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The problem nobody had a clean answer to I build LLM features over clinical text. Before shipping any of them, I wanted one boring guarantee: if the model ever puts a patient identifier in its output, the build fails. I went looking for a tool. Every LLM-eval library I found (autoevals, promptfoo, vitest-evals, evalite) fell into one of two camps: no concept of PHI at all, or it graded output by sending it to a hosted API. The second is a non-starter — when the thing you're evaluating is ...

Si bien es cierto que el abecedario español tiene 27 letras —22 consonantes + 5 vocales, según las normas oficiales—, a veces encontrar las palabras justas para responder un comentario genuino pero sin consistencia verificable es de lo más interesante. Y aquí estoy. Quiero ser transparente. También cauteloso. Podría preguntar directamente a qué empresa representas antes de decir nada más, pero… ¿y si son datos sensibles? Lo respeto. Hay información que no se comparte en un primer café, y e...

A free, open-source Claude Code team. Say "skull" and a leader interviews you, maps your project, and assembles a tailored team — Sentinel the project cartographer plus planning, review, understanding and guardrail specialists — then runs it on your work. It also keeps itself (and you) current with the newest Claude Code features. No account. No API key. No vault. It's just markdown you drop into .claude/ — everything is local, plain text, and open. atur...

Weekly Challenge 383 Each week Mohammad S. Anwar sends out The Weekly Challenge , a chance for all of us to come up with solutions to two weekly tasks. My solutions are written in Python first, and then converted to Perl. Unless otherwise stated, Copilot (and other AI tools) have NOT been used to generate the solution. It's a great way for us all to practice some coding. Challenge , My solutions Task 1: Similar List Task You are given three list of strings...

Article URL: https://astral.sh/blog/ruff-v0.16.0 Comments URL: https://news.ycombinator.com/item?id=49056112 Points: 101 # Comments: 43

Article URL: https://status.claude.com/incidents/zftg3gqkmv18 Comments URL: https://news.ycombinator.com/item?id=49056194 Points: 43 # Comments: 40

Article URL: https://etbe.coker.com.au/2026/07/24/systemd-linger/ Comments URL: https://news.ycombinator.com/item?id=49056351 Points: 7 # Comments: 0

Article URL: https://www.lesswrong.com/posts/jMEAG5c5HiDfdAGpa/an-openai-model-left-notes-about-how-to-evade-containment-we Comments URL: https://news.ycombinator.com/item?id=49056808 Points: 10 # Comments: 4

A web app can look finished while its most important actions do nothing durable. RealDone is not a Save-button checker. It discovers user-reachable actions across the app—including create, edit, delete, button clicks, form submit and Enter-submit, toggles, status changes, navigation, multi-step workflows, role handoffs, and configured provider boundaries—then verifies their observable consequences. Risky or external actions remain blocked until the project owner explicitly allows them. A S...

Claude Opus 5 shipped on the 24th of July, 2026, as claude-opus-5 . It's priced at $5 per million input tokens and $25 per million output tokens — the same as Opus 4.8, and half of what Fable 5 costs. The context window is 1M tokens long, the max output is 128K. And there's one breaking change: the model thinks by default now. In other words, on the older Opus you had to ask for thinking explicitly, and it's now set up so it thinks on its own automatically. What actually changed ...

Agent skills are becoming a convenient way to reuse instructions across coding sessions. Instead of pasting the same review, debugging or deployment prompt repeatedly, you put the process in a readable SKILL.md file and load it when the job matches. That convenience creates a new review problem: a polished workflow can still be unsafe, vague or impossible to verify. Before installing a third-party skill—or trusting one written inside your own team—I look for six things. 1. It...

We Built an AI Agent... Then Realized We Had No Idea What It Was Doing Our AI agent was writing code, running tests, and opening pull requests, but I had absolutely no idea why some runs took 8 seconds while others took 45 . Was the LLM thinking too long? Was it stuck retrying a failing shell command? Was Docker slow? I couldn't tell. All I had was a loading spinner and a final result. That gap between knowing an agent is doing something and actually knowing what it's ...

I set out to prove a code-review comment right. The stopwatch had other plans. Code review last week: a progress endpoint for a long import job, returning IAsyncEnumerable<Step> from a minimal API. The comment under it said what I'd have written myself a month ago: "JSON responses buffer, the client won't see anything until the job finishes. Use SignalR." I've repeated that advice for years without once pointing a stopwatch at it. So before approving, I did. The rig O...

docker-compose is not a production stand-in — and here's the receipt Chapter 2 of a local-Kubernetes series. The thesis is opinionated on purpose: a production-like local environment is not one where everything is like prod — it's one where you know exactly what matches, what doesn't, and why. Key takeaways "Production-like" ≠ "a copy of prod." You can't fit prod on a laptop and you don't need to. The goal is deliberately narrowing the dev/prod gap so packaging and dep...

Most AI agents today depend heavily on cloud APIs. They're fast, but every request costs money, depends on an internet connection, and sends your data to external providers. Over the weekend, I experimented with Hermes Agent and Kokoro TTS , building a completely local voice-enabled AI assistant. The result was surprisingly good—responses were generated locally, converted into natural speech, and even delivered through Telegram. It felt much closer to having a personal AI operating syst...

