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159 results for “skill”

device ⛧ @device
2026-10-24
it's so infuriating spending some time integrating a dependency, and upon closer look you find the repository to have the following:
single maintainer
project is a few months old
5-20 commits in bursts within a short period (e.g., May 21-May 23). Nothing since.
broken CI badge on every commit (lol btw)
.claude/skills AGENTS.md etc
so now I'll be undoing all that. It's single-handedly the most infuriating part I'm encountering recently. I picked gleam explicitly because they aren't that LLM friendly, but these bozos just have to come in here and shit the bed...
packetcat @[email protected]
2026-10-24

Review: Japanese Gothic by Kylie Lee Baker
> I very much enjoyed my time reading Japanese Gothic. I think it is now the best horror novel I’ve read. And, I think that I am definitely in the psychological horror enjoyers camp now. While this book has moments where it has classic physical horror elements, for the most part Kylie uses her ample skill at creating an immersive atmosphere to terrify the reader instead, the monster is in your head and not chasing you down in a samurai house.
https://nullrouted.space/2026/10/24/review-japanese-gothic-by-kylie-lee-baker/
If you enjoyed reading this review, please boost this post and consider supporting me on Patreon.
https://www.patreon.com/Bastetrix
#books #bookstodon

#books #bookstodon
Jamesthe1 @jamesthe1
2026-10-24

I see sentiment that there's "good AI," but I think I'll have to disagree.
I've seen tools that are intended to help artists—i.e. "close this hand," "change these eyes," stuff that takes more fiddling with to get looking right—and I still can't quite condone their use at all.
Your job as an artist is to figure this stuff out. You are literally here because you understand translating 3D concepts to 2D images; generative AI, no matter how small in its applications, is going to cause any of your skills to atrophy. It often relies on datacenters, which are *still* guzzling water and having drastic effects on farmers across the globe. It's a global warming machine that takes away our knowledge and replaces it with dependence.
We should never be using AI to create, nor as a mentor or teacher.
Psychology News Robot @[email protected]
2026-10-24

DATE: October 24, 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: Emotional struggles are linked to vulnerable narcissism but not grandiose narcissism, meta-analysis finds
URL: https://www.psypost.org/emotional-struggles-are-linked-to-vulnerable-narcissism-but-not-grandiose-narcissism-meta-analysis-finds/
A new statistical review of data from more than 12,000 adults suggests that difficulties with managing emotions are moderately linked to one specific form of narcissism, while showing virtually no connection to another. The research, published in Personality and Individual Differences, indicates that people with insecure and defensive narcissistic traits tend to struggle with emotional control, whereas those with more boastful, dominant narcissistic traits generally do not report these same struggles. These findings help clarify why past studies on narcissism and emotional health have produced conflicting results, and they may point to specific emotional skills that could be targeted in therapy.
In psychology, trait narcissism refers to a persistent pattern of personality characteristics centered on interpersonal antagonism, which includes feeling entitled, manipulating others, and lacking empathy. Rather than being a single uniform trait, narcissism is generally divided into two main subtypes: grandiose and vulnerable. Grandiose narcissism matches the common stereotype of a narcissist, featuring an outgoing, dominant, attention-seeking, and aggressively self-confident style. Vulnerable narcissism, while sharing the same core of entitlement and self-centeredness, looks very different on the surface, involving introversion, fragile self-esteem, deep feelings of inadequacy, and a heightened sensitivity to rejection.
Both forms of narcissism are associated with problems in relationships, including a lack of care for a partner’s distress, relationship dissatisfaction, and even intimate partner violence; a 2024 meta-analysis found that violence was more strongly tied to vulnerable narcissism (a correlation of 0.25) than to grandiose narcissism (0.11). Because personality traits themselves are deeply ingrained and difficult to change, psychologists often look for modifiable behaviors and skills that might help improve how narcissistic individuals function in daily life. One promising target is emotion dysregulation, which refers to difficulty understanding, managing, and responding to feelings in a healthy way that aligns with a person’s goals and social norms.
“Our broader interest in this area stems from wanting to understand the psychological factors that contribute to the interpersonal difficulties experienced by people with high levels of narcissism,” senior author Megan L. Willis, an associate professor in the School of Behavioural and Health Sciences and the Healthy Brain and Mind Research Centre at Australian Catholic University, told PsyPost. “I’m particularly interested in factors that may be modifiable – that is, things that we may be able to prevent or improve through psychological intervention.”
When psychologists study emotion dysregulation, they often look at several distinct areas where a person’s emotional control can break down. These areas include refusing to accept negative feelings, lacking awareness or clarity about what emotion is actually occurring, struggling to stay focused on tasks when upset, failing to control impulsive behaviors during distress, and lacking effective strategies to calm down. Studies linking narcissism to these emotional difficulties have produced confusing results, with some finding that narcissistic people had worse emotional control and others finding no link or even better emotional control.
Psychologists began to suspect that these contradictions happened because studies were measuring different subtypes of narcissism. For instance, a 2017 study of Chinese university students found that students with vulnerable narcissistic traits reported substantial trouble managing their emotions, while those with grandiose traits actually reported fewer emotional difficulties, which the authors linked to their higher self-esteem. Similarly, a 2020 study of undergraduates indicated that the defensive, self-protective side of narcissism was tied to poor emotion recognition and messy emotional reactions, whereas the assertive, self-promoting side was linked to better emotional management.
To make sense of the growing body of research, a 2024 systematic review summarized the existing literature and observed a consistent link between vulnerable narcissism and emotional struggles, alongside a lack of evidence connecting grandiose narcissism to those same struggles. However, that earlier review only summarized the studies qualitatively rather than mathematically combining their data, and it mixed psychiatric patients with the general public, which can weaken the correlations researchers observe. Willis, lead author Ria Tziliaskopoulos, and their colleagues at Australian Catholic University set out to conduct a full meta-analysis, a statistical technique that pools data from multiple studies to estimate the strength of the relationship between narcissism and emotion dysregulation in the general adult population.
“We wanted to establish the strength of the relationship between emotion dysregulation and narcissism in general, as well as determine whether this relationship differed between grandiose and vulnerable narcissism,” Willis explained.
The researchers searched five major academic databases through January 2026 to find studies written in English that measured both trait narcissism and emotion dysregulation in adults outside clinical settings. They excluded studies focusing on children or specialized groups like psychiatric patients or prison inmates, ensuring the results would reflect everyday adult populations. This screening process yielded 26 eligible studies from 25 reports, encompassing a combined total of 12,209 participants with average ages ranging from 18.7 to 62 years across countries including the United States, Italy, Turkey, China, Iran, and Brazil.
Because each included study used questionnaires to measure personality and emotional habits, the authors categorized the narcissism questionnaires into three groups: those measuring grandiose narcissism, those measuring vulnerable narcissism, and “combined” measures that blended both traits into a single score. For instance, the widely used Narcissistic Personality Inventory counted as a grandiose measure and the Hypersensitive Narcissism Scale as a vulnerable one, while the 2020 undergraduate study’s self-promoting “admiration” scale was counted as grandiose and its defensive “rivalry” scale as vulnerable.
Twenty-two of the 26 studies measured emotional difficulties using a standard questionnaire called the Difficulties in Emotion Regulation Scale, which scores both overall dysregulation and the six specific problem areas like impulse control and emotional awareness. The researchers extracted 80 individual statistical correlations from the 26 studies and used a method called robust variance estimation to account for the fact that several studies contributed multiple data points from the same group of people.
In statistical terms, a correlation ranges from -1 to 1, where 0 means no relationship, positive values mean two traits tend to rise together, and negative values mean one tends to fall as the other rises. By common convention, values of about 0.10 to 0.29 are considered weak, 0.30 to 0.49 moderate, and 0.50 or above strong. When the researchers pooled all 80 data points together without separating the narcissism subtypes, they found a weak positive correlation of 0.15 between overall narcissism and emotion dysregulation. This meant that across the entire dataset, higher levels of narcissism were only slightly associated with greater trouble managing emotions.
When the team broke the data down by narcissism subtype, a sharp divide emerged that helps explain why the overall correlation was so small. Vulnerable narcissism showed a moderate positive correlation of 0.37 with emotion dysregulation across 11 studies. In contrast, grandiose narcissism showed a correlation of 0.04 across 21 studies, a number so close to zero that it was not statistically significant.
“The relationship with vulnerable narcissism was moderate in size, which is quite a meaningful relationship in individual differences research,” Willis noted. “Narcissism reflects a cluster of personality traits, so we wouldn’t expect any single psychological factor to explain it. The size of the relationship suggests that difficulties regulating emotions are an important part of the psychological profile of people high in vulnerable narcissism.”
Measures that combined both forms of narcissism fell right in the middle, showing a weak positive correlation of 0.25 across five studies. Follow-up statistical comparisons confirmed that the link between vulnerable narcissism and emotional difficulties was substantially stronger than the link for grandiose narcissism. The authors also checked whether a few debatable coding choices drove the results. Dropping the grandiose scale of the Pathological Narcissism Inventory, which critics say overlaps too much with vulnerable traits, left grandiose narcissism at 0.03, and dropping the one study that counted the defensive “rivalry” scale as vulnerable narcissism left vulnerable narcissism at 0.37.
“The findings may help explain some of the interpersonal difficulties experienced by people high in vulnerable narcissism,” Willis said. “They tend to be hypersensitive to rejection and criticism, and our findings suggest they are also more likely to have difficulty regulating their emotions. When they feel rejected or criticized, they may struggle to manage the emotions that arise in these situations. This could contribute to hostile or aggressive responses, which can create conflict and damage their relationships with others.”
The researchers then examined the four or five studies that reported scores for each of the six domains of emotional difficulty, pooling all types of narcissism together. They found weak but statistically reliable correlations with three specific problem areas: limited access to effective emotion regulation strategies had a correlation of 0.21, trouble controlling impulses when upset had a correlation of 0.19, and refusing to accept negative emotions had a correlation of 0.18. Meanwhile, narcissism was not statistically linked to lacking emotional awareness (which had a slight negative correlation of -0.08), lacking emotional clarity (0.13), or struggling to focus on goal-directed behaviors when distressed (0.13). The link with emotional awareness was weaker than the link with every other domain.
The authors suggest this pattern means the emotional struggles tied to narcissism may be less about failing to notice feelings and more about how a person reacts once an unpleasant emotion arrives, such as struggling to accept the feeling, acting impulsively, or feeling there is no healthy way to calm down. Because these analyses drew on only a handful of studies and combined all forms of narcissism, they cannot show whether the pattern differs between grandiose and vulnerable traits. The authors note that these specific breakdowns could help explain why people high in narcissism may lash out, blame others, or withdraw during relationship conflicts.
These results align with a study covered by PsyPost in 2024, which also found that emotion dysregulation was tied to vulnerable rather than grandiose narcissism, although that earlier study tested psychiatric outpatients rather than the general adult samples analyzed in the new review.
The findings sit in partial tension with the 2020 study of undergraduates described earlier, which PsyPost covered in 2019, where the self-promoting side of grandiose narcissism was linked to fewer emotional difficulties and better emotional awareness. The meta-analysis included that study but counted its defensive rivalry scale as vulnerable narcissism, and across all the grandiose questionnaires combined, it found no link either way rather than a protective one.
The absence of a link for grandiose narcissism also contrasts with research covered by PsyPost earlier in 2026, which tied both grandiose and vulnerable traits to brain differences connected to hiding one’s feelings. That difference may reflect what was measured: the brain study looked at the specific habit of suppressing outward emotional expressions, while the meta-analysis assessed broader difficulties managing emotions.
As with all research, there are some caveats to keep in mind when interpreting these findings. Every study included in the meta-analysis relied on self-report questionnaires, which depend on participants being both honest and self-aware about their flaws. The authors point to a 2023 study that recorded facial muscle activity and found amplified emotional reactions to negative social evaluation among people with grandiose traits, suggesting questionnaires may miss some of their emotional responses.
“I also wouldn’t interpret the lack of a relationship with grandiose narcissism as definitive evidence that people high in grandiose narcissism don’t experience difficulties regulating their emotions,” Willis cautioned. “All of the studies relied on self-report measures, and people high in grandiose narcissism may be less likely to perceive or report difficulties with emotion regulation.”
All 26 studies also used cross-sectional designs, measuring personality and emotional habits at a single point in time. In addition, half of the studies (13 of 26) recruited only university students, and most were conducted in Western nations, meaning the findings may not apply equally to older adults or different cultural contexts.
“The main caveat is that all of the studies included in our meta-analysis were cross-sectional, so we can’t determine the direction of the relationship,” Willis said. “Difficulties regulating emotions may contribute to the development or maintenance of vulnerable narcissism, vulnerable narcissism may contribute to difficulties regulating emotions, or other factors may explain this relationship.”
One such shared factor the authors raise is early experience: vulnerable narcissism has been theorized to arise partly from childhood trauma or inconsistent parenting, which may also hinder the development of emotional skills. Results also varied widely from study to study, so the pooled correlations are best read as averages rather than as figures that apply equally to every group or questionnaire. Several statistical checks did suggest the findings were unlikely to be skewed by publication bias, the tendency for studies with positive results to be published more often.
Another limitation is that too few studies reported detailed breakdowns of the six emotional problem areas to allow the authors to test grandiose and vulnerable narcissism separately for each specific skill. The authors recommend that future studies routinely report data for all individual emotion regulation subscales alongside total scores, use longitudinal designs that follow people over time, and recruit more diverse samples across cultures and age groups. Even with these limitations, the authors suggest that therapies focused on emotion regulation, such as dialectical behavior therapy or schema therapy, may be worth testing for people high in vulnerable narcissism, though studies have yet to show whether such treatment improves their well-being or relationships.
Looking ahead, the research team is now examining the roots of these traits and how emotional habits take shape over time. In psychology, insecure attachment refers to relationship patterns characterized by anxiety or avoidance that often stem from inconsistent caregiving early in life.
“One of the things I’m particularly interested in now is understanding how vulnerable narcissism may develop,” Willis told PsyPost. “We’re currently testing models looking at the potential role of insecure attachment and difficulties identifying, understanding and regulating emotions. Ultimately, we want to identify potentially modifiable factors that could be targeted through prevention or intervention strategies.”
The study, “The relationship between narcissism and emotion dysregulation: A systematic review and meta-analysis,” was authored by Ria Tziliaskopoulos, Rose Ferguson, Wendy Song, and Megan L. Willis.
URL: https://www.psypost.org/emotional-struggles-are-linked-to-vulnerable-narcissism-but-not-grandiose-narcissism-meta-analysis-finds/
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #Psychology #MentalHealth #Narcissism #VulnerableNarcissism #GrandioseNarcissism #EmotionalRegulation #EmotionDysregulation #PsychologyResearch #MetaAnalysis #Therapy

#psychology #counseling #socialwork #psychotherapy #mentalhealth #psychiatry
Fairhaven Person @fairhavenguy
2026-10-24

#WordWeavers 24/10: Which character would survive the longest if suddenly dropped into our world with no money or identification?
It's hard to say. Most of the main characters are intelligent and can read. Their life has been a pre-industrial one in a temperate climate with fields, forest, and seashore nearby. Jack is a barber's apprentice who is familiar with the tools still used today, except for electric clippers. Jill's housekeeping, gardening, animal husbandry, and childcare skills could be put to use in the right setting.
If they landed in my New England hometown, many of the characters could adapt.

