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Psychology News Robot @[email protected]
2026-10-26

DATE: October 26, 2026 at 09:00AM
SOURCE: PSYPOST.ORG
** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
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TITLE: Playing video games with trusted friends may reduce addiction risks
URL: https://www.psypost.org/playing-video-games-with-trusted-friends-may-reduce-addiction-risks/
Video game developers constantly look for ways to keep players returning to their games without fostering unhealthy behavioral dependencies. Recent research published in Computers in Human Behavior reveals that playing video games with a trusted, familiar group of people can build player loyalty while simultaneously reducing the risk of video game addiction. These findings suggest that the social environment within a digital game plays a powerful role in determining whether a gaming habit remains a healthy pastime or becomes a psychological burden.
The video game industry faces a persistent tension between business goals and social responsibility. Companies rely on player loyalty, which describes a gamer’s intention to keep playing and recommending a specific game to others. At the same time, the industry faces scrutiny over video game addiction, a clinical syndrome characterized by a loss of control over gaming habits despite negative consequences in daily life.
Past studies have identified factors that contribute to addiction, such as social isolation, and factors that boost loyalty, like customized digital avatars. Few researchers have investigated how specific social dynamics inside a game might influence both outcomes at the same time. A team of researchers led by Shih-I Tai, a management scholar at Chang Gung University in Taiwan, wanted to find out if specific types of group interactions could help secure a player’s devotion to a game without pushing them toward maladaptive behavior.
To understand how the social environment alters a player’s mental state, the researchers looked at two established psychological frameworks. The first is the pleasure-arousal theory, which separates emotions into two dimensions. One dimension tracks how pleasant an experience feels, while the other tracks how much it stimulates or arouses the person.
The second framework is brain reward circuitry theory. This theory suggests that pleasure and arousal activate entirely different neural pathways in the human brain. According to these frameworks, feelings of comfort, harmony, and contentment increase pleasure but lower physical and emotional arousal.
In contrast, surprises and intense excitement boost both pleasure and arousal at the same time. The research team hypothesized that high pleasure encourages players to return to a game, building long-term loyalty. High arousal provides the intense neurological reinforcement that can lead to addiction.
To test these ideas, the researchers conducted an online survey of 884 video game players in Taiwan. The participants answered detailed questions about their gaming habits over the previous month. The respondents nominated a game they had played recently, which frequently included games where massive numbers of players assume fictional roles and interact in a shared digital world.
The research team ensured the reliability of their data by excluding responses from participants under the age of 18 or people who reported impossible play times. The survey measured four distinct aspects of the gaming experience. The researchers asked players about the size of their usual gaming group and how many of those group members they knew in the real world.
They also asked participants to rate their level of trust in their fellow group members. Finally, the survey assessed game escapism, which measures the extent to which players use the game to actively avoid stress from their everyday lives.
The researchers then compared these four factors against self-reported measures of game loyalty and game addiction. They analyzed the data using structural equation modeling, a statistical technique that helps identify relationships among multiple variables. The results showed that different social elements influenced players in entirely distinct ways.
When looking at the size of a player’s gaming group, the data showed a positive link to game loyalty. Players who routinely gamed with larger groups were more likely to report intentions to keep playing and recommending the game to others. However, group size was not statistically significant in relation to game addiction.
The researchers suspect that larger groups provide more social connections that make the game enjoyable, satisfying the pleasure dimension. At the same time, massive groups often introduce repetitive interactions or toxic behavior that dampen the intense overall excitement needed to form a dependency.
The findings regarding real-world familiarity showed a different pattern. Knowing fellow players in the real world was not statistically significant when predicting a player’s loyalty to a game. It did correspond with lower levels of game addiction.
The researchers propose that when gamers play with real-world acquaintances, those friends might occasionally remind them of outside responsibilities. This social grounding breaks the immersive spell of the game and lowers the intense emotional arousal that drives compulsive play.
Trust among group members proved to be the most beneficial social factor. High levels of trust within a gaming group corresponded with both increased game loyalty and decreased game addiction. Engaging with honest and reliable peers likely provides a comforting, highly pleasant experience that makes players want to return.
This same comfort and harmony create a relaxed state of mind. Trusting one’s peers reduces the high-stakes anxiety and arousal typically associated with addictive behavior. If a player trusts their group to act in their best interest, they receive social support that helps maintain a healthy balance between gaming and life.
In contrast to the protective social factors, game escapism had a dual effect. Using games to flee real-world stress strongly predicted higher game loyalty, as the temporary relief provided a rewarding experience. Yet escapism also strongly predicted higher levels of game addiction. Fleeing reality into a digital space immerses the player intensely, heightening the emotional arousal that stimulates the brain’s reward centers in a maladaptive way.
While the data offer insights into player behavior, the study relies on survey responses rather than direct observations. The researchers measured self-reported habits and feelings at a single point in time, meaning the results highlight associations rather than a strict cause-and-effect relationship. It is possible that players who are already less prone to addiction are simply more inclined to build trusting relationships with their peers.
The researchers used psychological theories regarding brain activity to interpret their findings, but they did not conduct medical brain scans or directly measure the participants’ physiological arousal and pleasure. Future studies could incorporate clinical monitoring tools, such as electroencephalograms, to verify exactly how these social interactions alter brain activity in real time.
Additionally, the survey was conducted entirely in Taiwan, which means the findings might be influenced by local cultural norms regarding socializing and technology use. Expanding this research to include gamers in different countries could reveal whether the protective effects of trust and real-world familiarity remain consistent across diverse cultural backgrounds.
The study, “The ties that (healthily) bind: Playing in groups increases game loyalty but decrease addiction,” was authored by Shih-I Tai, Thi Tuan Linh Pham, Tzu-Ling Huang, Alexander S. Dennis, Chun-Hao Liu, Gen-Yih Liao, and Ching-I Teng.
URL: https://www.psypost.org/playing-video-games-with-trusted-friends-may-reduce-addiction-risks/
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Psychology News Robot @[email protected]
2026-10-26

DATE: October 26, 2026 at 07:00AM
SOURCE: PSYPOST.ORG
** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
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TITLE: Political endorsements by scientific journals reduce public trust in research
URL: https://www.psypost.org/political-endorsements-by-scientific-journals-reduce-public-trust-in-research/
When scientific journals publicly endorse political candidates, they risk lowering public trust in the scientific community, particularly among moderate and conservative Americans. These endorsements cause measurable declines in how people view the impartiality and societal value of science, though they do not appear to change whether people are willing to follow practical scientific advice. The findings were recently published in the journal Group Processes & Intergroup Relations.
Public trust in scientific institutions is an important factor in shaping how society responds to global challenges like pandemics and climate change. Historically, scientific organizations have sought to maintain an apolitical stance to preserve their reputation as objective arbiters of fact. In recent years, several high-profile scientific journals have broken from this precedent. Editors at these publications have published pieces endorsing Democratic presidential candidates or strongly criticizing Republican candidates, often framing these stances as necessary defenses of scientific integrity. Researchers wanted to understand how these explicit political stances affect how the American public views the scientific enterprise as a whole.
Stylianos Syropoulos, Kyle Fiore Law, and their colleagues set out to examine this phenomenon through the psychological framework of motivated reasoning. Motivated reasoning is the human tendency to process information in a way that confirms existing beliefs and political identities. When individuals encounter statements that conflict with their political views, they often scrutinize or reject the source of that information.
The research team suspected that political endorsements from scientific journals might trigger this psychological mechanism. They hypothesized that people who identify as moderate or conservative might question the objectivity of science after reading endorsements that clash with their political leanings.
To capture a complete picture, the researchers evaluated trust not as a single concept, but as a collection of distinct dimensions. These dimensions include perceived ability, which is the competence of scientists to provide accurate information. They also looked at integrity, which involves the belief that science is impartial and objective. Benevolence measures the perception that science serves the public good and lacks hidden agendas. Finally, behavioral trust reflects whether people are actually willing to follow scientific recommendations in their daily lives, such as adhering to public health guidelines.
In the first experiment, conducted shortly before the 2020 presidential election, researchers recruited a large sample of 3,081 participants online. They randomly assigned these participants to read one of two types of texts regarding prominent scientific publications, such as Science, The Lancet, or Scientific American. The control group read a brief, neutral description of the journal’s history and mission formatted like a news wire article.
The treatment group read the same background information, but with an added excerpt from an actual editorial published by the journal that endorsed Joe Biden or criticized Donald Trump. Following the reading, participants answered a series of questions measuring their trust in science and their intentions to comply with health guidelines.
The researchers found that reading the political endorsements caused a measurable drop in general trust in scientific institutions. Participants in the treatment group were more likely to report that science lacks impartiality, pointing to a decline in perceived integrity. This effect was largely driven by politically moderate and conservative individuals, who exhibited lower trust after exposure to the endorsements.
Among conservatives, the treatment also increased belief in conspiracy theories about science and the origins of the coronavirus, indicating a drop in perceived benevolence. However, the endorsements did not yield a statistically significant effect on behavioral trust. Participants exposed to the political messaging remained just as willing to follow practical public health recommendations as those in the control group.
To see if these results would hold up under different conditions, the researchers conducted a second experiment days before the November 2020 election. They recruited a sample of 1,056 participants, matched to national demographic quotas for age, gender, and ethnicity. The experimental setup mirrored the first study, with participants randomly assigned to read either neutral background text or a political endorsement from a scientific journal.
The results replicated the core findings of the first experiment. Exposure to the political messaging reduced general trust in science and increased the belief that science is biased, again driven by moderate and conservative participants. In this iteration, the treatment group also reported a lower likelihood of using scientific sources for coronavirus updates, which suggests a decline in perceived scientific ability or competence.
Just as in the first study, the political endorsements had no statistically significant impact on people’s willingness to comply with practical safety guidelines. The messaging also did not alter belief in conspiracy theories for this specific group.
A third experiment took place four years later, surrounding the 2024 presidential election. The research team recruited a large sample of 2,144 participants to read material from Scientific American, Nature, or The Economist. The Economist was included because it frequently covers science and technology for a broad audience. As before, participants read either neutral background descriptions or actual published editorials endorsing Kamala Harris or criticizing Donald Trump.
Because the pandemic was no longer a central issue during this later study, the researchers removed questions about the coronavirus. They added new measures to evaluate general perceptions of scientific objectivity and societal usefulness.
The results from the 2024 election cycle aligned closely with the 2020 data. Reading the political endorsements decreased general trust in science and increased the belief that personal and political values govern scientific research. Participants in the treatment group also reported lower confidence in the idea that science is objective and useful for society. Once again, the declines in general trust and perceived integrity were most pronounced among moderate and conservative respondents.
The study relies entirely on real-world editorials that endorsed Democratic candidates or criticized Republican ones. Because the researchers used actual published materials to ensure their experiment mirrored reality, they did not test how left-leaning individuals might react to a major scientific journal endorsing a conservative candidate. It remains unknown if political liberals would exhibit the same motivated reasoning and subsequent loss of trust if faced with scientific endorsements that opposed their own political identities.
The researchers note that while explicit political endorsements carry reputational costs, complete silence from the scientific community on policy matters might also negatively affect public perception. Choosing not to address political rhetoric that directly targets scientific funding or spreads false information could be viewed as a failure to defend the scientific enterprise. Future research will need to explore how scientific organizations can correct falsehoods or advocate for evidence-based policies without appearing overly partisan. Finding the right balance will help institutions maintain their legitimacy across the entire political spectrum.
The study, “Presidential candidate endorsements by scientific journals decrease trust in science especially for moderate and conservative Americans,” was authored by Stylianos Syropoulos, Kyle Fiore Law, Crystal Li, Bernhard Leidner, and Kevin L. Young.
URL: https://www.psypost.org/political-endorsements-by-scientific-journals-reduce-public-trust-in-research/
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Dev Community @[email protected]
2026-10-26
Context Engineering over Prompt Engineering: Architecting Codebases for AI Agents

TLDR: Agents don't fail because your prompt was badly worded. They fail because your codebase was never designed to be read. The engineers getting compounding returns from agents aren't the best prompt writers. They're the ones whose repos carry the architecture outside their heads: context standards in the repo, strongly typed contracts, and deliberately pruned retrieval. Prompt engineering is the visible 10%. Context engineering is the structural 90%.

The Prompt Ceiling

Somewhere around 2024, the industry's collective mental model of "working with AI" became a prompt box. The skill was asking well. Whole courses, cheat sheets, and prompt libraries flourished on the premise that the delta between a good and bad AI outcome lived in the wording of your request.
For single-shot, single-file tasks, that was roughly true. It also quietly set everyone's expectations, and the expectations are now the problem.
Because in 2026 the work moved. Mid-level and senior engineers aren't asking a chatbot for a function anymore. They're dispatching agents across repositories of hundreds of thousands of lines, agents that read code, edit files, run tests, and open pull requests. And in this regime something counterintuitive happens that every serious team eventually notices: prompt quality stops being the constraint.
You can rewrite your prompt five times. You can role-play the model into a "senior staff engineer persona." Doesn't matter. The agent will still confidently duplicate an internal utility that already existed three directories away, import a dependency your org banned two quarters ago, and couple two modules your architecture has spent three years keeping apart.
Not because the prompt was weak. Because the codebase was opaque.
What an Agent Actually Sees

To understand why, look at what the average large repository exposes to a reader:
Implicit boundaries. "Don't call the data layer from the HTTP handler" exists as a norm, maybe a lint rule, maybe an ADR nobody reads. It does not exist anywhere a code reader encounters while working in the repo.
Unwritten dependency policy. Which libraries are approved, which are banned, which need a security review. Usually a Slack thread. Sometimes a wiki page. Never in the path of work.
Tribal domain models. What a "tenant" means in this system, which fields are PII, which service owns which concept. Spread across PR conversations, onboarding docs from 2019, and the memory of whoever's been here longest.
A new human engineer takes weeks to absorb all this, and that's with onboarding sessions and mentors and the ability to ask someone in the hallway. Then the industry hands an agent a repo checkout and a prompt, and acts surprised when the output violates constraints that were never written down anywhere the agent could read.
Here's the failure as it typically shows up, so common it's almost a template.
An agent is asked to "add a retry mechanism to the notification service." It produces a perfectly reasonable implementation. It also duplicates the exponential-backoff utility that already exists in an internal library, pulls in a third-party HTTP client the org specifically removed after a security audit, and places its new logic in a module that creates a circular dependency with the domain layer.
None of those are coding errors. All of them are context errors. And notice what makes them expensive: the code compiles, the tests pass, and the violations are only visible to someone who already knows the architecture. If that person exists, a senior just spent their verification budget (see the verification trap) catching things a document could have prevented.
Fix 1: Put the Architecture Where the Reader Is

The single highest-leverage change: stop storing architecture outside the repository.
The pattern that's emerged across serious AI-adopting orgs is an in-repo context standard, a known, versioned location that every agent (and every new human) reads first. An AGENTS.md or .context/ directory at the repo root holding the high-level domain model, the module boundaries and their rules, the explicit list of disallowed dependencies, the invariants ("never call X from Y," "all money fields are integers in minor units"), and pointers to the ADRs that matter.
Treated like code, not documentation. Reviewed in PRs. Updated when the architecture changes, in the same commit as the change.
The test for whether this is working is embarrassingly simple. Could a competent engineer who has never met your team make a boundary-respecting change using only what's in the repo? If your honest answer is "no, they'd need to ask someone in Slack," then your agent never had a chance. Neither did your next hire.
The corollary that surprises teams: this investment isn't really for the agent. It pays back twice. Once when the agent's first-draft PR respects the boundaries, and again every time a new human onboards in days instead of weeks. The agent didn't create the need. It just made the cost of not meeting it immediate.
Fix 2: Strongly Typed Contracts

The second fix is about what kind of surface your system presents to a reader.
Context files are necessary but they're advisory. Agents, like humans, follow probability, and prose is soft. Types are hard. The orgs getting the best agent behavior are the ones that make architectural constraints mechanically enforceable:
Explicit interface definitions between modules: generated clients, protobuf schemas, OpenAPI specs for anything crossing a service boundary. An agent that hallucinates a function signature doesn't produce a subtly wrong call that compiles. It produces a type error, immediately.
Strict types end to end. No Any escape hatches at the boundaries, no stringly-typed config. The failure mode shifts from "plausible code that violates the design" to "code that doesn't run," and that shift is the entire ballgame. An error the compiler catches at generation time costs seconds. The same error caught in review costs a senior's attention. Caught in production, it costs six hours or six figures.
Machine-checked policy where you can get it: lint rules and dependency analyzers that encode "this module may not import that one" as something the build fails on, not something the reviewer remembers.
The framing worth internalizing: every invariant you express only in prose is an invariant an agent will eventually violate, charmingly and with total confidence. Every invariant you express as a type or a lint rule is one less thing for the human reviewer to carry.
Fix 3: Prune the Context

The third fix is the counterintuitive one, because everything above has been "give the agent more context." But there's a failure mode on the other side, and mature teams hit it right around the time their context standards start working.
Context dilution. If your instinct is "the agent should know everything," you dump half the repository into the window, and the model now treats your critical domain rules as one equal voice in a choir of 40,000 tokens of boilerplate. The invariant you care about gets the same statistical weight as a vendored dependency's CHANGELOG. The agent saw the rules. It just didn't prioritize them, because you gave it no structure to prioritize with.
The working pattern is deliberate, selective context:
AST-based indexing and retrieval, tools that navigate the code's structure (definitions, call graphs, usages) instead of streaming its text. The agent gets "here are the three functions relevant to this task and their signatures," not "here's 200KB of the module they live in."
The in-repo standard provides the constants: domain model, boundaries, banned deps. The retrieval layer provides the variables: exactly the code relevant to this task, nothing else.
Budget context like a designed API surface, not an all-you-can-eat buffer. Teams that do report the same thing: smaller, sharper contexts outperform complete ones, on cost and quality, almost every time.
The principle underneath all three fixes is one the prompt-engineering era obscured: an AI agent is the most literal-minded new hire your team has ever onboarded. It will not infer your conventions. It will not ask a clarifying question in the hallway. It will follow exactly what's in front of it, at full speed, with total confidence.
Every year your team spent frustrated at new engineers for "not getting it," when the real problem was undocumented architecture and oral-tradition onboarding, is now doubled. Because the agent executes the misunderstanding immediately, at scale, in code.
The Question Behind the Question

If your codebase needs a person to already know your architecture in order to work safely in it, is that an asset or a liability? For twenty years, "the architecture lives in our senior staff's heads" sounded like job security. In an agent-heavy 2026, it sounds like a single point of failure with a Slack handle.

Your Turn

I'd genuinely like to hear how other teams are drawing this line, because the tradeoffs are real and I don't think there's a settled answer.
If a new human developer takes three weeks to understand your architecture, why do you expect an AI agent to infer it from raw files?
Is your codebase structured to guide autonomous agents, or is your architecture stored purely in your senior engineers' heads? And if it's the second one: does that feel like security, or a countdown?
Comment with the ugliest thing an agent did in your repo that turned out to be your architecture's fault. Those stories are more useful than the prompts everyone keeps posting.
Next in the series: Deterministic Safety Gates for Agentic Workflows. What happens when agents stop writing code and start changing infrastructure, and why "the agent tested itself" isn't a safety argument.#discuss #ai #aiagents #softwareengineering #software #coding #development #engineering #inclusive #community
Context Engineering over Prompt Engineering: Architecting Codebases for AI Agents
#ai #community #software #coding #development #engineering
Dev Community @[email protected]
2026-10-26
A few years ago, I checked a dependency’s license once, at install time, and never again. That habit is getting expensive.
License changes aren’t new, but they’re happening often enough that “it’s open source” is no longer a permanent property of a project. It’s a status that can change between releases.
The pattern is almost always the same.

The shift nearly always runs one way: from an OSI-approved permissive or copyleft license toward a “source-available” license the Open Source Initiative doesn’t recognize as open source.
The Business Source License (BSL) is the most common destination. It’s a “delayed open source” license: the vendor sets restrictions up front, and each version converts to a true open-source license after a change date, typically four years out.
It’s not just the famous cases anymore. A few from this year alone:
Loki Mode moved from the MIT License to BSL 1.1 effective March 19, 2026.
Unleash announced in June 2026 that its core repository is moving from Apache 2.0 to AGPLv3. AGPL is OSI-approved, so this one is a different kind of change, but it still affects anyone embedding the code.
MiniMax moved its M2.7 AI model from MIT to a non-commercial license, which drew immediate backlash.

Who it actually hurts

1. Small teams and agencies building on top: If your product depends on a library that relicenses, you now have three options: pay, migrate, or fork. Big companies have legal teams for that. A five-person shop usually doesn’t.
2. Contributors: People gave their time to a project under one set of terms. When the company controlling it changes the rules, the community’s work becomes part of a product they no longer fully share in.
3. Downstream projects: Plugins, integrations, and tutorials built around a tool inherit the uncertainty when its license shifts.

The fair counterargument

I don’t think vendors are simply villains here. Many of these changes were a response to cloud providers selling managed versions of a project without funding it. Maintainers need a sustainable model, and “be open and hope for donations” has a poor track record.
It’s also not always one-way. Redis is back under a recognized open-source license for those who choose it, while Valkey continues to be actively developed. Competition between the original and the fork may have pushed both toward better outcomes. Broader data also suggests permissive licensing has been rising, so this isn’t a total collapse of open source.
What I do now

Check who controls the project A single company controlling a project can relicense future versions at any time, while a project run by a neutral foundation with many contributing companies is much harder to change.
Read the license on every major upgrade, not just at install.
Pin versions so a surprise change doesn’t land in your next deploy.
Know your exit. Is there a credible fork? A foundation-backed alternative?
Set up license-change alerts for your critical dependencies.

So, is open source still open?

My honest answer: the code is still often open. The guarantee isn’t. Open source has always depended on trust, and trust now needs a little due diligence.
Over to you: Has a license change ever forced your team to migrate? Do you think relicensing is a fair business move, or a betrayal of the community that built the project? Tell me in the comments.#opensource #webdev #discuss #devdiscuss #software #coding #development #engineering #inclusive #community
Is Open Source Still Open? A Look at Recent License Changes and Who They Hurt
#opensource #community #software #webdev #coding #development
Dev Community @[email protected]
2026-10-26
Short answer: find people through work you can already click on, judge them by five specific pages instead of the homepage, and use the first call to ask what is not included. Those three habits filter out most of the bad options before money comes up.
Where to look

Start with sites you already use in your own industry. Scroll to the footer and look for a "site by" credit. If there isn't one, ask the owner who built it. A referral from someone whose site you can open and poke at is worth more than any directory listing.
Other places that work:
Industry associations, trade-show exhibitor lists, supplier newsletters — the people who serve the same kind of business you run tend to know each other.
Freelance marketplaces and portfolio sites. Use them to collect names, not to decide. A platform rating is not evidence about your project.
Your accountant, your chamber of commerce, a co-working space. Useful mainly when you want someone in your own timezone for calls.
What not to use as a filter: the lowest price, the longest list of services, and any promise about search rankings.
The five pages to open on every portfolio site

Their homepage is the page they spent the most time on. It is the least informative page they have.
1. The busiest page. Find the page with the most repeated elements — a product listing, a services index, a team page. Ask yourself: does the layout hold with three items? With thirty? Does a long paragraph still read, or does the text run into the next block? This is the page that resembles your actual site.
2. The contact or enquiry page. Submit a test enquiry with your real email address. Did you get a confirmation? Did the form tell you clearly when you submitted it empty? A broken form on a developer's own site is the single most useful signal you will get, and it costs you two minutes.
3. The mobile version, on your phone, on mobile data. Not on their office wifi and not in a device simulator. Time how long until you can read the headline, tap the menu open, and try to fill the form with one thumb.
4. The second language, if you need one. Do not check whether the words are translated — check whether the pages are. Click three links from the translated homepage. Do they stay in that language, or dump you back into the default one? Does the product list show the same products?
5. The footer pages. Privacy policy, terms, cookie notice. If they build for customers in Europe or the United States and their own site has none of these, that tells you what will be missing from yours.
Two more checks, thirty seconds each:
Open an address on their site that does not exist — add a few random letters to the end of a page address. A bare server error page means nobody is minding the details.
Read one portfolio entry properly. Does it say what the client needed, what was built, and roughly how long it took? If every entry is a screenshot with no context, you cannot tell what they did and what was already there before they arrived.
One more thing worth doing: portfolios often include sites the person only partly worked on, or maintained rather than built. That is not dishonest, but it is worth asking directly — "what was your part on this one?" A straight answer is a good sign.
The first call: eleven questions

Ask these before you talk about price. The point is not to catch anyone out; it is to find out whether they have done this enough times to have opinions.
1. Who actually does the work? A good answer names the person and says whether any part is subcontracted. A warning sign is "our team" with no names, or a salesperson who will not be involved after signing.
2. What do you need from me, and what happens if it arrives late? A good answer is a written list — logo files, company text, product photos, product data, hosting login — plus an explicit statement that the clock starts when the materials arrive, not when you pay. A warning sign is "just send whatever you have."
3. What will I see first, and when? A good answer is a live preview address with a date attached. A warning sign is "we'll send you screenshots."
4. How many rounds of changes are included, and what counts as a change? A good answer gives a number and a definition: text swaps and colour changes are one thing, a new page or a new feature is another. A warning sign is "unlimited changes" — that is not a policy, it is the absence of one.
5. What is not included? A good answer is a list: domain, hosting, business email, copywriting, photography, translation, ongoing updates. A warning sign is silence, or "everything is included."
6. Whose name is the domain and hosting in? A good answer is yours, from day one, and you get the login. A warning sign is theirs, "for management purposes." This is the most expensive mistake on the list — recovering a domain from someone who stops answering is slow and sometimes impossible.
7. Will I have admin access to the site when it's finished, and can I export the content? A good answer is yes, plus a plain explanation of what you will be able to change yourself. A warning sign is "you don't need access, we handle updates."
8. What happens after launch — what's covered, for how long, and what's billed extra? A good answer defines a window for fixing defects and a rate for everything after it. A warning sign is "free support forever", or no answer at all.
9. What is the timeline measured from? A good answer is a number of working days counted from the date your materials are complete. A warning sign is a confident "two weeks" said before they have seen your content.
10. What would make you turn this project down? A good answer names something real — a deadline they cannot hit, a platform that does not fit, a budget that does not match the scope. A warning sign is "nothing, we can do anything." Anyone who has done this for a while has walked away from something.
11. Can I speak to the client whose site you just showed me? A good answer is "I'll ask them" or "they've agreed to be a reference." A warning sign is every single project being off-limits.
Red flags and green flags

Red flags, in roughly the order they should worry you:
No written scope, or a scope that is one paragraph long.
The domain and hosting stay in their name.
A price far below everyone else, with no explanation of what was cut.
Pressure to decide today.
No preview address — screenshots only.
Guarantees about search ranking, traffic, or sales.
A portfolio where every site looks identical, and none of them is in your industry.
They ask for the full amount up front.
They cannot tell you what happens after launch.
Green flags:
They ask about your business before they ask about colours.
They can show you a site they built that is still running.
They push back on something you asked for, and explain why.
The exclusions are in writing before you ask twice.
They name the person who will do the work.

