An opinionated list of Python frameworks, libraries, tools, and resources
This article was originally published on https://forg.to/articles/how-to-stop-hitting-claude-usage-limits *You're Paying for Claude. You're Also Wasting Most of It. I used to hit my usage limit by 2pm every day. Not because I was doing too much work. Because I had no idea how Claude actually charges you. Once I understood the real mechanic, everything changed. I now hit my limit maybe once a mont
The Idea After deciding to build an iOS app using AI, the first thing I set out to create was a metronome app designed for dark stage environments. Back in college, I played drums — and while that was a while ago, there weren’t many metronome apps that felt both clean and professional. (Turns out, that’s still true today.) That’s what led me to the idea: a simple, black-and-white metronome where
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TL;DR — Superpowers and Compound Engineering aren't competitors. They're optimised for different worlds. Superpowers is gold for mature codebases with established methodology (TDD shops, large legacy systems, teams enforcing standards). Compound Engineering is gold for early-stage products where one person owns a feature end-to-end. Pick by what your codebase looks like, not by which README sounds
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