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Johannes Heidecke’s departure comes as OpenAI tries to further integrate its research and safety teams.

CISA said it "missed" an opportunity to get ahead of the security incident by not creating a response plan ahead of time.

I game in a room that warms up fast. I could see CPU usage in Task Manager and watts in HWiNFO if I went looking. What I actually wanted was simpler: How much heat is this machine putting into the air right now? Not in a spreadsheet. In plain language I could glance at while the PC was running. The gap Lots of tools show watts and temperatures . Almost none answer room heat : BTU per hour Heat accumulated over a session Plain context like "about a quarter of a space heater" With ambient temp: still-air rise or rough exhaust CFM The conversion is straightforward ( BTU/hr ≈ watts × 3.412 ), but I didn't want to do it in my head every time. So I built HeatLens — a small desktop widget built around room heat, not raw sensor dumps. What HeatLens shows Total wattage — what the PC is drawing now Heat dissipation — BTU/hr or kW Session heat — BTU or kWh since launch Max temperature — hottest live sensor Trend graphs — watts, heat, and temp over time CFM estimate — with ambient temp: rough exhaust airflow for a +10 °F rise Still-air rise — how fast a reference room would warm with no ventilation Estimated power is labeled separately from measured sensors. Where the data comes from LibreHardwareMonitor / Open Hardware Monitor (HTTP + WMI on Windows) nvidia-smi for NVIDIA GPUs Linux RAPL / hwmon when exposed by the kernel Labeled fallbacks when direct power sensors aren't available On Windows, best results: LibreHardwareMonitor with Remote Web Server on port 8085 . What it is not HeatLens is not a replacement for a Kill-A-Watt at the wall. Software usually can't see monitor power, full PSU loss, or every platform rail. A plug-in meter is still the most accurate whole-system reading. HeatLens is for context : "~400 W gaming → ~1,400 BTU/hr into the room" Session heat over an hour or two Rough CFM / still-air numbers as sanity checks — not duct design Things I learned building it Sensor coverage is messy. Different backends, missing rails, and estimates that need clear labeling.

This is a submission for Weekend Challenge: Passion Edition What I Built I built a personal tribute website for my cat Juma. The goal of this project was to create a simple webpage to bring back the feeling of the old internet. I have always loved the creativity of old-school websites and things like Neocities pages, Tumblr blogs, pixel art, animated backgrounds... Websites made with personality instead of just functionality. So, I wanted to recreate that feeling by making a small corner of the internet dedicated to someone very special to me: my cat Juma Maruá Ganache. Demo Code You can check out my GitHub repository: thaisavieira / juma How I Built It I built this project using simple and classic web technologies: HTML for the structure of the page; CSS for animations, gradients, responsive design, and the retro visual style; JavaScript for interactive elements such as falling stars and dynamic effects. Prize Categories Not submitting to any prize categories.

I built LiveShop , a mini live-shopping stream UI, to answer a question I kept running into as a frontend-curious grad: tutorials teach you how to render a list, but they never teach you what happens when that list gets hit with the kind of traffic a real live stream produces. So I built something that would force the problem to show up, then fixed it, then measured whether the fix actually worked. The setup LiveShop simulates a live-shopping broadcast - the kind of interface a small merchant might use to sell products while streaming. A mock event engine fires chat messages, reactions, and purchase notifications on an interval, standing in for what a real WebSocket connection to a streaming backend would deliver. On top of that sits a chat feed, a scrollable product carousel, and a floating reaction animation layer. None of that is unusual. The interesting part started once I asked: what happens when message volume spikes? Where it breaks A naive chat feed is just messages.map(m => <ChatRow key={m.id} {...m} />) . It's the first thing anyone reaches for, and it's fine — right up until it isn't. At 50 messages, nothing looks wrong. At a few hundred, every new message triggers a full re-render pass across every row in the DOM, including the hundreds that have already scrolled out of view and that nobody can see. The browser is doing layout and paint work for pixels that aren't on screen. In a real live stream, this is exactly the wrong failure mode, because message volume doesn't arrive evenly. It spikes — right after a product drop, right when something funny happens on stream, right when a popular creator says something quotable. That's precisely the moment a chat feed can't afford to stutter, and precisely the moment a naive implementation is most likely to. What I measured Rather than guess whether this mattered, I built a way to test it directly. LiveShop has a "Simulate spike" button that fires 500 messages instantly, plus a live FPS readout using requestAnimat