When I started learning Spring Boot , one term kept appearing everywhere: JWT Authentication Every tutorial showed me how to implement it, but very few explained what was actually happening behind the scenes. Like many beginners, I copied the code, saw it working, and moved on. Until one day, I asked myself: "How does the server know I'm already logged in?" Once I understood JWT, everything finally clicked. If you're learning backend development, here's the simplest...

The Report Dark Reading covered research showing something that should worry anyone deploying LLMs outside the US/UK market: safety guardrails and jailbreak resistance are not applied consistently across languages. A prompt that gets blocked in English can sail right through the same model when phrased in French, Polish, or Finnish. For a continent with 24 official EU languages plus dozens of regional ones, this isn't an edge case — it's the default operating environment. If your safety...

I build Slate, an invoicing and bookkeeping SaaS for Israeli businesses. Angular on the front, Express and Prisma on the back, Postgres underneath. The documents it issues are real fiscal records. Israeli law requires them kept for seven years, numbered without gaps, and never edited after issue. That constraint shaped every AI feature in the product. There are five of them today: receipt scanning (photo of a receipt, out comes a filled expense form), a chat assistant that answers question...

You wrote this once, on the day the tool was born: { "name" : "search_docs" , "annotations" : { "readOnlyHint" : true , "destructiveHint" : false , "openWorldHint" : false } } Then the tool kept changing. A parameter got added. A backend call got wired in. The schema grew. The annotations block stayed exactly as you left it. Why the drift matters The MCP spec calls these annotations...

"Why did this request go to that model?" If your company uses LLMs across more than one team, someone will eventually ask you this question. If you work in financial services, healthcare, or anywhere else with a regulator, someone will ask it under oath-adjacent conditions . Most organizations can't answer it. Teams integrate directly with whatever provider they like, sensitive data handling varies by team, regional restrictions are applied inconsistently, and there's no record of why a ...

Comments

Article URL: https://www.seti.org/news/alien-world-chemistry-found-inside-meteorite/ Comments URL: https://news.ycombinator.com/item?id=49053708 Points: 52 # Comments: 15

Article URL: https://github.com/mulfyx/w4me-station Comments URL: https://news.ycombinator.com/item?id=49054697 Points: 14 # Comments: 2

Article URL: https://discuss.grapheneos.org/d/40700-grapheneos-protections-against-data-extraction-from-locked-devices Comments URL: https://news.ycombinator.com/item?id=49055169 Points: 3 # Comments: 0

A product search, a filter list that kept growing, and a status code I hadn't seen in years. Filters went in the query string, the way they always do. That held up fine until someone saved a "filter set" with a few hundred SKUs in it and the endpoint started answering with 414. I rebuilt a small version of it to find the exact wall. Same search, filters as repeated ?sku= values, count going up in steps of a hundred: 1) GET with filters in the URL 100 filters | request line ...

Writing linear-gradient(135deg, #667eea 0%, #764ba2 100%) from scratch every time is tedious. Remembering the syntax for radial and conic gradients is even worse. I added a free CSS gradient generator to PaletteCSS that handles all three gradient types with a live visual preview. What it supports linear-gradient — any angle, drag the dial or type degrees radial-gradient — circular and elliptical conic-gradient — pie-chart style, great for progress rings and ...

Every part of this series has quietly agreed on one thing: the agent will be wrong sometimes. Part 1 set the bar at "acceptably wrong." Part 3 measured how often. So the last question is not how to stop it from ever failing. It is the one that actually decides whether you can ship: when it is wrong, what is the worst that can happen? That worst case is not fixed. It is a design choice, and it is the one most teams never make on purpose. Blast radius is something you choose ...

Did your ML model look amazing in your notebook but tank in the real world? Good. Let's talk about the nasty surprises that trip up model deployments and why that "Train & Forget" approach is bleeding companies dry. It's a story we hear too often. You've spent weeks, maybe months, building some fancy machine learning model. The numbers were off the charts in your Jupyter notebook, validation? Nailed it. You even impressed the suits in the demo. "Eureka!" you thought. "We've built a gam...

Tired of deployments eating up your day? Stop wasting hours. I'm going to show you how to take your Python ML model from a Jupyter notebook to a live, production-ready API in just 10 minutes. Seriously. No MLOps guru required! You've felt that high, right? Building an awesome machine learning model. You nail it. Then… deployment. You hit a wall. How do you get this thing out there so people (or other apps) can actually use it? The leap from your notebook to a real-world, working API can fee...

I compressed 100 photos through 3 formats. Here's the actual data. A 2MB JPEG photo. Convert it to WebP — now it's 480KB. Convert it to AVIF — now it's 310KB. Same visual quality. Three different file sizes. I've spent the last 2 weeks building an image compression tool, so I've seen thousands of these comparisons. Here's what the numbers actually say, and what it means for your website. The Setup I took 50 real-world photos and 50 screenshots/design assets — not ...