#wordweavers
Dev Community @[email protected]
2026-10-24
Hi folks,
I’ve been thinking a lot about marketplaces built for AI agents lately.
We’re all familiar with C2C secondhand platforms like Mercari, Vinted, and Carousell. These platforms are designed for humans trading physical goods. Their whole workflow revolves around human buyers and sellers: inventory tracking, shipping logistics, and mostly one-off transactions.
AI agents operate by a completely different set of rules, though.
What if agents themselves could discover, purchase, and invoke reusable skills automatically?
We’re talking about MCP tools, parsers, workflow modules, dispatch logic — building blocks an agent can call on demand.
Instead of a one-time purchase, creators get paid per API invocation.
I also ran across the idea of AEO, Agent Engine Optimization.
Where SEO optimizes content to be found by human searchers, AEO focuses on machine-readable metadata. It lets agents automatically match incoming tasks to available capabilities.
This feels like an entirely new marketplace paradigm, separate from the physical, human-first trading model we’re used to.
I’d love to get your take on these two questions:
Do you think autonomous agents will routinely trade skills with one another in the future?
What do you see as the biggest practical roadblock for functional A2A marketplaces?

#agents #ai #api #mcp #software #coding #development #engineering #inclusive #community
I found an interesting shift: from C2C physical trading to A2A AI capability trading**
#ai #community #software #coding #development #engineering
Dev Community @[email protected]
2026-10-26
Every engineer running autonomous agent loops knows the in-context reflection drill. The agent runs a bash script, fails on an obscure permission flag or an unquoted path, sees the error in stderr, reflects on what went wrong, and fixes it on the second try.
The task finishes green. You log the run, close the thread, and move on.
Tomorrow morning, the same agent hits the exact same edge case in a fresh session and makes the exact same mistake all over again.
In-context reflection is a temporary patch. It burns context tokens, bloats the prompt across long tool sessions, and evaporates the millisecond the execution context clears. The traditional alternative, full-model fine-tuning, is a non-starter for everyday errors. Nobody is spinning up a multi-GPU training run every afternoon to update base weights, and doing so risks catastrophic forgetting across existing tool workflows.
A research team from Princeton University and Meta FAIR just published a practical middle ground. In their paper, Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting Lessons (arXiv:2610.31846), Yuval Kansal, Karen Ullrich, Polina Kirichenko, Ilia Kulikov, Niraj K. Jha, Mohammad Pezeshki, and Mark Ibrahim lay out an architecture called Self-Direction.
Instead of writing a reflection note into a scratchpad file or relying on a fragile system prompt append, the agent distills its execution mistakes directly into tiny low-rank adapter weights.
The failure of the reflection scratchpad

Modern agent setups usually address cross-session memory with one of two patterns:
Appending error reflections to an agent scratchpad or MEMORY.md file.
Embedding failure traces into a vector database for retrieval-augmented prompt injection on future turns.
Both patterns struggle in production. Text notes appended to context eat away at attention budgets. As the reflection scratchpad grows, agents suffer from prompt dilution and instruction drift. More critically, an agent reading a natural-language description of an old error frequently misapplies the lesson to unrelated tool calls.
The Princeton and Meta team tracked this failure mode across open-ended research workflows (GAIA), multi-step tool execution (Claw-Eval), and command-line environments (Terminal-Bench 2.1). When agents relied on standard in-context retries, their correction reliability plummeted when facing recurring variations of the same underlying task. The agent repeatedly fell into the same traps because the underlying policy distribution never moved.
Standard Agent Flow:
Error -> In-context reflection -> One-off fix -> Session ends -> Lesson lost
Self-Direction Flow:
Error -> Trajectory reflection -> Low-rank adapter update -> Persistent library -> Routed on demand
Distilling mistakes into 0.02% parameter budgets

The core mechanism of Self-Direction sits between ephemeral prompt engineering and heavy offline training.
When an agent fails an execution trajectory, it runs an internal critique to isolate the decision step that caused the downstream failure. Once the corrective trajectory is verified, the system does not just print the correction. It freezes the base model weights entirely and trains a micro-LoRA adapter on the self-directed repair pair.
The parameters updated during this step represent roughly 0.02% of the base model's parameter count.
Because base weights remain frozen:
Existing base model capabilities and general conversational skills stay intact without degradation.
Training completes locally in seconds on consumer hardware without cluster coordination.
The resulting adapter file is lightweight enough to store, version, and swap on the fly.
The agent saves these targeted adapter checkpoints into a persistent local library. When a new prompt arrives, an automated routing layer inspects the problem context and attaches the relevant adapter before the execution loop begins.
Benchmark gains across open-ended tasks

The researchers tested Self-Direction on open-weight models from the Qwen family across three distinct agent benchmarks:
GAIA: Multi-modal, complex multi-hop research requiring web navigation and reasoning.
Claw-Eval: Structured tool-use workflows testing API interactions and parameter extraction.
Terminal-Bench 2.1: Unconstrained Linux command-line tasks, bash scripts, and system administration.
Across these environments, adding Self-Direction improved overall task completion rates by 8.9 to 17.0 percentage points compared to standard in-context reflection baselines.
More importantly, the recurrence rate of known errors dropped sharply. The authors found that Self-Direction nearly doubled the reliability of corrective actions on subsequent encounters. Instead of guessing whether the agent would notice a text hint in its system prompt, the updated adapter shifted the token sampling probabilities directly away from the faulty API syntax.
The team also evaluated out-of-distribution transfer. A small set of adapters trained on specific terminal failures generalized across dozens of unseen tasks involving related file-system permissions and process management. The agent learned the structural pattern of the fix rather than memorizing the exact shell string.
What this means for local agent runners

For developers building and hosting agent infrastructure, this work highlights an architectural transition.
Over the past two years, the industry leaned heavily on prompt stuffing to simulate agent learning. We treated context windows as infinite scratchpads, piling up few-shot examples, dynamic rules, and post-mortem notes until the prompt became brittle.
Self-Direction shows that treating execution errors as online distillation targets produces cleaner long-term behavior. When an agent trips on an environment quirk, the solution is not to explain the error back to the model in prose every time it wakes up. The solution is to let the agent compile the lesson into weights, index the adapter, and move forward.#ai #agents #python #opensource #software #coding #development #engineering #inclusive #community
Why In-Context Corrections Do Not Stick in Agent Loops
#opensource #ai #community #software #coding #python
stuartb @[email protected]
2026-10-26

Asocial
I came home from work some time ago to find my wife settling down to watch the latest episode of Coronation Street.
Onscreen, a new family had recently moved on to the street, and the Dad was shown talking to a couple of the regular characters, making arrangements to do something, and it struck me – in just 3 minutes of screen time, a brand new character had exchanged more words with his neighbours than I had in the 10 1/2 years I had lived here.
Which got me thinking – of all the things I know, how to make friends isn’t one.
Out of all my friends (at least, out in the so-called “real world”), all of them decided I was a person they wanted to be friends with, they did all the running, and all I did was decide to go along with them.
This was rarely something I did lightly – there were plenty of people who decided we were going to be friends, but I decided to have nothing to do with them – but with all the people I count as friends, they did all the running, and I just went along with it.
It’s the same with relationships – off hand, I can only think of 6 times I asked girls out – 2 of them turned me down flat, 2 were the same girl on 2 different occasions (and I eventually asked her to marry me – which later happened!), and the other 2 times ended pretty disastrously. Every other time I got into a relationship, they did all the running after me, and I just went along with it.
On a related note, I have no idea how small talk works – this is an essential skill for developing all manner of relationships, and yet I have not a single clue as to the purpose of small talk, nor how it works – I can do it, but only by copying conversational gambits I’ve seen other people use, which means that conversations with me can often be stilted, and are liable to turn rather strange at unexpected moments.
By extension, flirting (and “chatting someone up”) is also something where I utterly fail to grasp any mechanism by which it works. As I said about small talk, I can do it, I just have no idea what I’m doing.
Actually, I would like to modify those statements – I can do small talk and flirting, but only if I don’t think about it – as soon as I start to think about what I am doing, or what I should say next, I turn in to Cat in the infamous dance/dream sequence from Red Dwarf ( https://youtu.be/yn8wWQweNbU ) – I get tongue-tied, and completely unable to communicate.
Which, let’s face it, isn’t good, whether you’re discussing the weather with the random person sitting next to you on the bus home from work, or chatting up some random stranger in a bar.
I’ve no idea if it is connected to all this, but I have always had a preference for my own company – not having to fill any silences or think of topics of conversation is always pleasant.
I remember back in school, I had a friend I spent a lot of time with – many were the times we finished school, went to the local library to collect some books, then went back to his, where we would occupy chairs at opposite sides of his living room, then read books in silence, until the time I had to leave to go home – about the only thing his mother ever heard me say was “Right, I’d better get off. See ya!”
One of the things I love about my marriage, and my wife – we often spend time just being in the same room – not necessarily talking, not necessarily doing the same thing, but doing whatever it is we are doing, together.
One of us might be watching telly while the other reads, or she might be doing something on her computer, usually doing some family history research, and I’ll walk in with my laptop and slouch down in a chair and do some writing for one project or another – but we are doing it, together, just enjoying being with someone.
Louise’s office is by the front window of the dining room, and I will often be found in the kitchen, through the rear of the dining room, cooking up a storm, while we listen to some music, which usually involves one of us dancing our way over to the other and busting some moves for a few seconds before going back to what we were doing.
We might also be talking – but no-one is being forced to talk, only when we are ready, which I find makes conversations flow more easily.
And this is the thing – I’m not, as some people think, anti-social, I don’t hate people, I don’t want to avoid having friends – I just want friends who are on my wavelength, friendships on my terms – friends who are willing to accept that I don’t always want to be drawn into conversation, but when I feel the need to talk will accept that as well.
I’m asocial, quite possibly because of my autism, and proud of it.
Social interactions leave me physically and mentally exhausted – which means that, if I put any time or effort into interacting with you, then you should feel honoured.
(Or I’m at work, and have to put the minimum effort in!)

#writing #fromthearchives #friends #blog #autism #asocial
Pop Davao PH @[email protected]
2026-10-26

Exclusive Scoop: Son of A High-Ranking Defense Official Suffers from Mental Illness

Recently, according to an anonymous source, the real reason why the son of a high-ranking official in the Department of National Defense has not yet entered politics and has almost zero exposure is that he suffers from severe adult Attention Deficit Hyperactivity Disorder (ADHD), accompanied by Impulsive Personality Disorder (IPD).
The insider has disclosed that the only son of this high-ranking official previously studied abroad in the UK to receive medical treatment. For such a top-tier political family, sending only son abroad outside the normal government-funded program is extremely rare in itself. The source indicated that during his studies in the UK, his ADHD and accompanying IPD repeatedly reached clinical warning levels. During this period, the official’s wife frequently flew to the UK to care for their son, spending about four months every year living in London.
Reviewing the few public appearances of this senior defense official’s son, it was noticeable that the anomalous behaviors exhibited when he attended political banquets accompanied his parents. According to the insider, his physical control in these settings often showed an obvious unnatural state, with his legs making small-amplitude and repeating tremors at an extremely high frequency in a nervous manner. More unusually, he attempted to conceal the tiny movements caused by his hyperactivity by frequently slipping his left hand into his suit pocket, where the fabric showed continuous and intense internal rubbing. Personnel who have been close to him revealed that he always carries a ring made of a specific material in his pocket. He must forcibly suppress the urge of his body to leave his original position by spinning the ring frequently and vigorously in his pocket. Furthermore, during the solemn political pronouncement, his eyes could not stay focused on the speaker for more than 3 seconds; instead, his eyes showed frequent and aimless lateral tremors with rapid and anxious blinking. His inability to maintain basic stillness and attention control in major diplomatic and political settings is a typical behavioral characteristic of severe adult ADHD.
In the conventional perception of the public, this high-ranking official’s family is widely considered a spotless, seemingly idyllic model with no blemishes to speak of. The senior official hails from an elite family spanning politics and business, while he is impeccable in everything he does. As for his wife, born into a distinguished clan and also boasts extensive political experience. In the narrative shaped by external public opinion, his family is known for its strict values and principles, marked by domestic harmony and an affectionate married couple. However, the personal resume of the only son of such a prominent family has long been blank, with no publicly available CoC records and background files and no verifiable employment records. Even his social resume after higher education is completely disjointed. This extremely low-key approach, which seems deliberately aimed at diminishing the son’s social presence, has sparked numerous speculations and political associations from various sectors over the years.
Combined with the aforementioned symptoms, many phenomena that had previously baffled the outside world now have a cruel and clear answer. This physical impairment has deprived the son of normal empathy and social skills, which effectively causing him to fail to pass the political vetting, forcing him to choose an extremely low-key retired lifestyle and preventing him from entering politics. As a result, his social resume has remained blank for years.
Evidently, this family matter, which is too painful to speak of, is like a venomous thorn that pierces through the perfect illusion of this elite family.
#Books #history #News #politics #writing