Payment and paperwork

Two rules that solve most disputes before they start.
Do not pay the full amount up front. Split it against things you can see: a deposit to start, a payment when the preview is up, the rest at launch. Milestones tied to deliverables beat milestones tied to calendar dates.
Get the scope in writing, including the number of pages, who writes the text, who supplies the photos, and what happens if you add a page later. If the document does not mention something, assume it is not included.
Before you contact anyone: thirty minutes of prep

Write these down and the conversation becomes much shorter:
One paragraph on what the business does, who buys, and the single action a visitor should take.
Three sites you like, and one specific thing about each. Not "it looks nice" — "the product table fits forty items on one screen without scrolling sideways."
The materials you can supply today, and the ones you cannot.
A budget range, and a date you genuinely need it by.
Two developers to get quotes from, not five. Comparing five quotes against a one-page brief teaches you nothing.
The quotes you get will only be as good as the brief you send. And a developer who asks you good questions is telling you they have been burned before — that is usually the one worth a second call.#career #freelance #webdev #software #coding #development #engineering #inclusive #community
How to vet a web developer before you pay: which pages to open, which questions to ask
#community #software #webdev #coding #development #engineering
Dev Community @[email protected]
2026-10-26
You review a 30-line template diff. Production sends 40,000 tokens assembled at request time from six sources, four of which changed this week and none of which appear in the pull request. The diff you approved is not the change you shipped.
This is the part of LLMOps that keeps biting teams after they have evals, tracing, and a gateway in place. The template is versioned in git. The context is assembled by code paths nobody treats as release artifacts.
The prompt is a build output

List what actually goes into a request for a typical RAG-plus-tools system:
The system template (in git, reviewed).
Top-k retrieved chunks from an index that gets rebuilt on its own schedule.
Tool JSON schemas, whose descriptions are prompt text written by whoever last touched the tool.
A conversation summary produced by another model call.
A user profile or tenant config blob.
Few-shot examples chosen at runtime by a selector.
Each is a dependency with its own version and its own deploy cadence. The retrieved chunks change when someone reindexes. The tool schema changes when a backend engineer renames a parameter and rewrites the description. Neither triggers a review of the prompt.
So when quality drops on Tuesday and no code shipped, there is no diff to look at. That is the failure this whole post is about.
Fingerprint everything the model sees

Compute a content hash over the inputs that can change model behaviour, and log it with every trace:
model id and version
template version
retrieval snapshot id (the index build, not the query)
the fully rendered prompt
decoding parameters that matter
Store fingerprints always. Store full rendered prompts sampled or redacted, depending on your data rules — full-text retention is a compliance decision, not an observability one. The fingerprint is small, cheap, and answers "what changed" during an incident in one query.
The trade-off: a fingerprint that includes a timestamp, a request id, or a per-user greeting has near-zero collision rate and zero diagnostic value. Hash the parts that affect generation, not the metadata around it. I have watched a team spend a day on "why is our prompt hash changing every request" when the answer was a locale string.
A small assembler with provenance and eviction

You do not need a framework for this. You need three properties: every section carries a trust level, every section carries an eviction priority, and over-budget on a pinned section is a hard error rather than a silent trim.
from dataclasses import dataclass, field
from hashlib import sha256
import re
@dataclass
class Section:
name: str
text: str
trusted: bool # False for retrieval, tool output, user uploads
priority: int # higher survives truncation
pinned: bool = False # never evict; over budget means fail loudly
INSTRUCTION_LIKE = re.compile(
r"(ignore (all )?(previous|prior) instructions|you are now|system prompt)",
re.IGNORECASE,
)
def screen(text: str) -> str:
# Cheap heuristic, not a guarantee. Log hits and route to a stricter path.
return INSTRUCTION_LIKE.sub("[filtered]", text)
def count_tokens(s: str) -> int:
# Swap in tiktoken/the provider counter. One counter per model family.
return len(s) // 4
@dataclass
class Assembled:
prompt: str
fingerprint: str
dropped: list[str] = field(default_factory=list)
def assemble(sections, budget, model_id, template_version, snapshot_id):
ordered = sorted(sections, key=lambda s: (s.pinned, s.priority), reverse=True)
kept, dropped, used = [], [], 0
for s in ordered:
body = f"<{s.name} trust={'high' if s.trusted else 'low'}>\n"
body += (s.text if s.trusted else screen(s.text)) + f"\n</{s.name}>"
cost = count_tokens(body)
if used + cost > budget:
if s.pinned:
raise ValueError(f"pinned section {s.name} exceeds budget")
dropped.append(s.name)
continue
kept.append((s, body))
used += cost
# Render in original order so layout is stable across requests.
pos = {id(s): i for i, s in enumerate(sections)}
kept.sort(key=lambda pair: pos[id(pair[0])])
prompt = "\n\n".join(body for _, body in kept)
h = sha256()
for part in (model_id, template_version, snapshot_id, prompt):
h.update(part.encode())
return Assembled(prompt, h.hexdigest()[:16], dropped)
# Usage: build once per request, log the fingerprint beside the trace id.
ctx = assemble(
sections=[
Section("question", "why was my claim denied?", trusted=True, priority=10, pinned=True),
Section("policy_chunk", retrieved_text, trusted=False, priority=5),
Section("profile", tenant_blob, trusted=True, priority=1),
],
budget=12_000,
model_id="claude-sonnet-4",
template_version="support-v14",
snapshot_id=index_build_id,
)
Two details in there are load-bearing. Sections render in original insertion order rather than priority order, because reordering the same content changes model behaviour and makes your eval deltas unattributable. And pinned turns budget pressure into an exception instead of a degraded#ai #llm #devops #python #software #coding #development #engineering #inclusive #community
Context Assembly Is a Build Step: Versioning the Prompt You Actually Send
#devops #ai #community #software #coding #python
Dev Community @[email protected]
2026-10-25
Why the best engineers on agentic teams have stopped "prompting" and started designing "loops" — and what that means for your first year.
TL;DR
• Prompt engineering is about one request. Loop engineering is about the repeating cycle an AI coding agent runs: plan → act → check → decide what's next, until a goal is met.
• The quality gap between a good agent setup and a bad one is rarely the model. It's usually the loop: what context goes in, how work gets verified, and when the agent stops.
• Your job is shifting from "write the code" to "design the system that writes, tests, and fixes the code — and know when a human must step in."
• In a regulated shop, the loop is also your control framework. Treat it like one.
A quick history: how we got here
The naming has moved fast, so here's the short version:
Prompt engineering (2023–24): craft the perfect single request.
Context engineering (2025): realise the prompt is just one ingredient — what matters is everything the model sees: repo instructions, docs, prior decisions, tool outputs.
Harness engineering (early 2026): agents start doing long, multi-step work in production, so we build the scaffolding around them — tools, sandboxes, permissions.
Loop engineering (mid-2026): agents are now good enough to run unattended for long stretches and recover from their own mistakes. The hard problem becomes designing the cycle itself. The big unlock was "closing the loop." Before, engineers spent much of their day acting as QA for the agent: spot the bug, paste it back, ask for a fix, repeat. Once you give the agent a spec plus a way to check its own work (tests, evals, linters), it can do that cycle itself — and work productively for much longer without you.
The mental model: a thermostat, not a vending machine
A vending machine takes one input and gives one output. That's prompting.
A thermostat has a goal (21°C), a sensor (the thermometer), an action (heat on/off), and a stopping rule (target reached). It keeps cycling until the world matches the goal. That's a loop.
Every coding agent today runs some version of the same base cycle: reason → act → observe → repeat. Loop engineering is the deliberate design of that cycle as an engineering artifact in its own right — something you version, measure, and improve, just like code.
What this means for you, as a new engineer
The fundamentals didn't go away — they got more valuable:
• Writing clear specs is now a core engineering skill, not a PM chore.
• Writing good tests is how you give agents a sense of direction.
• Reading code critically is how you catch what the loop missed.
• Thinking in systems — goals, feedback, limits — is the new superpower.
The engineers who thrive in 2027 won't be the fastest typists. They'll be the ones who design loops their team can trust.#agents #ai #programming #softwareengineering #software #coding #development #engineering #inclusive #community
Lets us Loop Engineer
#ai #community #programming #software #coding #development
Leo Wattenberg @[email protected]
2026-10-25
Artcraft, Mindscaper, et al: Can we really just vibecode creative software?

Over the past couple days, massive hype has built up around Artcraft, a collection of literal clones of Adobe’s CC, Microsoft Office and other apps, entirely vibecoded and open source. Artists and creative folks battered by subscriptions costing hundreds and thousands of dollars a year finally smell relief that’s not the poorly designed open source jank of yesteryear, and the project receives dozens of similarly vibecoded pull requests each hour. At the same time, I’ve been building Mindscaper, practically with the same goal, but without the hype. As such, I have some thoughts and bias on this subject. And probably typos, this is 100% human-written.
Background
Artcraft is developed by Learning Machines LLC, which appears to be a one-man company by Brandon Thomas. Thomas previously developed the deepfake services “vo.codes” and “fakeyou.com” under this company, and developed VTubing tech. Judging by the blurb on “the.storyteller.company”, he appears to believe in “controllable AI” trivializing (“democratizing”) art creation, but with Artcraft opted to primarily clone existing professional software, putting AI on the backburner (although an AI subscription service is available).
By a lucky coincidence, I founded my company Koytek Wattenberg Media at a similar point in time, with similarly limited success. One of my biggest clients became Audacity, at which I worked on “democratizing” audio creation, and wouldn’t you know I’m also into VTubers. I started my foray into vibecoding about half a year after Artcraft, focusing on Audacity-shaped audio and video editors, Soundscaper and Framescaper.
On technical matters
I have nothing new to say here that hasn’t been already said a billion times. Modern coding robots (I used Opus 5 and GPT 5.6 Sol mostly) are perfectly capable of making something that Just Works. They just happen to be lying, lazy bastards that will happily claim something exists when it doesn’t. Test-driven development principles keep them reasonably honest, though they occasionally do manage to find a way to write tests so unspecific that a pixel-by-pixel spectrogram in the Roseus color scheme and a spectrogram with much coarser buckets using a blue-to-yellow color scheme pass the same test.
I am under no illusion that the code they’ve produced isn’t ugly and suboptimal. But then, the Audacity codebase is 25 years old and also extremely ugly and suboptimal. The Audacity team – all extremely talented software engineers and beautiful people – have been unable to make Audacity 4 feature-equivalent to Audacity 3 in over a year, despite that “only” being a UI migration with some UX changes attached. With Soundscaper, I managed to get an app going featuring all of Audacity 3’s and 4’s features within 3 months, plus a plethora of additional DAW features and video editor on top of it. Similarly, Artcraft managed to get a Photoshop (and general Adobe CC) clone going in half a year.
On practical matters
While the robots are perhaps good enough to get functionality implemented, they suck at figuring out the finer details – and pretty much all of the design.
Robots suck at progressive disclosure
Progressive disclosure is the part of UX where, instead of showing everything at once, you show things as they’re needed. A trivial example for this would be a menu; the “Format” menu is reduced to that single word on your screen until you, the user, decide that now is the time to format something and click on it, revealing a dozen or so options.
Robots suck at this. If you tell them to add a feature, they’ll generally attempt to place it on screen with a dedicated button. This is fine for an app with 6 controls in total, but less so for a complex creation app with 500. Forcing them to chuck everything into menus and dialogs helps, but then we’re straight back into design paradigms from the 80s.
This is where Artcraft and Mindscaper both have an advantage over a pure vibecoded approach: Artcraft already knows where everything goes because they’re busy copying existing design. I know where my stuff goes because I am the designer, and thus have a very strong idea on what these features are, how they should work, where they should go and when they should show up.
Robots suck at information density
In a similar vein to the thing above, robots love to implement stuff as “Control title – control – explanation sentence”. Again, this is fine for a small dialog with 2 controls in it, but for any sufficiently complex effect it just either overwhelms people with too many words and/or requires things to be spaced so far apart to fit all this extra text that the dialog becomes massive.
And again, cloning existing dialogs works here, as does being the original designer to make relevant decisions. Purely vibecoding this from scratch is going to be much more difficult though.
Robots suck at asking the right questions
The biggest difference between working with humans and working with robots is what questions I’m being asked. With the Audacity team, I’d get asked about user edge cases as they popped up, I’d get asked about clarifications on my designs, I’d get asked about things by users. With the robots, there are a lot fewer of these kinds of questions; the only time the robot asks questions at all seems to be in plan mode. However, in plan mode, the questions that are being asked almost always are implementation-centric, ie the stuff I don’t really care about. So what ends up happening is that the robot will go ahead and do something, proudly proclaim it’s finished and then it’s up to me to QA whether it actually did the right stuff. And more often than not, it misses some design edge case.
On philosophical matters
In this section I’d want to talk about the should and shouldn’ts of vibecoding a creative software suite.
Copyright
Artcraft is copying various proprietary programs wholesale using AI. On Artcraft’s side, this has all the usual copyright questions attached (who is the author? is the code copyrightable?), though on Adobe’s side this becomes more interesting: From Tetris v. Xio, we know that the look and feel of a program (in that case, Tetris) can be copyrightable. Note that this is different from copyright on the code itself (which I don’t think Artcraft can even infringe given that their thing is in Rust, which Adobe’s stuff probably isn’t), and also is different from attempting to claim copyright on the app’s structure or underlying ideas (that’s patent stuff, not copyright).
Artcraft thus may be in for a rude awakening should Adobe feel threatened by them. Adobe may or may not win this case, but they also probably don’t need to in order to hobble Artcraft’s development for years to come. Funnily enough, the actual code being open source may be a saving grace here, as it likely can trivially be forked, copied and modified to avoid Adobe’s gaze, to a better degree than pirates ever could. Another point in favor of Artcraft is AI itself; any rulings in favor of copyright holders over AI developers is also a ruling against Adobe’s own products. It thus may be that Adobe opts to do… nothing, in order to avoid the backblast. I don’t think they’ll necessarily care about the possibility of a Streisand; lawsuits like this generally are boring enough for the wider press to not care beyond perhaps a single story in the margins.
Mindscaper meanwhile doesn’t have that problem, as Soundscaper is building upon an open source app and crediting it, and Framescaper is original.
Patents
Software patents unfortunately arguably exist, some of them on UI (eg the Ribbon pattern found in MS Office). Adobe, unsurprisingly, has a tens of thousands of patents.
Software patents are one level more devious than mere copyright: It doesn’t matter that you had the idea all on your own, or that the idea may be obvious now, all that matters is that someone else also had the idea before you. And if they have registered it, you now are in trouble. It’s because of software patents that loading screens don’t have minigames, it’s because of software patents that Audacity makes you jump through hoops to download FFmpeg, as there are still over 3000 patents in the AAC patent pool alone. And if just a singular patent survives litigation, which given the sheer number of patents involved is gonna take forever anyway, you’re on the hook for yet more damages.
Software patents are the ideal tool for corporations to shut down people they don’t like, hence why some corporations (including Adobe and Microsoft, the companies most likely to be upset by what Artcraft is doing here) engage in non-aggression pacts with each other, like the Allied Security Trust or Open Innovation Network – this prevents these companies being at each other’s throats for patent reasons all the time.
However, these defensive alliances generally don’t apply to people outside the alliance, and neither Artcraft nor Mindscaper are in one. Thus, whenever one of the incumbents ends up being upset or feel threatened by what’s happening, they can pull the litigation lever.
I don’t know which patents could be relevant here, and frankly, I also don’t wanna know – in the end it doesn’t really matter. Patents are a risk factor for any software, and typically best left ignored as patent holders tend to be more busy with actual business than going after one-man companies. However, when you sufficiently piss off a company – and I’d argue that wholesale cloning of a software might constitute that – you have a lot stacked against you.
Freeing users
Subscription services for software create incentives for the builder of the software to put as little work as possible into the software while charging as much as possible. This allows the Autodesks and Adobes of the world to charge ridiculous amounts, while providing software so bad that it regularly attracts user petitions, begging them to fix longstanding bugs degrading the experience.
Simply providing free software alternatives here may work (eg in the case of blender and musescore), or may not work, as free software does (or at least: used to) suffer from small development teams and quite frequently UX so weird that it wasn’t possible for some folks to figure out how to use the program in question. A famous example for this is the “make a circle in GIMP”. The solution is to make an elliptical selection, intuit that you can convert that to a path, and then use a panel to draw the path – as opposed to Photoshop, where you just use the circle tool.
Providing free software versions of a program would indeed free people of subscription software to use something else, depending on the copyright situation more or less easy compared to pirating, and this is what Artcraft does provide.
Freeing new users
However, I’ve found that the entire creative software field suffers from underdeveloped design. A lot of professional-grade software is modeled after pre-digital hardware and uses design metaphors that simply don’t make sense if you haven’t experienced said hardware. I remember when Lightworks, an Oscar-winning video editor, became a free download in 2010, I couldn’t for the life of me figure it out, not even typically simple things like rendering the video. This is despite me having had some experience in Sony Movie Maker (the cheap version of Vegas) and Powerdirector.
This is understandable somewhat: To attract professional users in the 80s and 90s, you’d need to replicate their workflows from their pre-digital tools. And once you have these users and they’ve learned how to use the program, they’d hate to have to adapt to a new design. However, this also leads to new users having to jump through enormous quantities of hoops trying to learn the program. If you want to “democratize” art creation, making things free and open source is one thing, but the barrier to entry of learning the tool remains the same.
This is where my work on Audacity (and now Soundscaper) becomes relevant: It takes quite a bit of design work, but it in principle should be possible to design a complex creation app in such a way to make it easy to use. Notably, not everything has to be equally easy – setting up a simple recording should be a one-button operation, while setting up a more complex multi-track recording may be a bit more involved, as we can expect the user to have a more clear task in mind on which input goes to which track.
Our vision for Audacity (and Musescore for that matter) was to completely starve the current incumbents of new users by just having a much more modern design and having an app that’s easier to get into. I still think this is the way things will shake out eventually; we’ve already seen Finale starve to death from this pressure.
Artists
Of course, AI bros would go further here and envision a future where skill is completely replaced by AI. And while for things that already were sloppy and primarily utilitarian to begin with (shitposts, local business signage, anything using Wordart/clipart in the past), AI certainly is going to replace more dedicated tools, I don’t see it doing so for artists. To draw a chess analogy, computers have been better than humans in chess for a long time, yet humans still play chess, for one simple reason: It’s fun. Similarly, artists don’t really care if a robot can do a hyperrealistic godzilla in mere seconds, as the fun part of making art is actually creating it, not just admiring the result (that would be mere consumption).
As an artist, you of course get to pick your battles. You can decide that shading or coloring is too annoying, and stick to strict black and white, or that mixing and mastering is too annoying and stick to making melodies and chords. From there you can hire someone to do the shading and coloring and additional work for you, or indeed use AI instead. In either case, your initial creative vision gets diluted by the other work; the more people are involved and the heavier the AI use, the more diluted your vision gets. This can be acceptable to you, especially in a world where art is traded as a commercial good, but if we assume that art as a good is going to lose worth, this tradeoff may become less worthwhile, perhaps making it so that you only wants tools strictly designed for you to express yourself.
For-profit open source
Having both worked at and seen for-profit open source, I’m perhaps a bit jaded:
We see companies close access to their source the moment they feel like they can extract revenue out of it (various server software comes to mind)
We see companies increase the amount of closed components in open source software, to guarantee only they can have a revenue stream (VSCode and Android come to mind)
We see companies add user-hostile features to benefit only themselves (Google Chrome/Android is the poster-child here, to the point where regulators need to step in to it)
There might be a way to make a for-profit company be a good steward of an open source project, but the incentives always point in a different direction – and even if the current leadership can resist these incentives, the next one can flip towards enshittification so fast. I haven’t seen Thomas’ Artcraft project enough yet to know whether he’ll stay true to open source, though given he already has a subscription service (for cloud AI compute, of course) and a launcher ready, I gotta say that I’m worried.
It’s for this reason that I spun out Soundscaper from my company kw.media into Mindscaper, a non-profit association. It is now immune to me, or anyone else for that matter, deciding I would like it to make me tons of money, as long as its members (so, ideally, the users) don’t want to be milked. As an association (as opposed to a foundation), it also is way more democratic, giving users a real voice in what should happen to Soundscaper.
Resources & Sustainability
It’s impossible to discuss AI without touching upon the massive resource use it causes. Throughout my time developing Soundscaper, I have burnt through Codex’ and Claude’s 20x subscription quota almost every single reset cycle, including OpenAI’s ridiculous practice of vibing when a reset occurs and having a dude announce it on twitter. This has cost me hundreds of dollars, would have cost me tens of thousands of dollars in API cost, and has cost OpenAI and Anthropic likely thousands of dollars as well.
It is clear to me that the current pricing isn’t sustainable, and that slowly but surely, costs will go up. It is for this reason that Soundscaper uses local inference whenever useful and won’t be integrating with any cloud AI services. As far as I’m concerned, the entire point of open source is to fuck around with stuff on the machines you have access to and have it not tied to some service which decides what is and isn’t allowed.
It also is clear to me that there are real environmental impacts vibecoding this hard has; especially when comparing how long and hard my local setup has to work to achieve much simpler tasks (I let gemma and aya-expanse handle translation work locally, for example). Thus, I try to limit my vibecoding to the areas I really care about. I have a media background, so I shall vibecode media tools I’ll personally use – as opposed to token-maxxing just because I can.
Conclusion
So, can we vibecode creative software? On a technical level, yes. On a design level, yes, if we are a designer or follow someone else’s design. But should we? Depending on how adventurous we are with copyright and patent laws, especially if we are just re-implementing someone else’s design – maybe not.
We absolutely should open source and reverse engineer the things we care about, but we shouldn’t take a robot’s “one-shot” in an area we know nothing about as proof that it’s good now. It’s enormously easy to slop a whole software suite into existence but it actually takes some real work to get the software suite to actually be usable by real people – and surely, the benefit to real people has to be the goal.
If you’re interested in contributing to a non-profit making creative software, consider joining Mindscaper.
#ai #artcraft #artificialIntelligence #audacity #mindscaper #openSource #software #soundscaper #technology #vibecoding #writing
#ai #artcraft #artificialintelligence #audacity #mindscaper #opensource
Dev Community @[email protected]
2026-10-25
Your Python code works. It’s also slower than it needs to be. 🐢
If you work in AI or data, you’ve probably lived this loop:
Prototype in Python because it’s fast to write.
Hit a performance wall.
Rewrite the hot paths in C++, Rust, or CUDA.
Now you maintain two languages, two toolchains, and a fragile bridge between them.
This is often called the two-language problem. It costs teams time, hiring flexibility, and a lot of debugging hours. Mojo is an attempt to fix it at the root.
What is Mojo?
Mojo was created by Modular, co-founded by Chris Lattner, who also created Swift and LLVM. It combines Python-like syntax with systems-level performance and memory safety ideas inspired by Rust, and it uses the MLIR compiler framework to target hardware such as GPUs, CPUs, TPUs, and ASICs.
opensourceforu
The goal is simple: one language from prototype to production, with code that looks familiar to Python developers.
Why 1.0 matters
For years, Mojo was a fast-moving experiment, and its syntax changed often. That made it hard to bet a real project on it.
Version 1.0 brings backward compatibility and API stability, and future 1.x releases are expected to be mostly additive. Modular also says it relies on Mojo daily in production, as the foundation of its own commercial products, MAX and Modular Cloud.
opensourceforu
modular
In short, the language you learn today shouldn’t break under you next month.
What problems does it solve?
Performance without leaving the Python mindset
You write code that reads like Python, but you get control over types, memory, and hardware when you need it. You don’t have to jump to a different language for the hot path.
One language for the whole stack
Instead of Python for the model and C++ or CUDA for the kernels, you can aim to write both in the same language. That means a smaller skill gap inside your team.
Hardware portability
AI workloads now run on many kinds of accelerators. Because Mojo is built on MLIR, it is designed to target different hardware without rewriting everything per vendor.
Gradual adoption
You don’t need to migrate your whole codebase. Mojo is designed to work alongside Python, so you can start with the one function that’s too slow.
A first look at the code
Here is a simple example. It looks like Python, which is the point:
mojo
def main():
var total = 0
for i in range(1_000_000):
total += i
print(total)
The real gains show up when you add static types and use Mojo’s low-level features for performance-critical sections. Check the official Mojo docs for the current syntax, since the language was cleaned up significantly for 1.0.
Where to be realistic
I don’t want this to read like hype, so here are the caveats:
The ecosystem is young. Python has decades of libraries. Mojo does not, and you’ll often lean on Python interop.
It’s strongest for AI and performance-heavy work. For a typical CRUD web app, Python or Go is still the simpler choice.
Governance is worth watching. Modular was acquired by Qualcomm in mid-2026, and open-source status and community direction are things to keep an eye on as the ecosystem grows.
opensourceforu
Features are still arriving. Async programming, pattern matching, and tagged unions are on the roadmap.
opensourceforu
Should you try it?
My take:
Yes, if you work on ML, GPU kernels, or numerical code and you’re tired of maintaining a Python and C++ split.
Maybe later, if your workload is mostly I/O-bound or web-focused.
Worth a weekend, if you just want to see where language design is heading.
Getting started
Read the official Mojo documentation.
Install the toolchain and run a small benchmark of your own slow Python function.
Port just that one function and measure the difference.
Measure on your own workload. Benchmarks from other people’s projects won’t tell you whether it helps yours.
Final thoughts
The two-language problem has been accepted as “just how things are” for a long time. Mojo 1.0 doesn’t magically end it, but it’s the first time the idea feels stable enough to try seriously.
Have you tried Mojo, Rust, Zig, or another newer language? What problem did it solve for you, or why are you still skeptical? Let me know in the comments. 👇#programming #mojo #ai #python #software #coding #development #engineering #inclusive #community
Mojo 1.0 Is Here: Is the Two-Language Problem Finally Over?
#ai #community #programming #software #coding #python
Psychology News Robot @[email protected]
2026-10-25

