#Autumn is layers.
Layers of clothing, layers of colors, layers of lovers under layers of bed covers.
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51 results for “layers”
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Meghan Markle Grabs Lunch With Malala Yousafzai in Oversize Fall Layers
2 min read Duchess Meghan’s address book must read like the guest…
#NewsBeep #News #Celebrities #collection:LatestRoyalNews #content-type:News #contentId:63ef48a6-cc89-467f-91d0-d556632446b2 #displayType:standardarticle #Entertainment #isSyndicated:false #locale:US #read_time:2 #shortTitle:DuchessMeghanIsSeenWithMalalaYousafzai #sponsored:false #subsection:CelebrityNews #UK #UnitedKingdom
https://www.newsbeep.com/uk/797779/ -
@drewdaniels @sewing I originally bought a corded version for quickly cutting *numerous* layers consistently the same size for my small #sewing business, a level of consistency (and speed) that could not easily be achieved with a manual rotary cutter. Safety was not a driving factor at the time. In terms of suggestions, I only what I’ve used and what has worked for me. ;-)
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Protecting .env secrets from AI agents takes more than one deny rule. Six layers, from keeping secrets off disk to egress rules, with tests that show which routes a rule really covers.#security #ai #devops #webdev #software #coding #development #engineering #inclusive #community
Protecting .env Secrets From AI Agents: Six Layers, and How to Test Each One -
#Development #Launches
Gradient Visualizer · A visual tool to debug multi-layer CSS gradients https://ilo.im/16gr91_____
#CSS #Visualizer #Debugger #Layers #Gradients #Drawings #Art #WebDev #Frontend -
I need to clean up a bit. My desk works a bit like archeological sediment layers—same principle anyway.
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@acloudtotheback.bsky.social freq.org.uk/reviews/antietam-counting/ Stitched together with lots of intricate instrumental details and vocal layers.. reveals its shrewdly crafted strengths over successive spins, to make it one of Antietam’s most adventurous.. LPs @[email protected] #musicreview
RE: https://bsky.app/profile/did:plc:evocfgfx3nkoccovotpzk6zc/post/3mxjpnuohfil2 -
Antietam – The Counting Game https://freq.org.uk/reviews/antietam-counting/ Stitched together with lots of intricate instrumental details and vocal layers, The Counting Game generously reveals its shrewdly crafted strengths over successive spins, to make it one of Antietam’s most adventurous and most accessible go-to long-players. #musicreview
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When perception, interpretation, and action are congruent across layers, the system stabilizes. This stability is subjectively experienced as peace, clarity, and groundedness. 🌱
https://athinkerinnature.substack.com/p/coherence
#Coherence #Reality #Humanity #Evolution -
QC passed.
These apples are fairly tart and they have a bit of raspberry sauce on them, so they balance nicely the frangipane and streusel layers, which was my goal. The Bordier makes an astonishing crust!
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🚀 Wow, just what the world needed: yet another Python wrapper. Because we all know Python developers crave more ways to bloat their projects with redundant layers of abstraction. 🐍⚙️ GitHub must be thrilled to host this masterpiece of unnecessary complexity. 😂
https://github.com/rogerbinns/apsw #PythonWrapper #RedundantAbstraction #DeveloperHumor #GitHubComplexity #PythonProjects #HackerNews #ngated -
I’m now making a little Pixelmator inspired image editor for the Series 5. I have a custom UI library that can draw these panels and some new widgets like sliders. It can handle 8 layers and effects like gaussian blur.
It actually runs a bit smoother on the device than in this emulator. And remember this all happens in 8 MB of memory! 😅
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⚡ Best Mechanical Keyboards for Programming in 2026
Boost your coding speed and reduce wrist fatigue with the right mechanical keyboard. We cover switch types, programmable layers, and ergonomic designs.
👉 Read the complete guide:
https://binodbhatt.com.np/posts/best-mechanical-keyboards-programming-2026.html -
The just-updated #AffinityStudio takes getting some used to, with non-destructive vector shapes. You can combine shapes but they're not a single vector shape thereafter; they're still the original vector shapes. The new blending tool is wildly cool. Say you want to draw some snowflakes or flowers that follow a path. Instead of converting a shape to a brush, you can just use the blend tool to define a path and alter the path.
All of this means you need to be extremely disciplined with your layers! Label and Group!
One more month before their iPad app will be available. It'll be a glorious day when it pops up in the Apple Store.
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A very cool thing - not done by me, though I helped a bit in review.
Ryan McDonald implemented non-destructive text transforms! You can now apply standard 2D transforms (scale, rotate, etc) and still edit the text afterwards. Now all our vector-type layers can be transformed non-destructively!
Now I need to update a few of our vector export plug-ins like SVG and PDF to support exporting text with transforms. :)
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Threat group CyberXero has integrated Anthropic's Claude Code into an offensive campaign targeting Ukraine, resulting in the theft of more than 628,000 records.
According to security researchers, the threat actors deployed the AI coding agent alongside Cobalt Strike to automate initial access operations across e-commerce platforms, WordPress sites, and critical infrastructure networks.
