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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

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

Quando voce cria um container e depois outro, eles podem nao se enxergar. O motivo quase sempre e a rede. Entender os tipos de rede que o Docker oferece resolve isso de uma vez. Comece listando as redes que ja existem no seu Docker. docker network ls A rede bridge e a rede host vem de fabrica. Nenhuma delas oferece resolucao de nomes entre containers. Para isso voce precisa criar a sua propria rede. docker network create minha-rede Agora os containers que entrarem nessa rede conseguem se comunicar pelo nome do container. docker run -d --name app1 --network minha-rede nginx docker run -d --name app2 --network minha-rede alpine sleep 3600 Teste a comunicacao. docker exec app2 ping app1 -c 2 O ping funciona. O DNS interno do Docker resolve app1 para o IP interno do container. Isso resolve 90% dos problemas de comunicacao. A rede bridge padrao nao tem esse recurso. Crie sua propria rede sempre que precisar que containers se enxerguem. Agora a rede host. Ela elimina o isolamento de rede. O container usa a placa do host diretamente, sem NAT e sem mapeamento de porta. docker run -d --name web-host --network host nginx O nginx fica acessivel em http://localhost:80 direto. Sem -p 80:80. Mas so um container pode usar cada porta, porque a porta e a do host de verdade. Para ambientes com mais de uma maquina, existe a rede overlay. Ela precisa do Docker Swarm ou de um cluster. docker swarm init docker network create -d overlay rede-overlay docker service create --name api --network rede-overlay --replicas 3 nginx Os containers em maquinas diferentes se enxergam pelo nome como se estivessem na mesma maquina. No dia a dia, a rede bridge personalizada resolve quase todo caso de uso. Host e para performance ou casos especificos. Overlay e para quando voce escala para mais de uma maquina. That's all for now. Thanks for reading!

You ship all the time. A fix here, a new setting there, a feature you spent a whole weekend on. And your users mostly don't notice. That gap is expensive. When people can't see a product moving, it feels abandoned, even when you're shipping every week. They churn a little faster, they email asking for things you built a month ago, and all the momentum you're actually creating stays invisible. The fix is boring and old: a changelog. But not a changelog rotting in a Notion doc nobody opens. One that shows up inside your app , where users already are. Here's the approach I settled on. The idea: a widget, not just a page A "what's new" widget is a small button or badge in your UI. Click it, and a panel slides out with your latest updates. Users see it in the flow of using your product, not on some /changelog page they'd never visit. You really want three things: An in-app widget users actually see. A public page and RSS feed you can link from emails and docs. A way to write updates in plain language and publish in a click. The one-tag version I ended up building a tool for this (honest disclosure below), but the integration is the part worth showing, because it's the pattern any changelog widget should follow: <!-- Paste before </body> --> <script src= "https://cdn.patchlog.io/widget.js" data-project= "your-project-id" data-position= "bottom-right" async ></script> One script tag. No SDK, no npm install, no framework coupling. It behaves the same in React, Vue, Rails, or a plain HTML page. Two implementation details matter, whether you build one of these yourself or evaluate an existing one: Render it in a Shadow DOM. A changelog widget should not inherit or leak styles. If it uses the host page's global CSS, it will look broken on half the sites it lands on. Shadow DOM isolates it completely. Fail silently. A marketing widget must never break the host app. If the network call fails, it should quietly do nothing. What to actually write in it The tool is the easy part. T

Meta told Dylan Byers, of Puck News, that it had nixed the feature after backlash from its user base.

Following significant backlash, Meta is turning off the feature it announced this week that let users generate AI images based on content from public Instagram accounts just by tagging them. The feature, as originally set up, meant that content from any public Instagram account could be used in AI creations without the account owner's permission. […]

Data centers are driving up the company’s use of electricity—and carbon pollution.

Reinforcement learning uses error information to adjust control algorithms.

More police and firefighters use drones to catch and deter illegal fireworks.

The café was crowded that evening, but to me, the world had fallen completely silent. An open...