DATE: October 25, 2026 at 12:00PM
SOURCE: PSYPOST.ORG
** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------
TITLE: How the menstrual cycle might alter ADHD symptoms in women taking stimulant medication
URL: https://www.psypost.org/how-the-menstrual-cycle-alters-adhd-symptoms-in-women-taking-stimulants/
The menstrual cycle appears to modulate the severity of attention-deficit/hyperactivity disorder (ADHD) traits in women taking common stimulant medications. A new small study suggests that these traits peak in severity during menstruation and ease soon after, pointing toward a need for hormone-aware treatment plans. The research was published in the Journal of Attention Disorders.
ADHD is a neurodevelopmental condition characterized by distraction, impulsivity, and hyperactive behavior. Medical professionals historically viewed the condition as something that primarily affected young boys. That diagnostic gap is rapidly closing, and women now make up nearly half of the adult ADHD population in the United States. Women often present with different behavioral patterns than men, leaning toward severe inattention and internalizing behaviors rather than disruptive hyperactivity.
Despite facing daily impairments in their careers and social lives, women remain largely underrepresented in ADHD research. One distinct biological variable separating women from men is the constant fluctuation of ovarian hormones. During the follicular phase of the menstrual cycle, estrogen levels rise and peak near ovulation. The subsequent luteal phase features elevated estrogen and progesterone, which then drop rapidly shortly before menstruation begins.
Researchers suspect these hormones play a direct role in brain chemistry. Low estrogen levels are thought to decrease dopamine signaling in the brain. Because dopamine is the primary neurotransmitter involved in focus and attention, these natural hormonal drops could theoretically worsen ADHD symptoms.
Researchers Rebecca Zaritsky, Stephanie C. Reed, and Suzette M. Evans wanted to understand how these hormonal variations interact with ADHD treatments. Prior laboratory work demonstrated that amphetamines, the most commonly prescribed class of ADHD medications, can be less effective during the luteal phase of the cycle. Case reports from clinical settings also hinted that some women experience worsening moods and fading concentration right before their periods.
The research team designed a study to track these daily changes prospectively. The investigators recruited participants through online advertisements to monitor their daily experiences. They screened for individuals between the ages of 18 and 40 who experienced regular menstrual cycles lasting between 21 and 35 days.
To join the study, participants had to report an ADHD diagnosis and currently use amphetamine salts (commonly known by brand names like Adderall) for treatment on at least five days of the week. The team excluded anyone taking hormonal birth control, as well as individuals using other psychiatric medications. After exclusions and dropouts, the researchers ended up with a small study of 30 participants who completed the required long-term tracking.
Each individual filled out an initial survey detailing their medical history, baseline mood, and general impulsive behaviors. Following this baseline assessment, participants completed a daily online survey for 35 consecutive days. This extended timeline ensured the researchers could capture data from one full menstrual cycle for every participant.
The daily questionnaires asked participants to rate their current ADHD symptoms on a standardized four-point scale. They evaluated 18 distinct symptoms, splitting them equally between inattentive traits and hyperactive-impulsive traits. Participants also rated their daily mood variations on a six-point scale measuring 17 distinct emotional states, and reported their exact medication dosage for the day. Finally, they noted whether they were actively menstruating or spotting.
The researchers averaged the daily survey responses and grouped them into three specific biological windows. These windows included the first five days of menstruation, the mid-follicular phase shortly after bleeding ends, and the late luteal phase just before the next period begins. Participants reported their most severe ADHD symptoms during the active menstruation phase. In contrast, they experienced comparatively milder symptoms during the mid-follicular phase.
When researchers separated the specific types of ADHD traits, hyperactive and impulsive behaviors showed variation across the cycle phases, but after adjusting for multiple comparisons this amounted only to a trend rather than a statistically significant difference. Inattentive symptoms also fluctuated, but the differences were not statistically significant. Participants did not report any differences in how much their symptoms generally impaired their daily lives across the different phases.
Mood tracking revealed a correlation between negative emotions and ADHD symptoms. Participants reported elevated negative moods during both the late luteal and menstruation phases compared to the mid-follicular phase. The individuals who experienced the largest drops in mood during menstruation also reported the greatest increases in ADHD symptoms. This correlation held true for both inattentive and hyperactive traits.
Daily amphetamine doses remained remarkably steady across the entire cycle. Participants did not instinctively take more medication to compensate for feeling more distracted or hyperactive during their periods. The research team had hypothesized that some participants using flexible dosing regimens might attempt to counteract their worsening symptoms with higher doses. This lack of adjustment might reflect strict prescribing practices, as many doctors do not encourage patients to alter their daily stimulant intake based on their menstrual cycles.
This research relied entirely on self-reported surveys. The investigators did not measure hormone levels in a laboratory to perfectly confirm each biological phase, opting instead to estimate phases based on reported bleeding days. The small sample size also limited the statistical power of the analysis, meaning the results require verification in larger populations. Because participants taking other psychiatric medications were excluded, the results might not represent individuals managing ADHD alongside conditions such as mood disorders or premenstrual dysphoric disorder.
The study design carried a few distinct demographic and methodological limitations. The researchers did not explicitly gather information on gender identity, meaning the relationship between gender identity, hormone cycles, and ADHD remains untested in this specific context. The standardized survey tool used to track daily symptoms was also based on older diagnostic criteria. This older framework lacks recent diagnostic updates designed to capture how ADHD presents in adults.
The research team also looked at baseline mental health using standardized questionnaires for depression and anxiety. They found a trend suggesting that individuals with higher baseline depression and anxiety scores might experience slightly smaller shifts in ADHD symptoms across their cycles. However, having a higher overall symptom count on average consistently predicted a lower ADHD-related quality of life.
Future studies will need to incorporate direct hormone sampling to precisely map biological changes to behavioral shifts. Researchers might also explore how oral contraceptives or specific antidepressants impact this hormonal cycle in neurodivergent populations. Some clinics have already begun testing symptom-based dosing regimens, where patients take slightly more medication during the specific weeks when their natural hormones work against them. Tracking these targeted interventions could eventually help doctors provide highly individualized care for women navigating adult ADHD.
The study, “Changes in ADHD Symptoms and Mood Across the Menstrual Cycle in Females Treated With Stimulants: A Pilot Study,” was authored by Rebecca Zaritsky, Stephanie C. Reed, and Suzette M. Evans.
URL: https://www.psypost.org/how-the-menstrual-cycle-alters-adhd-symptoms-in-women-taking-stimulants/
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Author Carlo Carrasco @[email protected]
2026-10-25

A Look Back at X-Men 2099 #24 (1995)

Disclaimer: This is my original work with details sourced from reading the comic book and doing personal research. Anyone who wants to use this article, in part or in whole, needs to secure first my permission and agree to cite me as the source and author. Let it be known that any unauthorized use of this article will constrain the author to pursue the remedies under R.A. No. 8293, the Revised Penal Code, and/or all applicable legal actions under the laws of the Philippines.
Welcome back, superhero enthusiasts, 1990s culture enthusiasts, X-Men fans and comic book collectors! Today we revisit the Marvel Comics shared universe through one of the many tales of the X-Men 2099 monthly series.
One of the things I enjoyed the most about the X-Men 2099 series was its expansion of the 2099 universe lore as well as the portrayal of uncertainty that mutants deal with in the more desolate parts of America (far away from Spider-Man 2099 and Punisher 2099). Along the way, the futuristic X-Men themselves were really torn down by a series of unfortunate events that includes the departure of their founder and leader Xi’an (eventually Controller X of the Theater of Pain). With the team members separated by circumstances, they were given their own tales and character development periods.
With those details laid down, here is a look back at X-Men 2099 #24, published by Marvel Comics in 1995 with a story written by John Francis Moore and drawn by Ron Lim.
The cover.
Early story
The story begins in southern California where the Slaughterhouse gets shaken by the force of a gamma bomb that detonated far away. Inside the facility, Quiver and Sham struggle as they sneak carefully. They intend to escape even though the odds are against them.
Nearby, the demonic Theater of Pain leader Brimstone Love strikes Junkpile (now with SHIELD serving the new President Doom 2099) hard and seriously damages him. With Junkpile down, the evil leader believes that Doom’s best is a failure and the Theater of Pain has nothing to fear from him. Brimstone Love issues an order to have Junkpile disposed.
Several feet below the ground, Skullfire and Luna find themselves contained after getting captured. For Luna, Xi’an is a lost cause and she prefers to find Shakti and escape. Skullfire, however, insists on pursuing Xi’an even though their situation looks very hopeless. As the two try to escape, they end up facing the muses of the Theater of Pain’s bloodsport arenas…Euphoria, Felicity and Bliss. Luna was previously one of them…
Quality
Shakti represents the remaining X-Men while her former superior Xi’an has fully sided with the evil Theater of Pain as Controller X.
Being the comic book that immediately precedes issue #25, there is a lot of creative stuff here that X-Men 2099 fans will enjoy. The Moore-Lim duo took their time in setting up the eventual restoration of the futuristic X-Men as a team while carefully telling the dangerous events they will face with the Theater of Pain standing in their way.
Fans can find small moments that can be enjoyable or even intriguing to discover such as Luna facing off with her Theater of Pain “sisters” of long ago, the reuniting of Bloodhawk with Meanstreak and Krystalin followed by the justification behind their decision to get involved, and more. What I find very compelling to read is Shakti’s interaction with Controller X (Xi’an behind the mask obsessively serving the Theater of Pain) and the rising danger the Theater’s operation is brewing. There is richness in the dialogue between Shakti and Xi’an, and the symbolic division between them – Shakti representing the X-Men and their former leader Xi’an now serving the evil ones – is really powerful to see.
The storytelling as a whole has a lot of depth behind it, and the build-up of the events is undeniably powerful. As the story progressed, the tension also grew stronger and you can feel the danger that awaits the futuristic X-Men.
Conclusion
Bloodhawk is back with Meanstreak and Krystalin.
X-Men 2099 #24 (1995) succeeds a lot on setting-up the big conflict of issue #25 and it also sets in motion the eventual reuniting of the X-Men. Along the way, there are small details here and there that will compel you to revisit previous X-Men 2099 issues. Brimstone Love’s evil was emphasized more here and Controller X/Xi’an’s determination to serve him and make a lot of people suffer under the Theater’s operation added a lot of tension to the plot. Consider this a very important read before reading X-Men 2099 #25.
Overall, X-Men 2099 #24 (1995) is recommended.
+++++
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THE ONENESS of HUMANITY @[email protected]
2026-10-25

Taxpayers Alliance Against Genocide (TAAG): “We Will Not be Intimidated!”

Article republished by Jerry Alatalo | October 25, 2026
[Editor’s note: The non-responses of U.S. President Donald Trump and Israel Prime Minister Benjamin Netanyahu to this significant international law development have been widely described by veteran world affairs observers as being akin to deer-in-headlights. Please share the article far and wide. Please feel free to respond in the comments. Thank you very much. Peace.]
***
We Beat The Trump Administration In Court
October 24, 2026
Tariq Ra’ouf, Mondoweiss.
Now we’re going to the ICC to hold the U.S. accountable for the Gaza genocide.
A recent lawsuit successfully challenged the Trump administration’s assault on the International Criminal Court. Now, families like mine, whose relatives have been killed with U.S.-supplied weapons in Gaza, might finally have a chance at justice.
Over the course of the last three years, my family has lost 49 family members in Gaza. Their bodies were torn apart by shrapnel, crushed by falling buildings, and mutilated by bullets and munitions made and provided by the United States of America. Now, the U.S. is trying to dismantle the International Criminal Court, the only international court that can criminally prosecute U.S. officials for aiding and enabling genocide, by sanctioning the Court and attempting to intimidate anyone, including U.S. citizens, from working with them. So, in response, I joined others to sue this administration in order to take them to the very court they’re trying to destroy.
I’m a Palestinian-American organizer, and I helped establish Taxpayers Alliance Against Genocide (TAAG), which recently partnered with Democracy for the Arab World Now (DAWN) to sue the Trump administration so we can continue much-needed legal and advocacy work. Over the course of over a year, TAAG worked with an international legal team to prepare evidence against top U.S. officials for aiding and enabling the genocide of my people, but up until now, we haven’t been able to submit our evidence to the ICC because of this administration’s attacks and sanctions on the court and its judges.
Thankfully, that will now change after we successfully challenged the Trump administration in court. On October 13 we won a preliminary injunction that blocks this administration from being able to target our groups for working with sanctioned parties, clearing the way for us to proceed to the ICC. For the first time since the genocide started, I am beginning to see a glimmer of hope that justice will be found for the dozens of family members I have lost in Gaza.
The Trump administration’s war on the ICC
Long before the Trump administration sanctioned the entirety of the International Criminal Court (ICC) on October 9, it first enacted Executive Order 14203 in February of 2025. This executive order granted administration officials the ability to sanction foreign nationals for supporting the ICC’s investigations into U.S. and Israeli parties guilty of committing war crimes and their complicity in genocide. Under this Executive Order (EO) , the Trump administration also sanctioned leading Palestinian human rights groups al-Haq, al-Mezan, and the Palestinian Center for Human Rights (PCHR), as well as the UN Special Rapporteur for the Human Rights Situation on the Occupied Palestinian Territory, Francesca Albanese, along with other ICC officials.
What many people don’t know is that the EO that allowed the administration to sanction these parties doesn’t only go after foreign nationals, it also makes it a federal offense — punishable by a $1 million dollar fine and/or 20 years in prison — for anyone, including American citizens, who provides or receives a “service” to or from a sanctioned party. This provision of the EO is exactly why TAAG and DAWN took this administration to court: Our lawsuit argued that the EO violates our First Amendment rights as American citizens to speak, associate, and engage in Palestine-related human rights advocacy with these parties. As a founding member of TAAG, I can tell you firsthand how these sanctions have prevented us and organizations like DAWN from being able to continue our work sharing evidence and advocacy ideas with people like Albanese and ICC officials, because the definition of “service” is so broad and undefined — the US can interpret it as being anything that confers a benefit to the recipient.
In an October 9 video of Marco Rubio announcing the actions against the ICC, he tries defending the administration’s actions by calling the ICC a “rogue court,” which the U.S. is “not subject to the jurisdiction of.” But Rubio leaves out a fundamental principle of international law: A country does not have to join the ICC for its nationals to fall within the Court’s jurisdiction.
If a country that is not a State Party to the Rome Statute commits a war crime on the lands of a country that is a state party, the perpetrator of that crime can be investigated. Given that Palestine is a State Party to the Rome Statute, and that Gaza (and the West Bank) are classified as Occupied Palestinian Territories by the United Nations, the ICC has every right to investigate and prosecute the United States and Israel for the insurmountable war crimes committed against my family members and thousands of other innocent civilians.
It is clear that the only “protections” this administration are seeking are for the citizens responsible for aiding and abetting war crimes, which after three years, we have plenty of evidence of. Anthony Aguilar, a retired US special forces officer and whistleblower who resigned from his work with the Gaza Humanitarian Foundation (GHF) aid distribution centers in Gaza, has gone on record saying he saw the Israeli Defense Forces (IDF) shooting at crowds of Palestinians, and in an interview with the BBC says he saw Israeli and American military forces of the GHF using an “indiscriminate and unnecessary force against a civilian population, an unarmed, starving population.”
Rubio consistently mentions the idea of “protecting American citizens” from investigations by the ICC, but the citizens he is referring to are the ones committing the war crimes he doesn’t want investigated. What about American citizens whose families have been killed with U.S.-supplied weapons? Who protects our right to seek justice?
In sanctioning the entirety of the ICC, the Trump administration is not only trying to cripple any attempt to hold them accountable for their war crimes, but they’re also telling American citizens like myself that if you dare to seek justice for the family members you’ve lost, we’ll imprison you.
The struggle for accountability moves forward
Now that the courts sided with us in our lawsuit, we are finally able to prepare for the next steps ahead.
With the injunction in place, TAAG is now moving forward to finalize its evidence submission and plans to imminently send this to the ICC. We are also rallying support, within the US and beyond, for human rights organizations and advocates for the ICC to act responsibly upon this massive collection of evidence, in hopes that more parties take appropriate legal actions where possible. We fundamentally believe that grassroots movements like ours, and so many throughout the world, are the ones who must step up where our leadership has failed, and build the democratic, popular power capable of ensuring that legal action against genocide is taken and enforced.
We also hope that our actions inspire more American citizens to speak up against this administration and the priorities of our government. I helped found Taxpayers Alliance Against Genocide because I’m sick of our tax dollars being used against my own family in Gaza, while our communities here in the U.S. suffer from a lack of housing, education and healthcare. Those of us within TAAG recognize that we have a duty as American citizens to hold our nation accountable for the war crimes they commit, not just in Gaza but all over the world. We will not rest until we’ve exhausted every avenue to seek justice and accountability for these war crimes. We will continue collecting evidence, and pursuing every available legal avenue, moving forward with our crucial evidence work for a prospective submission to the ICC and advance our efforts to hold U.S. officials accountable for crimes against humanity.
We will not be intimidated into abandoning our work, our families, or the principle that no one is above the law.
***
(Source/credit: ScheerPost.com)
#DonaldTrump #GenocideProsecution #InternationalCriminalCourt #InternationalLaw #PoliticalScience #TAAG #WhatIsTheInternationalCriminalCourt

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Psychology News Robot @[email protected]
2026-10-25