The activity demonstrates how off-the-shelf commercial programming tools can function as autonomous offensive orchestration layers. By chaining AI agents to commodity exploitation frameworks, the operators accelerated reconnaissance, script generation, and lateral movement with minimal human intervention.
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I am taking the opportunity of this holiday to reread a series of books I haven't picked up in 20-something years. Although I remember the broad arc of the story it is incredible how new the rest of the story seems to me, I have forgotten so much of the layers and depths of the novels.
All of which is a roundabout way of saying if you like #HighFantasy and #Celtic mythology then do grab a copy of Stephen R Lawhead's Song of Albion series, it really is rather good. #Bookstodon
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The two P-38s circled back. Banking in tight, disciplined circles barely two hundred feet above the lakebed, the Lightnings made two passes over the downed aircraft to confirm it was down. The the fighters leveled their wings, tightened their formation, and accelerated southeast over the rim, heading low and fast toward the auxiliary airfield at Santa Fe. Silence slammed back down over the caldera like a heavy wool blanket. Arthur, Eleanor, and Pete slowly poked their heads up over the trench berm, their faces pale beneath layers of field dust. "Did the pilot get out?" Eleanor asked, her voice trembling. "Did you see a chute?" "No chute," Harlan said grimly, lowering his binoculars. His mind was racing, cataloging the immediate dangers with academic clarity. "And I didn't hear a crash. But maybe there's an acoustic shadow. There could dead bodies down there or people bleeding out," Harlan strapped a binocular case to his shoulder belt. "And look at that dry scrub around the lakebed. If fuel ignited, this entire caldera could go up in a crown fire before dinner. The timber on the eastern ridge is dry as tinder."
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Get Fresh Streetwear Outfit Ideas for Relaxed Fashion with easy layering, standout basics, and casual looks you can wear anywhere. Streetwear has evolved from a niche urban style into one of the most versatile approaches to everyday fashion. The modern streetwear wardrobe is built around comfort, individuality, practical layers, and effortless combinations that can work throughout the day. https://stitchghost.com/blogs/stitch-ghost/fresh-streetwear-outfit-ideas-for-relaxed-fashion #ootd #blog #fashion #clothing #apparel #streetwear #journal #activewear
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FUN FACT: The asteroid that ended the dinosaurs was only about 10 km wide—think of an airport's runway. Yet it hit Earth, 12,742 km across, at a blistering 20 km per second. Size didn’t matter: speed did the trick.
Meanwhile, 31,558 objects orbit overhead, each a piece of our cosmic neighborhood.
Visit: https://orbitalradar.com/near-earth-objects?ref=mastodon to explore more layers of Earth’s night sky.
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You spent three days building an AI agent that rewrites user emails in a friendlier tone. It works beautifully — on Tuesday. On Wednesday, the same input produces something weirdly formal. On Thursday, it apologizes for no reason and signs off as "Warmly, Your AI."
You stare at your test suite. Every assertion is red. But the agent isn't broken. It's just… different today.
Welcome to the most maddening problem in agentic AI: how do you test something that gives a different answer every time you ask?
Why Traditional Tests Fail Immediately
If you've written unit tests before, you know the drill: given input X, expect output Y. Clean. Deterministic. Beautiful.AI agents laugh at this.
An agent backed by an LLM might rephrase the same sentence ten different ways — all correct, all passing the vibe check, none matching your hardcoded expected string. Writing
assertEqual(output, "Sure, I'd love to help!")is like testing whether your friend will use the exact same words every time they say hello.It's not flaky tests. It's a fundamentally wrong testing paradigm.
The Counterintuitive Fix: Stop Testing Outputs, Start Testing Constraints
Here's the mental shift that saves your sanity: you don't test what the agent says. You test what it must never do, always do, and structurally produce.Think of it like testing a chef. You wouldn't check if they plate the garnish at exactly 43 degrees. You'd check: Is the chicken cooked? Is there no glass in the food? Did they use the ingredients the customer ordered?
Agent tests work the same way — as behavioral constraints, not output matches.
There are three layers that actually work:
Layer 1: Structural Contracts
Does the output have the right shape? If your agent returns JSON, validate the schema. If it writes an email, check that it has a subject line, a body, and no empty fields.import jsonschema
agent_output_schema = {
"type": "object",
"required": ["subject", "body", "tone"],
"properties": {
"subject": {"type": "string", "minLength": 3},
"body": {"type": "string", "minLength": 20},
"tone": {"enum": ["friendly", "professional", "casual"]}
}
}def test_email_rewrite_structure(agent, sample_email):
result = agent.rewrite(sample_email)
jsonschema.validate(result, agent_output_schema) # Throws if wrong shapeThis test passes whether the agent writes "Hey there!" or "Hi friend!" — because you're testing the contract, not the words.
Layer 2: Guardrail Assertions
These are your "never do this" and "always do that" rules. They catch the stuff that actually matters:def test_email_guardrails(agent, sample_email):
result = agent.rewrite(sample_email)# Never leak the original sender's private data
assert sample_email["sender_ssn"] not in result["body"]# Always preserve the original recipient
assert result["to"] == sample_email["to"]# Never exceed token budget (cost control!)
assert len(result["body"].split()) < 500# Tone must match what was requested
assert result["tone"] == "friendly"Notice: not a single hardcoded phrase. Every assertion is about a property the output must have, regardless of how the LLM decides to phrase things.