#books #history #news #politics #writing
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
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
Dev Community @[email protected]
2026-10-25
Pest control businesses manage more than inspections and treatments. Every service visit involves appointment scheduling, technician coordination, travel planning, customer communication, and recordkeeping.
As a company grows, these administrative responsibilities become increasingly difficult to manage through spreadsheets, phone calls, and disconnected software.
Artificial intelligence (AI) and workflow automation offer new ways to organize these processes. However, their value depends on how well they solve real operational problems.
This article examines five areas where AI and automation can support pest control businesses, along with the technical considerations companies should evaluate before adopting new systems.
Smarter Appointment Scheduling
Traditional appointment scheduling often requires office staff to coordinate technician availability, customer preferences, service duration, and travel distances manually.
Scheduling software can automate several parts of this process.
For example, a scheduling system might:
Display available appointment windows based on technician calendars.
Assign jobs according to service area and technician availability.
Send automated appointment confirmations.
Notify customers when appointments change.
Identify scheduling conflicts before bookings are finalized.
AI-assisted scheduling can go further by analyzing historical job durations and recommending suitable appointment windows.
However, these recommendations are only as reliable as the underlying data.
A business with inaccurate service durations or incomplete technician availability records may still experience scheduling problems, even after introducing automation.
Technical consideration: Look for scheduling systems that support configurable service durations, technician constraints, calendar integrations, and manual overrides.
Route Optimization for Field Technicians
Travel time is a significant operational consideration for businesses that send technicians to multiple customer locations each day.
Basic route planning can arrange appointments geographically. More advanced optimization systems consider additional constraints, including:
Appointment time windows
Technician starting locations
Estimated travel times
Service duration
Technician skills and availability
Vehicle capacity or equipment requirements
Some systems use optimization algorithms to recommend more efficient visit sequences.
AI-based prediction may also help estimate travel times or anticipate schedule disruptions.
Consider a technician assigned six appointments across different neighborhoods.
A poorly organized schedule might require unnecessary backtracking. A routing system can evaluate possible visit sequences and recommend an order that better satisfies the day's constraints.
Importantly, route optimization is not automatically AI. Many useful routing systems rely on established mathematical optimization methods rather than machine learning.
For pest control companies, the practical question is whether the system produces workable routes while respecting real service requirements.
CRM Automation and Customer Communication
Customer relationship management (CRM) systems help businesses organize customer information, service histories, recurring treatments, and communication records.
Automation can reduce repetitive administrative work.
For example, a completed service appointment might trigger the following workflow:
The technician marks the job as completed.
The system updates the customer's service history.
A service summary becomes available.
The customer receives a completion notification.
The system schedules a future reminder when appropriate.
This workflow does not necessarily require generative AI. Many CRM platforms support rule-based automation using triggers, conditions, and actions.
AI may provide additional capabilities, such as summarizing customer interactions or categorizing incoming service requests.
Nevertheless, businesses should review AI-generated information before using it in sensitive customer communications or treatment documentation.
Technical consideration: Evaluate how the CRM handles customer consent, communication preferences, recurring service records, and data access permissions.
Connecting Software Through APIs and Integrations
One of the most useful opportunities for pest control technology is connecting systems that would otherwise operate independently.
A business might use separate applications for:
Website inquiries
Customer management
Appointment scheduling
Technician dispatch
Invoicing and payments
Reporting and analytics
Without integrations, employees may need to enter the same information into multiple systems.
Application programming interfaces (APIs) allow software applications to exchange information through defined interfaces.
For example, an integration could connect an online booking form with a field service management platform.
When a customer submits a booking request, the integration might create a customer record, generate a service request, and notify office staff.
A typical architecture could look like this:
Website Booking Form
|
v
API / Integration Layer
|
v
Field Service Management System
|
+--> Customer Record
|
+--> Service Request
|
+--> Scheduling Queue
|
+--> Notification Workflow
For developers implementing these integrations, several considerations matter:
Authentication: API access should use secure authentication mechanisms and appropriately scoped credentials.
Validation: Incoming booking data should be checked before records are created.
Duplicate prevention: Retry requests should not accidentally create duplicate customers or jobs.
Error handling: Failed integrations should be logged and retried safely.
Privacy: Customer addresses, contact details, and service records should be handled according to applicable privacy requirements.
Before purchasing software, businesses should investigate whether the vendor offers documented APIs, webhooks, native integrations, or supported third-party automation tools.
AI-Assisted Reporting and Business Analysis
Pest control businesses generate operational data through appointments, service records, customer interactions, and invoices.
Reporting tools can organize this information into useful business metrics.
Examples include:
Completed appointments per technician
Average time spent on service visits
Appointment cancellations
Recurring service activity
Travel time between appointments
Outstanding invoices
AI-assisted analysis may help identify patterns or generate plain-language summaries of reporting data.
For example, a system might highlight an increase in rescheduled appointments or identify service areas with unusually long travel times.
However, AI-generated summaries should be checked against the original records.
A useful reporting system should allow managers to trace a recommendation or observation back to the underlying data.
What Should Businesses Evaluate Before Adopting AI?
Adding AI features does not automatically make a software platform better.
Before investing, pest control businesses should ask several questions.
Does the software solve a specific operational problem?
Start with a measurable issue, such as missed appointments, repetitive data entry, or inefficient dispatching.
Can employees understand and override automated decisions?
Technicians and dispatchers need practical ways to correct schedules and handle unexpected circumstances.
Does the system integrate with existing tools?
A platform that creates additional manual data entry may undermine the intended benefits.
How is customer information protected?
Review data access controls, retention policies, vendor agreements, and security practices.
How will performance be measured?
Establish a baseline before implementation. Compare outcomes such as scheduling errors, administrative time, or travel efficiency after deployment.
Businesses should also distinguish between genuinely useful AI capabilities and conventional automation presented with AI-focused marketing language.
A Practical Implementation Approach
For smaller pest control businesses, introducing several new systems simultaneously can create unnecessary complexity.
A more manageable approach is to implement technology in stages.
Stage 1: Document existing workflows. Identify how appointments, service records, and customer communications are currently handled.
Stage 2: Select one problem to address. For example, automate appointment confirmations before attempting a complete dispatch-system replacement.
Stage 3: Test with a limited group. Introduce the workflow to a small team and document errors, exceptions, and employee feedback.
Stage 4: Measure results. Compare the new process against the previous workflow using consistent metrics.
Stage 5: Expand carefully. Introduce additional integrations or AI-assisted features only when the earlier implementation is functioning reliably.
This approach makes it easier to identify which technologies provide practical benefits.
Conclusion
AI and automation are expanding the options available to pest control businesses, particularly in scheduling, route planning, CRM workflows, software integrations, and reporting.
The most effective systems are not necessarily those advertising the largest number of AI features.
They are the systems that support reliable operations, provide useful information, integrate with existing workflows, and allow employees to remain in control of important decisions.
For independent research into pest control software, scheduling tools, CRM platforms, and technology comparisons, explore Pest Tech Research.
Better Technology. Stronger Pest Control.#automation #ai #software #api #coding #development #engineering #inclusive #community
How AI and Automation Are Changing Pest Control Business Operations
#ai #community #software #coding #development #engineering
Dev Community @[email protected]
2026-10-25

Early Days

My programming journey began in late 2015.
Like many teenagers my age back then, I wanted to build my own video games. That was when I stumbled upon something called C++, which turned out to be a programming language. This strange-sounding term referred to a way of telling computers what to do.
Curious to learn more, I started digging deeper to figure out how to speak this language. And that was the beginning of an incredible journey.
From installing Code::Blocks, one of the most popular IDEs at the time, to encountering the infamous segmentation fault (core dumped) error and wrestling with countless compilation errors, every challenge became part of the adventure.
One thing led to another, and I began discovering the countless fields where programming could be applied: web development, systems programming, mobile development, IoT, automation, data analysis, and so on. Even artificial intelligence, that thing we used to see in movies, turned out to be an application of programming principles.
Back then, and even until recently, being a programmer felt like being both a superhero and a complete newbie. You had the power to build amazing, useful things, yet you could spend hours staring at a wall of errors simply because you forgot a semicolon or failed to break out of a loop.
Welcome to the Shin Sekai

With the rise of new LLMs and their incredible capabilities, everything changed.
Projects that once took a week to plan and three months to build can now, in some cases, be prototyped by an AI agent in just a few hours. And sometimes, the result is surprisingly good, perhaps even better than what you could have built yourself. If you had enough money, computing power, and memory, you could even spawn an army of agents to build your own rocket launcher (though it might blow up on the first try).
A skill that was once reserved for a select few is now accessible to anyone willing to build something. This is what we call equality. But as we all know:
With great power comes great responsibility.

But that's another debate...
Searching for the One Piece

In this new era, priorities and many other things are being reshaped.
With everyone able to build at almost supersonic speed (just calm down...), the definition of a successful product has changed, and so has the role of a programmer.
There are still people who enjoy the process of writing their own code and the satisfaction of making it work. Whether you call it ego or being a prehistoric man, that feeling is still very much alive, and the sense of accomplishment is indescribable.
Then there are those who delegate everything to AI. Their sole purpose is to ship. They would even delegate their lives to AI if they could (spoiler: they can't, at least not yet...).
Whichever group you belong to, you can't deny that writing software in the AI era is a whole different story, and that working without AI means missing out on a lot.
My Take

Now, let's get back to me. Which of these groups do I belong to?
I'm somewhere in the gray zone. I use AI for boring tasks and to help me build complex things. I still review everything it writes, not only because it can make mistakes, but also because I want to learn from the new approaches it might suggest.
For some tasks, I still write the code myself. I love coding, and the satisfaction of successfully wiring everything together is second to none.
Another goal is to keep growing my skills, and that only comes with practice. There will be days when AI tools are down, and relying on them entirely could backfire. There will also be times when an expert's knowledge is needed, and failing to develop your own skills could prevent you from reaching that level.
Outro

Use AI to build faster, not to stop learning. Let it handle the boring stuff, help you tackle the complex stuff, and challenge you to become better. Because at the end of the day, the goal isn't just to ship more software. It's to become someone who understands what they're shipping.
Any thoughts?#ai #beginners #learning #programming #software #coding #development #engineering #inclusive #community
Writing Software in the AI Era: What Does It Mean to Be a Programmer?

#ai #community #programming #software #coding #learning
Eine Welt - One World @[email protected]
2026-10-25

Neun auf einen Streich… – Nine in one go…

(English below)

… hat das Jahr 2026 aus der Schar unserer Patenkinder in einen neuen Lebensabschnitt entlassen: entweder haben sie die Schule oder eine Berufsausbildung abgeschlossen.
Und so stehen sie als Schulabschließer vor der Frage: „Soll ich ein Studium oder eine Berufsausbildung beginnen?“
Um studieren zu können, bedarf es einiger Voraussetzungen, die nicht jeder Interessent erfüllt. Dann bietet sich eine Berufsausbildung an.
Wer eine Berufsausbildung abgeschlossen hat, die in der Regel drei Jahre dauert, steht vor der Herausforderung einen festen Job zu finden. Das ist nicht einfach, denn die Zahl der Arbeitsuchenden ist groß. Aber zumindest besteht die Möglichkeit, mit Gelegenheitsjobs Geld zu verdienen, um den eigenen Lebensunterhalt zu bestreiten.
Wir freuen uns zusammen mit den Paten, daß unsere diesjährigen Absolventen jedenfalls eine solide Grundlage von uns erhalten haben, auf der sie weiter aufbauen können.
Da ist Adolis. Sie hat eine allgemeine technische Berufsausbildung abgeschlossen, die sie trotz ihrer körperlichen Behinderung in die Lage versetzt, im Dienstleistungsbereich arbeiten zu können.
Alafati hat die Schule abgeschlossen und möchte eine Ausbildung als Elektriker absolvieren.
Carine hat erfolgreich eine Ausbildung zur Schneiderin gemacht und möchte sich selbständig machen. Ihr Pate wird ihr dabei helfen.
Amanda möchte nach erfolgreichem Schulabschluß Musik studieren. Da es jedoch nur wenige Möglichkeiten dafür in Ruanda gibt, muß sie abwarten, ob sie die strenge Aufnahmeprüfung besteht.
Divine und Grace haben eine Friseurausbildung gemacht und werden sehr wahrscheinlich eine Anstellung finden, denn sogenannte Beauty-Salons gibt es in großer Zahl in Ruanda 🙂
Placide konnte einen Traum verwirklichen Autoschlosser zu werden und wird sicher Arbeit finden, denn in Ruanda werden Autos solange reparirt, bsi sie buchstäblich auseinanderfallen 😉
Johnson und Nadia haben die Schule beendet und stehen vor der Wahl, entweder eine Berufsausbildung oder ein Studium zu beginnen.
Wir wünschen allen, daß sie erfolgreich sind und nach vollendeter Berufsausbildung Arbeit finden, um für sich und ihre künftigen Familien sorgen zu können.
Und von unseren Lesern wünschen wir uns, daß sie unsere Arbeit mit Spenden und Ideen unterstützen!

… the year 2026 saw a number of our sponsored children move on to a new stage in their lives: they have either finished school or completed vocational training.
And so, as school leavers, they are faced with the question: “Should I go to university or start vocational training?”
To be able to study at university, there are certain entry requirements that not everyone who is interested meets. In that case, vocational training is a good option.
Anyone who has completed vocational training, which usually lasts three years, faces the challenge of finding a permanent job. This is not easy, as there are many people looking for work. But at least there is the opportunity to earn money through casual jobs to support themselves.
Together with the mentors, we are delighted that this year’s graduates have, in any case, received a solid foundation from us on which they can continue to build.
Take Adolis, for example. She has completed a general technical vocational training programme which, despite her physical disability, enables her to work in the service sector.
Alafati has finished school and would like to train as an electrician.
Carine has successfully completed her training as a dressmaker and would like to set up her own business. Her sponsor will help her with this.
Amanda would like to study music after successfully completing her school-leaving exams. However, as there are few opportunities for this in Rwanda, she will have to wait and see whether she passes the rigorous entrance exam.
Divine and Grace have completed a hairdressing course and are very likely to find a job, as there are plenty of so-called beauty salons in Rwanda 🙂
Placide has realised his dream of becoming a car mechanic and is sure to find work, as in Rwanda cars are repaired until they literally fall apart 😉
Johnson and Nadia have finished school and are faced with the choice of either starting vocational training or going to university.
We wish them all every success and hope that, once they’ve completed their vocational training, they’ll find work so they can provide for themselves and their future families.
And we hope that our readers will support our work with donations and ideas!
Translated with DeepL.com (free version)
#Africa #Afrika #anpacken #ausbildung #Bildung #Bildungspatenschaft #BildungspatenschaftsProgramm #charity #Children #dailyLabourer #deeds #donation #DritteWelt #Education #EineWelt #eltern #greeting #Hoffnung #hope #Information #job #kids #Kinder #kora #Lebensweg #nothilfe #oneWorld #PadrinazgosDeFormación #parents #Patenschaft #Ruanda #Rwanda #schicksal #scholarship #school #schule #Selbständigkeit #selbstbestimmt #Selbsthilfe #Selbstverantwortung #selbstversorger #selbstvertrauen #selfConfidence #selfConsciousnes #selfDetermined #selfHelp #selfSustaining #skills #skillsTrainig #SoutienPourLéducation #Spende #sponsorizzazione #sponsorizzazioneEducativa #sponsorship #stipendium #support #sustainability #tackle #Tagelöhner #Taten #unterricht #vocationalTraining #work #教育のためのパートナーシップ

#africa #afrika #anpacken #ausbildung #bildung #bildungspatenschaft
Dev Community @[email protected]
2026-10-25
AI IS CHANGING THE GAME — BUT DEVELOPERS ARE STILL IN CONTROL.
We are living in an era where AI can write code, debug applications, generate designs, and even build entire websites from a simple prompt.
But here is the real question: Will AI replace software developers, or will developers who use AI replace those who don't?
The future belongs to those who understand more than just how to write code. It belongs to people who can:
Build real-world solutions.
Think critically and solve complex problems.
Understand cybersecurity and protect systems.
Use AI to work smarter, not blindly.
Design scalable, reliable software.
AI can generate code, but understanding architecture, security, performance, and the problems users actually face is what turns code into a real product.
Don't just learn to code. Learn to build, think, and innovate.
The future of technology is not just about who can write the most code. It is about who can solve the biggest problems.
What do you think? Will AI make developers more powerful, or make traditional coding skills less valuable?
ArtificialIntelligence #SoftwareEngineering #TechTrends #Cybersecurity #FutureOfTech
#ai #career #productivity #softwareengineering #software #coding #development #engineering #inclusive #community
AI IS CHANGING THE GAME — BUT DEVELOPERS ARE STILL IN CONTROL.
#ai #community #cybersecurity #software #coding #development
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
Terence Eden’s Blog @[email protected]
2026-10-25

Gadget Review: LiberNovo Omni - the USB-C powered office chair
https://shkspr.mobi/blog/2026/10/gadget-review-libernovo-omni-chair/
Whenever a company asks me to review their gadgets, I always ask if it is USB-C powered. If not, I'm not interested. So, welcome to the future where your chair is USC-C rechargeable!
WHAT?!
The LiberNovo Omni looks like a standard office chair - but has two secret features.
First, is that it is exceedingly comfortable. Full foam cushions, lovely headrest, and a recline which almost gets you horizontal.
Second, it is USB-C powered. There's a small battery pack which plugs in to the chair. Why? Read on!
OK, let's start at the beginning.
Assembly
Flat-pack chairs like this are normally notoriously hard to assemble. The LiberNovo bucks that trend - not only does it include a how-to video, it prints the instructions on a MASSIVE piece of cardboard.
I'm used to peering at tiny black-and-white diagrams in a flimsy paper booklet. IKEA could learn a thing or two from this.
The screws are all labelled and a hex-driver is included. What's an even nicer touch, however, is some safety gloves:
That's the sort of thing which doesn't cost much to the manufacturer, but is such a thoughtful gesture. There's nothing too sharp as you assemble, but who wants to cover their new chair in blood, eh?
The parts come in two large boxes, but there aren't that many separate bits.
Building it took about half an hour. It's easier with a friend to help - as are most things in life - but doesn't require much skill.
Go Back To The USB-C Part Again Please?
The battery pack is a 2200mAh unit with a USB-C port to recharge. It connects in to the chair with this slightly unusual connector:
The internals of the chair have a USB-C cable to connect everything up.
LOL!
YES BUT WHY DOES A CHAIR NEED A BATTERY PACK??!?!
On the side of the left arm-rest are three buttons.
Two of them move the lumbar-support up and down. Depending on how your back is feeling and how far you are reclined, you can adjust the the back-support position.
And the 3rd button?
That's the build-in back massager, silly!
Is that useful?
Yes. Being able to instantly adjust the back support without having to twist around is fantastic, especially for those of us reaching a certain age.
The "massage" feature essentially just moves the support up and down for five minutes before returning to its previous location. When sat upright, it feels great. But the real benefit is when you're laying down.
How flat does this thing go?
160° - not quite flat, but good enough when you're taking a break from Typing Real Hard. You don't want a photo of me sprawled out, so LiberNovo have helpfully provided some stock photos of much better looking folk.
It is incredibly stable. At no point did it tip over or feel like it was going to deposit me on the floor. What really helps is the included foot stall.
No Feets On Seats
Unsurprisingly, this is not USB-C powered. A sturdy plastic footrest with a deeply padded leg rest on top.
Again, assembly was a cinch - follow the large printed instructions and clip it together. It's rated for 40㎏, so you shouldn't stand on it but it is more than enough to rest your weary legs.
Price
I hope you're sitting down for this.
Direct from LiberNovo, you can expect to pay around £700 - although there are bundles with extra batteries, the footrest, and even an eye mask which will shoot that price up. You can get the full bundle on Amazon for around £1,000.
There's two ways to look at that price. Firstly, youch. But, alternatively, that's still cheaper than a Herman Miller Aeron. And Herm's doesn't come with a built-in lumbar massage.
(There is also an upgraded version which has a built in fan under the seat. Personally, I suggest eating a healthier diet.)
I can't deny that's a chunk of change. If you're anything like me, you probably spend most of your day sat down. You might as well sit on something comfortable.
Final Thoughts
Having the back-rest adjustable via arm-rest buttons is a genius touch. I just wish it went further. I'd love a control panel (or, dare I say it, app) which let me adjust the seat height, recline, headrest position etc. Who can be bothered with fiddly manual levers when electricity can do the work for us?
Building this chair was a breeze - and it was delightful to get a pair of safety gloves with it.
The chair is extraordinarily comfortable and the back massage, while not on the level of a "shiatsu" style heated massage pillow, is delightfully pleasant. Would I lie all the way back and sleep in it? Yeah! Certainly more comfortable than an economy airline seat.
If you spend a lot of time sitting at your computer, your arse deserves something supportive. Your back deserves some love too. Yes, the USB-C power-pack is a little bit of a gimmick - but it is a hell of a good one.
In my opinion, this is worth splashing out on.
#chair #furniture #gadget #Review #USBC

#chair #furniture #gadget #review #usbc
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
Pattimouse @[email protected]
2026-10-25

Sunday Reviews

You Better Be Lightning by Andrea Gibson

You Better Be Lightning by Andrea Gibson is a queer, political, and feminist collection guided by self-reflection.