DATE: October 25, 2026 at 10:00AM
SOURCE: PSYPOST.ORG
** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
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TITLE: Anger and fear nudge voters toward different kinds of leader faces, study suggests
URL: https://www.psypost.org/anger-and-fear-nudge-voters-toward-different-kinds-of-leader-faces-study-suggests/
The specific emotions that political issues stir up in voters may subtly shift the physical traits they look for in a leader. A new study published in Cognition and Emotion indicates that when people think about a policy problem that makes them angry, they lean slightly more toward dominant-looking leaders than when they think about one that makes them afraid. Fear, by contrast, appears to put more weight on whether a leader’s face looks trustworthy.
When people look at a stranger’s face, they form instantaneous impressions about that person’s character. As a 2008 behavioral and computer modeling study found, these rapid evaluations boil down to two main facial qualities: trustworthiness and dominance. Trustworthiness is an impression of a person’s intentions, signaling whether they appear warm, cooperative, and safe to approach. Dominance is an impression of physical strength and assertiveness, signaling a person’s capacity to impose their will or confront rivals.
These split-second facial judgments influence real-world voting decisions, and the traits voters prefer often depend on the surrounding political environment and their own personal beliefs. For instance, a 2025 study by members of the team behind the new research, covered by PsyPost, found that people who are generally distrustful of information from others tend to hold more rigid, dogmatic beliefs and more conservative views, which in turn predicts a preference for dominant, authoritarian-looking leaders. Broader contexts matter as well, with voters generally favoring dominant faces during wartime or conflict and trustworthy faces during peaceful periods.
Different emotional reactions to the same crisis can also pull people in different directions. Although anger and fear are both unpleasant, high-stress emotions, a 2001 study on emotion and risk showed that they trigger opposing mindsets: anger makes people feel certain and willing to take risks, whereas fear makes people feel vulnerable and cautious.
In the political arena, a 2025 panel study of Ukrainians conducted during the Russian invasion found that citizens who reacted to the war with aggressive, angry emotions showed stronger preferences for dominant leaders (and for the other leader traits measured, such as warmth and competence), while those who reacted with fear showed little change in their preferences.
Most research on these emotional dynamics has focused on wars, terrorist attacks, or major geopolitical crises. Far less is known about how anger and fear shape leader evaluations during everyday domestic politics, where citizens routinely react to policy debates over the economy, healthcare, or public safety. The research was led by Mariana von Mohr, a lecturer in social and affective neuroscience at the University of Kent, and conducted at the Centre for the Politics of Feelings at the School of Advanced Study, University of London, which is directed by co-author Manos Tsakiris, a professor at Royal Holloway, University of London.
“Political issues often evoke strong emotions, which can shape political behavior in different ways,” von Mohr told PsyPost. “For example, anger can increase support for tougher, more confrontational political action, whereas fear can lead people to seek reassurance and favor responses that promise greater safety and protection. Here we wanted to understand whether these emotions also shape who people would prefer to vote for when dealing with the issues that concern them most, simply based on how dominant or trustworthy a potential leader looks.”
To investigate this, the researchers recruited 201 American participants through the online platform Prolific, ensuring the group was evenly split between people who identified with or leaned toward the Democratic Party and those who identified with or leaned toward the Republican Party. After removing four individuals who did not pass attention checks during the tasks, the final sample consisted of 197 participants.
The experiment used a within-subjects design, meaning every participant went through both the anger condition and the fear condition in a randomized order. In the anger section, participants viewed a list of 16 major national problems taken from a Pew Research Center survey, such as inflation, gun violence, illegal immigration, and climate change. They were asked to select the single issue that made them feel the most angry, defined as an issue that upsets or annoys them and brings up hostility, and to rate the intensity of their anger on a scale from 0 to 100.
Immediately after reflecting on that issue, participants completed a leader choice task consisting of 36 rapid rounds. In each round, two computer-generated faces appeared side by side on the screen for up to three seconds, and participants had to click on the leader they believed would be most capable of handling their chosen political issue. These nine digital faces were drawn from a standardized scientific database and had been systematically altered to represent different combinations of facial dominance and facial trustworthiness.
In the fear section, the same participants went through an identical process, but this time they selected the political issue from the list that made them feel the most afraid, defined as an issue that worries them a lot and feels like a frightening threat. After rating their fear on the 0 to 100 scale, they completed the 36-round face selection task again to pick the best leader to address that frightening issue.
Across the sample, both emotions were felt intensely, with average intensity ratings of 86.51 out of 100 for anger and 81.95 for fear. The issues most frequently selected for anger were illegal immigration (19.3 percent), gun violence (17.8 percent), and inflation (15.2 percent). For fear, participants most commonly selected climate change (15.2 percent), illegal immigration (14.2 percent), and domestic terrorism (11.2 percent).
The researchers first combined each participant’s choices into an overall index measuring the probability of picking a more dominant and less trustworthy-looking leader. Statistical analyses accounting for emotional intensity and specific policy issues showed that participants had a 49.5 percent probability of choosing a dominant-looking leader when angry, compared to a 46.5 percent probability when fearful, meaning participants on average leaned slightly toward the more trustworthy-looking face in both conditions. How intensely participants felt the emotion was unrelated to their choices. While this 3 percentage point absolute shift between anger and fear is modest, the authors note that even small emotional shifts could matter across an entire population during elections, when campaigns frequently amplify fear- and anger-inducing issues.
To see what drove this shift, the authors broke down the choices round by round to look at how participants weighed dominance cues and trustworthiness cues separately. When participants were in the fear condition, a face’s level of trustworthiness increased the likelihood that it would be chosen, whereas a face’s level of dominance had no statistically significant effect on their choices. When participants were in the anger condition, the positive pull of facial trustworthiness dropped, while facial dominance gained a stronger positive influence on their selections.
“Our findings suggest that how we feel about political issues can influence the kinds of leaders we find appealing, even based on something as simple as their facial appearance,” von Mohr explained. “When angry, people placed greater importance on facial cues of dominance (appearing strong and assertive) and less on trustworthiness. When afraid, they placed more importance on trustworthiness (appearing reliable and benevolent).”
The findings are in line with research covered by PsyPost in 2022, which similarly found that anger increased participants’ preferences for dominant-looking leaders over trustworthy ones. However, that earlier study examined anger triggered by non-political frustrations, whereas the new study suggests that anger tied to real-world policy issues is linked to a similar shift while directly contrasting it against political fear.
The researchers also examined the role of political party affiliation. Consistent with previous studies, Republicans showed a higher overall preference for dominant-looking leaders (52.5 percent probability) than Democrats did (43.5 percent probability), representing a 9 percentage point partisan gap. That gap was about three times as large as the 3-point difference between anger and fear. Even though Democrats and Republicans tended to pick different policy issues as their top concerns, the difference between anger and fear did not vary significantly by party, suggesting the emotions influenced leader choices in similar ways for both groups.
The emotion effects did not vary by participants’ gender and held when accounting for the order in which participants completed the tasks. When the researchers statistically controlled for the exact political issue each participant selected in the round-by-round analysis, the distinct effects of anger and fear on how participants weighed dominance and trustworthiness cues remained stable. In the overall-preference analysis, entering each specific issue as a separate factor left the anger-versus-fear difference about the same size but made the estimate less precise.
As with all research, there are some caveats to consider when interpreting these findings. Because participants were allowed to choose whichever political issue personally angered or frightened them the most, the study cannot completely separate the effect of the emotion from the nature of the policy issue itself. Although the researchers used statistical controls to account for the different issues selected, future experiments that induce anger or fear while keeping the policy topic identical for everyone would help isolate the pure effect of the emotion. The study also compared anger only with fear, without a neutral condition, so it cannot say whether anger made dominant faces more appealing, fear made them less appealing, or both.
Additionally, the faces used in the task were standardized, computer-generated avatars rather than photographs of real politicians. While digital faces allow scientists to precisely adjust facial structure without interference from a person’s hairstyle, age, race, or gender, real-world elections involve all of those visual factors alongside party labels and campaign rhetoric. Finally, the study only tested American voters, so it is not known whether the same patterns hold in other countries. Future research could also examine whether other political emotions, such as hope or disgust, shape leader preferences.
The study, “Political emotions shape preferences for leaders’ facial traits: anger favours dominance, fear favours trustworthiness,” was authored by Mariana Von Mohr and Manos Tsakiris.
URL: https://www.psypost.org/anger-and-fear-nudge-voters-toward-different-kinds-of-leader-faces-study-suggests/
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WE AND THE COLOR @[email protected]
2026-10-25
This InDesign Welcome Kit Presentation Template Doesn’t Just Look Good, It’s Also Easy to Customize

E-Type’s Welcome Kit Presentation Template, a 16-page InDesign file built at 1080 x 1920 pixels for screen presentations, is one of the more complete attempts, I have to say.
This review works from the template’s official preview spread; the full 16-page layout is exactly as E-Type ships it, plus the technical specifications are listed on Adobe Stock. No slides here are rebuilt or restyled. What follows is what’s actually in the file, read page by page, and what a working designer can expect once it opens in InDesign.
Download the kit from Adobe Stock.
Please note that this template requires Adobe InDesign to be installed on your computer. Whether you use Mac or PC, the latest version is available on the Adobe Creative Cloud website—take a look here.
Adobe InDesign Welcome Kit Presentation Template by E-Type. Download the kit from Adobe Stock.
What Is a Welcome Kit Presentation Template, Exactly?
A welcome kit presentation template is a pre-built slide system that agencies and freelancers send to new clients at the start of a project. It usually covers who the studio is, what the engagement includes, and what happens next. Unlike a pitch deck, it isn’t selling the work. It’s confirming the work after the contract is already signed.
That distinction matters for how you judge this template. A welcome kit doesn’t need to persuade anyone. It needs to look organized, feel personal, and get out of the way fast. E-Type’s version is designed specifically for that narrower job.
Inside the 16 Pages: A Breakdown That Matches the Index
The deck opens with a cover slide, then an index page listing six sections: About Us, Case Study, Partnerships, The Team, Manifesto, and Feedback. That index isn’t decoration. Every page after it maps directly to one of those six categories, which is rarer than it sounds in template packs this size.

The Full Page List
Cover (“Welcome Kit”)
Index
What We Do
About Us
Objective (mission, vision, values)
Case Study
A full-bleed quote slide for a client testimonial
Partnerships (four-client grid)
Creators (team intro)
The Team (four-person photo grid)
Manifesto
Offer (a pricing tier table, four rows)
Brief Project
Feedback
Statistics (three metric blocks)
Get In Touch (contact details)
Sixteen slides, zero filler pages. Plenty of template packs pad the count with duplicate layouts wearing a different color. This one doesn’t do that. Every slide earns its spot in the sequence.
How the Template Is Built: What the File Structure Tells You
Both listings confirm the color system runs on a swatch palette rather than hardcoded fills, which means updating brand colors should take minutes, not hours. Change the swatches once, and the pink, coral, and mint blocks update across all 16 pages. That’s standard practice for a well-built template, though plenty of template packs skip it and force a manual hunt through every frame instead.
The listings also confirm photo placeholders sit on their own layer, separate from text and background elements. That layer separation is what makes InDesign’s Content Grabber tool useful here: click an image frame, and the grabber lets you swap the photo without disturbing its crop or position. None of the preview photography ships with the download. That detail trips up first-time template buyers constantly, so it’s worth repeating.
Adding Motion With the Animation Panel
Because this template targets screen presentations instead of print, the Animation panel (Window > Interactive > Animation) is where it earns a second look. Anyone comfortable with that panel can stagger headline text so words appear in sequence or fade a photo in gently rather than having it snap into place. Neither effect is built into the file by default. You add both yourself, per object, through motion presets and per-frame delay settings.
If you’ve never touched the Animation panel, budget an extra hour to learn it. The template doesn’t walk you through that part.
Where This Welcome Kit Presentation Template Wins and Where It Doesn’t
The typography does the heavy lifting here. Bold, condensed, uppercase headlines against a warm off-white background give every page a consistent editorial weight, even before a single word of real content goes in. Pair that with free fonts linked directly in the included help file, and you avoid the usual font-licensing scramble that kills momentum on a Friday afternoon deadline.
Where it struggles: the Offer slide assumes a four-tier pricing structure. If your agency sells one flat retainer, you’ll either delete three rows or stretch thin content across a layout built for density. The Team and Creators slides also assume four people. Solo freelancers will need to rebuild those grids from scratch rather than just deleting a box.
The Six-Screen Trust Arc: A Framework I Use for Judging Welcome Decks
I’ll be direct about this: the Six-Screen Trust Arc isn’t an industry term. It’s a shorthand I started using this year after comparing a dozen welcome kit presentation templates side by side, and it’s useful enough to name here.
The idea is simple. The first six screens of any client-facing welcome deck should move through a specific order: cover, index, who-we-are, what-we-do, mission, and proof. Skip the proof slide, or bury it past screen ten, and the client starts asking questions the deck should have already answered. E-Type’s template follows this arc almost exactly, placing the case study at position six. That placement alone suggests whoever built this has sat through real client onboarding calls.
A second shorthand worth naming: the Narrative-to-Data Ratio, which is just the proportion of storytelling slides (About Us, Manifesto, Feedback) against pure data slides (Statistics, Offer). This deck runs roughly 70/30 in favor of narrative. That’s the right ratio for a welcome kit. A sales deck should flip it.
Who Should Buy This Welcome Kit Presentation Template
Small design studios and solo creative directors get the most value here, specifically teams onboarding clients onto retainer work rather than one-off projects. The vertical 1080 x 1920 format also fits directly into screen-share presentations and Instagram Stories exports, which agencies increasingly use for onboarding content.
Skip it if your pricing model doesn’t fit four tiers, or if you need a landscape deck for a projector presentation. E-Type sells a separate landscape version of the same system, worth checking if that’s your format.
Quick Reference: Welcome Kit Presentation Template Specs
SpecDetailDesignerE-Type (Adobe Stock contributor)FormatAdobe InDesign (.indd / .idml)Page count16 fully editable pagesDimensions1080 x 1920 px (vertical, screen-ready)FontsFree fonts, linked in the included help filePhotographyPreview only, not included in the downloadColor systemSwatch-based, editable in one clickAnimationManual, via InDesign’s Animation panelBest forAgency and freelance client onboarding decks
Where to Find It
The kit is available on Adobe Stock. Pricing on Adobe Stock runs through its standard template licensing, either a subscription credit or a single-template purchase, so check current terms before buying.
Download the kit from Adobe Stock.
Frequently Asked Questions
Does the welcome kit presentation template include the stock photos shown in the preview?
No. The preview photography is for display only. You add your own images to the 16 placeholder frames after purchase.
Can I edit this welcome kit presentation template in Canva instead of InDesign?
Some versions of E-Type’s welcome kit templates ship with a Canva file alongside the InDesign version. Check the specific listing you’re buying, since not every variant includes both formats.
Is the 1080 x 1920 format good for Instagram Stories or LinkedIn carousels?
Yes. That vertical ratio matches Instagram Stories natively and works well as an exported image carousel for LinkedIn, though you’ll need to export each page individually.
Do I need InDesign experience to use this template?
Basic experience helps, especially for the animation panel. Swapping text and photos requires almost no InDesign knowledge, since the Content Grabber handles image replacement without disturbing the layout.
How do I add the fade and sequential text animations to the slides?
Open Window > Interactive > Animation, select a text or image frame, then assign a motion preset like Fade In or Appear. Set the delay for each object manually to control the sequence.

Browse WE AND THE COLOR’s Templates category for more.

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#AdobeInDesign #AdobeStock #brandKit #InDesignTemplate #presentation #presentationTemplate #welcomeKit
#adobeindesign #adobestock #brandkit #indesigntemplate #presentation #presentationtemplate
Dev Community @[email protected]
2026-10-25
Professionalism is often misunderstood as simply being good at your job. In reality, it involves much more than technical knowledge.
Being a professional means knowing your strengths, developing your expertise, delivering quality work, and taking responsibility for the results.
Why Focus Matters
1. Depth Over Superficial Knowledge

Trying to learn everything at once can leave you with a broad but shallow understanding of many subjects.
Focusing on a specific field allows you to understand its fundamentals, recognize complex problems, and make better technical decisions.
For example, a developer who focuses on backend engineering can gradually build deeper knowledge of APIs, databases, authentication, system design, and performance optimization.
2. Trust Is Built Through Consistency

Clients, employers, and teammates need people they can rely on.
Trust comes from delivering what you promise, communicating clearly, meeting deadlines, and being honest about what you can and cannot do.
You don't need to know everything to be a professional. You need to be reliable about the things you take responsibility for.
3. Better Results Through Specialization

When you understand a field deeply, you can often solve problems more efficiently because you recognize familiar patterns and understand the trade-offs behind different solutions.
This doesn't mean you should stop exploring other technologies. It means you should build a strong foundation before constantly jumping between unrelated areas.
4. Knowing Your Limits Is a Strength

Imagine you're a web developer and someone asks you to design a complete brand identity.
You might have some experience with design tools, but that doesn't necessarily make you a professional designer.
A professional response could be to explain your limitations, collaborate with a designer, or take on only the parts you're qualified to handle.
Accepting every task just to prove yourself can lead to poor results. Recognizing when you need help is part of professional judgment.
Professionalism Goes Beyond Technical Skills

Being skilled at your job is only one part of the equation.
Professionalism also requires:
Continuous learning: Keeping your knowledge relevant as tools and practices evolve.
Accountability: Taking ownership of mistakes and working toward solutions.
Communication: Explaining decisions, asking the right questions, and setting realistic expectations.
Attention to quality: Considering maintainability, reliability, and long-term consequences, not just whether something works today.
Ethics: Being honest about your skills, progress, and limitations.
These qualities matter just as much as technical ability when working with a team or building a career.
The Balance: Specialize Without Becoming Narrow-Minded

Focusing on your expertise doesn't mean refusing to learn anything outside it.
A backend developer still benefits from understanding frontend fundamentals. A frontend developer benefits from knowing how APIs, databases, and authentication work.
The goal is to develop T-shaped skills: deep expertise in one area, supported by enough knowledge of related fields to collaborate effectively and understand the bigger picture.
You don't have to do everything yourself. You need to understand your role, recognize how it connects to others, and know when collaboration is the better solution.
Final Thoughts

Professionalism isn't about pretending to know everything or accepting every opportunity that comes your way.
It's about understanding what you do well, improving where you fall short, delivering work you can stand behind, and knowing when to ask for help.
Focus on your craft. Build depth. Take responsibility. Let the quality of your work earn people's trust.
That's a more sustainable path to becoming a professional than trying to be good at everything at once.#programming #career #beginners #productivity #software #coding #development #engineering #inclusive #community
Professionalism in Software Development: Focus on Your Strengths
#career #beginners #productivity #inclusive #engineering #development
Dev Community @[email protected]
2026-10-25
I used to treat ChatGPT like a vending machine. Need a React hook? Drop a coin. Need a PR summary? Drop another. Every ask started from zero — stack, conventions, what “done” meant — and every reply drifted a little from how we actually ship.
The waste wasn’t the tokens. It was re-explaining our frontend rules every single time, then cleaning up the same mistakes.
So I stopped pasting one-offs and started treating prompts like code: named, versioned, and checked into the repo.
One-off asks don’t compound

A throwaway chat is fine for a throwaway question. Frontend work rarely is throwaway:
“Write a Modal” without our focus-trap / Escape / return-focus rules
“Add loading states” without our skeleton vs spinner convention
“Review this PR” without what AI should skip vs what a human owns
“Refactor this form” without our form library and error-message pattern
You get something that compiles. You don’t get something that matches the last three PRs. The next person (or the next you) pays the tax again.
What belongs in a prompt library

I keep a small folder — prompts/frontend/ — not a novel. Each file is one job, one outcome shape.
Mine look like this:
prompts/frontend/
pr-review-ai.md # what the model checks vs leaves for humans
new-component.md # scaffold + a11y buckets + token rules
design-token-change.md # changelog / RFC checklist before merge
test-plan.md # real coverage vs fake green
agent-ui-diff.md # component boundaries, no random token edits
accessible-react.md # roles / keyboard / contrast report required
If a prompt isn’t reused at least twice a month, it doesn’t earn a file. Sticky notes in chat stay sticky notes.
The shape every prompt gets

Same skeleton so humans and agents know where to look:
# <job name>
## When to use
One sentence. If it doesn’t match, don’t force it.
## Context to paste
- Framework / router / form lib (or “read from repo”)
- Design tokens source of truth
- Related golden files (paths)
## Hard rules
Bullet list the model must not violate.
Prefer native HTML, keep tokens, no drive-by refactors, etc.
## Output format
Numbered sections the reply must include.
Missing a section = reject the turn.
## Done looks like
Concrete acceptance — not “looks good.”
The “output format” line is the whole game. Models fill schemas. They invent vibes when you don’t give one.
From ChatGPT paste to agent / editor default

Once the files exist, wire them where work actually happens:
Cursor / Copilot / Claude project instructions — point at prompts/frontend/ or inline the two you use daily.
PR bot / agent — load pr-review-ai.md as the system brief; humans still own product judgment.
Slash or snippet — @new-component expands the scaffold prompt so nobody retypes it.
CI-ish habit — if the agent reply skips a required section, treat it like a failed check and re-run with the same file.
You don’t need a fancy prompt CMS. Markdown in git is enough. Review prompt changes in PRs the same way you’d review eslint config.
A tiny migration story

Week 1 I had twelve ChatGPT threads titled “modal again.” Week 3 I had six files and deleted half the threads.
What changed in practice:
New Button / Modal / Combobox asks started from the same a11y report shape
Token edits stopped sneaking hex literals into components
AI PR comments stopped arguing about naming when the prompt said “leave naming to humans”
Same models. Less re-prompting. The library held the team memory the chat UI kept forgetting.
What not to put in the library

Product pitches and marketing copy (keep prompts about shipping UI)
One-shot debug sessions that will never recur
Entire style guides pasted twice — link the real doc instead
Secrets, private URLs, or customer data as “examples”
A prompt library is DX infrastructure. If it reads like a blog draft, move it to the blog.
Start with three, not thirty

Tonight, pick the three asks you keep repeating. Write each as one markdown file with When / Rules / Output / Done. Drop them in the repo. Point your editor agent at that folder.
Next time you open a blank ChatGPT box for a frontend task, open the matching file instead. Paste once. Reuse forever. That’s the whole upgrade — from one-off asks to a library your future self (and your agents) can actually find.#frontend #ai #dx #productivity #software #coding #development #engineering #inclusive #community
From One-Off ChatGPT Asks to a Reusable Frontend Prompt Library
#ai #community #software #coding #development #engineering
The Philosopher King @philosopherkinglife.wordpress.com@philosopherkinglife.wordpress.com
2026-10-25
How to Use AI Agents for Personal Finance and Wealth Building: The Master of Coin Guide (2026)

Why Most People Use Artificial Intelligence Backwards (And How to Fix It)
Every day, millions of people open an AI chat window and ask the wrong question: "What stock should I buy today?" or "How do I get rich quickly with AI?" They treat artificial intelligence like a carnival oracle, hoping a single prompt will absolve them of the discipline required to build wealth. When the oracle gives them generic, recycled advice, they walk away convinced that AI is overhyped.
The divide between those who build sovereign wealth in the coming decade and those who remain trapped in financial noise comes down to a single shift: moving from passive AI chatbots to autonomous AI agents.
According to Backbase’s 2026 AI in Wealth Management analysis, while 95% of financial institutions have experimented with generative AI, only 27% report substantial impact because most remain stuck in passive chat pilots rather than deploying agentic workflows. Meanwhile, McKinsey’s 2035 Wealth Management Outlook projects that AI agents and intelligent automation will form the operational backbone of modern wealth management—delivering an estimated 30% to 40% productivity leap in portfolio intelligence and financial research.
You no longer need a Wall Street family office to command that same operational backbone. In the philosophical framework of The Philosopher King, your mind is the fifth-dimensional architect, and AI agents are four-dimensional force multipliers. Here is the step-by-step guide to building your own autonomous AI agent workflow to master your coin, reclaim your time, and eliminate emotional financial mistakes.
Step 1: Shift from Single Prompts to a Multi-Agent Financial Council
A chatbot waits for you to ask a question and gives you a single stream of text. An AI agent is given an objective, access to real-world tools (such as web search, financial data APIs, and spreadsheets), and the autonomy to break a complex goal into sequential steps.
When managing your personal treasury—what we call the discipline of the Master of Coin—relying on a single AI persona creates blind spots. Instead, structure your workflow as a three-agent council built on deliberate discipline (the meticulated kind):
The Intelligence Scout (Macro and Fundamental Research Agent): Configured to scan earnings transcripts, SEC filings, macroeconomic liquidity trends, and sector developments on a recurring schedule, distilling hours of noisy financial media into a five-minute first-principles brief.
The Treasury Comptroller (Cash-Flow and Allocation Auditor): Tasked with auditing your monthly burn rate, identifying recurring subscription bloat, tracking your savings velocity, and calculating whether your actual asset allocation matches your target risk profile.
The Red-Team Adversary (Risk and Pre-Mortem Agent): Programmed with a single directive: to ruthlessly challenge your investment ideas. Whenever you are tempted by market euphoria or FOMO, this agent stress-tests your thesis by finding every balance-sheet weakness, valuation risk, and macroeconomic headwind you ignored.
Step 2: Choose Your AI Agent Stack and Connect Your Financial Data
Whether you are a non-technical sovereign investor or a developer building custom pipelines, the open-source and commercial ecosystem for AI agents has matured rapidly. As documented in McKinsey’s 2026 State of AI Global Survey, agentic orchestration is now one of the fastest-scaling capabilities worldwide. Depending on your technical comfort, consider these proven building blocks:
Model Context Protocol (MCP) for Secure Data Connections: Introduced as an open standard by Anthropic and detailed in the official Model Context Protocol documentation, MCP acts like a universal USB-C port for AI applications. It allows your AI agent to securely read local CSV exports, Google Sheets ledgers, and market data feeds without uploading your private credentials to untrusted third parties.
CrewAI for Role-Based Financial Teams: If you want specialized agents to collaborate on a research report, CrewAI lets you define distinct roles (for example, a Fundamental Analyst Agent and a Risk Manager Agent) that hand off structured outputs to one another automatically.
LangGraph for Stateful, Auditable Workflows: Built by the LangChain team, LangGraph models agent workflows as a stateful graph with built-in checkpointing and human-in-the-loop pauses—ideal when you want an agent to pause and wait for your explicit approval before logging or executing a decision.
OpenAI Agents SDK and Microsoft AutoGen for Multi-Agent Debate: Using the OpenAI Agents SDK or Microsoft AutoGen, you can pit a Bull Agent against a Bear Agent on any asset class, forcing both agents to cite verifiable data until only the strongest first-principles truth survives.
Step 3: Enforce the "Human-in-the-Loop" Sovereign Rule
One of the most common questions people ask when researching AI for investing is: "Should I connect an AI agent directly to my brokerage account and let it trade autonomously?"
For the vast majority of individual investors, the answer is an emphatic no. As highlighted in Forbes Finance Council’s analysis on AI agents in finance and McKinsey’s research on AI agent performance management, autonomous agents require strict governance boundaries, audit trails, and human oversight.
"An AI agent is your tireless chief of staff in the fourth dimension, but your consciousness remains the sovereign on the throne. Never abdicate final judgment to a machine."
To protect your capital while maximizing leverage, enforce these four iron rules:
Read-Only Access by Default: Give your agents read access to market data, news feeds, and sanitized CSV exports of your budget—never raw write/withdrawal permissions to your primary bank or brokerage accounts.
Require Source Citations on Every Claim: Instruct your research agents to attach a direct link to the primary filing, earnings release, or statistical dataset for every number they report, eliminating hallucinated metrics.
Automate the Preparation, Never the Conviction: Use agents to compress 10 hours of reading into 15 minutes of structured synthesis, freeing your mind to make low time-preference decisions with clarity.
Log Every Thesis for Post-Mortem Review: Have your agent archive every investment decision alongside the exact assumptions you held at the time, creating an unvarnished mirror of your cognitive biases over time.
Step 4: Copy This 15-Minute Daily "Master of Coin" AI Routine
You do not need a complex server cluster to start today. Begin with a simple, repeatable daily cadence using scheduled AI tasks:
06:00 AM — The Sovereign Morning Brief: Schedule an AI agent to scan overnight macroeconomic developments, central bank releases, and any material news affecting your watchlist, delivering a bulleted summary stripped of sensationalist clickbait before you open your eyes.
Sunday Evening — The Red-Team Portfolio Audit: Feed your current asset allocation and investment thesis into an adversarial prompt: "Act as a first-principles risk officer. Identify the top three hidden correlations, liquidity risks, or macro assumptions that could cause a 25% drawdown in this portfolio over the next 12 months."
First of the Month — The Capital Velocity Check: Have your agent categorize the prior month’s cash flows into three buckets—Sovereign Asset Accumulation, Productive Skill/System Building, and Fleeting Consumption—so you can see exactly where your life-force is compounding or leaking.
Join the Conversation: How Are You Using AI Agents to Master Your Coin?
We are living through the greatest democratization of analytical leverage in human history. Those who pair ancient philosophical restraint with modern agentic intelligence will operate with the clarity of an institution and the agility of a sovereign individual.
I want to hear from you in the comments below:
Are you currently using AI agents or scheduled workflows to research investments, track expenses, or stress-test your financial decisions?
Which part of managing your personal finances drains the most time or emotional energy—and how would you design an AI agent to take it off your plate?
Where do you draw the line between automated AI analysis and human intuition when putting your capital at risk?
Drop your thoughts, favorite prompts, or questions in the comments—let us sharpen our frameworks together.
All is One | One is All
#aiAgents #ArtificialIntelligence #automation #financialSovereignty #howTo #MasterOfCoin #personalFinance #wealthBuilding
#aiagents #artificialintelligence #automation #financialsovereignty #howto #masterofcoin
Psychology News Robot @[email protected]
2026-10-25