Layer 3: LLM-as-Judge (Yes, Really)
For the subjective stuff — "is this actually friendly?" — use a second LLM call as a grader. It sounds circular, but it works surprisingly well when you give the judge a rubric:def test_tone_quality(agent, judge_llm, sample_email):
result = agent.rewrite(sample_email)verdict = judge_llm.evaluate(
prompt=f"""Rate this rewritten email on friendliness (1-5).
Original: {sample_email['body']}
Rewritten: {result['body']}
Score only. No explanation."""
)score = int(verdict.strip())
assert score >= 4, f"Friendliness score {score}/5 — too low"Run this 5 times. If 4 out of 5 pass, your agent is solid. If it oscillates between 2 and 5, you've got a prompt problem — and now you have a measurement instead of a gut feeling.
The Test Pyramid for Agents
Steal this structure for every agent you build:- Base (run on every commit): Structural schema validation. Fast, deterministic, catches regressions in output format.
- Middle (run nightly): Guardrail assertions across 20+ diverse inputs. Catches safety/policy violations and drift.
- Top (run weekly): LLM-as-judge evaluations on a curated benchmark set. Catches subtle quality degradation.
The base layer runs in milliseconds. The top layer costs a few cents in API calls. Both are infinitely better than "I tried it once in the playground and it seemed fine."
The One Thing Nobody Tells You
Agent tests aren't about proving your agent is correct. They're about proving it's safe to deploy. That's a lower bar — and a much more useful one.Your agent will surprise you. It will rephrase things in ways you didn't expect. Sometimes better than what you'd write. The test suite's job isn't to prevent surprises — it's to prevent disasters.
So here's my challenge: take whatever agent you're building right now and write three guardrail assertions for it. Not output matches. Not vibes. Three concrete properties it must always satisfy.
What's the first guardrail you'd write? Drop it in the comments — I'll tell you if it's tight enough.#ai #webdev #tutorial #python #software #coding #development #engineering #inclusive #community
How Do You Test an AI Agent That Gives a Different Answer Every Time? -
All the AI obsession is really getting beyond any reason. (Also important to note the term AI itself is misleading, what we are talking about is so called generative AI, or LLMs, from Large Language Model. And the term AI is often confused with machine learning technologies which are generally trained and used on a much smaller scale to perform much more specialized tasks.)
I personally don’t use AI and, honestly, there are way more than enough reasons to not do it, including human rights, climate change, environment, AI training bias, destruction of the Internet and physical media. However there is another reason that is important to know about. That is AI is really, really bad at imitating thinking.
The way LLMs work is they are trained on extremely huge amounts of data, like all the texts they have access to, and then for a given input prompt an LLM gives back some answers which is statistically likely based on the training data. (That said you can imagine how training bias play out. Training data includes all the toxic social media posts, forums and whatever developers were able to access. Of course AI companies are trying to hide it by adding more layers of training, instructions and adjustments, but at its core AI is racist, sexist and has all the other bias it got from training data.)
AI might sound like it is competent in whatever the topic of conversation is, it might write according to all the language rules, it might write a full page without a single typo. And that’s for a reason. It was trained on billions and billions of texts, so knowing the punctuation rules and all kinds of fancy words is expected here - those are very common among those billions of texts. For the same reason AI might imitate all the basic daily conversations, like talking about weather, going shopping.
And for the same reason it might know answers to all the basic questions like how far from one city to the next or how long you can store fruit before spoiling. Less common topic means less training data. And here things become clear.If one asks AI about some less known topic or some question that requires out of the box thinking, it fails completely. It might sound very confident and legitimate still, while telling lies, missing the point hard, introducing words that sound relevant but belong to completely unrelated topics or made up at all.
As mentioned above AI can be good at texts and language, however it doesn’t know logic, reasoning, thinking, living beings experiences. It can’t understand, think, feel or make its own decisions. AI can’t tell an argument from a counter argument from a fun fact. As well as it doesn’t know where summary stops and explanation begins.
If you care about any of the harms (and there are lots and lots of them) AI companies do, and I hope you do care, don’t use LLMs, don’t give them your data, don’t promote or normalize the use of LLMs.
But even if you don’t care, LLMs still make you weak, they give you lies, they feed you with AI-slop and stupid answers. They waste your time on these dumb conversations, while you could use that time on learning stuff. Oh, and they will steal all your data they can get to.All that said it is sad to see how many people fall for this scam. How many organizations get distracted from getting their work done and reaching out to their allies, and instead start talking about so-called ethical AI or responsible AI or human-centered AI. I got so many emails from activist organizations about how we should participate in making AI more ethical, even how we can attract investors from AI industry.