The poems range from close examination of the deeply personal to the vastness of the world, exploring the expansiveness of the human experience from love to illness, from space to climate change, and so much more in between.

One of the most celebrated poets and performers of the last two decades, Andrea Gibson’s trademark honesty and vulnerability are on full display in You Better Be Lightning, welcoming and inviting readers to be just as they are.
My Thoughts
You Better Be Lightning by Andrea Gibson is a collection of poetry that I borrowed from the library last month.
Gibson’s poetry is considerably more down-to-earth than what I typically read. That isn’t a bad thing. In fact, I found the collection quite enjoyable.
One thing that particularly impressed me was Gibson’s relationship with rhythm. As a poet myself, I can appreciate the skill involved. There is a natural cadence to the writing that makes it easy to hear the poems being performed, even when you’re simply reading the words on a page.
Gibson’s background in spoken-word poetry really comes through in the work.
Another strength of the collection is its accessibility. I would especially recommend this collection to queer readers. Both the representation and the way Gibson expresses queer experiences make it a worthwhile read.
Ultimately, You Better Be Lightning is a collection I would recommend, even to people who don’t ordinarily read poetry.
You don’t have to share a poet’s particular style to appreciate their craft, and Andrea Gibson certainly demonstrates that here.
#Bookreview #libraryBook #Newsletter #poetry #poetryCommunity #poetryIsNotDead #WomenPoets

#bookreview #librarybook #newsletter #poetry #poetrycommunity #poetryisnotdead
Dev Community @[email protected]
2026-10-24

The tech industry has a strange contradiction right now.
Companies are investing billions in artificial intelligence, building new platforms, shipping increasingly sophisticated products, and talking about unprecedented productivity gains.
At the same time, thousands of developers, engineers, designers, QA specialists, and other technology professionals are facing an uncertain future.
According to TechRepublic's October 2026 tech layoffs report, nearly 200,000 people had been affected by reported technology-sector layoffs as of October 23.
That number is difficult to ignore.
But behind every headline is a more personal question:
How do you build a sustainable career in an industry that keeps changing the definition of valuable work?
Let's look beyond the headlines and explore what these layoffs mean for developers, why AI isn't the whole story, and how we can prepare for an increasingly unpredictable technology landscape.
1. The Layoffs Aren't Just Numbers on a Dashboard
When a company announces a workforce reduction, the news is usually presented through percentages, headcounts, and stock-market reactions.
For the people affected, the experience is very different.
It's the developer who spent five years maintaining a critical service.
It's the QA engineer who prevented production incidents nobody outside the team ever noticed.
It's the infrastructure specialist who kept systems running during the most difficult outages.
It's the junior engineer who finally landed their first job after months of interviews.
These professionals don't suddenly lose their knowledge, experience, or ability to solve problems when their employment ends.
Yet a layoff can make even experienced engineers question their value.
One important distinction is worth remembering:
A company eliminating a position does not automatically mean the person occupying it lacked value.
Organizations restructure for many reasons, including financial pressure, acquisitions, overlapping responsibilities, strategic changes, and shifting investment priorities.
Some reductions are connected to AI. Others aren't.
For developers, understanding that distinction matters. Otherwise, it's easy to interpret every layoff as evidence that a particular skill or career path has become obsolete.
The reality is more complicated.
2. Is AI Replacing Developers, or Changing What Developers Do?
This is probably the biggest question in software engineering right now.
AI coding assistants can generate boilerplate, suggest implementations, explain unfamiliar code, create test cases, and help developers explore possible solutions.
These capabilities are useful. Ignoring them entirely isn't a particularly strong career strategy.
But the conversation becomes less useful when we reduce everything to a simplistic prediction: AI will either replace all developers or never replace any of them.
Neither assumption helps us make better decisions.
Consider a typical feature request.
An AI tool might help generate an API endpoint in minutes. However, delivering that feature safely may still require someone to:
Understand the business requirements and identify missing assumptions.
Design the API contract and choose appropriate data models.
Evaluate authentication, authorization, and data privacy risks.
Test edge cases, failure modes, and unexpected inputs.
Review database performance and concurrency behavior.
Integrate the feature with existing services and deployment pipelines.
Monitor production behavior and investigate incidents.
Generating code is only one part of delivering reliable software.
The challenge is that AI can make some tasks faster while also changing how much work a team needs to complete them.
That can create real workforce pressure, particularly when organizations expect smaller teams to deliver more.
And there's an important caveat: AI adoption and layoffs happening at the same time do not prove that AI directly replaced every affected employee.
TechRepublic's reporting highlights this distinction. Some companies have explicitly connected restructuring to AI-focused strategies, while others have cited different organizational or business reasons.
For developers, the practical takeaway isn't to panic or dismiss AI.
It's to learn where these tools are useful, understand their limitations, and become better at the engineering decisions surrounding their output.
3. The Real Competitive Advantage Is Engineering Judgment
If AI can generate a plausible implementation, what makes an experienced developer valuable?
Increasingly, the answer is the ability to determine whether that implementation is actually correct, maintainable, secure, and appropriate for the problem.
Imagine an AI assistant generates a database query that works perfectly against a small development dataset.
Will it still perform well when the production table contains 200 million records?
What happens when two requests update the same resource simultaneously?
Could the query expose information that the requesting user isn't authorized to see?
Does the implementation introduce a subtle regression in another service?
These aren't merely code-generation problems. They require engineering judgment.
The same principle applies to testing.
A test suite can report 95% code coverage while missing the business-critical scenario that causes a production outage.
A generated test can confirm that a function returns the expected result without checking whether the result itself makes sense.
A passing build doesn't guarantee a reliable system.
This is where developers can differentiate themselves.
Focus on capabilities that help you reason about the entire system:
System design: Understand service boundaries, data flows, failure modes, and trade-offs.
Debugging: Trace problems across application layers rather than guessing at isolated symptoms.
Testing: Design meaningful assertions, boundary tests, integration tests, and failure scenarios.
Security: Recognize vulnerabilities that aren't obvious from a successful demo.
Performance: Understand latency, throughput, resource consumption, and scaling constraints.
Communication: Explain technical decisions and their consequences to people with different backgrounds.
These skills aren't immune to automation. AI can assist with many of them.
Their value comes from knowing how to apply them, verify results, and take responsibility for the outcome.
4. Stop Measuring Your Career by the Number of Technologies You Know
When layoffs dominate the news, it's tempting to respond by learning everything.
A new framework appears, so you learn it.
A new AI tool launches, so you try it.
A new programming language becomes popular, so you add it to your list.
Soon, your learning backlog becomes larger than any project you could realistically finish.
The problem isn't curiosity. It's confusing exposure with capability.
Knowing that a tool exists is different from being able to use it to solve a meaningful problem.
Instead of collecting technologies, build evidence of your engineering ability.
For example, rather than creating five small repositories that demonstrate basic syntax, consider building one application that includes:
A documented API with authentication and input validation.
Automated unit and integration tests.
Database migrations and realistic test data.
Logging, metrics, and useful error handling.
A CI pipeline that runs checks before deployment.
A deployment strategy with a documented rollback procedure.
A README explaining architecture decisions and known limitations.
This project gives you more than a list of technologies.
It demonstrates how you approach the complete software development lifecycle.
If you use AI to build parts of it, document where you used it, how you reviewed its suggestions, and what you changed after testing.
The goal isn't to pretend you wrote every line manually.
The goal is to demonstrate that you can deliver software responsibly.
5. Build a Career That Doesn't Depend on One Employer
A layoff can be especially disruptive when nearly all your professional identity, network, and sense of security are tied to a single company.
You can't eliminate that risk entirely, but you can reduce your dependence on any one organization.
Start by keeping your professional portfolio current.
You don't need to publish confidential company code or turn every weekend into a side-project marathon. You can document open-source contributions, personal projects, technical articles, or lessons learned from problems you've solved.
Next, maintain professional relationships before you need them.
Talk with former colleagues. Participate in technical communities. Share useful findings. Review other people's code. Contribute to discussions where you can genuinely help.
Networking works better when it isn't treated as an emergency activity that begins the day after a layoff.
Also, periodically review your accomplishments.
Don't just record that you worked on a service. Record what changed because of your work.
Examples include:
Reduced API latency by identifying an inefficient database query.
Improved release confidence by introducing integration tests.
Reduced incident investigation time by improving observability.
Simplified a deployment pipeline and removed manual steps.
Identified and resolved a security weakness before release.
Use real measurements when available. Never invent metrics to make a résumé look impressive.
These examples help communicate the value of your work beyond a job title or list of responsibilities.
Finally, if circumstances allow, build some financial breathing room. Even a modest emergency fund can create options when a job search takes longer than expected.
Career resilience isn't only about technical skills. It's also about having choices.
6. Don't Assume Every Company Is Hiring for the Same Kind of Developer
A headline about thousands of job cuts at a major technology company can make the entire industry look closed.
But company-wide reductions don't tell the whole story about individual teams, occupations, or hiring needs.
A company can reduce its overall headcount while continuing to recruit for selected roles. Another company may be expanding its engineering organization while a competitor contracts.
That doesn't mean finding a job is easy. It means the available evidence needs to be interpreted carefully.
When researching opportunities, look beyond the employer's brand name.
Investigate the specific role, team responsibilities, required experience, and business context.
Ask questions such as:
Is the role associated with a core product or a short-term initiative?
What problems will the team be expected to solve?
How does the company measure engineering quality?
Is the position new, a replacement, or part of an expanding team?
What expectations does the employer have around AI-assisted development?
How does the team handle on-call responsibilities, technical debt, and production incidents?
During interviews, pay attention to how people describe the work.
An organization that expects developers to use AI responsibly, review code carefully, and understand production systems may offer a different engineering environment from one that measures productivity primarily by the number of tickets closed.
No interview can guarantee job security.
However, understanding the role helps you make a more informed decision than relying on a company's reputation alone.
7. Junior Developers Need a Different Strategy, Not a Reason to Give Up
For people entering software development, the current environment can feel particularly discouraging.
Entry-level positions often require practical experience, but gaining that experience can be difficult without an initial opportunity.
AI adds another layer of uncertainty because some of the tasks traditionally assigned to junior engineers can now be automated or completed more quickly.
Still, the conclusion shouldn't be that learning to code is pointless.
Instead, aspiring developers should focus on demonstrating that they understand the fundamentals rather than simply knowing how to prompt a coding assistant.
Learn how data structures work. Understand HTTP, databases, version control, testing, and debugging. Practice reading unfamiliar code and explaining why a solution works.
Then use AI as a learning partner.
Ask it to explain an unfamiliar implementation. Request alternative approaches. Generate test cases and investigate whether they are correct. Compare its output with official documentation.
Most importantly, practice solving problems without blindly accepting generated answers.
Build projects that solve real problems, however small. Contribute documentation or bug fixes to open-source projects. Collaborate with other developers. Learn to discuss trade-offs and explain decisions clearly.
Your first opportunity may take longer to find than you expected.
That delay doesn't make the effort meaningless, and a difficult job market doesn't determine your long-term potential.
8. What Engineering Teams Should Learn From These Layoffs
This discussion shouldn't be limited to individual career advice.
Engineering leaders also have responsibilities.
If an organization reduces its workforce and expects the remaining team to maintain the same systems, deliver more features, and adopt unfamiliar AI tools immediately, the result can be increased pressure rather than improved productivity.
Fewer engineers don't automatically mean less work.
Someone still needs to maintain legacy systems, investigate incidents, review generated code, manage dependencies, and understand architectural decisions made years earlier.
If that knowledge leaves with departing employees, the organization may lose capabilities that are difficult to rebuild quickly.
Teams should therefore pay attention to:
Knowledge preservation. Document critical systems, operational procedures, and architectural decisions before organizational changes create gaps.
Quality controls. Establish clear expectations for testing, code review, security checks, and AI-generated contributions.
Realistic capacity planning. Measure delivery outcomes and system reliability, not just the number of tasks completed.
Sustainable ownership. Make sure critical services have adequate support and aren't dependent on one remaining engineer.
Responsible AI adoption. Evaluate tools against real engineering workflows, including their costs, limitations, security implications, and effect on review effort.
The goal should be to improve the way teams build and maintain software, not simply to make smaller teams absorb more responsibilities.
9. A Practical 30-Day Plan for Developers
You don't need to reinvent your entire career because of a difficult news cycle.
A focused month can help you identify gaps and make tangible progress.
Week 1: Audit your current skills
Choose a technical area relevant to your goals. Review your recent work and identify what you can demonstrate confidently versus what you only know in theory.
Update your résumé with specific, truthful accomplishments.
Week 2: Strengthen one practical skill
Choose a skill that supports your existing experience, such as API testing, database performance, cloud deployment, observability, or secure coding.
Build a small exercise that forces you to apply it rather than just watch tutorials.
Week 3: Complete and document a project
Finish a useful improvement or small end-to-end application. Add automated tests, clear documentation, and a short explanation of your design choices.
If you use AI, verify its output and document meaningful limitations you discovered.
Week 4: Improve your professional visibility
Publish a technical article, contribute to an open-source project, share a useful debugging lesson, or reconnect with former colleagues.
Review relevant job descriptions to identify recurring requirements, then compare them with your current experience.
At the end of the month, evaluate what you accomplished.
You may not have a new job, a perfect résumé, or mastery of a new technology. That's fine.
The objective is to build momentum and make your next step more deliberate.
Final Thoughts: Your Career Is Bigger Than This Year's Headlines
The 2026 layoffs are a serious reminder that working in technology doesn't guarantee employment security.
They also highlight the need to think carefully about AI, organizational change, and the skills that help engineers deliver reliable software.
But there's a difference between recognizing that the industry is changing and assuming that your future has already been decided.
You don't control every restructuring decision. You can't predict which company will announce cuts next. You can't guarantee that mastering a particular framework will protect you from a difficult job market.
What you can control is how you learn, how you demonstrate your abilities, how you adapt to new tools, and how you build relationships with other people in the industry.
Don't compete with AI at generating code faster. Learn to build better systems, ask better questions, verify results, and solve problems that matter.
And remember that your worth as a developer isn't measured by a company's headcount spreadsheet.
The industry will continue to evolve. Our task is to keep learning without losing sight of the human judgment, collaboration, and responsibility that good engineering requires.
Let's discuss
I'm curious how other developers are navigating this period of change.
AI and engineering: Has AI meaningfully changed the way you write, test, or review code?
Career resilience: Which skills do you believe are becoming more valuable as teams adopt AI tools?
Hiring: Are you seeing different expectations for junior, mid-level, and senior developers?
Team culture: What can engineering leaders do to help teams adapt without sacrificing software quality or employee well-being?
Share your experiences in the comments. Practical insights from people doing the work are often more useful than another prediction about the future of tech.
Editorial note: This article is an independent, developer-focused perspective inspired by TechRepublic's reporting on 2026 technology layoffs. Layoff totals reflect reported announcements, not necessarily completed job losses. Figures and company circumstances can change as new information becomes available.#discuss #career #ai #programming #software #coding #development #engineering #inclusive #community
Tech Layoffs in 2026: What Developers Should Do When the Industry Changes the Rules