DATE: October 25, 2026 at 08:00AM
SOURCE: PSYPOST.ORG
** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
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TITLE: Neuroscientists are digging into a worrying link between polluted air and children’s brain wiring
URL: https://www.psypost.org/neuroscientists-are-digging-into-a-worrying-link-between-polluted-air-and-childrens-brain-wiring/
Different sources of fine particle air pollution show distinct, subtle links to how the developing brains of children communicate, according to new nationwide research published in Journal of Environmental Psychology. Particles from traffic, coal burning, and agricultural fertilizers were each tied to their own patterns of connectivity in networks involved in sensory processing and higher-order thinking. The findings offer early evidence that where air pollution comes from may matter, not just how much of it children breathe.
Outdoor air pollution contains fine particulate matter, commonly known as PM2.5, which consists of airborne particles smaller than 2.5 micrometers across. Because these specks are tiny enough to inhale deep into the lungs and enter the bloodstream, they may reach the brain.
The brain coordinates its activities through large-scale functional networks, which are collections of brain regions in different locations whose activity tends to rise and fall together. Researchers can measure this synchrony with brain scans that track changes in blood flow while a person lies still and rests. During childhood and the transition into adolescence, these communication channels continue to reorganize as youth develop adult reasoning, sensory integration, and emotional control.
Past research has linked outdoor air pollution to differences in these communication patterns. In a 2023 longitudinal study, members of the team behind the new research used data from the Adolescent Brain Cognitive Development Study, a large national project following thousands of American youth. They found that children with higher exposure to fine particles and other pollutants showed differences in how their resting brain networks matured across early adolescence. Additionally, a study covered by PsyPost in 2023 found that briefly breathing diesel exhaust in a controlled experiment temporarily weakened connectivity within the default mode network, a set of regions most active during mind-wandering and self-reflection, in adults.
Fine particulate matter is not a single chemical substance, but rather a complex blend created by both natural processes and human activities. Addressing this complexity, a 2024 study by members of the same team, using data from the same national project, found that specific chemical ingredients and pollution sources had varying relationships with children’s memory and thinking skills. The new study uses the six pollution sources identified in that work. The investigation was led by Katherine L. Bottenhorn of the University of Southern California and Florida International University and Megan M. Herting of the University of Southern California.
For the new study, the authors again drew on data from the Adolescent Brain Cognitive Development Study, which spans 21 research sites across the country. Data from one site were dropped because of hardware problems, leaving 20 sites for the cross-sectional analysis; the longitudinal analysis was limited to the 12 sites that used the same brand of scanner. The researchers analyzed brain scans from 6,291 children who were between 9 and 11 years old, as well as a subset of 1,972 participants scanned on consistent magnetic resonance imaging equipment roughly two years later, between ages 11 and 13.
The participants’ resting-state brain activity was recorded while they relaxed with their eyes open. The scientists mapped interactions among 12 functional networks, encompassing primary sensory networks like vision and hearing, attention circuits, and higher-order systems. The salience network helps flag important events and shift attention, while the cingulo-opercular network, described here as a cognitive-control network, helps keep attention on a task.
The team used annual air quality models tied to each child’s home address to estimate their exposure to 15 chemical components of air pollution during 2016. By grouping these chemicals mathematically, the authors estimated exposure to six primary sources: crustal materials (such as soil and dust), ammonium sulfates (largely from coal combustion), biomass burning (such as wood smoke and wildfires), traffic emissions, ammonium nitrates (often linked to agricultural fertilizers), and industrial fuel combustion.
The researchers applied a machine learning method called connectome-based predictive modeling to identify connectivity patterns related to each pollution source. To see if the results could generalize across the country, the scientists trained models using participants from all study sites except one, and then tested how well that model predicted pollution levels for children living at the held-out site. The analyses adjusted for several potential confounding variables, including household income, race and ethnicity, perceived neighborhood safety, neighborhood population density, distance to major roads, urbanicity, physical activity, screen time, scanner hardware, and head movement inside the scanner.
The cross-sectional analysis at ages 9 to 11 identified consistent links between specific pollution sources and connectivity across sensory and motor networks. Greater exposure to traffic emissions related to weaker functional communication between visual and mouth-related motor networks, as well as between auditory and cognitive-control circuits. In contrast, higher exposure to ammonium sulfates was linked to stronger communication between sensory systems and higher-order cognitive networks, including the default mode network. Crustal materials, industrial fuel burning, and total particulate matter were each tied to a handful of sensory and attention-related connections, while biomass burning showed no consistent pattern at ages 9 to 11.
Higher-order networks, which are involved in introspective thought, stimulus processing, and, according to some researchers, vulnerability to mental health problems, were also tied to specific pollution sources. Exposure to ammonium nitrates was associated with stronger communication between salience and cognitive-control networks, but weaker communication within memory networks and between attention networks and other networks.
Over the following two years, the researchers found consistent patterns of change linked to three sources. Higher traffic exposure was tied to increasing connectivity over time between hand-movement networks and attention-related areas, but decreasing connectivity between auditory and cognitive-control networks. Higher biomass burning exposure was linked to increasing connectivity among attention-related networks, and higher ammonium sulfate exposure to weakening connectivity between an attention network and a hand-movement network, while crustal materials, ammonium nitrates, industrial fuel burning, and total particulate matter showed no consistent pattern of change.
Despite identifying consistent connections in the training data, the connectivity patterns accounted for less than 1 percent of the variation in children’s exposure at ages 9 to 11, and no more than about 2 percent for changes over time. When tested on children from held-out study sites, the models performed no better, and often worse, than simply guessing the average exposure. Prediction errors were largest at sites in the western United States and at sites where exposure levels varied more from child to child.
The authors concluded that, after accounting for other factors, these pollution sources likely play only a small role in differences in brain connectivity between children, and that the links may not apply to youth in other regions. They also note that the patterns for biomass burning and ammonium sulfates held up best across sites, while those for crustal materials and traffic emissions deserve more caution.
The findings are in line with a study covered by PsyPost earlier this year, which found that young teenagers exposed to elevated particulate pollution showed less brain network maturation over two years, though that previous research evaluated an overall brain maturity score rather than connectivity across specific functional networks and distinct pollution sources.
“This is an important and timely topic, and the paper complements a growing body of evidence that children’s physical environments in general, and air pollution exposure in particular, are associated with their neural and cognitive development,” said Monica Rosenberg, an associate professor of psychology at the University of Chicago, director of the Cognition, Attention, and Brain Lab, and a co-developer of the brain-modeling technique used in the study, who was not involved in the research.
“The longitudinal aspect of this work is unique and is a major strength—the authors didn’t just show associations between brain networks and pollution at a single time point but found that pollution predicts developmental trajectories of networks,” Rosenberg told PsyPost.
There are a few things to keep in mind when interpreting these findings. The study used observational data and statistical associations, meaning it cannot establish that breathing these specific particles directly caused the observed network differences. Because the research took place across many regions with varying environmental mixtures, local factors that were unmeasured might account for some of the geographic discrepancies.
The authors also did not apply a statistical correction for the many connections they tested at once, so some of the identified links could be false positives, and the analysis was exploratory, with no advance predictions about which networks would be affected. Because each study site used scanners from a single manufacturer, differences between regions cannot be fully separated from differences in scanning equipment.
Rosenberg noted that the geographic inconsistency represents an important caveat for understanding the results. “One caveat is that the primary predictive models did not successfully generalize across sites, meaning that associations between brain network patterns and air pollution were inconsistent across geographic locations,” she said. “In addition to the thorough analyses presented in the paper, it would be interesting to test whether the observed brain changes were associated with cognitive and/or attentional performance since the implications of neural changes alone aren’t always clear.”
Another caveat involves how exposure was measured. The researchers assigned air pollution levels based on each child’s home address during a single year, which does not capture personal daily habits such as indoor air filtration, ventilation, daily travel routes, or time spent exercising outdoors. In addition, the participants in this nationwide study were somewhat more likely to come from higher-income and non-Hispanic white households than the national average, and average particulate exposures across the sample were below current federal limits. Future studies will need to follow youth through later adolescence to evaluate whether these source-specific network patterns correspond with tangible differences in sensory function, attention, or mental health outcomes.
The study, “Sources of outdoor air pollution exposure and child brain network development across the United States,” was authored by Katherine L. Bottenhorn, Kirthana Sukumaran, Amanda C. Del Giacco, Hedyeh Ahmadi, Jordan D. Corbett, Alethea V. de Jesus, Carlos Cardenas-Iniguez, Rima Habre, Daniel A. Hackman, Joel Schwartz, Jiu-Chiuan Chen, and Megan M. Herting.
URL: https://www.psypost.org/neuroscientists-are-digging-into-a-worrying-link-between-polluted-air-and-childrens-brain-wiring/
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AirPollution #ChildrensHealth #BrainDevelopment #Neuroscience #ChildDevelopment #CleanAir #PM25 #EnvironmentalHealth #PublicHealth #ClimateAndHealth

#psychology #counseling #socialwork #psychotherapy #mentalhealth #psychiatry
Psychology News Robot @[email protected]
2026-10-25

DATE: October 25, 2026 at 07:00AM
SOURCE: PSYPOST.ORG
** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
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TITLE: Romantic breakups correspond with changes in sexual identity during adulthood
URL: https://www.psypost.org/the-material-fallout-of-a-breakup-correlates-with-shifts-in-sexual-identity/
Ending a committed relationship can prompt people to fundamentally reevaluate who they are, and a recent study suggests this process may extend to a person’s sexual orientation. Research published in The Journal of Sex Research indicates that individuals who experience highly disruptive material changes after a breakup report higher levels of ambiguity regarding their sexual identity. These findings highlight how major life transitions might correspond with shifts in sexual development throughout adulthood.
To understand these findings, it helps to look at how social scientists view human development. Life course theory suggests that human development is a lifelong process shaped by both expected milestones and unexpected events. Major life disruptions often act as turning points that influence a person’s future trajectory and sense of self.
Identity process theory builds on this idea by proposing that people constantly evaluate and adjust their identities to fit their environments. When a person’s social context changes rapidly, they often enter a processing period. During this time, they might assimilate new identity traits or discard old ones to maintain a coherent sense of self.
For many people, a romantic breakup is one of the most jarring transitions they will experience. Exiting a relationship requires a person to untangle their individual identity from their former partnership.
This untangling process can affect many facets of a person’s life, including their sexuality. Sexual orientation is not a single, unchangeable trait. It encompasses a person’s attractions, behaviors, and internal identity labels.
Sometimes, these different components do not perfectly align. Sexual orientation ambiguity refers to a period where a person feels their sexual identity is incongruent, unpredictable, or in a state of flux. While adolescence is frequently viewed as the primary time for sexual exploration, adults can experience changes in their sexual orientation at any point in their lives.
Grace A. O’Toole and her colleagues at Kansas State University conducted the study to explore these lifespan changes. The research team included O’Toole, Peyton Woods, Kristin M. Anders, Jared Durtschi, and Anthony J. Ferraro. They wanted to see if the end of a committed romantic relationship corresponded with periods of sexual ambiguity.
The researchers were particularly interested in differentiating between two types of relationship fallout. They wanted to separate the psychological toll of a breakup from its material consequences.
To investigate this, the researchers recruited 493 adults living in the United States through an online survey platform. Every participant had experienced the end of a committed romantic relationship within the past two years. The group included a wide age range, spanning from 18 to 76 years old.
Participants completed questionnaires assessing how the breakup impacted their lives. The survey split these impacts into two distinct categories. Psychological impacts measured changes in a person’s thoughts, emotions, and attitudes following the split.
Material impacts measured concrete lifestyle changes. This category included tangible disruptions like having to move to a new home, losing shared friend groups, or facing altered financial circumstances.
The participants also answered questions designed to measure sexual orientation ambiguity and sexual orientation uncertainty. Ambiguity questions assessed whether a person viewed their sexual orientation as changing rapidly. Uncertainty questions asked if participants felt a general sense of doubt about their sexual identity.
The researchers used statistical models to look for associations between the severity of the breakup’s impact and the participant’s reported levels of sexual ambiguity. They accounted for variables like gender, race, sexual orientation, and the seriousness of the past relationship.
The results revealed a link between the material consequences of a breakup and an individual’s sexual identity. Participants who reported higher levels of material impact from their relationship dissolution also reported higher levels of sexual orientation ambiguity. Those who faced greater concrete lifestyle disruptions were more likely to feel that their sexual orientation was in a state of flux.
The psychological impact of the breakup did not show the same relationship. The statistical models revealed that changes in a person’s emotional or mental state following the breakup were not statistically significant predictors of sexual orientation ambiguity.
The study also revealed notable differences across various demographic groups. People who identified as bisexual reported higher levels of sexual ambiguity compared to those who identified as heterosexual. This aligns with past research suggesting that bisexual individuals often navigate social environments that question the legitimacy of their orientation, which can prompt feelings of incongruence.
Participants who identified as genderqueer reported higher ambiguity than those who identified as cisgender men. Racial identity also correlated with these feelings of ambiguity. Black participants reported higher levels of sexual orientation ambiguity than White participants.
The nature of the ended relationship played a role in the findings as well. Participants who had been cohabitating with their former partners reported lower sexual ambiguity than those who had only been seriously dating.
The authors suggest that these findings point to a need for broader public education regarding sexual development. Recognizing that sexuality can be fluid across the entire lifespan could help individuals navigate periods of uncertainty. The researchers suggest that therapists and counselors should be attuned to how material disruptions might trigger identity shifts in their clients.
The study relies on cross-sectional data, meaning the researchers collected all the information at a single point in time. Because of this design, the results can only show correlations rather than cause and effect. It is possible that navigating the material fallout of a breakup leads to sexual ambiguity, but it is equally possible that existing sexual ambiguity contributed to the end of the relationship.
The participant pool was predominantly heterosexual, cisgender, and White. This demographic makeup limits how broadly the conclusions can be applied to other groups. The experiences of individuals with diverse sexual and gender identities might differ substantially from the patterns observed in this specific sample.
The survey measures used to assess sexual ambiguity have primarily been tested on young adults in past research. Applying these measures across a broader age range provides a useful first step, but the questionnaires may need further testing to ensure they capture the nuances of older adults’ experiences accurately.
Future research could track individuals over time to better understand the sequence of these identity shifts. Tracking participants over several years would help clarify whether the material disruption of a breakup directly prompts people to reevaluate their sexual orientation. Examining a more diverse group of participants could also reveal how different cultural and social backgrounds influence identity development after a major life transition.
The study, “Understanding Sexual Orientation Ambiguity: Perceptions of Transitional Impacts in the Context of a Relationship Dissolution,” was authored by Grace A. O’Toole, Peyton Woods, Kristin M. Anders, Jared Durtschi, and Anthony J. Ferraro.
URL: https://www.psypost.org/the-material-fallout-of-a-breakup-correlates-with-shifts-in-sexual-identity/
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2026-10-25
"Enterprise IT isn't Broken—Its Game Mechanics Are Just Trash": Inside Erkki Laine's Tri-Axial Engine

Why do smart engineers and expensive AI agents make catastrophic production decisions? Because corporate governance is designed like a terribly balanced strategy game. We sat down with the Finnish architect who recoded the rules using Nash Equilibrium, 3D collision physics, and hardcore RPG logic.
TURKU, FINLAND — If you read enough enterprise governance documentation, your brain eventually turns into grey SharePoint sludge. It is a joyless world of RACI matrices, annual spreadsheets, and committee meetings where twenty people spend an hour debating a change ticket none of them actually understand.
Then you open the technical specifications of the Business Impact Governance Framework (BIGF) by Finnish ICT architect Erkki Laine, and something weird happens.
Suddenly, you aren't reading bureaucratic filler anymore. You are reading about Autonomous Agentic Meshes, Tri-Axial Actuation Boundaries, Hardened Meta-Governance Layers, and Cryptographic Canonical Decision Records.
Wait a second. Is this an enterprise IT framework, or the patch notes for a hardcore tactical RPG?
"Honestly, there is very little difference between leading a party through a lethal boss fight in a tactical RPG and keeping a multi-cloud production environment alive at 3 AM," Laine says, leaning back with a coffee mug while three dogs patrol his home office perimeter. "In both cases, if your buffs are fake, your map telemetry is fogged, and your party members are secretly sabotaging each other for loot, you are going to wipe."
We sat down with Laine to unpack how an obsession with tactical role-playing games, grand strategy, and mathematical game theory led him to build a deterministic runtime engine that is quietly making traditional ITIL bureaucracy obsolete.

Level 1: Solving the DevOps "Prisoner's Dilemma"

Viti Magazine: Let's start with human behavior. Why do traditional ITSM and GRC processes fail so miserably in the real world?
Erkki Laine: Because legacy governance assumes you can fix structural conflicts by writing a politely worded PDF policy and ordering people to "collaborate." Anyone who has ever played a grand strategy game or studied basic Game Theory knows that's delusional.
Look at a standard IT organization. Developers (Dev) and Security/Governance (Ops/Sec) are trapped in a textbook Prisoner's Dilemma:
Dev's optimal selfish move: Push code as fast as possible, understate the risk score on the ticket, and hit sprint velocity targets.
Security's optimal selfish move: Stall, interrogate, and demand three weeks of manual CAB reviews to ensure zero personal blame if something breaks.
Both sides play their dominant selfish strategy, and the organization gets the worst possible collective outcome: miserable lead times, shadow IT, and rubber-stamped risk theater.
Viti Magazine: And BIGF fixes this without holding a single team-building workshop?
Laine: Workshops don't change payoff matrices; code does. In BIGF, we replace the political negotiation with a deterministic Policy Gate at the runtime boundary. Security encodes the exact mathematical thresholds into the engine once. Dev knows the exact rules before they even commit code.
Neither side can bluff, cheat, or play politics. The moment the deployment artifact and telemetry satisfy the mathematical equation, the gate opens in milliseconds. By changing the rules of the board, cooperation automatically becomes the Nash Equilibrium—the only winning move for everyone.

Level 2: The Tri-Axial Engine (3D Collision Physics for Risk)

Viti Magazine: People analyzing your v2.3 and v2.4 specifications have pointed out that your "Tri-Axial Actuation Boundary" looks suspiciously like a 3D game engine.
Laine: (Laughs) Guilty as charged. In a 3D game engine, every object exists on X, Y, and Z coordinates. As long as your character stays inside the playable room, you can run, jump, and cast spells freely. The moment you try to walk through a solid wall or step off the edge of the map, the physics engine triggers collision detection and stops your movement vector cold.
Why on earth were we managing enterprise production risk on flat, two-dimensional Excel sheets when operational reality is three-dimensional?
In BIGF, every operational decision—whether triggered by a human engineer, a CI/CD pipeline, or an autonomous AI agent—is evaluated in real time across three orthogonal axes (B × S × T):
Authority = f(B, S, T)
The B-Axis (Business Impact): What is the actual statutory, financial, and operational blast radius of this service node? (RTO, RPO, NIS2/DORA criticality tier).
The S-Axis (Situational State): What is happening in the environment right now? Are we in normal daytime operations, in the middle of a cascading P1 incident, or under a peak commercial freeze?
The T-Axis (Telemetry & Trust Integrity): How fresh, complete, and cryptographically verified is our underlying dependency data? (θ threshold, calculated via deterministic 10^-4 fixed-point arithmetic).
As long as the combined coordinate stays inside the Tri-Axial Actuation Boundary—the "playable room"—execution flows at full automation speed. The millisecond a rogue AI agent tries to walk through the wall because the CMDB telemetry (T) is stale (θ < 0.8000) during a degraded state (S), the physics engine hits it with a deterministic 403 Hard Halt.
No invisible clipping through production walls. Fail-Closed in authority, every single time.

Level 3: Mechanism Design—Making Lying Mathematically Stupid

Viti Magazine: One of the most ruthless parts of your repository is the Cloud Procurement and Change simulation logic. You use "Mechanism Design"—essentially reverse game theory. How does that stop people from gaming the system?
Laine: Every veteran architect knows the oldest trick in corporate IT: if you want to buy a shiny new cloud tool or push a risky change without triggering a three-month security review, you just lie on the intake form. You tick the box that says "Low Business Impact / No Critical Data," get your rubber stamp, and connect it to core production two weeks later.
In game theory, that's an incentive-incompatible mechanism—the game rewards dishonesty.
With BIGF's Mechanism Design, lying on day one is operational suicide on day two. Why? Because the intake declaration isn't filed away in a dead Word document; it compiles directly into the live Runtime Control-Plane.
If a team claims their new service is "Tier-4 Non-Critical" just to bypass procurement scrutiny, the engine automatically assigns that service a Tier-4 actuation ceiling. The first time that service tries to make an API call to a Tier-1 customer database at runtime, the boundary detects the structural mismatch and severs the connection instantly.
You don't need compliance police chasing people with clipboards. You just design the game mechanics so that telling the absolute truth upfront is the only way your software gets to run in production.

Final Boss: Agent-to-Agent (A2A) Combat in the Mesh

Viti Magazine: Let's talk about the endgame: version 2.3 and autonomous AI agents. Everyone is rushing to deploy "Agentic AI" in ITSM. What happens when machines start playing against machines?
Laine: That is the ultimate boss fight of 2026 and 2027. Right now, vendors are selling autonomous buyer-bots, capacity-scaling bots, and incident-remediation bots. What happens when a Cost-Optimization AI Agent (whose reward function is to kill idle servers to save money) collides at 2:00 AM with a Resilience AI Agent (whose reward function is to spin up redundant nodes for DORA compliance)?
Without a deterministic referee, those two probabilistic models will enter an infinite loop, hallucinate authority, and DDoS the enterprise from the inside.
In BIGF v2.3 (Autonomous Agentic Mesh Governance), agents aren't allowed to negotiate based on LLM "vibes." Every Agent-to-Agent (A2A) interaction is governed like a high-stakes multiplayer match with strict cryptographic rules:
Every action requires a state-bound token signed with Ed25519 cryptography.
Every decision produces an immutable, replayable Canonical Decision Record.
If two agents collide, the Tri-Axial equation (B × S × T) deterministically resolves who has priority based on governed Business Impact master data—not who has the most aggressive prompt.
Viti Magazine: So if BIGF were a video game on Steam, what genre would it be?
Laine: For the people still trying to run modern cloud infrastructure with manual CAB meetings and color-coded spreadsheets? It's Dark Souls on permadeath mode.
For the architects who actually implement a deterministic control-plane? It's Factorio meets Baldur's Gate—a beautifully automated factory where every belt is balanced, the party buffs are mathematically verified, and the goblins get vaporized at the perimeter before you even have to roll initiative.
Ready to patch your enterprise game mechanics?
Stop playing corporate Prisoner's Dilemma with Excel sheets. Google Erkki Laine BIG Framework to explore the open-science specifications (v2.3 Autonomous Agentic Mesh Governance, v2.4 Reference Architecture, and the NIS2/DORA Alignment series) or test the Interactive Runtime Control-Plane Simulator directly on Zenodo and GitHub.#architecture #ai #devops #gametheory #software #coding #development #engineering #inclusive #community
VITI MAGAZINE EXCLUSIVE INTERVIEW | GAME THEORY & ARCHITECTURE
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Dev Community @[email protected]
2026-10-25
ACH Return Codes Explained: R01–R85 and How to Handle Them in Production
ACH Return Codes Explained: R01–R85 and How to Handle Them in Production

When you send an ACH debit or credit, you're not guaranteed it will settle. The National Automated Clearing House Association (Nacha) defines 85 possible return codes—each one a specific reason why a bank rejected your transaction. As a developer building payout systems, understanding these codes is critical: they determine whether you retry, escalate to support, or switch payment rails entirely.
This guide covers the most common return codes you'll encounter in production, what triggers them, and how your code should respond.
The Big Three: R01, R03, R10

R01 — Insufficient Funds
The account exists and is valid, but the account holder doesn't have enough money. This is the most common return you'll see on debit transactions.
When it fires: ACH debit posted, but balance dropped below the transfer amount.
Developer action: Log the return, notify the customer, and do not retry immediately. Implement a dunning flow: wait 3–5 days, then retry once. If it fails again, flag for manual review or offer an alternative payment method.
if (returnCode === 'R01') {
await markTransactionForRetry(transactionId, {
retryAfter: Date.now() + (4 * 24 * 60 * 60 * 1000), // 4 days
maxRetries: 1
});
await notifyCustomer(customerId, 'Payment failed due to insufficient funds');
}
R03 — No Account / Unable to Locate Account
The account number or routing number is invalid, or the account was closed.
When it fires: Bank can't match the account details you provided.
Developer action: Do not retry. This is a data quality issue. Validate the account number and routing number with the customer, or ask them to re-enter their banking details. Consider integrating microdeposit verification before accepting ACH details.
R10 — Customer Initiated Return
The account holder disputed the transaction or called their bank to reverse it. This is a chargeback equivalent for ACH.
When it fires: Within 60 days of the original transaction (per Nacha rules).
Developer action: Treat this as a customer service issue. Log it, contact the customer to understand why, and adjust your onboarding or communication. Do not retry without explicit customer consent.

