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My scrapers run on PythonAnywhere. My phone runs Termux. I wanted them to talk to each other. The standard options all had the same problem: they required infrastructure I didn't want to maintain. Firebase — cloud lock-in, SDK overhead, costs money at scale Ngrok — exposes a port on my phone, dies when the tunnel resets A VPS with Redis — another server to maintain, SSH into, keep alive Webhook to
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
The 3 AM Nightmare Last week, I let an AI agent run loose on my production server. It was fine — until 3 AM. To interact with the agent, a user must first authenticate across Gmail, a support desk, and a payment platform — all before the agent takes its first action. Permission denied. Permission denied. Permission denied. Three different connectors. Three different auth systems. One very tired
Hi everyone! I wanted to share a small project I’ve been working on lately. The premise is simple: every time we share a photo or a document, we inadvertently leak a massive amount of personal data — from home GPS coordinates to camera serial numbers and even the edit history of a PDF. Using "online privacy services" to clean your files always felt like a paradox to me (sending private data to a s
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
Vibe coding is a good starting point, but it is not where serious AI-assisted development ends. The next step is agentic engineering: using AI coding agents inside a controlled engineering workflow, with context, tests, review and clear boundaries. Vibe coding often focuses on the generated output: Ask for feature -> get code -> run it -> ask for fixes Agentic engineering focuses on the system ar