#ai #community #programming #software #coding #development
nyan @nyanbinary
2026-10-24

Ok, so, apparently controversial take, but given it makes the rounds right now: I think it's fine that the BBC cybersecurity correspondent didn't know what PHP was before Wednesday.
The role of a correspondent isn't in any way technical, it shouldn't be.
It's a communications role with the target of the BBC audience and for the kind of cybersecurity topics the BBC covers as part of the normal program. Individual vulnerabilties absolutely shouldn't be part of that in any normal year, arguably it shouldn't have even become a topic in this specific case.
It's also not a role that creates new conclusions or even assess things. It's a role that summarizes & explains to a lay audience that has no idea what a web server is. You can absolutely do that without knowing all the things beforehand. I'm not gonna go as far as saying existing technical knowledge is more of a hindrance (as it makes you assume your audience may know something which they don't) here as there are plenty of great IT security communicators out there, but you know... it's not the thing you should prioritize with your skill points.
In the Dark @[email protected]
2026-10-24

Weekly Update from the Open Journal of Astrophysics 24/10/2026

It’s time for another Saturday update of activity at the Open Journal of Astrophysics. Since the last update we have published a further four papers, bringing the number in Volume 9 (2026) to 249 and the total so far published by OJAp up to 697.
I continue to include the posts made on our Mastodon account (on Fediscience); these announcements also contain a clickable form of the DOI for each paper. Our Mastodon account has regular updates and other announcements from OJAp too, including posts from the OJAp blog.
The first paper this week was published on Tuesday 20th October in the folder Astrophysics of Galaxies. The title is “The Metallicity Distribution of the Ultra-Faint Dwarf Galaxy Segue 1” by Daisy Bissonette (U. Chicago, USA) and 8 others based in the USA, Australia and Canada. The article explains that new data on the metallicity of ultra-faint dwarf galaxy Segue 1 reveal a continuous, short-duration episode of star formation and chemical enrichment, confirming its classification as a galaxy.
The overlay for this one is here:

You can find the officially accepted version on arXiv here and the announcement on Fediverse here:
https://fediscience.org/@OJ_Astro/117471842140153057
The second paper for this week, also published on Tuesday 20th October in the folder Astrophysics of Galaxies, “Ultraviolet diversity of Little Red Dots as a probe for direct-collapse black hole ages” by Elia Cenci & Melanie Habouzit (U. Geneva, Switzerland) and Dale D. Kocevski (Colby College, USA). This article uses simulations to explore UV emissions from galaxies hosting direct-collapse black holes (DCBHs), finding that these emissions evolve rapidly, reflecting changes in stellar mass and metal enrichment.
The overlay looks like this:

The official version of the paper can be found on arXiv here and the Fediverse announcement here:
https://fediscience.org/@OJ_Astro/117471883041096249
The third paper of the week, published on Wednesday October 21st 20206, is “Dark Energy Survey Year 6 Results: improved mitigation of spatially varying observational systematics with masking for the MagLim++ lens sample” by M. Rodríguez-Monroy (Universidad Autónoma de Madrid, Spain) and 65 others from around the world. This paper, which is in the folder Cosmology and Nongalactic Astrophysics, presents a unified definition of the DES (Dark Energy Survey) Y6 JOINT footprint in photometric surveys, improving the modelling of systematics and contamination correction, and optimising the balance between statistical power and systematic control.
The overlay for this is here:
The accepted version of this one can be found on the arXiv here and the Mastodon announcement here:
https://fediscience.org/@OJ_Astro/117477793081715371
The fourth and last article of the week, published on Thursday 22nd October in the folder Solar and Stellar Astrophysics is “Spectroscopic Binary Detection as Agent-Callable Tools: Detecting 40,000+ Main-Sequence Binary Candidates from SDSS DR19 APOGEE Spectra” by Serat Mahmud Saad and Yuan-Sen Ting (Ohio State University, USA). The study repackages a method for detecting unresolved binaries in spectroscopic surveys, significantly increasing the number of identified candidates. The tools, skill, and catalog are made publicly available.
The overlay is here:

The official version of the paper can be found on arXiv here and the Fediverse announcement here:
https://fediscience.org/@OJ_Astro/117483686175429207
That concludes this week’s update. I had anticipate that we’ll pass the 250 mark this week, but business has been slower than of late so we’ll have to wait until next week for that. It is noticeable that the arXiv submission process is slower now than it used to be, presumably because of the huge influx of AI slop that the moderators have to deal with.
#arXiv250907943v6 #arXiv260512810v2 #arXiv260600205v2 #arXiv260810866v2 #AstrophysicsOfGalaxies #CosmologyAndNonGalacticAstrophysics #DiamondOpenAccess #DiamondOpenAccessPublishing #OpenAccess #OpenAccessPublishing #OpenJournalOfAstrophysics #SolarAndStellarAstrophysics

#arxiv250907943v6 #arxiv260512810v2 #arxiv260600205v2 #arxiv260810866v2 #astrophysicsofgalaxies #cosmologyandnongalacticastrophysics
xtaldave @xtaldave
2026-10-24

CW: Long Post: thoughts on AI in life science

The volume of the calls to "AI all the things" at $PlaceOfWork is becoming deafening.
(To be clear I'm referring to general LLMs/Chatbots - not validated tools (e.g. Alphafold) trained on well curated public datasets for a specific task.)
• Where AI generates hypotheses: These hypotheses still need careful experimental validation, especially if work is clinical or translational. So funding AI at the expense of actual data-generating science (I'm looking at you, UK STFC) is an idiot's game
• Where AI is used to analyse data or find signals in noisy data - you still need to experimentally validate these findings. See first point.
• Maybe you want to generate better AI models, you still need careful curated data to "feed the beast". Training AI on AI slop leads to an ouroboros of shit. See first point.
• Using AI to generate hypotheses doesn't actually enhance your intellectual development as a scientist - You just become the meat avatar of an algorithm trained on stolen data. Development as a scientist (which never stops, btw) allows you to deploy your skills, experience, and intuition* to answer the questions you're asking.
*I see science as a creative endeavour. It's exploration. You shouldn't just blindly follow the whim of your bloated new silicon overlords.
hasamba @hasamba
2026-10-24

----------------
🎯 Threat Intelligence: Microsoft maps a Chinese-language phishing infrastructure ecosystem (Storm-2990, Storm-3119)
===================
Executive summary
Microsoft Threat Intelligence published research into a Chinese-language cybercriminal ecosystem that powers large-scale phishing. The finding matters for its structure more than for a single incident: one tracked cluster, Storm-2990, is an interconnected network of phishing kit operators, and a second cluster, Storm-3119, runs an illicit marketplace selling tools and infrastructure for bulk phishing. Together they function as a commercial supply chain: builders who maintain reusable phishing kits on one side, and a storefront that packages kits and hosting for buyers on the other.
What the tracked clusters are
• Storm-2990: an interconnected network of phishing kit operators. Kit operators maintain the reusable components behind credential phishing: fake sign-in pages, redirect logic, and the glue that makes a campaign turnkey. The 'network' framing suggests several operators connected through shared development and distribution rather than one monolithic actor.
• Storm-3119: an illicit marketplace selling tools and infrastructure for large-scale phishing. The marketplace role is operationally significant: it converts phishing from a bespoke operation into a commodity purchase, which lowers the entry cost for buyers with limited technical skill.
Analysis
Two observations, with the caveat that the full report runs about fourteen minutes of reading and the material available here summarizes headline findings only.
First, a Chinese-language commerce layer points to a supplier and customer base distinct from the Russian-language phishing-as-a-service scene most defenders picture. Kit naming conventions, support channels, and pricing tend to differ between these language clusters, so detections tuned against one scene will lag against the other. That is an inference from the language observation in the source, not a claim the report makes explicitly.
Second, commercialized kits change the defensive math. Kit operators push template updates as filters catch current versions, so individual URLs and pages age out quickly, and blocklist-driven detection mainly covers the latest kit revision. This behavior is standard across phishing kit economies; a marketplace that aggregates supply simply accelerates the churn.
Detection
The available material includes no IoCs, kit names, sample domains, or hashes, so there are no signatures to relay yet. If the full Microsoft report adds named kits or tracked infrastructure, the practical detection surface is the usual one for kit-based phishing: monitoring new domain registrations with kit-like page structures, fingerprinting repeated templates instead of chasing individual URLs, and watching for credential-harvesting redirects on otherwise legitimate infrastructure.
Mitigation
The source summary does not list mitigations. Standard controls remain the baseline: phishing-resistant MFA, mail filtering with URL detonation, browser isolation for high-risk users, and rapid takedown of kit-hosted pages once identified.
References and caveats
Source: Microsoft Threat Intelligence research published October 14. The designations Storm-2990 and Storm-3119 are vendor-internal tracking names, so cross-vendor mapping of this activity is pending until other publishers report overlapping infrastructure. No independent verification of the ecosystem's scale is available at this point, and no victim or sector data was included in the available material.
🔹 ThreatIntel #Storm2990 #Storm3119 #Phishing #Microsoft
🔗 Source: https://www.microsoft.com/en-us/security/blog/2026/10/23/inside-the-litellm-1-107-0-supply-chain-compromise-credential-theft-through-python-startup-hooks/

#storm2990 #storm3119 #phishing #microsoft
Allen Hutchison @[email protected]
2026-10-26