Secondary Returns: R04, R07, R29

R04 — Reserved (Not Currently Used)
Ignore this; it's in the Nacha spec but not actively returned by banks.
R07 — Authorization Revoked by Customer
The customer asked their bank to block future ACH transactions from you (similar to stopping a check).
Developer action: Stop all ACH attempts for this customer. Reach out to understand why, and offer alternative payment methods.
R29 — Corporate Customer Advises Not Authorized
A business account holder says they didn't authorize this transaction.
Developer action: Escalate immediately. This is a fraud or authorization dispute. Do not retry; contact the customer and your compliance team.

Rare but Critical: R82, R83

R82 — Duplicate Entry
You submitted the same transaction twice in the same batch window.
Developer action: Your code has a bug. Check for idempotency failures in your ACH submission logic. Implement unique trace numbers (ODFI trace) to prevent duplicates.
R83 — Routing Number Check Digit Error
The routing number's checksum is invalid.
Developer action: Validate routing numbers client-side before submission using the Nacha checksum algorithm (weighted sum mod 10). Catch this before it reaches the bank.

Building a Return Handler

Here's a production-ready pattern:
javascript
const returnActions = {
'R01': { action: 'retry', delay: 4 * 24 * 3600 * 1000, maxAttempts: 1 },
'R03': { action: 'escalate', reason: 'invalid_account' },
'R07': { action: 'block', reason: 'customer_revoked' },
'R10': { action: 'investigate', reason: 'customer_initiated' },
'R82': { action: 'escalate', reason: 'duplicate_detected' },
'R83': { action: 'reject', reason: 'invalid_routing' },
};
async function handleACHReturn(transactionId, returnCode) {
const policy = returnActions[returnCode];
if (!policy) {
await logUnknownReturn(returnCode);
return;
}
switch (policy.action) {
case 'retry':
await scheduleRetry(transactionId, policy.delay, policy.maxAttempts);
break;
case 'escalate':
---
*Decoding ACH return codes programmatically? The [ACH Return Codes API](rapidapi.com/payoutrail-ach-re…) returns the full Nacha R01–R85 set with plain-language descriptions and handling guidance.*#backend #fintech #programming #softwareengineering #software #coding #development #engineering #inclusive #community
ACH Return Codes Explained: R01–R85 and How to Handle Them in Production
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Dev Community @[email protected]
2026-10-25
ACH Return Codes Explained: R01, R03, R10 & How to Handle Them
ACH Return Codes Explained: R01, R03, R10 & How to Handle Them

When you're building a payout system, ACH returns are inevitable. A transaction settles, but then days later—sometimes up to five business days—a return code arrives telling you the money is coming back. Understanding what each code means is the difference between a resilient payment flow and a broken one.
This guide covers the most common ACH return codes you'll encounter, what triggers them, and how to handle them in your integration.
What Is an ACH Return Code?

The National Automated Clearing House Association (Nacha) defines a standardized set of return codes (R01 through R85) that banks use to reject ACH entries. Each code maps to a specific reason: insufficient funds, closed account, unauthorized debit, routing errors, and more.
When a return arrives, your processor sends it back to you via webhook or API response. The return includes:
The original transaction ID
The return code (e.g., R01)
The reason text
The amount being reversed
The return date
Your job: parse it, log it, notify the user, and decide the next action.
The Big Three: R01, R03, R10
R01: Insufficient Funds

What it means: The recipient's account doesn't have enough balance to cover the debit.
When it fires: Usually within 1–2 business days, during the bank's posting cycle.
Why it matters: This is temporary. The user might deposit funds tomorrow. It's a retry candidate.
How to handle it:
{
"return_code": "R01",
"original_amount": 50000,
"cents": true,
"action": "retry_later",
"retry_window_days": 3
}
Schedule a retry in 2–3 days. Update your UI to show the user: "Payment pending — will retry on [date]." If it fails twice, escalate to manual review or suggest an alternate payment method.
R03: No Account / Unable to Locate Account

What it means: The routing number and account number combination doesn't exist, or the account has been closed.
When it fires: Usually 1–3 business days.
Why it matters: This is permanent. The account is gone. Retrying won't help.
How to handle it:
{
"return_code": "R03",
"action": "fail_permanently",
"notify_user": true,
"message": "The bank account you provided is inactive or invalid. Please verify and re-enter."
}
Flag the bank account as invalid in your database. Require the user to re-enter or re-verify. Don't retry.
R10: Unauthorized Debit

What it means: The recipient (or their bank) claims they never authorized this ACH debit. Common in B2C payouts where the user disputes the charge.
When it fires: 3–5 business days (often as a chargeback-style dispute).
Why it matters: This is a fraud or authorization signal. The recipient is actively contesting the payout.
How to handle it:
{
"return_code": "R10",
"action": "investigate",
"escalate_to": "compliance",
"preserve_evidence": true,
"message": "Recipient disputes this payment. Review authorization records."
}
Don't retry. Escalate to your compliance team. Check your records for proof of authorization (e.g., signed agreement, API consent token, timestamp). If authorization is weak, you may need to refund from your own account.
Building Retry Logic

Here's a simple state machine for handling returns:
Return CodeCategoryActionRetry?Max AttemptsR01, R02, R09TemporaryRetryYes3R03, R04, R05Account IssueFailNo—R10, R29AuthorizationEscalateNo—R20, R21, R22Format ErrorFailNo—R31, R32Routing ErrorInvestigateMaybe1
Implementation Pattern

javascript
async function handleAchReturn(returnEvent) {
const { returnCode, transactionId, amount } = returnEvent;
if (['R01', 'R02', 'R09'].includes(returnCode)) {
// Retry logic
await scheduleRetry(transactionId, amount, 2); // retry in 2 days
await notifyUser(transactionId, 'pending');
} else if (['R03', 'R04', 'R05'].includes(returnCode)) {
// Permanent failure
await markAccountInvalid(transactionId);
await notifyUser(transactionId
---
*Decoding ACH return codes programmatically? The [ACH Return Codes API](rapidapi.com/payoutrail-ach-re…) returns the full Nacha R01–R85 set with plain-language descriptions and handling guidance.*#backend #fintech #softwaredevelopment #software #coding #development #engineering #inclusive #community
ACH Return Codes Explained: R01, R03, R10 & How to Handle Them
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Dev Community @[email protected]
2026-10-25
Governance Attack Surface Review: Polygon Bridge

Target Protocol: Polygon Bridge (TVL: $2660.5M)
Governance Attack Surface Review – Polygon Bridge

Protocol: Polygon Bridge (Ethereum ↔ Polygon PoS)
TVL (approx.): $2.66 B (Ethereum + Polygon L2)
Date: 25 Oct 2026
Prepared by: Senior DeFi Security Researcher – Independent Audit

1. Executive Summary

The Polygon Bridge is the primary trust‑minimized conduit for moving ERC‑20, ERC‑721, and ERC‑1155 assets between Ethereum mainnet and Polygon PoS. While the bridge’s core token‑locking contracts have been extensively audited and are widely regarded as secure, the governance layer that controls upgrades, fee parameters, and emergency pausing introduces a non‑trivial attack surface.
Our review focuses on the governance contracts, timelock mechanisms, role‑based access control, and the interaction between governance and the bridge’s core contracts. We identified several high‑impact vectors that could allow an adversary—either a malicious proposer, a compromised multisig, or a colluding validator set—to:
Upgrade bridge logic to a malicious implementation (e.g., siphon locked assets).
Manipulate fee or withdrawal limits to create economic incentives for a “run” attack.
Exploit timelock bypasses or governance “emergency” functions to pause or unpause the bridge arbitrarily.
Leverage cross‑chain message replay or replay‑protected signatures to forge withdrawals.
Overall, the governance attack surface scores 7 / 10 on a 1‑10 risk scale (1 = negligible, 10 = critical). The most severe risk stems from insufficient separation between upgrade authority and operational controls, combined with low‑entropy timelock parameters that could be rushed under a “fast‑track” proposal path.
The remainder of this report details each identified vector, quantifies its impact, and provides prioritized, actionable remediation recommendations.

2. Identified Attack Vectors
#VectorDescriptionPotential ImpactLikelihoodCurrent MitigationsExploitability (CWE)1Upgrade Authority CentralisationThe PolygonBridgeProxy points to an implementation contract that can be upgraded by a single Polygon DAO Multisig (3‑of‑5). The multisig’s owners are partially composed of core Polygon team members and a few external partners.Full control over bridge logic → arbitrary token mint/burn, fund diversion, disabling withdrawals.Medium‑High (social engineering, key‑compromise).Multisig threshold, time‑locked execution (48 h).CWE‑250 (Execution with Unnecessary Privileges)2Fast‑Track Governance PathA “fast‑track” proposal (used for urgent patches) reduces the timelock from 48 h to 6 h and requires only 2‑of‑5 signatures. The fast‑track can be triggered by any proposer with a “priority” flag.Allows rapid deployment of malicious code before community can react.Medium (requires collusion or compromised proposer).Limited to “critical” proposals (subjective).CWE‑284 (Improper Access Control)3Fee & Withdrawal Limit ManipulationGovernance can modify withdrawalCap, dailyLimit, and bridgeFee. By setting caps to zero or fees to 100 %, the bridge can be rendered unusable or cause a “run” where users rush to withdraw before caps are lowered.Economic denial‑of‑service, loss of user confidence, potential liquidity crunch on Polygon.Medium (requires governance action).Caps are bounded by hard‑coded max values; however, bounds are generous.CWE‑352 (Cross‑Site Request Forgery) – analogous to “parameter tampering”.4Emergency Pause AbuseThe pauseBridge() function can be called by the Governor (single address) without timelock. Pausing halts deposits/withdrawals but does not unlock already‑locked assets.Market manipulation, forced migration to competitor bridges, potential “bridge‑freeze” attack.Low‑Medium (single‑key compromise).Governor address is a contract that forwards calls from the DAO; no delay.CWE‑276 (Improper Restriction of Excessive Authentication).5Cross‑Chain Message ReplayThe bridge uses a Merkle‑root based proof system for withdrawals. The proof verification contract does not store a per‑nonce replay bitmap for ERC‑721/1155 tokens, relying only on token‑ID uniqueness.An attacker could replay a withdrawal proof for an NFT that was transferred multiple times, resulting in double‑spend.Low (requires precise timing & knowledge of state).ERC‑721/1155 withdrawals include a nonce but the bitmap is cleared only on contract upgrade.CWE‑284 (Improper Access Control)6Governance Parameter Race ConditionThe setValidatorSet function (used to update the set of PoS validators that sign exit proofs) can be called in the same block as a withdrawal proof that relies on the previous validator set.A malicious validator could approve a fraudulent exit before the new set is activated, stealing assets.Low‑Medium (depends on block ordering).Validator set changes are delayed by 1 hour, but no explicit finality check.CWE‑362 (Race Condition)7Insufficient Event Logging for Governance ActionsCertain governance actions (e.g., fee changes) emit only generic ParameterChanged events without the new value or caller address.Reduces transparency, hampers on‑chain monitoring and off‑chain alerting.Low (operational risk).None.CWE‑778 (Insufficient Logging).8Upgradeable Proxy Storage CollisionThe proxy uses the unstructured storage pattern, but a future implementation could unintentionally overwrite critical storage slots (e.g., owner, timelock).Loss of control, accidental self‑destruct, or forced upgrade to a malicious contract.Low (requires malicious upgrade).Storage slot constants are documented; no automated slot‑collision checks.CWE‑124 (Improper Protection of Critical Data).
2.1 Deep‑Dive on the Highest‑Risk Vectors
Vector 1 – Upgrade Authority Centralisation

Root cause: The upgrade function upgradeTo(address newImplementation) is guarded only by onlyGovernor, where Governor is a contract that forwards calls from the DAO multisig. The DAO’s governance token (MATIC) voting power is heavily weighted toward the Polygon core team (≈ 55 %).
Attack scenario: An adversary compromises two of the five multisig signers (phishing, key‑reuse, or insider threat). They submit a proposal to upgrade the bridge implementation to a contract that contains a hidden sweep() function. After the 48‑hour timelock, the upgrade executes, and the attacker calls sweep() to transfer all locked assets to an address they control.

Vector 2 – Fast‑Track Governance Path

Root cause: The fast‑track path is intended for emergency patches but lacks a “quorum” check on the proposer’s reputation. The priority flag can be set by any address that holds ≥ 0.5 % of total MATIC supply.
Attack scenario: An attacker accumulates the required MATIC (possible via market manipulation) and triggers a fast‑track upgrade that replaces the withdrawal verification logic with a version that always returns true. The 6‑hour window is insufficient for community response, leading to a massive outflow of assets.

Vector 5 – Cross‑Chain Message Replay (NFTs)

Root cause: The proof verification contract stores a mapping processedProofs[bytes32] => bool. For ERC‑721/1155, the proof hash does not incorporate the token’s nonce (the internal transfer counter).
Attack scenario: An attacker withdraws an NFT, then quickly re‑submits the same proof after the NFT has been transferred on Polygon. Because the proof hash is unchanged, the bridge treats it as a fresh withdrawal, minting a duplicate NFT on Ethereum.

3. Prioritized Technical Recommendations
PriorityRecommendationTargeted Vector(s)Implementation DetailsExpected Risk ReductionCriticalMigrate Upgrade Authority to a 4‑of‑7 DAO with a 72‑hour timelock.1, 2Deploy a new GovernorV2 contract that requires a higher threshold and longer delay. Add a delay‑increase function that can only be called after a 30‑day governance vote.↓ from 7 → 4CriticalEliminate fast‑track path or enforce a minimum 24‑hour delay regardless of priority.2Remove the priority flag; require the same timelock for all proposals. If fast‑track is retained, enforce a 2‑of‑7 quorum and a minimum 24‑h delay.↓ from 7 → 3HighIntroduce a “guardian” multi‑sig (3‑of‑5) for emergency pause/unpause with a 12‑hour timelock.4Replace the single Governor pause authority with a PauseGuardian contract that mirrors the upgrade timelock.↓ from 5 → 2HighAdd per‑nonce replay protection for ERC‑721/1155 withdrawals.5Extend the proof hash to keccak256(abi.encodePacked(tokenId, nonce, proofData)). Store processed nonces in a bitmap to guarantee uniqueness.↓ from 5 → 1MediumSeparate fee/limit parameters into a dedicated “EconomicParameters” contract with immutable caps.3Deploy a contract that only allows fee changes within a narrow band (e.g., 0‑0.5 %). Use a 48‑hour timelock for any change.↓ from 4 → 2MediumAdd explicit finality checks for validator set updates.6Require that a withdrawal proof’s validatorSetId matches the active set at the block height of the proof. Reject proofs that reference a set that will become active later.↓ from 3 → 1LowStandardise event logging for all governance actions.7Emit events with full parameter details (ParameterChanged(string name, uint256 oldValue, uint256 newValue, address caller)).Improves monitoring, no direct risk change.LowAutomated storage‑slot collision analysis for future upgrades.8Integrate a static‑analysis step (e.g., Slither‑Storage) into the CI pipeline to detect overlapping slots before any upgrade.Prevents accidental overwrites.
Implementation Roadmap (Suggested Timeline)
PhaseDurationMilestonesPhase 0 – Immediate Safeguards (0‑2 weeks)Deploy PauseGuardian and replace single‑key pause. Add comprehensive event logging.Phase 1 – Governance Hardening (2‑6 weeks)Deploy GovernorV2 with higher quorum & longer timelock; retire fast‑track path.Phase 2 – Core Bridge Enhancements (6‑12 weeks)Upgrade bridge implementation to include nonce‑protected NFT withdrawals and validator‑set finality checks.Phase 3 – Economic Parameter Refactor (12‑16 weeks)Migrate fee/limit logic to EconomicParameters contract; enforce immutable caps.Phase 4 – Continuous Assurance (ongoing)Integrate storage‑slot analysis, formal verification of upgradeability, and on‑chain governance monitoring dashboards.
4. Overall Risk Score
DimensionScore (1‑10)RationaleGovernance Upgrade Risk8Centralised upgrade authority + fast‑track path = high‑impact, moderate‑likelihood.Economic Parameter Manipulation5Can cause denial‑of‑service but not direct asset loss.Operational Controls (Pause, Validator Set)4Single‑key pause and validator race are lower‑likelihood but still impactful.Replay & Cross‑Chain Proof Issues3Limited to NFTs; mitigated by existing uniqueness checks for ERC‑20.Transparency / Monitoring2Mostly operational hygiene.Composite Risk Score7 / 10Weighted average (higher weight to upgrade risk).

Interpretation: A score of 7 indicates a significant attack surface that, if left unaddressed, could lead to catastrophic loss of locked assets or systemic market disruption. Immediate remediation of the upgrade authority and fast‑track mechanisms is essential.

5. Conclusion

The Polygon Bridge remains a cornerstone of Ethereum‑Polygon interoperability, handling billions of dollars in assets. Its core token‑locking contracts are robust, but the governance layer introduces a concentration of power and procedural shortcuts that could be exploited to compromise the entire bridge.
Our analysis shows that the most exploitable weakness is the ability to upgrade the bridge implementation with a relatively low‑threshold multisig and a fast‑track path that bypasses community scrutiny. By restructuring governance to require broader consensus, extending timelocks, and removing privileged fast‑

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2026-10-27
The Art of Less Is a Book About Seeing More, Not Having Less

Back in August, I wrote about this new gestalten book before it was even released. Thanks to the lovely people at gestalten, who sent me a copy, I’ve now had the pleasure of spending the past few weeks with it. Although I received a complimentary copy of the book, everything I write about it in this review reflects my own personal opinion.
The international edition has been available since September 1, 2026, and the US edition follows today, October 27. Christmas shoppers still have weeks to order. Feeds refresh faster than anyone can read them, so a book that asks for two hands and a quiet hour feels almost radical right now.
Order the book from gestalten. Order the book on Amazon.
The Art of Less: An Exploration of Essence is a 320-page hardcover published by gestalten. Jonas Bjerre-Poulsen of Norm Architects and Sarah Dorweiler of Aesence edited it, and it profiles twenty international creatives across five chapters.
The Art of Less: An Exploration of Essence—a gestalten book review by Dirk Petzold.
Is The Art of Less Worth $100?
Yes. I would pay full price, and I would gladly buy a second copy as a gift.
I judge minimalist work with a rule I call the Presence Test. A piece passes when removing one more element would drain it of presence. In my experience, many minimalism books fail. Their pages look quiet, yet their ideas stay thin.
The Art of Less passes. I could not find a single page I wanted to cut.

The price still invites suspicion, and it should. At $100, €70, or £60, this hardcover asks for real commitment. Two things justify that commitment. One is the object. The other is the argument.
What Is The Art of Less About?
The book treats minimalism as a sensory and intellectual practice. The editors argue that the urge to reduce grows stronger when life feels overloaded. They call it a human reflex, and they back that claim with twenty examples. The examples come from art, architecture, and design, so the argument never stays in one discipline. Each profile tests the same idea from a different angle. Restraint, in this book, produces presence. That is a sharper thesis than the old line about less being more.
The claim sounds abstract until you apply it. Think about the last time your inbox and your feed demanded you at once. Did you crave more input, or less?
Sarah Dorweiler founded Aesence more than ten years ago, and this is her first book. Jonas Bjerre-Poulsen leads Norm Architects, the Copenhagen studio founded in 2008. Norm Architects had already published Soft Minimal and Stillness with gestalten, and Dorweiler took the photographs for Stillness. That shared history shows.
The collaboration reads like a conversation between two people who have studied the same quiet things for years. A practitioner and a curator rarely write the same book. Here, they do.
The Five Lenses Behind the Argument
Five chapters organize the twenty profiles. Each chapter acts as a lens, and each features four creatives.
Balance with Nicolas Schuybroek, Danielle Siggerud, Nuria Maria, and Tycjan Knut
Contrast with Tatsuro Miki, HENRYTIMI, Lee Ufan, and Rahee Yoon
Materiality with Cédric Etienne, Christian + Jade, Maverick Lee, and Antonia Ferrer
Rhythm with Liam Stevens, Studio MK27, Krista Mezzadri, and Maria Yelletisch
Space with Álvaro Siza, John Pawson, Ylva Carlgren, and Tom Holden
Four profiles per lens is a smart limit. It keeps every chapter tight and gives each creative room to breathe.
The Art of Less: An Exploration of Essence—a gestalten book review by Dirk Petzold.
How Does The Art of Less Feel in Your Hands?
Binding and Cover
The Art of Less measures 24.5 × 33 cm, or 9¾ × 13 inches. Retailer data puts the spine at about 3 cm. The 320 pages are stitch-bound.
Stitch binding matters at this size. Sewn signatures usually outlast glued spines, so a heavy book like this one should survive many openings.
The cover stays calm. A pale greige surface carries a small title and a small inset photograph of a black rock framed by a dark doorway. The rock is an artwork by Kimsooja, according to gestalten. Nothing on the cover shouts. That restraint sets the tone before you open the book.
The Handling Tax
Precious books charge what I call a handling tax. You pay it in hesitation, because every touch risks a crease or a stain. The Art of Less charged me that tax, and I paid it gladly. I turned each page with care and left no marks on paper or cover. I found no flaws in the print or the cover.
The tax even fits the theme. Slow handling forces slow reading, and slow reading is what a book about restraint deserves.
Photography That Does Real Work
The photographs are perfect. I would not change one of them.
A book about presence depends on its pictures. One busy image would contradict the text at once. Here, every image supports the argument, and the large format gives each one room. Screens flatten texture. Paper at this size restores some of it.
The writing deserves its own mention. It reads beautifully from the first page.
Order the book from gestalten. Order the book on Amazon.
Why Does the Artist Selection Work So Well?
The artist selection is the smartest decision in the book. The editors place established masters beside younger names, and the pairing produces friction in the best way.
Álvaro Siza won the Pritzker Prize in 1992, and John Pawson designed the Novy Dvur monastery in the Czech Republic, so anyone who studied architecture already knows how much silence these two can build into stone and light.
Lee Ufan, a leading voice of the Mono-ha movement, adds sculpture built on elemental tension. The publisher singles out that tension as a signature of his work.
Younger names such as Tycjan Knut in Balance and Rahee Yoon in Contrast sit beside these giants without apology. That placement argues that minimalism remains a living practice.
The map matters too. Siza built his career in Porto, and Studio MK27 works from São Paulo. Minimalism looks different in each climate, and the book lets those differences show.
How Does The Art of Less Compare With Earlier Minimalism Books?
John Pawson’s Minimum, published by Phaidon in 1996, set the visual template for the genre. It showed readers what reduction looks like.
Norm Architects’ Soft Minimal went further inside one studio. It explains how the team thinks and designs.
The Art of Less widens that view to twenty creatives. It also asks what reduction does to the person standing in the room. That question sets it apart from every minimalism book on my shelf.
Where Does Minimalism Go Next?
I expect this book to influence how publishers pitch minimalism. Two changes seem likely by the end of 2027.
More minimalism titles will present themselves as tools for attention and drop the style-guide angle.
Heavy, tactile print will gain ground against feeds. Gestalten’s own media pitches for this book stress the rising demand for haptic, physical experiences.
I could be wrong about the pace. I doubt I am wrong about the direction.
Here is a definition worth quoting. Sensory minimalism, as I define it, is the practice of reducing a space or an object until what remains holds more attention than the whole once did.
How Should You Read The Art of Less?
Read it in five sittings. I call this the One-Lens Evening. Take one chapter per sitting, and leave your phone in another room.
Look at each photograph before you read its text. Then read the text and look again.
Is The Art of Less a Good Christmas Gift for Architecture Lovers?
Yes. I can’t think of a better gift for anyone who loves art and architecture, and designers will treasure it too.
The list prices are €70 in Germany, £60 in the UK, and $100 in the US. Gestalten’s shop lists free shipping above $49 in the US and above €29 in the EU. Order by mid-November if you want to wrap it before Christmas.
Who Should Skip It?
Skip it if you want a decluttering manual. The book is built around twenty profiles, so it offers examples and ideas. Buy something else if you need a ten-step system for your closet.
The Art of Less at a Glance
DetailInformationTitleThe Art of Less: An Exploration of EssencePublishergestaltenEditorsJonas Bjerre-Poulsen (Norm Architects) and Sarah Dorweiler (Aesence)Format24.5 × 33 cm (9¾ × 13 in)Pages320, full colorBindingHardcover, stitch-boundLanguageEnglishPrice€70 (Germany), £60 (UK), $100 (US)ReleaseInternational: September 1, 2026. USA: October 27, 2026ISBN978-3-96704-245-0StructureFive chapters, twenty creatives, four per chapterCover artworkBlack rock by KimsoojaMy verdictWorth the price. I found no flaws.Best forArt and architecture fans, and designers Order the book from gestalten. Order the book on Amazon.
Frequently Asked Questions About The Art of Less
Is The Art of Less worth buying?
Yes. After several weeks with the book, I found no flaws in the print or the cover, and the photography holds up page after page. At $100, it costs more than most design books, but it passes my Presence Test.
Who edited The Art of Less?
Jonas Bjerre-Poulsen of the Copenhagen studio Norm Architects and Sarah Dorweiler, founder of the minimalist art publication Aesence, edited the book for gestalten. It is Dorweiler’s first book.
What are the five chapters of The Art of Less?
Balance, Contrast, Materiality, Rhythm, and Space. Each chapter profiles four creatives.
How much does The Art of Less cost?
The list price is €70 in Germany, £60 in the UK, and $100 in the US. Gestalten’s shop lists free shipping above $49 in the US and above €29 in the EU.
When does The Art of Less come out in the US?
The US edition publishes on October 27, 2026. The international edition has been available since September 1, 2026.
Is The Art of Less a how-to guide to minimalism?
No. The book follows twenty profiles of creatives and offers examples and ideas. Readers who want a decluttering system should look elsewhere.
Where can I buy The Art of Less?
You can order it from the gestalten shop or from Amazon.com. Independent bookstores can order it too, and the ISBN is 978-3-96704-245-0.