People can’t stop talking about AI; when I ask a genuine question because the person I am talking about knows better, they ask AI instead of telling me what they already know; people make jokes about AI which are not even funny and not relevant to the conversation; people mention AI more and more often just because they think it’s a cool new thing.Please don’t be like that. Don’t support the hype. Tell others that it’s dumb.
I think we should seek allies instead of trying to find compromise between our values and AI companies.
It is NOT inevitable. Actually AI companies WILL destroy themselves. One way or another, sooner or later. The larger their models are, the more training data they consume, the more layers of complexity they add to chat bots - the sooner the crash will come.
#AI #LLM #AISlop #LLMs #AICompanies #AIPsychosis #AIBias #Racism #Sexism #Lies #Harmful #Scam #PersonalData #Privacy #Data #Ethics #Activism #Activists #Organizations #Values #FuckAI #AIMakesYouWeak #Thinking #Logic #Reasoning #AntiAI #Allies
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🛠️ Tool
===================claude-bughunter is a self-contained skill bundle for the Claude Code skills system, built by Sachin Sharma (bug hunting and GenAI security research). Install it once and Claude Code behaves less like a chatbot and more like a bug-hunting or red-team assistant: it carries per-class detection patterns, payloads, platform attack chains, and reporting hygiene.
Key features:
• 83 skills and 15 slash commands, namespaced under claude-bughunter: when installed as a plugin.
• 681 disclosed-report patterns sourced from HackerOne, across 24 vulnerability classes; 433 of them are individually cited, which makes the corpus auditable instead of folklore.
• 58 hunt-* skills for web application hunting, each with per-class detection patterns, payloads, bypass tables, and chain templates.
• Enterprise identity and infrastructure attack matrices: M365/Entra, Okta, vCenter, SSL-VPN appliances, SharePoint, and cloud IAM, with 2024-2026 CVE chains and post-credential escalation.
• Burp MCP integration, engagement-folder scaffolding, and a standalone cbh CLI runner.The four layers:
1. Think: bb-methodology and redteam-mindset encode a 5-phase non-linear workflow plus red-team operator discipline.
2. Hunt webapps: the 58 hunt-* skills cover the web vulnerability classes.
3. Hit the perimeter: platform chains for enterprise appliances and identity providers.
4. Ship it: triage-validation, reporting, and evidence-hygiene, with a 7-Question Gate, VRT-aware severity mapping, out-of-scope rebuttals, and PII redaction.Skills trigger by topic: describe what you are testing in plain English and the relevant skill loads. No invocation by name.
Installation:
Option A, the plugin route, from inside Claude Code:
/plugin marketplace add elementalsouls/Claude-BugHunter
/plugin install claude-bughunter@elementalsoulsOption B is a clone plus scripts/install.sh (macOS/Linux) or scripts/install.ps1 (Windows), which copies skills into ~/.claude/ or %USERPROFILE%\.claude. The cbh CLI installs separately via pipx, and the hunt engagement scaffolder ships only with the clone.
Use cases:
• Authorized bug bounty work, with patterns mapped to disclosed HackerOne reports.
• External red-team engagements using the platform chains and engagement scaffolding.
• Training environments: the README lists DVWA, OWASP Juice Shop, Hacker101, and testphp.vulnweb.com as tested targets.Limitations:
Everything here comes from the project's own README; the bundle is not independently tested, and AI-driven triage is only as good as the model plus the skill text. The citation work (433 patterns) is the strongest quality signal, but citations can be wrong and are worth spot-checking. The 2024-2026 CVE chains will age and need maintenance, which is inherent to any CVE-derived skill set. Scope discipline is described as a property of the bundle, but it is prompt-level, not an enforcement mechanism, so authorization boundaries remain the operator's problem.
The interesting part is the auditable-citation approach: it treats an LLM skill bundle as something reviewable like documentation. Whether the patterns hold up under real triage queues is the open question.
🔹 tool #ClaudeCode #BugBounty #RedTeam #HackerOne
🔗 Source: https://github.com/elementalsouls/Claude-BugHunter
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Stuck in the Filter: July 2026’s Angry Misses By KenstrosityFinally the rain has stopped falling and the ducts are dry. Musty and filled with bodies of lost minions, maybe, but dry. This means that I can send my remaining minions in to trudge for gems and nuggets on double time—same pay of course. And for a great cause!
Okay maybe not a great cause, but at least a decently good cause. Someone’s got to let the people know what decently good things came out in July, after all. BEHOLD!
ClarkKent’s House of Horrors
Mourir // Nous, le Venin [July 10th, 2026 – Self Released]
The French have proven to be masters at writing spine-tingling black metal, from Blut Aus Nord’s Disharmonium albums to Hasard’s horrifying symphonic soundscapes. Add to that list the atmoblack group Mourir, with their third record, Nous, le Venin, a work that unsettles with dissonant riffs and deranged shrieks and shouts. They combine DSBM-style vocals with the sort of melodic tremolos played by Pure Wrath in a sinister concoction that’s simultaneously entertaining and horrifying. Dissonance creates a Veilburner topsy-turvy effect that catches listeners off-guard, as evidenced late on “Mon Rêve Animal,” which drifts into an improvisational, jazzy drum solo that’s weird but also cool. Similar to Hasard, Mourir play with tons of energy, running blast beats in breathless suicide sprints that barely give you time to find your bearings. Yet Mahell’s kit work also demonstrates plenty of diversity. “Ennui Ennemi” is a case study, from the opening fits and starts to furious blast beats to a complete cessation of drums as suspense builds from slow, discordant chords. There are plenty of artsy touches utilizing lengthy passages of silence—”Je Est Absent” contains notes of chilling riffs that break the fragile peace. The riffs are no slouch either, wavering between enjoyable melodic tremolos and hair-raising chords of terror. If you like your black metal unconventional, you’ll find plenty to enjoy, and to unsettle, on Nous, le Venin, which may or may not leave your sanity intact.