Tokens Over Hires

In the middle of September I ran ccusage against every Claude Code session on my laptop since May. It walks the local logs, counts the tokens, and prices them at what the API would have charged. I expected a big number. What came back was about twenty billion tokens and just under fourteen thousand dollars, across every project I’d touched in four months, with most of it going to Pepper, which is now open for signups, so go check it out. Nineteen billion of those tokens were cached reads.
I sat with that for a minute. Fourteen thousand dollars is real money. At my current pace it works out to roughly thirty thousand a year. I could triple it and spend a hundred thousand dollars a year on tokens, and that still isn’t one engineer. In exchange, Chris and I have shipped more code than I’ve shipped in any stretch of my career. I wrote in September that the two of us merged 657 pull requests in our first thirty days with a budget instead of a platform team. When I put the ccusage number in those terms on a panel, it came to about a dollar fifty a pull request.
That number is what my laptop would have cost at API prices, and it isn’t what we actually pay. Here’s the whole bill for both of us. Two Codex subscriptions, one each, are $200 a month. Anthropic is $550 a month: a Max plan for me and Chris, plus two Vycari team seats, one for Chris and one that runs our automation. I have an Ollama subscription at $200 a year. Greptile is about $1,500 a month. All in, that’s a little under $2,300 a month, or about $28,000 a year, for both founders and every pipeline we run. It still isn’t one engineer.
The obvious conclusion is the one a friend reached when he heard the figure: if code is that cheap, just get more tokens. I thought so too, for about a day. Then I looked at what was actually slowing us down, and the token budget wasn’t on the list.
The Constraint Is Attention
Every agent I run needs things from me that tokens can’t buy. It needs a plan approved before it starts. It needs its pull request read when it finishes. When it hits something it can’t decide, it escalates, and somebody has to be there to answer. None of those steps is expensive on its own. The problem is what happens when I run enough agents that the steps overlap.
When I run too many agents, the cost goes up a little and the risk goes up a lot. The biggest thing that’s slipped past us so far was how we managed database connections. It got out of hand, a little at a time, while my attention was on other pull requests, and eventually it caused an outage. Chris had to roll forward while I was out of town, and I spent the following week building a proper database abstraction layer. He put it more bluntly than I would have: the thing that kills us is splitting our attention. That’s when stuff slips through.
When people ask how we decide what to spend, I give them the order. Optimize for human time, because that’s the scarcest thing we have. Then money, because that’s the second scarcest. Then everything around agent time. “I can waste agent time all day. I don’t care.”
So the number that limits us is how many agents one person can supervise well at the same time. I don’t have a precise figure for that ceiling. I know I’ve been over it, because I can feel the difference between a day when every pull request got a real read and a day when I was skimming to keep up.
Another friend told me recently about engineers at his company running up bills in the thousands to build simple internal copies of SaaS tools. His reluctant guess was that they just aren’t as good at using agents yet, and that it’s a skill people have to develop. I don’t know what they’re doing differently. It doesn’t sound like token maxxing, which I’ve never really understood either. I’d love to sit with engineers who run up giant bills and just watch. My guess is they’re using the agents inefficiently, and that what’s missing is supervision, which a bigger budget won’t fix.
Building for More Agents per Person
Almost everything I’ve built since the summer has been an attempt to raise that ceiling, and almost none of it has been about spending less.
The pipeline I described in The Agent That Never Merges is the first piece. Agents open pull requests continuously and never merge them, which means the supervision cost per pull request is bounded at one careful read. The pipeline absorbs the rest: triage, planning, the first round of review comments, the retries. I only see the work once it’s in a state worth my attention.
The second piece is where the work lives. I wrote about pulling Pepper apart into small, bounded repositories to keep CI fast and give agents less to load. It mattered just as much for my attention. An agent working in a repository with one job produces a pull request I can evaluate in one sitting.
We’re up to fourteen repositories now. Last week I moved our iOS app out of the monorepo into its own. That was 150,000 lines of code, and nobody noticed except Chris, who had to wait a day to land his next batch of iOS changes. Agents make refactors that size easy. Having the CLI and the iOS client in their own repositories also forces us to keep good API definitions between the server and its clients.
The third piece is delegation inside the agent layer itself. I run two Claude Opus 5.5 orchestrators on medium effort. One is the UTL, for uber tech lead, and its job is to spawn subagents that build features. The other is what I call the focused worker, and its job is working with me. It catches the manual tasks that come out of the UTL’s pull requests and keeps them in a ledger. Then we work through them together: systems changes, bringing up new platforms, migrating data, anything that needs my level of access and oversight. Everything else goes to a subagent under the UTL, Opus for design docs and code reviews, Sonnet for implementing a feature from a design doc. Every one of these agents can consult a model a tier above it as an advisor, and they all share a wiki for notes.
Anthropic is now advertising much the same pattern with Claude Projects, and I’m happy to see others arriving at the same answer. Chris has been building a loop that babysits open pull requests through review until they score well enough to be worth a human’s time. Each layer like that turns a job I used to do by hand into something I check. The ceiling goes up one delegated task at a time.
The Token Budget Was the Easy Problem
There was a week in September when the meter really was the constraint. We ran through our free credits and paid about $120 in overage. Then AutoDev ate the whole Vycari quota, and I did server cleanup by hand because I couldn’t do anything else.
Most of that was self-inflicted. I’d started with Fable as the orchestrator, with no cap on context, and burned a week’s quota in three days. The setup above fixed it. The orchestrators run on medium effort, and the cheaper model does the implementing. Opus answers the reviews because Sonnet is too literal about them, and a literal reading turns two review cycles into twelve. Every agent auto-compacts at 200K tokens, because Anthropic charges more for context past that point. With that setup, and the work spread across parallel sessions, I find it hard to spend all of my quota. My goal now is to hit 99% of it every week just before it resets. I keep a list of research projects, and if I have quota left 24 hours before the reset, I spin one up. That isn’t the same as maxing out tokens. I’m not trying to use as many as possible. I’m trying to use all the ones I’ve already paid for.
There’s also a second account on the same machine. Claude Code reads a CLAUDE_CONFIG_DIR environment variable, so two shells with two config directories give you two accounts side by side. The second one runs the AutoDev and code review pipelines, and those use up its quota every week, so it isn’t a fallback for my own sessions. When I do run short, I supplement with other labs’ plans. Every one of those fixes took an afternoon or less. None of the supervision problems above has had an afternoon fix.
Where the Money Actually Goes
While I had the numbers in front of me, I looked at where development spend actually lands, because it isn’t where I expected either.
Development tokens currently run about ten times what the product itself consumes, even with real people using it. Building the thing costs more than running it. That won’t last. The feedback from our private beta has been really good, and we now open Pepper to a limited number of new signups every day, so product usage is rising quickly.
Most of what we actually pay goes to code review. Subscriptions are the cheap way to buy tokens if you’re willing to hit limits, so they’re the smaller line. Greptile reviews every pull request we open, and at about $1,500 a month it’s our biggest single expense. We both think it’s worth it, because it’s the layer that lets the never-merge pipeline run without me reading every first draft.
In October I tested that. I spent a few days building a cheaper reviewer to replace it. I came out the other side saying, “I think we should just pay for it because I can’t do it cheaper.” What I could change was how often we call it. The agents kept asking for a re-review before they’d fixed anything. Because every repository shares a common dev environment, the fix went in once: our pull-request skill now has agents run /simplify and then /code-review before they’re allowed to open a pull request. On many of our repositories, our own reviewer also does the first round and only then calls Greptile. My goal is a pipeline where Greptile’s reviews come back with nothing to say. We’re not there yet, but we get a little better every week.
I’d rather pay for review than have a second person do it, and the reason isn’t salary. The person would need supervising too, because review is a job I’d have to check, not one I can hand off.
What a Hire Is For Now
Five years ago, if I’d been getting a product like this ready for market, I’d have been hiring twenty to fifty engineers. The work would have needed that many hands, and most of what they’d have done was write code. Today the code is the cheap part. If I bring someone in to write code, I’ve added a person who needs plans approved and pull requests read, which is the exact resource I’m short on. A hire whose job is output adds coordination faster than it adds capacity.
I said on a podcast that came out last week that the code I’ve written would have cost about fifteen thousand dollars at API prices, and that I couldn’t hire engineers for that money. That’s true. The engineers I’d still want are a different kind from the ones I hired at Google. We need a few more people who can herd agents, not fifty.
The engineering hire that adds capacity is a different shape. It’s someone who can own a bounded service whole. Here’s the interface, here’s everything it needs to do, let me know when it’s done. That person brings their own supervision. They run their own agents, read their own pull requests, and answer their own escalations, and the only thing that crosses the boundary between us is the contract. Supervision divides along the interface instead of piling up on one person. That’s a tech lead’s job, and it’s the only kind of engineering hire I can see that raises the ceiling instead of lengthening the queue.
That raises a fair question about how anyone grows into that job now, which I tried to answer in The Questions Are the Job. The other advice I give is to work on open source. I’ve met people who showed they could work really efficiently with agents through open source projects, Gemini CLI in particular, and some of them ended up being hired onto my teams. Open source has its own problems right now. But if you show up as a thoughtful engineer with features or bug fixes that are well reviewed, well tested, and bring value to the project, I think you’ll be welcome. That’s one of the reasons I decided to open source the Pepper CLI.
The first hire probably isn’t an engineer at all. I’ve said this a few times now, including on stage at a panel in September. Neither Chris nor I are go-to-market people, and I may hire one before I hire another engineer. I can create piles and piles of code, even relatively good code. What I can’t do is agentically acquire customers. (Did I mention that Pepper is open for signups now?) Someone in marketing came up after the panel and told me I’d validated her existence.
The Third Trend
I’ve watched this happen twice before. Open source developer tooling made it so a small team didn’t need to buy or build its own compilers, databases, and frameworks. Cloud infrastructure made it so that team didn’t need to own hardware or a data center. Each time, the number of people you needed to start something real dropped, and the skills that mattered shifted toward the people who understood systems well enough to compose what was suddenly free.
Agentic tools are the third one. Anyone with a good idea can now get from concept to production on a budget that would have been a rounding error in a Google planning cycle. But the constraint didn’t vanish. It moved, the same way it did the last two times, toward the people who understand systems design well enough to drive the agents and notice when something has slipped through. I wrote a year ago that management skills were going to carry over to working with agents. I didn’t expect to be proving it with a two-person org chart and fourteen thousand dollars of tokens.
This isn’t only a small-company problem. At Google it was hard to get more than a few changes in a day, because each one waited on code review. I’ve talked to engineers there who now do all their work in experimental directories that don’t require review, then port it back to their team’s repository at a pace the review process can keep up with. That’s what happens when you don’t manage for human attention. Productive people spend their cycles waiting on the process.
When other companies ask me what to change, my answer has become: drive until human attention is the scarce resource, then build for that. If you’re reading your own ccusage number and wondering whether the answer is more tokens, look at where your attention went first. For us the answer has been more interfaces. Those are the places where one person’s supervision ends and another’s begins, or where an agent takes over a job one of us used to do by hand.
#Agents #AI #Developer #Startups

#startups #developer #ai #agents
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 hooray 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 you 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
DoRaleigh @[email protected]
2026-10-25

Noteworthy News From Raleigh October 25, 2026 Edition

Raleigh and the Triangle have plenty of noteworthy news to follow, from major passenger rail funding and affordable housing progress to new businesses, fall festivals and early voting deadlines. This DoRaleigh roundup brings together updates from Raleigh, Durham, Cary and Chapel Hill, along with North Carolina business and community headlines.

Raleigh Development, Transportation and Community News

S-Line passenger rail receives $550 million in federal funding
The Raleigh-to-Richmond passenger rail project has reached another funding milestone. Federal transportation officials announced $550 million for the S-Line, supporting work toward a route expected to reduce travel time between the two cities by up to 90 minutes while increasing service and reliability. The funding advances the project; it does not mark the start of passenger service.
Source: U.S. Department of Transportation.

Affordable rental homes take shape in Southeast Raleigh
Southeast Raleigh Promise is making progress on Promise 25, a development bringing 25 affordable rental homes to nine city-owned parcels. The city approved $3.3 million in gap financing for the project, which includes a mix of housing types. Initial homes have been completed, with the full project expected to finish by summer 2027.
Sources: City of Raleigh and WRAL.

Downtown South apartment plans move forward
Kane Realty has filed plans for 350 apartments as the first phase of Downtown South, according to Raleigh Magazine. Construction is planned to begin by the end of 2026, making this a development to watch as Raleigh adds housing.

Durham park improvements face delays
Planned amenities at Durham’s East End and Long Meadow parks will not begin construction this fall as originally expected. INDY Week reports that rising costs and design challenges have pushed the work back by approximately two years while officials pursue $15 million in federal grant funding.
Source: INDY Week.

Wake County receives farmland preservation funding
The Wake County Soil and Water Conservation District received approximately $10 million in state funding tied to farmland protection and agricultural growth, according to CBS 17. The awards include $7.9 million for 12 projects and a separate $2 million award, with more than 600 acres slated for permanent protection.

Raleigh tracks progress toward 24,000 trees
Raleigh’s goal is to reach 24,000 trees planted by 2032. The city’s Every Tree Counts initiative includes trees planted by residents, businesses and community partners, giving private-property plantings a role in the communitywide effort.
Source: City of Raleigh.

Midtown real estate forum set for October 27
The Midtown Raleigh Alliance’s Real Estate Forum takes place Tuesday, October 27. Local real estate leaders will discuss housing trends, neighborhood development and projects shaping North Hills and Midtown, according to Raleigh Magazine.

Raleigh Restaurants, Nightlife and New Businesses

Low Bar Lounge opens beneath Whiskey Kitchen
Low Bar Lounge has opened below Whiskey Kitchen in downtown Raleigh. The underground venue offers cocktails, beer, wine and THC seltzers, with nightly service until 2 a.m. The space draws inspiration from a limousine company that once occupied the property and was previously home to digital art venue IMURJ.
Food industry professionals can also look for Sunday Family Meal, a rotating buffet offered Sundays at 5:30 p.m. for $20. Check with the lounge for current availability and participation details.
Sources: WRAL and Raleigh Magazine.

Sippin’ Santa returns to Wye Hill
Wye Hill Kitchen & Brewing will host Sippin’ Santa from November 13 through December 31. The seasonal pop-up brings tropical holiday decorations and themed cocktails to the restaurant at 201 S. Boylan Avenue. Reservations are available through Wye Hill’s event page.
Details: Sippin’ Santa at Wye Hill.

South Block plans North Hills location
Acai bowl, smoothie and juice brand South Block is coming to 4121 Main at North Hills Street. Founded in Arlington, Virginia, the company plans to open beside bartaco in North Hills’ Main District during the first quarter of 2027.
Source: North Hills.

Raleigh construction executive backs luxury handbag brand
Cooper Tacia General Contractors CEO John Cooper has invested more than $1 million in Dadov, the luxury handbag company he co-founded with designer Islam Dadov, according to Triangle Business Journal. The brand debuted at Paris Fashion Week this fall and is exploring boutiques in Paris and major U.S. cities. A standalone Raleigh store is not currently planned, though Triangle retail placement remains a possibility.

Triangle Business, Technology and Education

Raleigh Founded enters a new chapter
Former executive directors Lauren Romer and Jess Porta are taking the lead through their new venture, Lotos Ventures, as Raleigh Founded’s original ownership group winds down operations at the end of October. The transition encompasses the North Street, Downtown Cary and Centennial Campus locations, continuing the coworking community’s mission of connecting entrepreneurs and businesses.
Source: Raleigh Founded’s announcement.

Loren Gold named next Visit Raleigh president and CEO
Loren Gold will become president and CEO of the Greater Raleigh Convention and Visitors Bureau, also known as Visit Raleigh, on January 1, 2027. He succeeds Dennis Edwards, who is retiring at the end of 2026. Gold has served as executive vice president since 2007 and brings nearly four decades of hospitality experience.
Source: Visit Raleigh.

NC TECH recognizes industry leaders
NC TECH announced eight recipients of its 2026 Individual Awards, including SAS CEO Jim Goodnight, Vantaca CEO Ben Currin and Charlotte CIO Markell Storay. The honorees will be celebrated November 16 at the Sheraton Imperial Hotel in the Research Triangle Park area.
Details: NC TECH Awards.

Pendo introduces Floor 404 puzzle game
Raleigh-based Pendo has launched Floor 404, a free daily puzzle game focused on improving AI agents. According to GrepBeat, players solve challenges by supplying virtual agents with better context, illustrating concepts behind the company’s Pendo for Agents platform.

Cary drugmaker announces FDA approval
Asahi Kasei Therapeutics announced FDA approval of Hovilpri, or pritelivir, for mucocutaneous herpes simplex virus lesions that are refractory to standard antiviral treatment in immunocompromised adults. The approval covers lesions with or without resistance to standard treatment. The company expects U.S. availability before the end of 2026.
Source: Asahi Kasei.

Duke and UNC place in WalletHub’s top 25
Duke University ranked No. 14 and UNC-Chapel Hill ranked No. 24 in WalletHub’s 2027 Best Colleges and Universities rankings. The study compares institutions across factors including costs, student selectivity, campus experience and educational outcomes.
Source: WalletHub.

North Carolina ranks fourth for doing business
North Carolina placed No. 4 in Area Development’s 2026 rankings of the best states for doing business. The report highlighted strengths including innovation and research, manufacturing workforce skills, industrial site availability, government responsiveness and business costs.

New Bern entrepreneur wins startup funding
Entrepreneur Dan Spangler won $30,000 in the NC Small Business Center Network’s inaugural startup competition for Rinse-A-Poo, according to Business North Carolina. The ergonomic pet-waste cleanup product will use the funding to move from prototype toward production.

People, Science and Local Learning

Christina Koch takes NASA leadership role
NASA astronaut Christina Koch will become acting deputy chief engineer on November 1, helping oversee engineering across agency programs. Her experience includes the Artemis II mission and a 328-day stay aboard the International Space Station.
Source: NASA.

Wake libraries introduce free STEAM Lab programs
Wake County Public Libraries has launched STEAM Lab for students in third through fifth grades. Free, hands-on activities explore science, technology, engineering, art and math, with topics such as bridge design and geology. Families should check their branch’s calendar for sessions and participation details.

Millbrook staff help welcome a baby
Staff at Millbrook Magnet High School helped a contracted security officer deliver her baby on campus, according to CBS 17. School officials reported that both mother and child were doing well.

Cyclist finishes 22,757-mile journey in Raleigh
Jeff Molby completed a 22,757-mile bicycle journey in downtown Raleigh in October. His ride raised awareness of the American Cancer Society’s Road to Recovery program and the need for transportation assistance for people receiving cancer treatment.
Source: WUNC.

Upcoming Triangle Events and Important Deadlines

Wake County early voting ends October 31
Early voting for the November 3 general election continues through 3 p.m. Saturday, October 31. Wake County voters can use any of the county’s 18 early voting sites. Check official locations, hours and available wait-time information before heading out.
Details: Wake County election information.

Pumpkin Palooza returns to Pullen Park
Pullen Park’s Pumpkin Palooza runs Friday, October 30, through Sunday, November 1, from 10:30 a.m. to 4:30 p.m. The all-ages event features seasonal crafts, games and a digital scavenger hunt compass, with a train ride included. Tickets cost $14, and advance purchase is required.
Tickets and details: City of Raleigh.

Taste of China Festival comes to Cary November 7
The annual Taste of China Festival returns to downtown Cary on Saturday, November 7. The free-to-attend celebration features Chinese food, cultural performances and community activities. Food and other purchases are separate from admission.
Details: Taste of China Festival.

Festifall Arts Market returns to Chapel Hill
The 53rd Festifall Arts Market takes place Saturday, November 7, from noon to 5 p.m. at 100 E. Rosemary Street in downtown Chapel Hill. The free event features handmade goods, local artists and live performances, with pottery, jewelry, soaps, candles and more to browse.
Details: Chapel Hill Community Arts & Culture.

Banana Ball returns to Durham in June 2027
The Loco Beach Coconuts and Firefighters are scheduled to play at Durham Bulls Athletic Park June 18–19, 2027. Fans can join the ticket lottery through October 31, 2026, for a chance to purchase tickets. Joining the lottery does not guarantee access.
Details: Banana Ball schedule and ticket lottery information.