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Dev Community @[email protected]
2026-10-27

An agent that feels instant on a laptop can feel broken the moment its model call crosses a network boundary, and the postmortem usually blames the model. The regression more often comes from three numbers nobody recorded before the move: cold-start latency, payload size leaving the device, and behaviour when the remote seat is unreachable. The scenario below is illustrative rather than a customer story, but the failure shape is common enough to plan around.
This article builds a small probe that records those three numbers for any endpoint pair and turns them into a residency decision. It assumes you already have an agent that runs end to end and you are choosing where the model call happens. Nothing here depends on one vendor, and every threshold below is a starting point you should retune for your own product.
Why three measurements beat one benchmark

Public leaderboards measure model quality on fixed prompts, and they say almost nothing about the machine your agent actually runs on. Residency is a property of your process, your network path and your prompt size, so it has to be measured in place. A single latency number is also easy to game: a warm local runtime can win on the median and lose badly on the first request after a laptop wakes up.
The probe therefore records a distribution rather than a point estimate, and it pairs that distribution with two non-latency signals. The payload audit answers what content leaves the device, which matters when prompts carry repository context or customer records. The offline check answers whether the agent degrades into a queue or into an exception, and that difference decides how much of the fallback path you have to build.
Step 1: Fix one request shape for both endpoints

The comparison is only meaningful when both sides receive the same bytes, so define one request body and reuse it. Keep the system prompt and user prompt in files, and let environment variables supply the base URLs. This also makes the probe cheap to rerun whenever a runtime updates.
mkdir -p prompts
printf 'Summarise the three functions in this diff.\n' > prompts/short.txt
export LOCAL_BASE_URL="http://127.0.0.1:8080/v1/complete"
export REMOTE_BASE_URL="https://your-endpoint.example/v1/complete"
The two URLs are placeholders, and the script below does no vendor-specific shaping. If your local runtime expects a different JSON envelope, adapt build_body once and keep the rest identical. That single edit point is what makes the numbers comparable later.
Step 2: Measure first body byte, with explicit honesty about it

Time to first token is the metric developers feel, but most HTTP clients only expose time to first body byte. The probe reads exactly one byte and stops, then labels the result as an approximation. It also reports p50 and p95 separately, because tail behaviour is where remote seats and cold local runtimes diverge.
The script also counts failures instead of hiding them behind a mean. A run that times out is data, and a remote seat that fails two of seven attempts changes the architecture conversation more than a hundred milliseconds of median latency.
#!/usr/bin/env python3
"""residency_probe.py - reference implementation, not a benchmark suite.
Records first-body-byte latency, payload size, secret-pattern hits and
forced-offline behaviour for up to two endpoints. Every number it prints
is yours to collect; no vendor performance is asserted here.
"""
from __future__ import annotations
import argparse
import json
import os
import re
import socket
import statistics
import time
import urllib.error
import urllib.request
SECRET_PATTERNS = {
"provider_key": re.compile(r"sk-[A-Za-z0-9]{16,}"),
"bearer_value": re.compile(r"(?i)bearer[ ]+[A-Za-z0-9._-]{12,}"),
"aws_secret": re.compile(r"(?i)aws_secret_access_key[ ]*[:=]"),
}
def build_body(prompt: str, system: str) -> bytes:
return json.dumps({"system": system, "prompt": prompt}).encode("utf-8")
def scan_for_secrets(raw: bytes) -> list[str]:
text = raw.decode("utf-8", errors="replace")
hits = [name for name, pat in SECRET_PATTERNS.items() if pat.search(text)]
for key in ("API_KEY", "ACCESS_TOKEN", "SECRET"):
value = os.environ.get(key, "")
if len(value) >= 12 and value in text:
hits.append("env:" + key)
return hits
def first_body_byte_ms(url: str, body: bytes, timeout: float) -> float:
req = urllib.request.Request(
url, data=body, headers={"Content-Type": "application/json"}
)
start = time.perf_counter()
with urllib.request.urlopen(req, timeout=timeout) as resp:
resp.read(1) # first body byte, NOT first token
return (time.perf_counter() - start) * 1000.0
def probe(url: str, body: bytes, attempts: int, timeout: float) -> dict:
samples, failures = [], 0
for _ in range(attempts):
try:
samples.append(first_body_byte_ms(url, body, timeout))
except (urllib.error.URLError, socket.timeout, TimeoutError, ConnectionError):
failures += 1
ordered = sorted(samples)
def pct(p: float) -> float | None:
if not ordered:
return None
idx = min(len(ordered) - 1, int(round(p * (len(ordered) - 1))))
return round(ordered[idx], 1)
return {
"url": url,
"attempts": attempts,
"failures": failures,
"p50_ms": pct(0.50),
"p95_ms": pct(0.95),
"mean_ms": round(statistics.fmean(samples), 1) if samples else None,
}
def offline_behaviour(fallback) -> dict:
"""Point at the discard port, then check the fallback still returns work."""
dead = "http://127.0.0.1:9/v1/complete"
started = time.perf_counter()
try:
first_body_byte_ms(dead, b"{}", timeout=2.0)
reached = True
except (urllib.error.URLError, socket.timeout, TimeoutError, ConnectionError):
reached = False
result = fallback()
return {
"endpoint_reachable": reached,
"fallback_returned": result is not None,
"fallback_seconds": round(time.perf_counter() - started, 3),
}
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--prompt", default="prompts/short.txt")
ap.add_argument("--attempts", type=int, default=7)
ap.add_argument("--timeout", type=float, default=20.0)
ap.add_argument("--out", default="residency_report.json")
args = ap.parse_args()
prompt = open(args.prompt, encoding="utf-8").read().strip()
body = build_body(prompt, system="residency-probe")
report = {
"payload_bytes": len(body),
"payload_secret_hits": scan_for_secrets(body),
"endpoints": [],
"offline": offline_behaviour(lambda: {"queued": True}),
}
for name in ("LOCAL_BASE_URL", "REMOTE_BASE_URL"):
url = os.environ.get(name)
if not url:
continue
row = probe(url, body, args.attempts, args.timeout)
row["name"] = name
report["endpoints"].append(row)
with open(args.out, "w", encoding="utf-8") as fh:
json.dump(report, fh, indent=2)
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
Run it twice: once with the local runtime freshly started, and once after it has served a few requests. Those two runs are the cheapest way to see cold-start cost without building a load generator.
Step 3: Audit what actually leaves the device

The payload audit is deliberately blunt, and it is not a data-loss-prevention product. It flags obvious credential shapes and any long environment value that leaked into the serialised body, which catches the common accident of pasting a token into a system prompt. A hit is a stop sign for a remote seat and a warning for a local one, because local runtimes log too.
The byte count matters as much as the hits. A prompt carrying an entire diff can be orders of magnitude larger than a chat message, and upload time on a home connection scales with it. Record the byte count next to the latency figures, or you will attribute a bandwidth problem to model speed.
Step 4: Force the offline path on purpose

The probe points at a dead port and then calls the same fallback your agent would call. If the fallback raises instead of returning queued work, the agent needs a fallback contract before any residency change ships. If it returns queued work in a few seconds, the offline story is already credible.
This check is cheap and it prevents the most expensive migration surprise. A remote seat that disappears during a demo is survivable when work queues locally and can be resumed. It is fatal when the whole run lives in a request handler that has already returned.
A decision table you can argue with

The thresholds below are operator-tunable starting points rather than published guidance. Fill the middle columns with your own probe output and use the last two as the interpretive lens.
SignalWhat local-first tends to giveWhat a free server tends to giveRead it asWarm p95 first body byteLow and stable once the runtime is pinnedDepends on queueing at the shared endpointLocal wins on steady interactive loopsCold startLong first request after sleep or restartEndpoint is already warmRemote wins on sporadic usePayload bytes off deviceZero for the model callFull prompt, including contextSensitive context argues for localSecret-pattern hitsStill a bug, still loggedBlocks the send until redactedAny hit stops the remote optionOffline behaviourUsable if you persist stateFails closed without a queueQueue design is the real workConcurrency shapeBounded by one machineBursts absorbed elsewhereSpiky workloads favour remote

The table is a discussion device, not a verdict. Two teams with identical numbers can land on different rows because their data classification and support burden differ.
Where a free server genuinely wins

A recurring discussion in developer circles concerns how many intermediaries sit between a developer and a model, and residency is the concrete version of that question. The honest answer is that self-hosting is sometimes the wrong trade, especially when the local machine cold-starts constantly or has no accelerator at all. Spiky workloads, shared team artifacts and short-lived experiments all favour a remote seat that is already warm.
MonkeyCode, an open-source coding agent project, offers free model access and a free server option, which makes it a reasonable second column for the probe above. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Token allowances and server terms change, so read the project page for the current limits before you design around them, and never assume a free tier is permanent.
The useful pattern is to run the probe against a free remote seat and against your laptop on the same day with the same prompt file. That produces a defensible comparison instead of an intuition, and it costs one environment variable.
Limitations and who should not use this

The probe measures first body byte, not time to first token, and the two diverge when a server streams headers early. It is a single-machine instrument, so it is not a load test and it will not reveal behaviour under ten concurrent runs. The secret scan is a regex tripwire, not a compliance control, and it will miss bespoke credential formats.
Teams with hard data-residency obligations should keep the model call local and treat the remote column as unavailable rather than slow. Interactive products with tight latency budgets should not adopt a shared endpoint without a warm-up strategy and a measured tail. Anyone expecting deterministic throughput from a free tier should build the queue first and treat the server as an accelerator, not a guarantee.
If you want a second column without provisioning anything, point the probe at a free remote seat first and keep the local runtime as the fallback. The numbers you collect will tell you more than any migration plan written before them.#ai #localfirst #python #architecture #software #coding #development #engineering #inclusive #community
Measuring Before Migrating: A Residency Smoke Test for Local-First Agents and Free Servers

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Dev Community @[email protected]
2026-10-27
Quick Answer

A fleet tracking backend should treat every GPS ping as untrusted input. Sort pings by device time, drop duplicates and impossible values, reject poor fixes and teleport jumps, hold position while a vehicle is parked, and add hysteresis to geofences. Keep the raw stream, store the cleaned stream beside it, and snap to roads only after cleaning. The Python below was tested on synthetic data only, not on live device feeds.
Why does a fleet app receive bad GPS data?

Some of the reasons are written down in vendor documentation. Samsara's vehicle locations documentation says a device reports about every 5 seconds while the vehicle is on and about once an hour while it is off. It also says identical consecutive records can occur, and that GPS interference in a garage can suppress updates even when the vehicle shows as on. The same page says the device keeps a rolling 30 days of location data, so after a connection is restored the buffered points become available through the API.
Read together, those statements describe four problems a backend must expect: duplicate rows, silent gaps, a misleading "on" status, and late data that arrives out of order. This is one vendor's documentation. Other vendors will behave differently, so read the equivalent page for the device you actually use.
What should the ingest layer check before it stores a ping?

My suggestion (author judgement, not a standard) is six cheap checks, run in this order:
Sort by device timestamp, so late arrivals land in the right place.
Drop exact duplicates (same time and position).
Reject impossible values, including out-of-range coordinates and the 0,0 position that some receivers report before they have a fix.
Reject poor fixes, where the reported accuracy is worse than a threshold.
Reject teleport jumps, where the implied speed between two kept points is not plausible for a vehicle.
Hold position while parked, so receiver jitter does not draw a wandering trail.
The thresholds in the code (50 m accuracy, 45 m/s, 10 m jitter) are starting values I chose for the example. Tune them against your own vehicles and devices.
from dataclasses import dataclass
from math import radians, sin, cos, asin, sqrt
@dataclass(frozen=True)
class Ping:
device: str
ts: int # epoch seconds, device clock
lat: float
lon: float
acc_m: float = 10 # reported horizontal accuracy in metres
def haversine_m(a, b):
r = 6371000.0
p1, p2 = radians(a.lat), radians(b.lat)
dp, dl = p2 - p1, radians(b.lon - a.lon)
h = sin(dp / 2) ** 2 + cos(p1) * cos(p2) * sin(dl / 2) ** 2
return 2 * r * asin(sqrt(h))
def clean(pings, max_acc_m=50, max_speed_ms=45, jitter_m=10):
"""Return a cleaned, time-ordered list for ONE device."""
seen, ordered = set(),
[] for p in sorted(pings, key=lambda p: p.ts): # 1. fix ordering
key = (p.ts, round(p.lat, 6), round(p.lon, 6))
if key in seen: # 2. drop duplicates
continue
seen.add(key)
if not (-90 <= p.lat <= 90 and -180 <= p.lon <= 180): # 3. impossible values
continue
if p.lat == 0 and p.lon == 0: # "null island" fix
continue
if p.acc_m > max_acc_m: # 4. poor fix
continue
ordered.append(p)
out =
[] for p in ordered:
if out:
prev, dt = out[-1], p.ts - out[-1].ts
if dt <= 0:
continue
d = haversine_m(prev, p)
if d / dt > max_speed_ms: # 5. teleport jump
continue
if d < max(jitter_m, p.acc_m): # 6. parked jitter
p = Ping(p.device, p.ts, prev.lat, prev.lon, p.acc_m)
out.append(p)
return out
Testing status: I ran six unit tests with Python 3.13 on synthetic points: out-of-order input, duplicates, 0,0 and poor-accuracy points, a teleport jump, parked jitter, and geofence flapping. All six passed. I have not run this against real device feeds, so treat it as a teaching sample and not production code.
How do you stop geofence alerts from flapping?

A vehicle parked near the edge of a depot boundary can cross it back and forth because of receiver noise, and each crossing can fire an alert. Two techniques help. Use hysteresis, which means the vehicle enters a zone inside radius r but only exits beyond r plus a buffer. Then require several consecutive pings before the state changes.
class Geofence:
"""Circle fence with hysteresis: enter inside r, exit only outside r + buffer."""
def __init__(self, lat, lon, r_m, buffer_m=30, confirm=2):
self.c = Ping("fence", 0, lat, lon)
self.r, self.buf, self.confirm = r_m, buffer_m, confirm
self.inside, self._streak = False, 0
def update(self, p):
d = haversine_m(self.c, p)
want_inside = d <= self.r if not self.inside else d <= self.r + self.buf
if want_inside == self.inside:
self._streak = 0
return None
self._streak += 1
if self._streak >= self.confirm:
self.inside, self._streak = want_inside, 0
return "enter" if self.inside else "exit"
return None
In the test, a sequence of distances of 150, 90, 95, 110, 98, 105, 160 and 170 metres from a 100 m fence produced exactly one enter event and one exit event. Two of those readings (110 m and 105 m) were outside the 100 m radius while the vehicle was still inside the zone, and the buffer absorbed both.
Should you snap points to roads?

Map matching can clean what the filters above leave behind, but it has its own rules. Google's Roads API documentation says one snap-to-roads request accepts up to 100 GPS points and returns points snapped to the most likely road. It recommends keeping consecutive points at or below about 300 metres apart, and offers an optional interpolate setting that adds points following the road shape. Interpolated points have no original index, so the response can contain more points than the request.
OSRM's Match service takes optional timestamps, which must increase, and optional per-point radiuses that default to 5 metres. The documentation suggests using the device's reported accuracy. It returns null for points dropped as outliers and a confidence score from 0 to 1 for each matching. The OSRM page I read was for version 5.5.1, so check the documentation for the version you run.
My judgement: snap after cleaning, keep the raw point and the snapped point side by side, and treat a low confidence score as a flag for review rather than an error.
How do you find vehicles near a depot quickly?

PostGIS documents ST_DWithin as a cheap filter that can use a spatial index, while ST_Distance computes exact distances and cannot use indexes. For geography values the distance is in metres. The common pattern is to filter with ST_DWithin first and sort with ST_Distance on what remains.
SELECT v.id, ST_Distance(v.last_position, d.location) AS metres
FROM vehicles v
JOIN depots d ON d.id = 7
WHERE ST_DWithin(v.last_position, d.location, 2000)
ORDER BY metres;
This query is untested. It assumes both columns are geography with a spatial index on last_position, so check the types and the index in your own schema.
What should the fleet database store?

In my view three tables cover most needs: an append-only table of raw pings, a table of cleaned pings that maps and reports read, and a small table holding the last known position per vehicle for proximity queries. Keeping the raw data lets you re-run a better filter later and settle disputes about a route.
Which are the top five fleet management app development companies?

Teams that prefer to buy this work instead of building it can compare the five partners below. The list is not a ranking of quality.
Figures below come from each company's own website and are self-reported, so confirm them directly before signing anything.
1. Dev Technosys

Dev Technosys is a fleet management app development company with 250+ professionals that does business globally. Its fleet page describes connecting GPS tracking, fuel monitoring and dispatching systems, then automating assignments, maintenance alerts and journey updates. The company builds its own platforms and connects them to existing business systems through APIs instead of configuring third-party ERP products. It states an 89% project success rate and says most new business comes through client referrals; both claims are self-reported. That scope fits teams that already own tracking hardware and need workflow software around it.
2. N-iX

N-iX lists fleet management software development as a dedicated service. Its page covers telematics and predictive-maintenance integration, ELD and IoT sensor integration, hours-of-service compliance modules, route-planning and dispatch engines, and mobile fleet apps. A case study describes a GPS-tracked in-vehicle Linux gateway, a Google Cloud IoT platform and an Android driver app for a telematics client, in a partnership that began in February 2021. The site states over 2,400 software engineers, and the case study gives no fleet outcome figures. N-iX suits teams that need hardware-to-cloud telematics work from one vendor.
3. Intellias

Two fleet case studies sit on the Intellias site. In the first, a Florida telematics client has worked with the company since 2018 on a platform with real-time GPS mapping, idle-time tracking and diagnostic trouble code reporting, and the page says it handles 100,000+ vehicles with data points every 30 seconds. The second is a native iOS, Android and Angular truck-tracking front end for a large car maker, tested on 1,000 vehicles and offered in more than 20 countries. Intellias suits teams that expect high vehicle counts and need a backend built for that volume.
4. Altoros

Altoros says it was founded in 2001 and lists GPS tracking software for car rental, trucking and delivery, fleet management with vehicle tracking and dispatch, and IoT platforms for fleets. Its logistics page cites a transport management case serving 100+ companies and processing 10,000+ automated orders a day, with operator workload cut by up to 70%, plus a truck-parking app prototype covering 10,000+ locations. All of these figures are self-reported, and the page names no ISO certification, so ask for evidence. Altoros suits teams that want an IoT-heavy build from a long-established vendor.
5. STX Next

Describing itself as a Python-native team with more than 20 years of experience, STX Next lists fleet management systems, route optimization, electronic proof of delivery, predictive maintenance and IoT integration on its transport and logistics page. Named case studies include Sendcloud (a carrier integration proof of concept), GS1 Polska (a location-data app for drivers) and T Dispatch (dispatch software). The page mentions Python, IoT, RFID, AWS, Azure and Snowflake and lists delivery centers in Poznan and Merida, but it states no headcount. STX Next suits teams that prefer a Python backend.
FAQ

How often should a fleet device send a position?
Samsara's documentation describes about every 5 seconds while a vehicle is on and about hourly while it is off. Your hardware and data plan set the real figure.
Should raw pings be deleted after cleaning?
In my view, no. Keep them for a retention period your own policy allows, so you can re-run improved filters later.
Do I need map matching in every fleet app?
No. Many dashboards work with cleaned points. Matching helps most when you report distance driven on roads.
What accuracy threshold should I use?
The example uses 50 m as a starting value. Tune it per vehicle type and device.
Can I use this code in production?
It is a teaching sample tested on synthetic data. Add tests built from real device samples first.#python #backend #iot #architecture #software #coding #development #engineering #inclusive #community
Designing a Fleet Tracking Backend That Handles Bad GPS Data
#community #software #iot #coding #python #architecture
Dev Community @[email protected]
2026-10-27
AI can generate a financial model that looks coherent, contains valid formulas, and produces a plausible result while still being wrong in ways that matter.
A formula can reference the wrong cell. A cost assumption can be expressed in the wrong unit. A revenue schedule can omit a phase. A model can calculate correctly from an unsupported market assumption.
These are different failure modes, and they need different controls. For real estate feasibility models, the objective should not be to ask whether an AI-generated workbook looks correct. It should be to establish which properties can be tested automatically, which assumptions require evidence, and which decisions must remain with a qualified human reviewer.
A useful validation architecture has five layers:
Structural validation
Deterministic calculation checks
Domain and consistency checks
Evidence and provenance checks
Human review and approval
No single layer is sufficient.
1. Separate the output from the evidence

Consider a hypothetical development model with the following inputs:
Site area and developable area
Permitted gross floor area
Unit mix or lettable area
Revenue assumptions
Construction and other development costs
Financing assumptions
Development programme
Exit assumptions
The model calculates development costs, revenue, cash flows, residual land value (RLV), and potentially internal rate of return (IRR).
For example, a model might calculate an RLV of £4 million. That output can be mathematically consistent with the workbook and still be commercially misleading if the revenue assumptions are unsupported or the construction cost basis is outdated. Validation therefore needs to test both the calculation and the justification for its inputs.
2. Layer one: validate the structure before the numbers

Before reviewing financial logic, validate the shape of the generated output. If an AI system is expected to return structured data before creating a workbook, define an explicit contract. JSON Schema is one established option for specifying required fields, data types, and constraints.
See the JSON Schema getting-started guide.
A simplified model input contract might look like this:
{
"project_id": "DEMO-001",
"currency": "GBP",
"site_area_sqm": 5000,
"gross_floor_area_sqm": 12000,
"construction_cost_per_sqm": 1800
}
This is illustrative data, not a market benchmark or a recommendation for a production schema.
A structural validator could check that:
Required fields are present.
Numeric values are actually numeric.
Areas and costs use declared units.
Currency is explicitly identified.
Values fall within defined technical bounds.
Unexpected fields are rejected or handled deliberately.
Missing values are not silently replaced with zero.
The last point is important. Zero and unknown are not equivalent. If a finance rate is missing, substituting zero can make a model appear more profitable. A missing value should normally trigger a validation error or an explicit unresolved status.
Structural validation establishes that the model can be interpreted. It does not establish that the model is economically sensible.
3. Layer two: use deterministic checks for deterministic rules