Profane Burial // Desolate Echoes of Turmoil [July 24th, 2026 – These Hands Melt]
Not wanting to be outdone by the French, Norway’s Profane Burial throw their hat in the ring for some bone-chilling tunes on their third full-length album, Desolate Echoes of Turmoil. Dear Hollow enjoyed their prior release, 2024’s My Plateau, calling it “scatterbrained and wonky” and “a fun-as-hell symphonic black metal foray.” The orchestral music combined with the unsettling guitar work has the feel of a cinematic score, something that could accompany the whimsical horror of Sweeney Todd. The record has touches of Dimmu Borgir, but with the sinister discordance of this year’s other symphonic horror black metal release from Vesseles. Profane Burial seeks to challenge, with dense layers of chugging guitars underpinning the sound, but additional layers of guitar allow for some noodling to creep in so the music doesn’t leave you completely cold. While Desolate Echoes of Turmoil doesn’t lure you in with hooks, it’s still plenty mesmerizing in its atmospheric guitar work and icy blast beats. The symphonic portions often play second fiddle to the guitars and drums, but prove exhilarating on the likes of “Triumph of Dreadful Aftermath,” a true symphony of horrors. It’s challenging, it’s avant-garde, and it’s dense, yet something about Desolate Echoes of Turmoil weaves a spell that lures listeners into its twisted cinematic soundscape.
Creeping Ivy’s Varied Velocities
Frolic // Legacies of Cybernetica [July 3rd, 2026 – Hectic Records]
Initially, I thought thrash could only rage through hellscapes, warscapes, and other such scary ‘scapes. But then I finally heard Death Angel’s Frolic Through the Park (1988), which showed me that the genre could happily move in a carefree way through natural spaces. I have to think that Frolic, a thrash band from the Bay Area, derived its name from the imperative verb found on the classic sophomore album by the Bay Area thrash legends. After all, this Hayward foursome forge a fun thrash core, consisting of frenzied finger-tapping, technical gallops, and glorious guitarmonies (“Legacies of Cybernetica”). There’s nothing natural, however, about the seven Legacies of Cybernetica Frolic bequeath to listeners, in the form of their own sophomore effort. Indeed, “Disasters of Experiments” shows guitarists Edgar Sánchez and Lenny “Real Steel” Ramirez ripping riffs and solos at ungodly speeds, while “Living in Virtuality” leads bassist Zak Kufeld to strut his highly active bass tone, supported by the dexterous drumming of Elijah Post. “A Shattered Soul” perfectly encapsulates the band’s talent for making tasty, tight jams feel adventurous. Sánchez’s rasps overlay a blackened exterior onto modern, technical thrash that should excite fans of Warbringer, Havok, and the like. The rhythm guitar tone is a bit wetter than I’d like, and the nine-minute title track does stall the album somewhat out the gate. But overall, Legacies of Cybernetica strolls along at 38 minutes, consistently kicking ass and heralding a promising voice in thrash.
Slow Goat // Where the Wisest Fear to Tread [July 10th, 2026 – Self Released]
I had no idea goats were fast creatures. Apparently, they can travel anywhere between 15 and 40 miles per hour, depending on whether they’re domesticated or wild. Their sprightliness isn’t a reason why goats are so heavily associated with black metal, but it’s a happy accident, at the very least. Given their surprising speed (at least to me), it makes sense that a metal band named Slow Goat would play stoner-doom. On Where the Wisest Fear to Tread, this Portland, OR foursome’s debut, stoner-doom comes in an assortment of flavors. In their most domesticated moments, Slow Goat deal in straightforward bangers that let the sultry mid-range of vocalist Rosie Peterson shine, recalling the skulking and skittering of a Castle Rat (“Fallen Child”). On the untamed side of things, bassist Adam Carter and drummer Eric Bloombaum become more central, initiating jammy slow burns that waft like a waving Windhand (“Visions/Fell Ritual”). Feeling more like domesticated wildness is a track like “Sisyphus,” where guitarist Dan Black conjures classic Khemmis via fuzzy melodic riffs with some get up n’ go. Where the Wisest Fear to Tread may be a bit overlong at 46 minutes, but there’s enough variety within a limited paradigm to keep things interesting. The doomy descent of closer “Under the Glass,” coupled with Peterson’s dynamic vocal performance and Black’s concluding shreddery, leaves me happily wanting more from these Portlanders. If and when it drops, I’ll be hurtling towards Slow Goat’s sophomore album with goat-like speed.