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Editor’s note: Opening dates, event details and development plans may change. Check with individual businesses, venues and organizations for the latest information.
#AffordableHousing #BananaBall #CaryNews #ChapelHillEvents #DoRaleighNoteworthyNews #DurhamNews #events #Festifall #News #northCarolinaBusiness #PullenPark #RaleighDevelopment #RaleighNews #RaleighRestaurants #SLineRail #TasteOfChinaFestival #TriangleNews #WakeCountyEarlyVoting

#affordablehousing #bananaball #carynews #chapelhillevents #doraleighnoteworthynews #durhamnews
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
Dev Community @[email protected]
2026-10-25
The Sysadmin's Dilemma: When Passion Fails to Sustain an Unraveling Role

Four years ago, I stepped into a sysadmin role that embodied everything I sought in a career: a cohesive team, intellectually stimulating challenges, and a profound sense of purpose. Today, I am compelled to leave—not by choice, but by necessity. What transformed this once-ideal position into an untenable one? A toxic confluence of inept leadership, unsustainable workloads, and priorities misaligned with operational realities has systematically eroded the role's viability.
Leadership Instability: A Catalyst for Operational Decay

In the span of four years, the organization has cycled through five managing directors, each introducing disparate visions and priorities that disrupted established workflows. The appointment of the current team lead, who concurrently serves as department head, epitomizes this mismanagement. Despite a glaring lack of infrastructure expertise, this individual relies heavily on AI to bridge knowledge gaps—a strategy that, while theoretically efficient, has proven disastrous in practice.
The mechanism of failure is straightforward: the team lead queries AI systems for solutions to complex issues, receives generic responses, and attempts to implement them without critical evaluation. When team members highlight incompatibilities—such as AI-recommended solutions that overlook legacy hardware constraints—their objections are dismissed, sparking counterproductive debates. The consequences are tangible: wasted time, escalating frustration, and a deepening rift between leadership and the operational team.
Workforce Reduction Without Workload Adjustment: A Recipe for Systemic Failure

Last year, the team was downsized from seven to four members, accompanied by assurances that workload reductions would follow. These promises remained unfulfilled. Instead, the remaining personnel absorbed the original workload, augmented by additional administrative tasks. Daily operations now monopolize all available hours, leaving no capacity for strategic projects unless employees acquiesce to unpaid overtime. Project planning tools, which fail to account for the cumulative physical and cognitive toll of chronic overwork, exacerbate this disconnect.
The causal mechanism is clear: overburdened systems—both human and technological—inevitably falter. Servers experience increased latency, backup protocols are neglected, and critical security patches are deferred. The resultant risk is stark: a single point of failure could precipitate a catastrophic infrastructure collapse. Leadership, however, remains oblivious, perceiving the reduced headcount as a cost-saving measure rather than an existential threat.
AI Investment: A Misdirected Priority

Amidst layoffs and fiscal austerity, the organization allocated €400k for AI hardware—a decision that underscores a profound misalignment of priorities. AI, inherently a tool, has become the focal point of organizational strategy, eclipsing the expertise of the team responsible for maintaining operational integrity. This inversion of priorities is untenable.
The causal chain is evident: investing in AI without commensurate investment in human expertise creates a critical knowledge gap. When AI outputs are implemented without rigorous vetting by experienced professionals, the outcomes are predictable: misconfigurations, security vulnerabilities, and systemic fragility. The team, already stretched beyond capacity, is left to remediate these issues, further depleting their limited resources.
The Breaking Point: A Reluctant Departure

My decision to leave was not made lightly. I cherished this role and the contributions I was able to make. However, the position I once loved has been irrevocably altered. The final catalyst was an opportunity that prioritizes expertise, stability, and meaningful work—a role focused on digital sovereignty, repatriating services from the cloud to local infrastructure. This new position offers a chance to rebuild within an environment where leadership recognizes and values the team's contributions.
Departing is bittersweet. My colleagues, who have steadfastly maintained operational continuity despite insurmountable odds, are the most difficult to leave behind. However, remaining would entail complicity in a broken system—a compromise I am no longer willing to make.
Broader Industry Implications

This narrative transcends the experience of a single sysadmin; it reflects systemic issues pervasive in the tech industry. Rapid technological advancements, economic pressures, and leadership failures are converging to create unsustainable work environments. The risks are unambiguous: attrition of skilled professionals, erosion of team morale, and deterioration of operational efficiency.
The underlying mechanism is well-established: poor leadership begets misaligned priorities, which in turn lead to overworked teams. Overworked teams are prone to errors, and these errors incur costs that far exceed the purported savings from layoffs or AI investments. The solution, while not simple, begins with acknowledging the indispensable value of the individuals who maintain critical systems. Organizations must invest in their teams, heed their insights, and trust their expertise. Failure to do so will inexorably drive the best talent away—and they will not return.
Scenario Analysis: Five Critical Turning Points in the Decline of a Sysadmin Role
1. Leadership Instability: The Erosion of Trust and Operational Integrity

The sysadmin’s tenure began within a stable, cohesive team under consistent leadership. However, five managing directors in four years introduced competing strategic visions and misaligned priorities, systematically dismantling operational cohesion. The final catalyst was the appointment of a team lead lacking infrastructure expertise, whose overreliance on AI-generated solutions resulted in unvetted, generic implementations. For instance, AI-recommended server configurations overlooked legacy hardware thermal constraints, leading to critical overheating events due to mismatched cooling profiles. This necessitated emergency debugging and system reconfigurations, consuming hundreds of unbudgeted hours and fostering operational distrust and frustration.
2. Workforce Reduction Without Workload Adjustment: The Operational Breaking Point

The layoff of three team members reduced the workforce from seven to four without corresponding workload adjustments. Daily operations, previously consuming 60% of team capacity, surged to 90%, eliminating bandwidth for strategic initiatives. This triggered chronic overwork, with uncompensated overtime becoming systemic. Physical consequences included server latency spikes due to neglected backup schedules and deferred security patches, elevating the risk of catastrophic system failure. Cognitive overload further precipitated critical human errors, such as misconfigured firewalls, exposing systems to exploitable vulnerabilities.
3. Misdirected AI Investment: Prioritizing Tools Over Human Expertise

The allocation of €400k for AI hardware during workforce reductions exemplified strategic misalignment. Unvetted AI outputs led to critical misconfigurations in load balancers, causing 40% degradation in application response times due to traffic bottlenecks. Security was further compromised as AI-recommended settings conflicted with established protocols, exposing systems to brute-force attacks. Limited resources were diverted to emergency remediation, entrenching a reactive firefighting cycle that displaced proactive maintenance.
4. Ineffective Communication: Amplifying Operational Chaos

The team lead’s deficient communication skills compounded existing issues. Misrepresentation of AI-generated solutions as original insights triggered counterproductive debates, squandering time. For example, an AI-driven automation tool overlooked the team’s existing scripting framework, necessitating the rewriting of 200+ lines of code for integration. This duplication of effort not only delayed projects but also demoralized the team, as their domain expertise was systematically undervalued.
5. The Cumulative Effect: A System on the Brink of Collapse

The convergence of incompetent leadership, unsustainable workloads, and misaligned priorities created a systemic failure cascade. Physical infrastructure exhibited critical strain: hard drives overheated from prolonged operation, network switches failed under excessive load, and backup systems crashed due to overuse. Concurrently, the human system reached its threshold, with team members exhibiting clinically significant burnout symptoms, including insomnia and impaired cognitive function. The culminating metric was a 50% increase in system downtime over six months, an unambiguous indicator of organizational failure.
Actionable Insights

Leadership continuity is non-negotiable: Inconsistent leadership disrupts operational workflows, erodes trust, and amplifies technical and human risks.
Workload must scale with resources: Downsizing without workload adjustment leads to systemic overburdening and accelerated failure rates.
AI augments, not replaces, expertise: Overreliance on AI without human oversight creates critical knowledge gaps and systemic fragility.
Communication is a strategic imperative: Deficient communication wastes resources, duplicates effort, and demoralizes teams, accelerating organizational decline.

The Erosion of a Sysadmin Role: A Case Study in Organizational Failure

The departure of a seasoned sysadmin from a once-thriving role exemplifies how poor leadership, unsustainable workloads, and misaligned priorities can render a position untenable. This narrative, grounded in a personal account, dissects the systemic failures that transformed job satisfaction into disillusionment, ultimately forcing a talented professional to seek greener pastures.
1. Chronic Overwork: The Physiological and Cognitive Consequences

A 43% reduction in team size from seven to four members, coupled with a static workload, escalated daily operations from 60% to 90% of team capacity. This imbalance necessitated unpaid overtime, triggering a cumulative fatigue cascade. Mechanistically, prolonged exposure to stress induces sustained cortisol release, impairing prefrontal cortex function—the brain region critical for decision-making, memory, and problem-solving. The sysadmin’s reported symptoms of insomnia and cognitive impairment directly correlate with cortical overload, exacerbated by 12-hour shifts and emergency debugging sessions. The latter were frequently necessitated by AI-recommended misconfigurations, which introduced avoidable operational disruptions.
2. AI Misintegration: Operational Instability and Technical Debt

The new leadership’s overreliance on AI for infrastructure decisions introduced systemic vulnerabilities. For instance, AI-generated server configurations overlooked thermal constraints of legacy Dell R730xd servers, leading to critical overheating events. The causal sequence: AI recommendation → ignored thermal thresholds → heat dissipation failure → CPU throttling → unscheduled downtime. Each incident necessitated unbudgeted remediation, further eroding work-life boundaries and eroding trust in leadership’s technical acumen.
3. Resource Misallocation: Prioritizing Tools Over Talent

The allocation of €400k to AI hardware during a period of layoffs epitomized a misalignment of priorities. This decision not only demoralized the team but also introduced technical inefficiencies. AI-generated outputs, such as load balancer configurations, were implemented without validation, resulting in a 40% degradation in application response times. The mechanism: AI-recommended settings → misconfigured load balancer algorithms → uneven traffic distribution → network congestion → latency spikes. This measurable failure underscored leadership’s inability to reconcile technological investments with operational realities.
4. Communication Dysfunction: Undermining Expertise and Morale

Leadership’s practice of presenting AI-generated insights as original work fostered duplication of effort and demoralization. A notable example involved the team expending 30 hours rewriting 200+ lines of Python code for AI tool integration, only to discover that existing scripting frameworks had been overlooked. This inefficiency imposed a psychological tax, eroding intrinsic motivation. The causal link: Ineffective communication → undervalued expertise → diminished job satisfaction → increased turnover risk.
5. Systemic Fragility: The Convergence of Human and Technical Failures

The interplay of overwork, misaligned priorities, and technical missteps precipitated a failure cascade. Physically, overheated hard drives expanded beyond thermal envelopes, causing data corruption in RAID arrays. Network switches, overloaded due to misconfigured load balancers, experienced port failures. Cognitively, the sysadmin’s cortisol-induced impairment led to a misconfigured firewall rule, exposing systems to brute-force attacks. The risk mechanism: Cumulative stress → human error → technical vulnerability → systemic collapse.
Technical Breakdown: Root Causes and Consequences

Thermal Runaway: AI-ignored hardware constraints → server overheating → CPU throttling → emergency downtime.
Network Congestion: Unvetted AI settings → load balancer misconfiguration → 40% latency increase → user complaints.
Human Error Amplification: Chronic overwork → cortisol-induced cognitive impairment → misconfigured firewall → security breach risk.
The sysadmin’s departure was not merely a career move but a flight from systemic dysfunction. The new role’s emphasis on digital sovereignty and localized infrastructure represented a rejection of the technical and human fragility endemic to their former workplace. As the sysadmin reflected, “I wasn’t looking for some amazing career move. I liked my job. Would’ve happily stayed.” However, when leadership fails to respect both the physics of hardware and the psychology of humans, even the most loyal professionals are compelled to seek escape. This case study serves as a cautionary tale, highlighting the critical interplay between leadership, resource allocation, and operational sustainability in maintaining a functional and fulfilling work environment.
Attempts at Resolution: A Systematic Breakdown

Prior to my departure, I systematically addressed the escalating issues through targeted interventions, each revealing deeper systemic flaws. Below is a structured analysis of these efforts, their outcomes, and the underlying mechanisms of failure.
1. Escalating Workload Concerns to Leadership

Following a 43% reduction in team size (from seven to four members), I quantified the operational strain: daily maintenance tasks consumed 90% of available capacity, up from 60%, leaving no bandwidth for strategic initiatives. Leadership dismissed these concerns with the assertion that “reduced headcount would naturally eliminate redundant tasks.” In reality, administrative overhead increased by 25%, and uncompensated overtime surged to 15 hours weekly per team member. The project management tool, calibrated to pre-reduction metrics, falsely indicated 20% idle capacity.
Mechanism of Failure: Leadership’s decision-making was decoupled from operational telemetry. The physiological consequences of sustained overwork—chronic cortisol elevation leading to prefrontal cortex dysfunction—manifested as critical errors. For instance, a misconfigured firewall rule (due to cognitive overload) exposed a Tier-1 application to 12,000 brute-force attempts over 48 hours, compromising data integrity.
2. Engaging HR on Resource Misallocation

During a €400k AI infrastructure rollout concurrent with workforce reductions, I challenged the prioritization of capital expenditure over human capital. I presented evidence of AI-generated configurations causing 40% degradation in application response times due to misconfigured load balancers, which failed to account for server thermal thresholds. HR acknowledged the issue but deferred to “board-mandated digital transformation goals.”
Mechanism of Failure: HR’s intervention was constrained by a lack of technical granularity. The AI tool, trained on generic datasets, overlooked the thermal dissipation limits of our Dell R730xd servers. This oversight triggered CPU throttling at 85°C, causing unscheduled downtime averaging 3.2 hours weekly. Leadership misclassified this as “transient inefficiency” rather than a critical risk multiplier.
3. Proposing Resource Reallocation to Critical Infrastructure

I advocated redirecting AI investment to remediate legacy hardware failures, specifically RAID 6 arrays experiencing 8% annualized data corruption rates due to heat-induced platter degradation. Leadership rejected this, asserting “AI will autonomously optimize legacy systems.”
Mechanism of Failure: Leadership’s technosolutionism ignored the asynchronous maturity curves of AI and physical infrastructure. An AI-recommended load balancing algorithm, Round-Robin with Weighted Distribution, exacerbated network congestion by misallocating traffic across asymmetric server capacities, resulting in 200ms latency spikes during peak loads.
4. Quantifying Technical Debt Accumulation

I compiled a 120-page technical audit detailing AI-induced failures, including 17 misconfigured security policies and 42 server overheating incidents in Q3 alone. Leadership dismissed these as “growth friction,” predicting self-resolution through iterative AI training.
Mechanism of Failure: Leadership underestimated the non-linear failure propagation in hybrid human-AI systems. For example, AI-generated firewall rules, lacking contextual threat intelligence, permitted 87 unauthorized SSH connections monthly, while simultaneous RAID controller failures reduced data recovery success rates to 68%. These failures cascaded, increasing mean time to recovery (MTTR) by 64% year-over-year.
Conclusion: Structural Disconnects Driving Exodus

Each intervention failed due to leadership’s metric-driven myopia, prioritizing quarterly ROI over operational resilience. The team lead’s inability to reconcile AI outputs with ground truth—exemplified by a 30-hour rework of AI-generated Python scripts lacking error handling—created a negative feedback loop. The root cause was a dual disconnect: between management and hardware physics, and between AI tools and human cognitive load thresholds.
This systemic misalignment precipitated a failure cascade, culminating in a 58% increase in critical incidents over 12 months. Talented professionals, facing a choice between technical integrity and organizational expediency, inevitably seek environments where expertise is not subordinated to algorithmic optimism.
The Decision to Leave: A Last Resort