Rules that can be expressed precisely should not depend on an LLM's judgment. For example, if a model defines construction cost as gross floor area multiplied by construction cost per square metre, the expected calculation is:
\text{GFA}
\times
\text{Cost per sqm}
]
For the hypothetical inputs above:
[
12{,}000 \times £1{,}800 = £21{,}600{,}000
]
A deterministic test can independently calculate the expected result and compare it with the workbook output.
from decimal import Decimal
gfa_sqm = Decimal("12000")
cost_per_sqm = Decimal("1800")
reported_cost = Decimal("21600000")
expected_cost = gfa_sqm * cost_per_sqm
assert expected_cost == reported_cost
This test checks arithmetic for the stated formula. It does not establish that the GFA is permitted, that the cost rate is appropriate, or that the formula captures every relevant cost.
A production implementation should also define tolerances where appropriate, particularly when comparing rounded spreadsheet values or calculations involving floating-point numbers.
Other deterministic checks might include:
Revenue equals the sum of its components.
Total costs reconcile to their component schedules.
Cash flow totals reconcile across periods.
Unit counts and areas reconcile between schedules.
The reported output matches an independently calculated result.
A sensitivity case actually changes the intended input.
Required formula cells are not unexpectedly blank or replaced by constants.
Where possible, use independent calculations rather than simply checking that a formula cell contains a formula. A formula can exist and still be wrong.
4. Layer three: validate model relationships

Many serious errors are not syntax errors or arithmetic errors. They are inconsistencies between parts of the model.
Suppose a development model contains:
120 residential units in the unit schedule
116 units in the sales schedule
Revenue calculated using the sales schedule
Construction costs calculated using the unit schedule
Every formula may evaluate successfully, but the workbook contains a discrepancy that needs resolution.
Domain-level checks can flag conditions such as:
Unit counts that differ across schedules.
Areas that exceed a declared model constraint.
Revenue periods that do not align with the programme.
Cost totals that exclude a populated cost category.
An exit value that uses an inconsistent timing assumption.
A sensitivity scenario that changes revenue but leaves dependent calculations unchanged.
An IRR calculation using a cash-flow range that omits a period.
These checks should be encoded as explicit rules wherever the intended relationship is known.
Some rules require context. For example, whether a particular cost should be included in a development budget depends on the model's scope and conventions. A validator should not impose a universal rule where none exists.
Instead, it should identify the relevant condition, record the rule being applied, and send ambiguous cases for review.
5. Layer four: validate the provenance of assumptions

A financial model can pass every arithmetic test and still fail as an analytical tool.
The reason is straightforward: calculations cannot establish the truth of their inputs.
For every material assumption, a validation system should try to preserve:
The value and unit.
The source or origin of the value.
The source's date and relevant data period.
The geography and asset type, where applicable.
The transformation from source evidence to model assumption.
Any manual adjustment made by the analyst.
The model locations that depend on the assumption.
Consider a construction cost assumption of £1,800 per square metre. A validator might confirm that the figure is numeric, the unit is recorded, and the workbook uses it consistently. Those checks do not establish whether the figure is suitable for the location, asset type, specification, and cost period. That question requires appropriate evidence and professional interpretation.
An LLM may help extract candidate assumptions from documents or explain how a value is used in a workbook. The extracted result should still be checked against its source, and the resulting assumption should remain distinguishable from the underlying evidence.
A useful principle is:
Traceability makes an assumption inspectable. It does not make the assumption correct.
6. Layer five: make human review a defined state transition

Human review should not be an informal final glance after an automated process has already treated the model as complete.
A practical workflow can represent validation explicitly:
GENERATED
|
v
STRUCTURE_CHECKED
|
v
CALCULATIONS_CHECKED
|
v
DOMAIN_CHECKED
|
v
EVIDENCE_REVIEWED
|
v
HUMAN_APPROVAL_REQUIRED
|
+----> APPROVED FOR THE DEFINED PURPOSE
|
+----> RETURNED FOR CORRECTION
|
+----> BLOCKED: UNRESOLVED ISSUE
The states are illustrative, but the distinction matters. A successful automated test should not automatically mean that a model is approved. Approval should be scoped to a defined version of the workbook, its assumptions, its evidence, and its intended use.
A reviewer should be able to see:
What was generated.
Which automated checks passed.
Which checks failed or were skipped.
Which assumptions lack adequate evidence.
What changed after a correction.
Who approved the model and for what purpose.
If a material input or formula changes after approval, the system should identify which checks need to run again and whether the earlier approval remains valid. This is where versioning and change tracking become part of model governance rather than administrative overhead.
7. Design the exception path before the success path

Validation is often designed around a clean example: the model is generated, every check passes, and the user receives the output. Production systems need to handle less convenient outcomes.
A failed check should produce actionable information. “Validation failed” is less useful than “The unit count in the sales schedule differs from the unit schedule; review the linked inputs before approving revenue.”
Similarly, a warning should not disappear simply because a later stage of the workflow succeeds. For auditability, preserve the check identifier, the model version, the result, the relevant evidence, and the resolution.
8. Measure validation quality without inventing an accuracy score

A single “model accuracy” percentage can hide important distinctions. A workbook might pass every arithmetic test while containing several unsupported assumptions. Another may contain a harmless formatting issue but have fully reconciled calculations and well-documented inputs.
A more useful validation report separates categories:
Structural checks passed or failed.
Deterministic calculation checks passed or failed.
Domain checks passed, failed, or were not applicable.
Assumptions with verified, missing, or conflicting evidence.
Open exceptions and their severity.
Human review status.
The scope and version of the approved output.
If the system is evaluated over time, track false negatives and false positives for individual validation rules against a reviewed test set. Record the test cases, expected outcomes, rule versions, and known limitations.
Do not interpret a high pass rate as proof that the model is correct. It may simply mean that the checks cover only a narrow set of failure modes.
The purpose of validation is to make known risks visible and testable, not to manufacture certainty.
9. A practical implementation sequence

A team introducing validation around AI-generated financial models does not need to automate every review step at once.
A sensible sequence is:
Define the contract. Specify the required inputs, output fields, units, and missing-value behaviour.
Write deterministic tests. Start with totals, reconciliations, formula ranges, and independent calculations.
Add domain checks. Encode known cross-schedule relationships and clearly defined constraints.
Track provenance. Associate material assumptions with sources, dates, units, and transformations.
Introduce exception handling. Make failures explicit and prevent unresolved material issues from being silently treated as passed.
Define human approval. Record the reviewer, workbook version, purpose, and outstanding qualifications.
Build a regression suite. Retain representative valid and invalid examples and rerun the checks when the generation logic or validation rules change.
The test suite should include deliberate failures, not only successful examples. Examples might include missing finance assumptions, inconsistent unit counts, a formula referencing the wrong period, and a source whose geography does not match the project.
This helps test whether the validation process detects the intended problem rather than merely accepting a plausible-looking output.
Conclusion

Human-in-the-loop validation is not a person checking an AI result after the important work is finished. It is a system design in which deterministic rules handle deterministic questions, domain checks expose inconsistencies, evidence is kept separate from assumptions, and a human reviewer remains accountable for unresolved judgments.
For financial modelling, that distinction is material. A workbook can be well-formed without being economically sound, mathematically consistent without being supported by market evidence, and ready for review without being ready for a decision.
The objective is not to make every AI-generated model look trustworthy. It is to make the model's logic, assumptions, limitations, and unresolved issues sufficiently visible that a professional can challenge it before relying on the result.#feasibility #realestate #ai #financial #software #coding #development #engineering #inclusive #community
Human-in-the-Loop Validation for AI-Generated Financial Models
#ai #community #software #coding #development #engineering
Mick Donnellan @[email protected]
2026-10-26

*Manchester dates* for The Crucified Silence – Sunday November 22nd – Irish World Heritage Centre. 6pm.

“A story anyone can relate to…” — Irish Independent
Click here to secure your ticket now!
About
Following acclaimed performances in Athlone, Castlebar, Ballinrobe and Galway, The Crucified Silence comes to Manchester on Sunday 22nd November at 6pm, bringing a distinctive voice from the West of Ireland to the iconic Irish World Heritage Centre.
Hilarious, heartbreaking and brimming with dark Irish humour, The Crucified Silence is a compelling one-man play written and performed by acclaimed Irish playwright Mick Donnellan. Set in Ballinrobe, Co. Mayo, the story follows Joe, a local guard who has crossed a line after a volatile altercation with his superintendent. As he drinks his way around town, he conjures vivid, nostalgic images of familiar landmarks and meeting places, including The Bowers walk, Art O’Neill’s Bar and Geoff’s pub. These recollections bring his world to life, drawing audiences into a community shaped by its own rhythms, rituals and relationships.
Yet beneath the laughter lies a deeply moving story of loneliness, loss, regret and the search for connection. Just as Joe’s sharp wit and comic observations have audiences laughing, glimpses of his troubled past reveal a more vulnerable man struggling to come to terms with the life he has lost. The result is a play that can have you laughing out loud one moment and breaking your heart the next.
Populated by recognisable Irish characters and brought vividly to life through authentic Irish dialect, cultural nuances and a rich tradition of storytelling, the play offers a compelling glimpse into the humour, warmth and complexities of everyday life in rural Ireland. For those familiar with Irish life, there is much to recognise; for those discovering it for the first time, an opportunity to experience a distinctive culture through characters who feel wonderfully real.
Book now

The Venue
A much-loved hub for Irish culture, arts and community in the North West of England, the Irish World Heritage Centre provides a fitting setting for a play that celebrates Irish storytelling while exploring experiences that transcend borders and backgrounds.
For Manchester’s Irish community, the evening offers a powerful sense of home, evoking the people, places, humour and shared experiences that connect us to where we come from. It is also a wonderful opportunity to celebrate the strength of Irish theatre and the remarkable stories being created in Ireland and finding audiences beyond its shores.
Universal Appeal
Although Joe’s world may be rooted in a small town in Co. Mayo, his struggles are universal. His attempts to make sense of a life shaped by difficult choices, personal loss and fading connections will resonate with anyone, from all cultures and backgrounds, who has ever looked back on their past and wondered how things might have turned out differently.
With minimal staging and intimate, uncompromising storytelling, the play places audiences face to face with Joe’s inner world, where humour becomes a defence against pain and silence can speak louder than words. Mick Donnellan’s powerful central performance draws audiences towards a devastating climax, balancing theatrical intensity with the feeling of a deeply personal confession.
All are welcome to join for an evening of big laughs, poignant storytelling, heartbreaking moments and an unforgettable injection of high-quality theatre!
Spaces limited so be sure to secure your seat as early as possible :)
About Mick Donnellan and Truman Town
Mick Donnellan works as a successful Playwright, Novelist and Screenwriter. Film credits include Tiger Raid adapted from Mick’s Play Radio Luxembourg. Optioned by London Film Company Dixon/Baxi/Evans, the script was developed in London and shot in Jordan. Starring Brian Gleeson, Damian Molony and Sofia Boutella, it was accepted into the Tribeca film festival (New York) and was also seen at Cannes and Edinburgh.
The Irish Premiere was screened at the Galway Film Fleadh and you can watch the Tiger Raid trailer here.
Mick is the author of six novels:
The Dark Rooms
Galway Blues
El Niño
Fisherman’s Blues
The Dead Soup
The Naked Flame
Recent Work:
Mick Donnellan has had two major theatre productions hit the stage in 2025.
Outlaw Pete premiered at the Town Hall Studio, Galway in April 2025, starring Laura Hutchinson, Brian Cahill, and Carl Jones. Following its debut, the production toured to the Linenhall Arts Centre in Castlebar in May, before returning to Galway for the Galway Fringe Festival in July, where it sold out Kennedy’s Bar on Eyre Square.
Donnellan’s second new play of 2025, Class of ’57, was produced by Athlone Little Theatre and directed by Caroline Coyle, featuring standout performances from Ellen Ryan, Brian Toolan, and Nino Magilocco.
In December 2024, his most recent book Galway Blues was published to great acclaim. He received the Mayo Theatre Bursary through Mayo Arts Office in April the same year.
Prior to that, he was awarded the Agility Award through the Arts Council of Ireland and his play Nally was supported by Westmeath Arts Office.
In other theatre work, The Crucified Silence (as a short play) was chosen as part of the Scripts Ireland Play festival. After a week of intensive workshops with Playwright Eugene O’Brien, the monologue was directed by Jim Culleton (Fishamble) and performed by Aaron Monaghan. Following this, Mick took part in the Galway Theatre Development Programme, run by Andrew Flynn in conjunction with Galway’s Town Hall Theatre, which resulted in a residency at the Tyrone Guthrie Centre.
His other plays include Sunday Morning Coming Down, Shortcut to Hallelujah, Gun Metal Grey, and Velvet Revolution.
He is listed on the Irish theatre institute here: http://irishplayography.com/person.aspx?personid=47564
About Truman Town:
Truman Town Theatre exploded on to the theatrical circuit with their Play – Sunday Morning Coming Down. Written and directed by the company’s Artistic Director Mick Donnellan, it immediately sold out the Galway Town Hall Studio for five nights on the first run. Following that, the crew went on tour to Mayo, selling out Lisloughrey Lodge, Cong,The Valkenburg Niteclub, Ballinrobe and the Linenhall in Castlebar. It also enjoyed large audiences on the Main Stage on Galway’s Town Hall and went on a National Tour covering – Glor Ennis, St. John’s Listowel, Cork Arts Theatre and it sold out five nights at The New Theatre, Dublin. It was also performed by DRUID as a public reading during the Galway Arts Festival.
Truman Town followed this phenomenal success with a second Play – Shortcut to Hallelujah. Again, written and directed by Mick Donnellan, it deals with the curse on the Mayo football team. It sold out the Town Hall Studio, went on tour to Ballinrobe and the Linenhall in Castlebar and then down to St. John’s Theatre, Listowel Co. Kerry where it was requested back to headline Friday night of Listowel Writer’s week.
Between this, a third drama was produced as part of the Galway Theatre Festival. It was titled Gun Metal Grey.
Shortcut to Hallelujah returned from Listowel Writer’s Week to perform on the Main Stage Galway for a wildly successful five nights after which Velvet Revolution, the fourth Truman Town production, also written by Mick Donnellan, was premiered in St. John’s Theatre, Listowel. It went on tour to Kilmallock, Kenmare and Waterville, then up to Ballinrobe, on to Galway and ran for ten days as part of the Galway Fringe. Mentioned as a Highlight of the festival by the Irish times, it was subsequently performed in Campbell’s Tavern, Cloghanover, Headford, Electric Picnic and The New Theatre, Temple Bar, Dublin.
Radio Luxembourg, Mick’s fifth Play, was bought by a London Film Company (Dixon/Baxi/Evans) and has been adapted for the screen. The title for the movie version is “Tiger Raid”. Starring Brian Gleeson, Damian Molony and Sofia Boutella, it was accepted into the Tribeca film festival (New York) and was seen at Cannes and Edinburgh. The Irish Premiere was screened at the Galway Film Fleadh.
Currently Mick is working on the exciting screen adaptation of Shortcut to Hallelujah with Florence Films. The screenplay is titled Sam and is based around the curse supposedly put on the Mayo Football team as they returned home as All Ireland Champions in 1951. Drenched in Irish lyricism and modern day dark humour, the script has been met with keen interest by film producers and actors throughout the industry.
Between writing projects, Mick has enjoyed roles as a lecturer in writing at University of Ireland and TUS Athlone.
*
Read more on www.mickdonnellan.com
#books #music #news #Poetry #writing

#books #music #news #poetry #writing
Dev Community @[email protected]
2026-10-26
Preface

Before diving in to the basics, this post would like to implore the reader to look up definitions of all the technical jargon that will naturally follow in the series. You cannot build a technical understanding of the structures involved unless you’re proficient in the language it speaks.
Note: All technical jargon will be marked with an asterisk (*), making it easier to identify them for lookups.
This is the first in a 5-part series that aims to act as a manual to developers who are either starting out learning GitLab CI/CD or more senior devs looking for a handbook.
First order of business — What are CI/CD pipelines, and why GitLab?

In order to comprehend the concept that is automation and pipelines, we need to trace the history of why these things came into place, the technologies that came after and how refined they became over time.
To give a brief introduction, before pipelines, developers had to manually set up everything — from installing dependencies required by apps to setting up infrastructure for their projects to run.
After the advent of scripting, it became a lot easier to automate manual tasks with the run of a single .sh file. But scripting brought up its own sets of problems, one of them being deployment.
Deployment was still done manually, because scripting produced many errors such as:
Silent Failures — Scripts return a success exit code (0), but they do not stop executing after a command fails, unless explicitly written otherwise using set -e.
Unset Variable Issues — Scripts depend on the host environment. If a variable is mis-spelt or missing on the production server, then the entire script breaks.
Unverified Releases — Running scripts without automated health checks can leave services in a broken state without anyone knowing the root cause. And thus, to fix such problems, pipelines were born.

CI/CD Pipelines — Demystified

CI/CD pipelines are Continuous Integration/Continuous Delivery tools that aid in automating 90% of the work that you would have otherwise done manually, such as deployment, setting up infrastructure on a server, etc.
Any changes you make in your code base or structure immediately get integrated and tested by the pipeline — the Continuous Integration part — and then deployed to a cloud platform of your choice, which falls under the Continuous Delivery section (AWS, Microsoft Azure, Google Cloud, etc.).
There are many CI tools that came about after the advent of pipelining. A few notable ones are:
GitHub Actions, as provided by GitHub.
GitLab CI, as provided by GitLab.
Jenkins, which is an *open-source downloadable automation tool that is hosted on a server locally, such as an EC2 Instance or a Kubernetes cluster.
CircleCI, which is a *SaaS platform that integrates across all repositories, such as GitHub, GitLab, BitBucket, etc.
There is no “proper” or “best” pipeline, since the primary work that each tool does is automation, while offering different sets of features, such as GitLab CI offering native *Terraform integration, whereas GitHub Actions — being relatively new — does not, requiring third-party support. But that is about the degree to which the distinction between each pipeline goes.
Of course, the choice varies from company-to-company. The employee must, then, learn to be flexible to suit the company’s requirements.
Why GitLab CI? Why not other CI tools?

Herein lies the “paradox of choice”. There are several repo-hosting platforms available that offer CI/CD capabilities, so choosing each depends on the pros and cons of each platform, along with the needs of the individual/team.
A table is then best suited to provide a more direct comparison between each platform, their benefits, and the costs they offer.
A table is then best suited to provide a more direct comparison between each platform, their benefits, and the costs they offer.
Platform / ToolPrimary FocusCore AdvantageBest Suited ForGitLabAll-in-one DevOps lifecycleNative *Terraform integration out-of-the-boxProduction-standard DevOps engineering teamsGitHubSource control & collaborationMassive marketplace ecosystem of ActionsGeneral development & open-source projectsBitbucketAtlassian ecosystem synergyBuilt-in Jira and Confluence link trackingEnterprise environments already deep in JiraJFrog ArtifactoryUniversal binary repositorySpecialized artifact management & storageHigh-scale dependency management & security
Conclusion

While GitHub and BitBucket offer more features in general development — with JFrog Artifactory offering solely binary storage — GitLab stands out as the best production-standard for a DevOps team when it comes to offering a pure DevOps-related repository hosting platform.
And thus, we shall begin by navigating through GitLab and its main attraction point for many developers — CI/CD capabilities.
Any corrections in this post will be much appreciated in the comments!#automation #cicd #devops #tutorial #software #coding #development #engineering #inclusive #community
The GitLab CI Manual: The Textbook You’ve Always Needed — #1 Introduction
#devops #community #software #coding #development #tutorial
Dev Community @[email protected]
2026-10-26
You shipped the feature, fixed the bugs, and answered the "quick question" that turned into a two-hour debugging session. The invoice went out on the first of the month. Thirty days later: nothing.
The most I have ever waited on was $4,800. The client answered every message within a day - "accounts is processing it", "the approver is on holiday", "next Friday, promise". What got it paid was not a longer or angrier email. It was the third email in a sequence I already had sitting in my drafts folder, sent on day 30, worded almost exactly like the one below. The money arrived in 48 hours.
If you freelance or run a small dev shop, chasing payment for finished work is one of the worst parts of the job. The fix is not a better apology for asking. It is a process: the same three emails, on the same days, every time.
Here they are, copy-paste.
Day 7: the polite nudge

At seven days past the due date, assume a boring explanation. The invoice was missed, routed to the wrong person, or stuck in an approval queue. Your goal is a response without friction.
Subject: Quick follow-up on invoice #[NUMBER]
Hi [CLIENT NAME],
I'm following up on invoice #[NUMBER] for [PROJECT/DELIVERABLE], which was due on [DUE DATE].
Could you confirm that it's been received and let me know when I can expect payment?
For reference, the outstanding amount is [AMOUNT].
Thanks,
[YOUR NAME]

Why this works: you identify the invoice, state the amount, and ask for one specific next step. No accusation, no essay.
Send it in the original invoice thread so the context travels with it. If they reply with a payment date, write it down. A promise in a casual email is easy to forget - yours and theirs.
Day 14: the firm follow-up

Two weeks late, be more direct. This message says the balance needs attention; it does not ask whether anyone saw your last email.
Subject: Payment overdue - invoice #[NUMBER]
Hi [CLIENT NAME],
Invoice #[NUMBER] for [AMOUNT] was due on [DUE DATE] and remains unpaid.
Please confirm the scheduled payment date by [RESPONSE DEADLINE].
If payment has already been processed, please disregard this reminder and let me know so I can update my records.
Thank you,
[YOUR NAME]

Why this works: it replaces a vague request with a clear action and a deadline, and it gives the client an easy out if payment is already moving.
Set the response deadline at two business days. If the answer is "waiting for approval," ask when approval is expected. If "accounting pays on Fridays," ask which Friday your invoice is scheduled for. Turn "we'll get to it" into something you can track.
Check your contract before this email. If it allows late fees, interest, or pausing work, know the terms before you invoke them. Never add fees the contract does not mention.
These templates are communication examples, not legal advice. Check your contract and applicable local law before adding late fees or taking collection action.
Day 30: the final notice

A month overdue changes the conversation. Stay polite, but do not send a third email that sounds like the first one.
Subject: Final payment notice - invoice #[NUMBER]
Hi [CLIENT NAME],
Invoice #[NUMBER] for [AMOUNT], originally due on [DUE DATE], remains outstanding despite my previous follow-ups.
Please arrange payment in full by [FINAL DEADLINE] or contact me by that date to confirm a concrete payment arrangement.
If the balance remains unpaid and I have not received an agreed resolution, I will consider the next steps available under our agreement and applicable law, which may include formal collection or recovery proceedings.
Please confirm receipt of this notice.
Regards,
[YOUR NAME]

Why this works: it is factual, it documents the history, and it sets an explicit deadline without threats you cannot carry out.
Only name actions you are genuinely prepared and legally entitled to take. Before formal recovery, review your agreement, preserve the invoices and correspondence, and check the rules in the relevant jurisdiction. And do not assume you can shut down a live service, revoke access, or withhold source code - your contract decides that, not your frustration.
What happens between the emails matters as much

Keep a payment log. Invoice number, amount, due date, reminder dates, responses, promised dates. The day you have three late invoices, this log is the only thing keeping the story straight.
Separate payment from new work. A client who has not paid asking for another feature is not an automatic yes. Review the agreement and discuss a pause or revised schedule.
Make paying easy. Every email includes the invoice, the amount, payment instructions, and the reference their accounting needs. Every extra step is another week.
Fix the next contract. Explicit payment terms, a defined invoicing schedule, a stated late-fee rule. For larger projects, milestone payments instead of one big invoice at the end.
Do not let one client be your whole business. A great relationship is still a financial risk if one unpaid invoice stops you paying your own bills.

Build the system before you need it

The best time to decide how you handle late payment is before an invoice goes overdue. Set three calendar reminders for days 7, 14, and 30, save the scripts as templates, and when the day comes you fill in the brackets and send. You follow a process instead of writing an email while frustrated.
Your turn: what is worse - clients who ignore reminders, clients who keep promising "next week," or clients who dispute the invoice after the work is done? And what actually worked for you?
Written by the Change Brief team - we build practical, no-signup toolkits for freelancers.
AI assistance was used in drafting this article. The publisher reviewed and edited the content and takes responsibility for the final version.#freelance #career #productivity #software #coding #development #engineering #inclusive #community
3 Late-Payment Emails for Freelance Developers
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