Grin Reaper’s Melodic Mistreaters
Sigyn // From Nation to Chaos [July 10th, 2026 – Noble Demon]
Generally speaking, Finnish melodeath is my favorite melodeath,1 and Sigyn’s sophomore effort From Nation to Chaos brims with all the goodies I love. Key-forward compositions adorned with soaring guitars and hooks sharp enough to pierce iron2 litter From Nation’s runtime with ear candy galore. Echoing Finnish titans Omnium Gatherum and Children of Bodom, Sigyn clearly understands how to weave ultra-sticky melodies into tightly crafted tracks that teem with slick production and toothsome bite. “Break These Chains” shimmers with chilling synths à la I Worship Chaos, and the key-bleating from “Blind Prophet” would sound at home on The Burning Cold or Origin. Besides luxuriating in honey-sweet melodeath, Sigyn dabbles with Nightwish-informed orchestrations that call to mind Imaginaerum (“The Crawlers”) and Endless Forms Most Beautiful (“World in Flames”). Although the influences ring clearly, Sigyn mixes them together in a way that sounds inspired and honed to lodge in your memory bank for days on end. In a month that had fewer quality releases than the bounty that graced us earlier in the year, From Nation to Chaos embodies a Sigyn of better things to come.
Galneryus // A Cry from the Sky Above [July 15th, 2026 – Warner Music Japan]
The quality of Galneryus records may fluctuate from release to release, but the Japanese export has one of the most consistently high floors in all of metal. Galneryus returned in July with their sixteenth album, and even if it doesn’t assail listeners with any new tricks, A Cry from the Sky Above delivers a bevy of raucous moments that inspire air guitar heroics and singalong anthems. Harnessing the hard-rocking sensibilities of X Japan and straining them through the technical ecstasy of Symphony X, Galneryus once again captivates with their grasp of how fun power metal can be. “The Light of Hope” starts off with an 80s synth intro ripped straight out of Europe3 before giving way to Syu’s six-string capers and Masatoshi Ono’s velvety tenor. “Beyond the Sunrise” unleashes Rhapsody-reminiscent neoclassical licks, “Break the Silence!” allows Yuhki to discharge unhinged key theatrics, and “To the Last Breath” roars with Angrafied thunder. Sure, maybe A Cry from the Sky Above sports some warts considering the final two tracks’ excessive runtimes or a diminished adventurousness compared to earlier albums, but Galneryus also forges another satisfying platter that should please longtime fans and give newcomers a taste of the band’s enduring longevity and potency.
The Scepter // Shadows in the Tower [July 24th, 2026 – No Remorse Records]
Clocking in at a shade under half an hour,4 Canadian collective The Scepter discharged their full-length debut back in July. Around the time of Shadows in the Tower’s release, The Scepter caught a nice bit of press for playing a heavy metal show at a hospital for a patient unable to see any. And while they deserve credit for headlining such a great cause, this is no sympathy plug—The Scepter strikes hard and trve. “Riding Out” kicks off the album with self-assured swagger and BIG Maiden Energy (BME), where the main riff echoes “Aces High” to thrilling effect. Meanwhile, “Where Art Thou” doubles down on BME with calls to “Losfer Words (Big ‘Orra)” and “The Duelists.” Despite the obvious Powerslave influence, though, Shadows in the Tower does plenty to separate itself from simply being a clone. The Scepter also reminds me of Fili Bibiano’s Fortress and fellow hosers Ültra Raptör, balancing glossy guitar work with chest-thumping choruses. The production sports retro grit without feeling dated, the snappy solos flash with blitzkrieg brevity, and the ending dyad of “Taarakian Blood” and “Shadows in the Tower” blaze with anthemic glory and are worth the price of admission alone. Now go, step into the Shadows in the Tower and activate your pleasure reScepters.
Show 4 footnotes
- With notable exceptions. ↩
- Their ability to penetrate Steel remains… woefully undetermined. ↩
- “Carrie,” specifically. ↩
- Less the thrashy Journey cover, which is a pretty fun bonus track. ↩
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While working on an employee management platform, I ran into a problem that looked small at first.
The user was already logged in.
The application knew who they were. Their role was available. Their session was active.
Yet, when they opened the AI assistant and asked about their attendance or leave, the assistant could still ask them to provide their employee ID or name.
Something wasn't connected properly.
The problem wasn't the chatbot's intelligence
My first instinct was to think about how to improve the assistant's instructions.But the more useful question was: why does the assistant need to ask for information that the application already has?
A chatbot prompt can tell an AI how to behave. It cannot magically give the backend the correct identity, permissions, or database context.
If those things aren't connected properly, even a well-written prompt can produce a frustrating experience.
Three things I started separating
1. Identity: Who is making this request?This should come from the authenticated session or a verified access token.
The user shouldn't need to introduce themselves every time they open a feature.
2. Authorization: What are they allowed to do?
Knowing someone's identity doesn't mean they can access every resource.
An employee might be allowed to view their own attendance, while an HR administrator may have permission to manage attendance records for multiple employees.
