Claude models reached GA on Microsoft Foundry with Azure-native billing and governance, but no European data zone exists. Anthropic's own documentation confirms data residency guarantees apply to Bedrock and Vertex AI but not Foundry. European practitioners from banking and healthcare report the offering is unapproved for production. By Steef-Jan Wiggers

I am delighted to announce that a user reported dysfunction so that I could go down the rabbit hole...

I made a 10MB LoRA adapter for Qwen3.5-4B plus a small orchestration layer. It decides, per query, whether to answer directly, search the web, or retrieve from your own local documents and it refuses to make things up when it can't verify an answer. It runs locally (Apple Silicon / MLX, with a GGUF build for llama.cpp/Ollama). Basically small instruct models are poor at telling users how confident they really are. They can't verbalise it and tend to say they are confident for everyhting. In my past research I tested seven 3-9b models and they all hit a confidence ceiling. But the information is there in the internal activations. The adapter reads the internal signal directly and gates tool use on it. The main elements are that: - it catches its own errors better than the base model's tool calling (d′ improvement of 0.46 (95% CI [0.01, 0.89])). Of the cases the gate flagged that the base model didn't, 87% were genuinely wrong answers. - it is less likely to leak your private queries to public search. A two-signal version routes personal information related questions such as "what did my discharge summary say" to a local retriever instead of a websearch. It cut the rate of private questions sent to public search from 22% to 10% (reduction 0.12, 95% CI [0.02, 0.22]). This is useful for those who are using the LLM for confidential docs. - every answer is traceable. When it retrieves, it cites the specific passage ( report.md ¶2 ), verifies the answer is actually in that passage, and shows a confidence band. Worst case, it says "I couldn't verify that". It is built to say "I don't know," instead of lie. limitations: - Privacy result is n=60; the retrieval/competence dissociation is n=126 hand-authored items. Screened and CI'd, but small. - GGUF reproduces the MLX gate's decisions at --lora-scaled ...:8 (found by sweep — scale 1 does nothing; effective scale ≈ the training scale). Agreement 0.83 on a 24-item probe; disagreements are all conservative-direction (GGUF answer

Built a visual editor that validates tensor shapes, counts params, estimates FLOPs/VRAM while you design. Catches incompatible residuals, mismatched Linear layers, all that before you waste GPU time. 63 ops. Proper shape inference. Exports PyTorch code that actually runs. URL- tensey.vercel.app Github- github.com/aarocy/tensey – MIT licensed. submitted by /u/uselessfuh [link] [留言]

I build one small browser tool a day and write down what I learned. Day 25 was a UUID generator. What started as "make some random IDs" turned into a proper look at how the bits are laid out, and why the newer v7 format is quietly the better default for a primary key. Live tool: https://dev48v.infy.uk/solve/day25-uuid.html A UUID is just 16 bytes with a few fixed bits A UUID is a 128-bit number, written as 32 hex digits grouped 8-4-4-4-12 . That is about 3.4x10^38 possible values, which is the whole point: any machine can pick one and trust it will not clash with any other UUID minted anywhere, ever. It carries no meaning — it is an identifier, not data. The reason UUIDs exist at all is coordination. The classic database ID is 1, 2, 3... from a central counter, and that works great until you have more than one writer. Two servers, an offline mobile app, or a sharded database cannot all ask one counter for the next number without a round-trip and a lock. UUIDs sidestep that entirely: each node generates its own IDs locally, with zero coordination, and they still do not collide. A client can even create the ID before the row ever reaches the server. Version 4: 122 random bits v4 is the one most people mean by "UUID". Fill all 16 bytes with cryptographic randomness, then overwrite two small fields so tools can recognise the format: const b = new Uint8Array ( 16 ); crypto . getRandomValues ( b ); // never Math.random() b [ 6 ] = ( b [ 6 ] & 0x0f ) | 0x40 ; // version 4 b [ 8 ] = ( b [ 8 ] & 0x3f ) | 0x80 ; // variant 10xx Two things get pinned. The high nibble of byte 6 becomes 4 — that is the digit right after the second hyphen, and it is how any parser knows the scheme. The top two bits of byte 8 become 10 , which is why the 17th hex digit of almost every UUID you see is 8 , 9 , a or b . Everything else stays random: 122 bits of it. Is "random and never collides" a contradiction? The birthday paradox says collisions become likely around the square root of the space, w