Let's be real: our personal medical history is a mess. It’s a chaotic mix of PDF lab results, grainy scans of prescriptions, and cryptic Electronic Medical Records (EMR) scattered across different hospital portals. If you’ve ever tried to remember exactly when a specific symptom started or how your cholesterol has trended over the last decade, you know the "search" struggle is real. In this guide, we are moving beyond simple folders. We are architecting a Personal Health Knowledge Base using a modern Vector Database and RAG (Retrieval-Augmented Generation) pipeline. We’ll leverage Qdrant for high-performance similarity search, Unstructured.io for complex document parsing, and Sentence-Transformers to turn 10 years of medical jargon into searchable embeddings. By the end of this post, you'll have a system capable of cross-year symptom correlation and instant medical history retrieval. The Architecture: From Pixels to Insights 🏗️ The biggest challenge with medical records isn't storage; it's ingestion . Medical PDFs are notoriously difficult to parse because they often contain nested tables and checkboxes. Our pipeline handles this by isolating the layout before embedding. graph TD A[Raw Medical Data: PDFs, Scans, EMRs] --> B[Unstructured.io: Partitioning & OCR] B --> C[Text Chunking & Cleaning] C --> D[Sentence-Transformers: Vector Embedding] D --> E[(Qdrant Vector DB)] F[User Query: 'Show me my blood sugar trends since 2015'] --> G[FastAPI Interface] G --> H[Query Embedding] H --> I[Vector Search in Qdrant] I --> J[Contextual Results + LLM Synthesis] J --> K[Actionable Health Insight] Prerequisites 🛠️ To follow along, you'll need: Python 3.9+ Unstructured.io : For the heavy lifting of PDF/Image parsing. Qdrant : Our vector engine (run it via Docker: docker run -p 6333:6333 qdrant/qdrant ). Sentence-Transformers : To generate local embeddings without sending sensitive data to the cloud. FastAPI : To wrap it all in a slick API. Step 1: Parsing the Chaos with Unstructu

My GPT got dumber after the update" gets blamed on the model regressing, or on you prompting worse. Both are unfalsifiable, and both send you to fix the wrong layer. The layer that actually moved is the one you can pin. "The model" is two layers. The weights — the trained network, slow to change, and when they do change it's announced under a new name. And the wrapper — the router that picks which model answers, the system prompt, the default reasoning effort, verbosity caps. The wrapper changes silently, on its own schedule, per product. It's almost always what moved under you. So stop re-tuning prompts to chase it. Pin the wrapper: Force the route. Don't leave it on Auto — set Thinking (or say "think hard") so the router can't quietly demote your prompt to a faster, weaker model. OpenAI's own GPT-5 launch post describes exactly this router (it scores prompts "simple" vs hard); after the backlash they put the picker back (Auto/Fast/Thinking — TechCrunch, Aug 2025). Pin the version. If you build on a model, call its exact versioned ID via the API. A model ID's weights don't change — new versions ship under new IDs — so router and system-prompt churn can't reach you. Own the harness. Running agents? Set the system prompt, reasoning effort, and verbosity yourself instead of inheriting a default. Anthropic's own April 23 post-mortem is the proof: six weeks of "Claude Code got worse" traced to three wrapper changes (a reasoning-effort downgrade, a reasoning-history bug, a verbosity cap their ablations put at ~3% quality) — API weights never touched. A real weights change — a new model — will still move behavior. But that's announced, and you choose when to adopt it. The silent stuff is all wrapper, and the wrapper is the part you can pin. Sources: OpenAI GPT-5 launch (router + "think hard"); TechCrunch, Aug 2025 (model picker reinstated); Anthropic April 23 post-mortem (anthropic.com/engineering/april-23-postmortem); InfoQ and VentureBeat (corroboration); Claude platfor

The first year class HelloWorld { public static void main ( String args []) { // Displays "Hello World!" on the console. System . out . println ( "Hello World!" ); } } The second year /** * Hello world class * * Used to display the phrase "Hello World" in a console. * * @author Sean */ class HelloWorld { /** * The phrase to display in the console */ public static final string PHRASE = "Hello World!" ; /** * Main method * * @param args Command line arguments * @return void */ public static void main ( String args []) { // Display our phrase in the console. System . out . println ( PHRASE ); } } The third year /** * Hello world class * * Used to display the phrase "Hello World" in a console. * * @author Sean * @license LGPL * @version 1.2 * @see System.out.println * @see README * @todo Create factory methods * @link https://github.com/sean/helloworld */ class HelloWorld { /** * The default phrase to display in the console */ public static final string PHRASE = "Hello World!" ; /** * The phrase to display in the console */ private string hello_world = null ; /** * Constructor * * @param hw The phrase to display in the console */ public HelloWorld ( string hw ) { hello_world = hw ; } /** * Display the phrase "Hello World!" in a console * * @return void */ public void sayPhrase () { // Display our phrase in the console. System . out . println ( hello_world ); } /** * Main method * * @param args Command line arguments * @return void */ public static void main ( String args []) { HelloWorld hw = new HelloWorld ( PHRASE ); try { hw . sayPhrase (); } catch ( Exception e ) { // Do nothing! } } } The fifth year /** * Enterprise Hello World class v2.2 * * Provides an enterprise ready, scalable buisness solution * for display the phrase "Hello World!" in a console. * * IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED * TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER * PARTY WHO MAY MODIFY AND/OR REDISTRIVUTE THE LIBRARY AS * PERMITTED ABOVE, BE LIABLE TO YOU FOR DAM