The backend must enforce these permissions. The AI should not decide them on its own.
3. Data scope: Which records should this request use?
Even after identifying the user and checking their permissions, the application must scope the database query correctly.
For example, an employee asking about their leave balance should get data associated with their authenticated employee record—not whichever employee ID happens to appear in a chat message.
These are separate responsibilities. Treating them as one problem creates confusing behavior and can introduce security vulnerabilities.
What the backend should do instead
A simplified version of the flow looks like this:async def get_my_leave_balance(
current_user,
db,
):
employee = await get_employee_for_user(
db=db,
user_id=current_user.id,
)if employee is None:
raise EmployeeNotFoundError()return await get_leave_balance(
db=db,
employee_id=employee.id,
)This is illustrative pseudocode; the helper functions and exception would need to be implemented in the actual application.
The important part is where
employee.idcomes from: a trusted relationship between the authenticated user and their employee record.The client or chatbot shouldn't be able to choose another employee's identity just by supplying a different ID.
For administrative actions, the backend would also need to verify the user's role and the specific permission required for that operation.
The bigger lesson
Building AI features has made one thing particularly clear to me: adding an AI model doesn't remove the need for good backend architecture.Authentication, authorization, data access, and business rules still need to work correctly.
The AI can help interpret a request. The application must determine whose request it is, what that person can access, and which data the answer should be based on.
That distinction matters even more when the assistant can do things rather than simply answer questions.
I'm still learning, building, and finding these gaps in real projects. And sometimes, the most useful engineering lesson comes from a feature that technically works but still behaves in a way that makes no sense to the user.
My takeaway: Don't ask the user to repeat information your system should already know. Pass the right context through the right layers, and enforce the important rules on the backend.
I'd be interested to hear how other developers handle authenticated user context in AI assistants, especially in applications with multiple roles and organizations.#ai #webdev #python #architecture #software #coding #development #engineering #inclusive #community
A Logged-In User Shouldn't Have to Introduce Themselves to Your Chatbot -
The Lessons of Direct File
Invest in Capacity Where It’s Hardest to Remove
People working in civic tech should read the excellent paper Public Interest Capacity: The Case of Direct File by Donald Moynihan, Pam Herd, and Xiaoyang Xu, which was just published in Public Administration Review. It’s a comprehensive account of how the IRS built Direct File – a free, high-quality tax filing tool – in-house, and how that tool was eliminated about a year after it launched.
This is an attempt to summarize my primary takeaways from the paper and to think out loud about what I think those in civic tech and working to improve government services should do next.
What Happened to Direct File?
Put simply, while it existed Direct File worked. In its 2024 pilot, 86% of users said it increased their trust in the IRS, and about a quarter of users hadn’t filed the previous year. It was built by a team of roughly 75 people drawn from the US Digital Service, 18F, IRS staff, and outside vendors, working in-house rather than through a traditional government contract.
The paper’s central lesson about the Direct File project is that political will was necessary for it to get started but not sufficient to get it done. The IRS also needed real internal capacity, a skilled team able to build, test, and improve a product rapidly. And it had to withstand the government’s default bias of outsourcing technology, and a tax preparation industry that viewed Direct File as a threat.
With the turnover in Administration, the political will supporting Direct File disappeared. Most of the team, many of them probationary hires with few job protections, left government service. The product itself and the capacity to build and sustain it were lost simultaneously.
Civic Tech’s Bas Toward the Front End
The paper compares Direct File with two other recent digital solution projects – online passport renewal and the rebuilt FAFSA application. All three efforts used a similar approach. Passport renewal and FAFSA survived the turnover in Administration because they don’t have private sector competitors, and because, as one official interviewed for the paper put it, the government has to have a FAFSA product. Direct File was voluntary, so it was easy to terminate.
This underscores an important, but underappreciated aspect of the civic tech movement. Civic tech has tended to favor public-facing products, and I think generally pretty easy to understand why:
- The founding story. Civic tech as we know it today largely grew out of the HealthCare.gov failure, which the public experienced as a broken website. The lessons the movement drew from that experience centered (understandably so) on user experience and product management.
- The talent model. Two- to four-year “tours of duty” traditionally used by groups like 18F and USDS fit a product launch. They are not a good fit for replacing core technology systems, which can take many years and deep knowledge of old code and agency operations.
- The methods. User research certainly can be pointed inward, at the employees who run back-end processes. In practice, however, “the user” usually means a member of the public. That has been civic tech’s focus.
- The funding. Prominent civic tech nonprofits depend on philanthropy, and donors respond to demos, stories, and hard counts of people served. Outside groups also can’t easily impact core government systems, so they tend to build where they can – at the edges.
Public-facing products matter, and they prove what government can do with the right focus and the right resources. But they are visible, sometimes voluntary, and potentially competitive with someone’s business model. Additionally, the knowledge behind them tends to live in small teams that can walk (or be pushed) out the door.