The problem with a data breach is not finding evidence. It is connecting it. But let me start where I actually was: 4 AM, last day of the hackathon, staring at this in my terminal. RateLimitError: GroqException - Rate limit reached for model `llama-3.3-70b-versatile` on tokens per day (TPD): Limit 100000, Used 99787, Requested 1616. Please try again in 20m12s. Used 99,787 out of 100,000. My deployment was half done, my demo graph was empty on the server, and the free tier had 213 tokens left. The submission deadline was hours away. I had not slept. I had not eaten. My friends were asleep and I was swapping API keys like a gambler swapping chips. This post is the story of how we got there, and how it ended at 7 in the morning with the best sigh of relief I have ever taken. First, some honesty about how I got here When I joined my first WeMakeDevs hackathon, I did not believe in it. I thought it was one of those ordinary online events. Fake prizes, no follow-through, what would I even get out of it. I joined anyway, mostly out of boredom, got into the Discord, talked to people, made a few connections. I landed in the top 50. A few days later an email showed up: a free Claude Max subscription as a gift. I read it twice. I genuinely could not believe a hackathon had actually delivered something. So when this hackathon opened, I did not hesitate. I messaged my friends and said we are joining as a team this time. Three of us: me (Mehraan), Aqib, and Ubaid. The spark We spent the first evening in our group chat throwing ideas around and shooting most of them down. Then one of my friends dropped a thought that stuck: what happens after a company gets hacked? I started digging into it. The answer is honestly depressing. After a breach, the evidence is everywhere. VPN records. File access logs. The email gateway. Badge readers at the office doors. CCTV. HR notes. Anonymous tips. Each system tells one small piece of the story, and a human analyst has to stitch all of it togeth

In this full guide, you’ll learn: 📛 Why most AI browser agents fail on modern websites. 🧱 How browser fingerprinting and anti-bot systems work. ⛑️ How to build an AI browser agent using JavaScript (Node.js) that combines Gemini, Playwright , and Bright Data to browse real websites, extract live data, analyze, reason, and generate reports locally without maintaining fragile anti-bot infrastructure ourselves that breaks 5 days later. 🗃️ How to setup Bright Data production-ready browser sessions for AI agent automation without user’s assistance manually. 🪁Introduction Building unrestricted anonymous browser automation has developed far beyond writing Playwright scripts that click buttons and scrape HTML. Modern websites actively detect automated traffic using browser fingerprints , TLS signatures , IP reputation, and behavioral analysis, making reliable automation significantly more challenging than it was just a few years ago. Modern AI browser agents don’t usually fail because they’re arbitrary. Their reasoning, prompts, and planning loops are often sophisticated. The execution layer underneath is fragile. Most tutorials show how to connect an LLM to a browser, execute a few Playwright commands , and declare you’ve built an autonomous agent. await page . goto ( url ) await page . click ( selector ) await page . type ( selector , text ) In reality, you’ve ONLY automated a browser. Commercial sites don’t gauge how intelligent your agent is. They judge whether they believe your browser is genuine. Before a page even finishes loading, they inspect what your browser actually is: the TLS handshake , IP reputation, browser fingerprints, canvas and WebGL fingerprints , cookies, device characteristics, and even the rhythm of your connection. Dozens of signals are examined in the time it takes the page to start loading. If those signals don’t look authentic, your agent rarely reaches the real application. Instead, it encounters CAPTCHA challenges, verification pages, silent re

I have been building a small Honor of Kings Global site called HOKMeta: https://hokmeta.com/heroes/ At first, I built it like many game sites: hero pages, counters, tools, and a tier list. But after working on the site for a while, I removed the tier list as a main page. The reason is simple: a tier list looks useful, but it does not always match how players actually choose heroes. A Marco Polo player will still play Marco Polo even if people say he is weak. A Hou Yi player usually does not search for “is Hou Yi S tier?” first. They search for things like: Hou Yi build Hou Yi arcana Hou Yi counter what to build against tanks what to change against assassins best build for ranked That made me rethink the site structure. Instead of making the tier list the center of the site, I moved the focus toward: hero build pages counter pages item pages damage calculator build compare counter picker For a small SEO site, this feels more useful too. A tier list is one page. Hero builds and matchup questions create many real long-tail pages. For example, “Hou Yi build 2026” is a clearer search intent than just “Honor of Kings tier list”. The current direction is: hero page -> build, arcana, counters, FAQ counter page -> who beats this hero and why tool page -> test builds instead of guessing item page -> understand what the item actually does It is still early, and the data is still being cleaned up, but this direction feels closer to what players need before a ranked match. If you build content/tool sites, this was a useful lesson for me: Do not blindly copy the obvious page type. Look at what users are really trying to decide.