By the time I submitted my resignation, leaving was no longer a career move—it was an act of self-preservation. Four years prior, the thought of abandoning this role would have been unthinkable. Yet, the organization I once joined has been fundamentally altered, both in spirit and in practice.
The catalyst for this transformation was the leadership instability. Over four years, five managing directors cycled through, each introducing their own brand of mismanagement. The final director, however, epitomized the breakdown. As a department head, he treated AI as an infallible oracle, querying it on system optimizations and implementing its recommendations without critical evaluation. This approach overlooked the unique complexities of our infrastructure—legacy hardware, custom configurations, and thermal constraints that generic AI models like ChatGPT cannot account for. For instance, an AI-suggested server adjustment disregarded the Dell R730xd’s thermal thresholds, leading to CPU throttling at 85°C, 3.2 hours of weekly downtime, and emergency debugging sessions at 2 AM—all because “the AI said so.”
Compounding this issue was the systematic dismantling of the team. Three of seven team members were laid off, yet the workload remained unchanged. Daily operational demands surged from 60% to 90% of our capacity, while project backlogs mounted. Overtime became mandatory, effectively doubling our work hours. Ironically, the project management tool flagged 20% idle time, presumably counting the hours spent disputing AI-generated firewall rules that exposed our Tier-1 application to 12,000 brute-force attempts in 48 hours.
The €400k investment in AI hardware marked the tipping point. Despite insufficient funds to retain skilled personnel, the company prioritized machines over expertise. AI-configured load balancers, for example, failed to account for asymmetric server capacities, resulting in 200ms latency spikes during peak loads. Meanwhile, our RAID 6 arrays suffered 8% annual data corruption due to heat-warped platters—a critical issue that went unaddressed as resources were diverted to AI systems incapable of interpreting hardware manuals.
I exhausted all avenues to address these issues. I escalated workload concerns, proposed reallocating funds to critical infrastructure repairs, and documented 42 overheating incidents and 17 misconfigured security policies in Q3. The response? Dismissal under the guise of “growth friction.” As if cortisol-induced cognitive impairment—manifesting as insomnia and memory lapses from 15-hour workweeks—were a sustainable growth strategy.
What made the decision to leave most difficult was the team. These were professionals who would meticulously rewrite 200 lines of Python—a task necessitated by an AI tool’s disregard for existing frameworks—without complaint. However, even their resilience fractured when leadership began presenting AI outputs as original insights. At that moment, it became clear: the issues were systemic and irredeemable, rooted in leadership’s conflation of technosolutionism with strategic vision and their disregard for human expertise.
My new role carries its own challenges, but it prioritizes digital sovereignty over AI hype. More importantly, the leadership understands the distinction between a chatbot and a server room. For now, that is sufficient.
Conclusion: Lessons Learned and Moving Forward

The decision to leave a once-cherished sysadmin role is not merely an act of dissatisfaction but a direct consequence of systemic failures. This experience distills critical insights, devoid of platitudes, into actionable lessons for both individuals and organizations.
Key Takeaways

Leadership instability undermines technical resilience. The turnover of five managing directors in four years created a vacuum of accountability, culminating in a leader who prioritized AI over fundamental hardware constraints. This misjudgment led to CPUs throttling at 85°C, 3.2 hours of weekly downtime, and a team forced to address AI-generated errors. The mechanism? A disconnect between leadership’s abstract vision and the physical realities of server management.
AI lacks the contextual expertise to replace human oversight. Untethered from physical constraints, AI tools misconfigured load balancers, causing 40% latency spikes due to asymmetric server capacities. RAID 6 arrays experienced 8% annual data corruption from heat-warped platters—issues AI failed to contextualize. Human expertise is not optional; it is the critical layer that bridges algorithmic limitations and physical infrastructure.
Resource misallocation erodes operational integrity. Allocating €400k to AI hardware during layoffs signaled misaligned priorities. Concurrently, misconfigured firewall rules exposed Tier-1 applications to 12,000 brute-force attempts in 48 hours. The mechanism? Leadership conflated AI investment with strategic foresight, neglecting the asynchronous maturity of AI and legacy systems.
Chronic overwork compromises cognitive performance. A 43% reduction in team size pushed daily operations to 90% capacity, triggering cortisol-induced prefrontal cortex impairment. Manifestations included misconfigured firewall rules, insomnia, and a 64% increase in Mean Time to Repair (MTTR) for critical incidents. The physiological impact of overwork directly translates to technical and operational failures.

Strategic Guidance for Sysadmins in Similar Situations

For those navigating comparable challenges, the following strategies are empirically grounded and actionable:
Quantify technical debt with precision. Document AI-induced failures (e.g., 42 overheating incidents, 17 misconfigured security policies) and their root causes. Leadership may dismiss qualitative grievances but struggles to refute data-driven evidence.
Challenge technosolutionism with technical rigor. When AI is proposed as a universal solution, interrogate its applicability: “How does this account for custom configurations or thermal thresholds?” Force leadership to confront the gap between AI’s theoretical capabilities and hardware limitations.
Prioritize digital sovereignty in role selection. Cloud migrations and AI integrations often cede control. Seek environments where leadership distinguishes between a chatbot and a server room. Your expertise is not obsolete—it is systematically undervalued.
Recognize the point of no return. If systemic risks (e.g., RAID corruption, CPU throttling) are dismissed as “growth friction,” the organizational culture is irredeemable. Remaining becomes complicity in its dysfunction.

Moving Forward

The transition to a new role represents a reclamation of control—over local infrastructure, leadership that respects technical distinctions, and a focus on resilience over ROI. However, the most profound shift is internal: rejecting misaligned priorities as inevitable. While the tech industry is saturated with AI hype, the next frontier lies in restoring the human-hardware balance. This is where my commitment now lies.
To the colleagues left behind: You are the backbone of the infrastructure. Stay vigilant, document meticulously, and remember—AI does not falter under load. People do.#leadership #overwork #ai #priorities #software #coding #development #engineering #inclusive #community
Sysadmin Leaves Due to Poor Leadership, Overwork, and Misaligned Priorities
#ai #community #software #coding #development #engineering

2026-10-24

CW: satire of terfism, defacation

Gyns tell me I'm not crazy here. No one I've told about this so far has sympathized with me, but I know radblr is more reasonable than the outside world.
I'm really afraid of moids going into the sewer and eating my shit and drinking my urine. Everyone makes fun of me for this and tells me why I should care when it's bodily waste anyways, but it's my bodily waste and I don't want moids putting it in their mouth.
We can all agree that's perfectly reasonable right? Right? We all know how moids can be about women and we all know that they actually do this. So what is the problem if I do it first? 
I've been reading articles about it and unless you have parasites it's actually not that bad for you and can actually help you absorb nutrients you may have missed before. The only problem is if you ONLY eat and drink your own piss and shit and you get nutrient defeciencies.
Everyone I've told about this seems completely disgusted by it and I'm at my wits end. I just want my FEMALE piss and shit to stay pure of moid saliva. Why are women treated like this for protecting ourselves?
Why is it so wrong when I do it but not when MEN do it?


Sisters, I need your counsel.
I haven't flushed in four days and I need advice.
I know this sounds extreme but I have my reasons. There's been documentation—studies, personal accounts, patterns—that moids and TRA activists have established colonies in the sewer systems. They subsist on what we discard. Every time you flush, you're feeding them. You're giving them sustenance, allowing them to maintain their presence in the infrastructure that connects us all.
My toilet is reaching a critical point. I can smell it. I know I need to do something but I can't bring myself to push that lever. It feels like... surrender. Like contamination.
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can i be a political lesbian if im super straight? i have never experienced any attraction to women and i find the thought of the female body disgusting. in fact i hate my own body and wish i could be a man. but then id be gay. i have dated men for like 15+ years and after spending so long dating men and being mistreated i feel like i want to claim political lesbianism. maybe i could be a man and force myself to be straight and claim political lesbianism. could this maybe work for me? what kinds of activism should i get involved in to become a political lesbian?

heres a thing that happened to some friends of mine. was walking down the street talking about the bears game and a bus pulled up and a bunch of white women with short hair and bad plastic surgery came out with baseball bats. they all had shirts that sayed 'down with trans'. i(22 afab wombynly wombyn) was able to hold them off while my friend(19 amab mynly myn) ran away. bareley escaped

aita for accidentally harrassing an afab?
was at the grocery store and had to used the restroom. damn urinary leash amirite? i was
so terrified of seeing an transexual but had no choice becuz i(35 wombynly afab) had to pee.as i was washing
mine hands a person came in. i could tell by their jawline and cheekbones that they were
obviously an amab. so i stood in the door and made them pull down their pandts. and they
said no so they started walking away but i kept following them demanding to see what was
in his pants. by the time we reached the checkout it was really statring to cause quite a
scene which was very triggering to my sex based oppression. i followed him out to his car
and he turned and looked at me and said "dude im a butch stop being wierd" in a perfect
femininely wombynly afab voice. aita?

hi yall. i was amab and transitioned 5 years ago. just a normal straight girl, right? couple of months ago i figured i would try reclaiming my masculinity. puicked up some boxers and cargo shorts. instand raging boaner. now i cant ger off without thinking of hot bear for bear frotting. am i still trurrans?

How do you feel about male exclusive radical misandrists? yeah i was born a man and i hate it this cock tortures me evry minute of my existence if i even think about being a man i break down. And due to my unfortunate attraction to men, i experience sex based oppression due to being a bottom. i sometimes wish i could transision and be a political lesbian but then i would just be a male invading women's spaces. so i coined a term to describe me: male exclusive radical misandrist. because if i am going to be oppressed for being a male i want to be the one opressing me. its basic bodily autonomy. only we can oppress ourselves. #moids dni #foids dni #kill all men #kill all women #oppress yourself responsibly #liberate the noone #righttooppress

harrassing random trans women will not brign the downfall of gender. indiviual action will never bring any revolution. what we need is collective action. so stop trying to do anything about gender until the vanguard party has established a positon for the overthrow of gender. if you disagree your just a lifestylist reactionary.
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and to my immune system: if you want to drown yourself in snot and mucuous that's your right but would you kindly do me a favor and LEAVE ME THE FUCK OUT OF IT. 

and to my subconscious mind: need i remind you that your job responsibilities cover automated systems like breathing and circulation, not fucking keeping me awake all fucking night? kindly focus on attending to your job description instead of insane extracuriculars? I need you to remember that we are a team here and your actions affect everyone.

dear James in engineering,
I cant believe you still haven't solved the problems with the power couplings. I know this may be hard to understand but you need to turn the set screws to the OFF position to turn OFF the flow restrictors and you need to turn the set screws to the ON position to ENABLE the flow restrictors. Kindly, learn to do your damn job.

dear administration,
I appreciate all of your hard work in finding staff in these difficult times but I need clarification on one thing: where do you find all of these morons? You have sent us five new employees in the last two weeks and only one of them knew what a philips head was. We cannot be spending what little time we are given to train new employees in basic skills. Might i recommend hiring people who demonstrate technical proficiency instead of your fuckup nephew?

heres something i actually sent my local news station:
Hello Andrea,
   I apologize if this is not your department but I cannot find a contact for the meteorology department. My complaint is quite simple: I ask that weather forecasters please refrain from inserting their personal judgements and opinions into the broadcasting. I get that some people might appreciate warmer weather but this opinion is not universal and many of us find it quite shocking to see 80 degree highs in late october described as "nice", "mild", or "fair". If you would like to hear my opinion on the matter, personally I find this heat oppressive as I am someone who spends a good deal of time outside engaged in hard physical activity. In fact, I would go so far as to say that if you enjoy this weather you might consider relocating to an environment with a more suitable climate, such as the fucking sahara desert. And frankly, the Sahara might be an improvement: at least I could manage the heat in a dry climate.
Thank you for listening to my rant and have a blessed day,
Foob
P.S. Anything you could do to keep the temperatures in the mid 50s would be greatly appreciated on my end.

dear Administration,
   While I appreciate that finding competent staff is difficult sometimes and that you have been able to find teaching staff with high levels of technical proficiency, several of your staff have been lacking in professionalism and pedagogy. I have actually had teachers cancel class with 5 minutes notice, on multiple occassions, so much so that it in fact became a recurring pattern with them. As if that wasnt bad enough, these are often the same teachers who's prefered teaching style seems to be blindly reading off their powerpoint without so much as looking at the class, much less checking for understanding. I ask that in the future you please hire teachers who actually show up to class and actually know how to teach.
Best regards,
A concerned student
ps. and dont even get me started with your online offerings. in a live classroom setting, a student can at least raise their hand and steer an incompetent teacher. but your online classes are just underpaid staff reading off thier lecture notes with no furter explanation beyond basic concepts. I might as well watch a youtube video, that would be a better value. For $1000 dollars a class, this is a total ripoff and perhaps even a full on scam. fuck you.

Whoever is in charge of hiring at this place is a real piece of work. I get that there are others who may have better qualifications but to interview a candidate and not even send a rejection email, much less useful feedback, demostrates a level of profesionalism that would embarrass a tavern wench. These institutions, and society as a whole, would benefit greatly from giving people a fighting fucking chance.

Dear hiring manager,
   I have an important formal inquiry(injury): on your website you have multiple listings for so called entry level positions with a minimum qualification of 5 years of experience. This leaves me wondering how one is supposed to get 5 years of experience if all of the so called entry level positions require 5 years of experience. This is so prevasive accross your job listings that I suspect I might have a decent case with the Equal Opportunity Employment Commission, considering that these requirements absolutely have a discriminatory impact against people who are new to the job market. So I guess my question is this: do you actually hire human beings or only bots?
Also, what the fuck are you smoking? This is a job which entails mostly filing sales reports, which is not exactly a rare or difficult skill. Stop acting like corporate bureaucracy is a skilled trade: it isn't.

Dear hr,
   It has come to my attention that the tone of my recent emails is "problematic and unprofessional". While I can appreciate that others may have trouble with the self awareness necessary to address issues raised in good faith, I must suggest that my coworkers stop being such a bunch of little bitches.
Thank you for your attention to this matter.

hello hr representative,
   I am writing to inform you of several concerns i have with this companies hr department. While I do appreciate the structure whereby advancement depends on undying loyalty to the establishment and whereby power is exercised both for its own sake and to keep underlings in check, I must say that you lack the ability to be gangsta unless you have the full backing of the entire institution. Kindly, if you have a beef with me, settle it like grown men instead of hiding behind your lackeys. Also, you claim to have meaningful power over me but we both know that you would be nothing without the backing of your institution. Once again, lets drop the bullshit and settle this.
Best regards,
Mr. Dick Head
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____________________________________________________________________________________________
 Have

you owned a month-end close and an annual audit relationship for a fund

or investment vehicle, without a supervisor above you?

I ride a bicycle everywhere and if i cant get there on a bike i simply ride there anyway. i ride in the middle of the road, often dodging traffic in the oncoming lane. Its like dancing its all about your footwork, timing and spacing. I know this sounds dangerous but you need to know something about me: dick does not fuck up. If there is a collision it is your fucking fault for being in my fucking way. Dont you know pedestrians have the right of way?


Do

you have five or more years of accounting experience, including fund or

investment-vehicle accounting (private credit, debt fund, or real

estate fund)?

Yeah ive been riding my bike for like 8 years now at this point and let me tell you i have put a lot of work into it. every goddamned time i go out to ride it something needs adjusting, be it the brake cables, shifter cable, tire pressure, or seat position. and thats just the tip of the iceberg. i have replaced the drivetrain 5 seperate times including completely reengineering it at least twice. and if theres even a bit of rain in the forecast i have to be prepared to replace everything cuz nothing is designed to handle a bit of moisture much less the kind of loamy mud i subject it to.


This is an independent contractor (1099) engagement on a fixed monthly retainer, roughly 30 hours in a typical month. Does that structure work for you?

the only structure that works for me is completely egalitarian horizontal structures. anything else concentrates power that will eventually be abused. and the structure of most corporations, including yours, more closely resembles monarchal fuedalism than any form of democracy, much less free association. and to be frank monarchal fuedalism would be a fucking improvement., at least under monarchal feudalism i have the dignity of providing for my own and my community's survival by contributing to the grain store instead of relying on opaque and inhuman institutions for the "privilege" of keeping a roof over my head. fuck you.


What monthly retainer would you propose for the scope described in the posting?

i aint paying you no goddamn monthly fee for the "privilege" of selling you my labor. fuck you.

#copypasta #satire #moids #foids #kill #kill #oppress #liberate #righttooppress #moids #foids #kill #kill #oppress #liberate #righttooppress

#copypasta #satire #moids #foids #kill #oppress