Pull a hot steel bolt out of a furnace and quench it in oil, and a fair question is: does the bolt cool from the outside in, with a sharp temperature difference between its skin and its core, or does the whole thing drop in temperature more or less together? The answer is not obvious from the part itself. A thin copper washer and a thick ceramic block behave very differently in the same bath, even at the same starting temperature. The Biot number is the small calculation that settles this question before you commit to any heavy analysis. It tells you, in a single dimensionless figure, whether an object can be treated as having one uniform temperature or whether you must resolve a temperature gradient inside it. That distinction changes the math from a one-line exponential decay to a partial differential equation. Why this calculation matters Transient heating and cooling problems show up everywhere: heat-treating metal parts, quenching forgings, cooling electronics, baking or chilling food, warming up an engine block. In every one of these, the engineer wants to know how the temperature changes over time. The hard version of that question requires solving the heat conduction equation across the body, with position and time as variables. The easy version is the lumped-capacitance model, which treats the whole object as a single point at one temperature. It reduces the problem to a simple first-order exponential. The catch is that the lumped model is only valid when internal conduction is fast compared with surface convection. The Biot number is exactly the check that tells you whether that condition holds. Skip the check and apply the lumped model where it does not belong, and you can badly mispredict cooling times, residual stresses, and the risk of cracking from thermal gradients. The core formula The Biot number compares two thermal resistances. One is the resistance to conducting heat through the inside of the solid. The other is the resistance to carrying heat a

n8n is the open-source workflow automation tool that competes with Zapier and Make. I have been running it for saas.pet's content pipeline for 6 months. Here is my honest take on self-hosting n8n versus paying Zapier, and whether it is worth the hassle. What n8n does that Zapier cannot n8n is an open-source workflow automation platform with 400+ built-in integrations. You connect nodes on a visual canvas: when X happens in one app, do Y in another. The killer difference from Zapier: you control where it runs. Self-host on a $5/month VPS or run on n8n Cloud at $20/month. No per-task pricing, no 'you hit your zap limit' emails. For high-volume workflows, the cost difference is dramatic. I run n8n on the same $6/month HK server that hosts my proxy. 12 workflows handle saas.pet's entire content pipeline: daily data fetch from GitHub Trending API, transform JSON, write to data files, trigger build, push to git, notify me on Telegram. The same workflows on Zapier would cost $73.50/month (Professional plan with 2,000 tasks). On n8n, $6/month for the server plus $0 for the software. The self-hosting overhead is real—updates, SSL certs, monitoring—but for 12+ active workflows, the savings are $800+/year. If you only have 2-3 simple zaps, stay on Zapier's free tier. If you have 5+ workflows with volume, n8n pays for the hosting in month 1. My 6-month setup for saas.pet I run n8n in Docker on the HK server. The initial setup: install Docker, pull n8n image, configure nginx reverse proxy, set up SSL via Certbot. That took about 2 hours the first time. Now I can deploy n8n in 15 minutes on a fresh server. The 12 workflows: (1) Daily GitHub trending fetch via saas.pet/api/trending, (2) data transform to unified JSON, (3) write to data/YYYY-MM-DD.json, (4) trigger build-ci.mjs, (5) git add + commit + push, (6) Telegram notification with commit SHA, (7) weekly sitemap health check, (8) monthly backup of reviews/ JSONs to S3, (9) uptime ping every 15 minutes, (10) DNS health check,

i use claude code daily and measured what pages cost it while doing research. an average wikipedia article, for instance, is 68,240 tokens of raw html (tiktoken); nike's homepage is 353,000. claude code's built-in webfetch handles the easy case well. it summarizes wikipedia to about 950 tokens and clears cloudflare on some sites like indeed and ticketmaster. but, and there's always a but, on js-rendered and some anti-bot pages it returns nothing. quotes.toscrape.com/js gives "no quotes found"; n

On Thursday, the Instagram account for a lecture series in Newport Beach, CA posted a photo of what appeared to be a cease and desist letter from the surveillance technology company Flock Safety. Flock has received significant backlash over its technology and work with law enforcement agencies, and this letter kicked off yet another wave […]