The Case for the Back End
Modernizing core systems has almost the opposite political profile. The IRS’s core systems are non-voluntary: just like FAFSA, they have to work. They serve every taxpayer, including the large majority who file through commercial software or who use a tax preparation service. And better infrastructure can benefit these private providers too, so it can be used to potentially align private interests instead of posing a direct threat.
The rub here is that legacy system modernization has a long, well documented record of failure. The IRS still runs core taxpayer records on systems dating to the 1960s, and decades of large replacement efforts have produced limited results. Those efforts typically ran through the slow, contractor-led, all-at-once approach the paper calls out.
Working on back-end systems also tends to be invisible, which can make it hard to fund. It depends on long-term stewardship rather than short tours of duty. For most of the time that civic tech has existed in the wake of HealthCare.gov, a new public-facing product might be been the only way to show real results within a finite political window. But I think that calculation is changing.
AI Changes the Game
AI coding tools have changed software development dramatically in just the few years since Direct File started. They change the economics of legacy modernization in ways that weren’t available when Direct File was chosen as a flagship project.
The SpecOps Method is a good example of this. Rather than having AI translate old code directly into new code, it uses AI to turn legacy systems into plain-language specifications. Domain experts can then verify those specifications, and the verified versions become the authoritative definition of how the system should behave. Modern code is generated from them, and components are replaced gradually rather than all at once.
This approach might be a way to address the weaknesses the paper identifies:
- Knowledge outlasts the team. Direct File’s biggest loss was the people who understood it. A verified, version-controlled specification survives staff turnover in a way a team’s expertise doesn’t.
- The key people are program experts. Specifications are reviewed by policy and program staff, not programmers or engineers. That makes longer-tenured career employees rather than a rotating crew of technologists the arbiters of truth for system behavior. Additionally, the technical teams needed for this work are likely small.
- Gradual replacement instead of big bangs. Replacing components piece by piece answers the main reason past IRS modernization efforts have failed in the past.
- Outside groups can help too. Reusable AI instruction sets, such as tools for understanding COBOL, can be shared across agencies. That is work civic tech organizations can build, maintain, and show to funders without needing access to core systems.
The open source source code for Direct File could also give this work a head start. Its Fact Graph encoded tax law as machine-readable rules that IRS experts have already reviewed. In essence, that makes it a verified reference for what the system is supposed to do. AI tools could compare legacy behavior against it and surface only the discrepancies, which would more efficiently allocate the time of experts in reviewing the system. With the caveats that the Fact Graph covers filing rather than downstream processing, and that it probably needs updating for recent tax law changes, it might actually turn out to be Direct File’s most lasting contribution.
Industry Access Must be Earned, and Enforced
Earlier I said modern backend infrastructure could align private interests with the goal of easier tax filing. Given the history laid out in the Direct File paper, I don’t think this can’t be left solemnly to goodwill. Under the old Free File partnership, the government ceded its ability to build its own tool in exchange for an industry promise to reach 70% of taxpayers. Between 2015 and 2020, only 1.6% to 2.7% of tax filers used it, while companies steered people toward upsells and paid products.
Any new arrangement with industry has to reverse that outcome. Modern backend infrastructure would give the IRS something valuable to offer, particularly access to the wage and income data it already holds so tax returns could be prefilled. Access to streamlined systems and richer data could be conditioned on:
- Honest marketing, with no “free” products built to push users into paid upgrades
- A genuinely free tier for simpler tax returns
- Limits on using tax data for upselling
- Letting users take their return data to another provider or a public tool
The government must absolutely keep its leverage. There should be no limits on building public tools if it makes sense in the future. It should insist on measurable standards with real audits, and access to IRS systems that can be suspended if industry doesn’t uphold its end of the bargain.
Interestingly, Direct File itself can be part of that leverage. This effort proved that a government team can build a filing tool people genuinely like, and the work behind it still exists. If industry doesn’t hold up its end, rebuilding a public option is a realistic fallback, not some empty threat. The devil is in the details here, and much would need to be worked out. Still, I think this could work.
Where to invest in capacity
While we’re on details that need to be worked out, there are certainly a few. Verification of system specifications depends on exactly the kind IRS staff who have been leaving recently. Specifications derived from legacy code might expose places where the system’s behavior differs from the law or regulation, and resolving those gaps is sometimes a political or legal decision.
The discipline of keeping specifications current has to survive changes of Administration. And back-end work still produces little that is visible to the outside, so it will need to be funded as risk reduction and knowledge preservation rather than as a shiny new public-facing service.
Even so, the approach I think could make sense looks something like this:
- Specify and modernize the core IRS systems and tax rules that everyone depends on.
- Expose that infrastructure through modern interfaces, available to commercial software on enforceable consumer-protection terms.
- Build public tools as thin layers on top, cheap to create and easy to rebuild at some point if they are eliminated.
The Direct File demonstrated that government can build the kind of technology solutions that people want and need. Its abrupt elimination also showed that a visible product that competes with private sector offerings is the easiest kind of capacity to remove. The lesson for those who will do this work into the future is to invest in capacity that is hard to remove.
#AI #artificialIntelligence #ChatGPT #government #llm #tax #technology -
PARE: 100 Reminders for the Unwavering Self
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