Before you train a model, you need data in the right format. This took me longer than I expected and taught me a lot about how LLMs actually learn. I used MedQA USMLE — real medical licensing exam questions used to certify doctors in the US. It's available on HuggingFace for free. from datasets import load_dataset dataset = load_dataset("GBaker/MedQA-USMLE-4-options") Each sample looks like this:
Series: How Machines Learn: A Complete Guide from Zero to AI Engineer Phase 6: Machine Learning (The Core) You've been hearing "machine learning" for years now. Your phone uses it. Netflix uses it. Your spam filter uses it. Every tech company puts it in their job posts. And yet, if someone asked you right now to explain what machine learning actually is in plain words, you might freeze up a little
Paste. Fix. Download. — Meet Tree2Zip If you’ve ever copied a file tree from ChatGPT and tried to use it… …you already know the problem. Weird indentation Broken nesting Inline comments in filenames Missing folders What looks like a clean project structure turns into a mess when you actually try to recreate it. So I built Tree2Zip: 👉 Paste any file tree → get a clean, working .zip instantly I k
The Problem If you're like me, you live in your terminal. You've got Docker containers running for databases, Redis instances for caching, microservices doing their thing — and you're constantly context-switching to check on them. # The old way: docker ps docker logs my-app -n 50 docker stats docker inspect some_container # ... back and forth, breaking your flow Now imagine you're working with
Si tu as 30 secondes. La mémoire versionnée d'un workflow Claude Code a un effet de bord que personne ne signale : une règle mémorisée qui colle au symptôme de manière plausible court-circuite la vérification, même quand elle ne s'applique pas au compteur précis que tu regardes. Je me suis coûté vingt minutes d'exploration SQL la semaine dernière parce qu'une règle de la forme du bug — sans en êtr
In today’s busy Kubernetes setups, downtime hits hard. A single hour of outage can cost big companies millions in lost sales and fixes. Traditional monitoring tools often leave teams scrambling, with mean time to recovery (MTTR) stretching to hours or even days in tangled microservices. You know the drill — alerts flood in, but the real problem hides in the noise. This article shows you how AI for
Most AI news tools try to solve information overload by summarizing more content, faster. That was not the product I wanted to build. I wanted something closer to a personal news radar: a system that could watch Hacker News, Reddit, RSS, GitHub, Telegram, and other sources for me, reduce the noise, connect the context, and still leave room for human judgment. So I built Horizon. Horizon is an ope
The pitch for full agentic coding sounds clean: you write specs, agents write code, you review and steer. The human stays "in the loop" as the expert orchestrator. But buried in Anthropic's own research on how AI is transforming work at Anthropic is a sentence that should give every engineer pause: "Effectively using Claude requires supervision, and supervising Claude requires the very coding skil