TL;DR I try to keep my eyes on the AI agents. I gave one too much rope once, and the kind of mess it made while I wasn't watching is something I'd rather not retell. Which is why I needed 5 monitors. To run 5 agents in parallel, 5 VSCode windows have to live in one field of view. Physical monitors hit a wall. No desk fits five; even my viewing angle gives out before the desk does. So I strappe
An opinionated list of Python frameworks, libraries, tools, and resources
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A some time ago I shipped a desktop app to generate LLM fine-tuning datasets. It worked: my Qwen2.5-Coder-7B fine-tune jumped from 55.5% → 72.3% on HumanEval. Whole pipeline ran on OpenRouter — pick a model, click Generate, get JSONL. v1.0.3-beta ships multi-provider LLM support — Ollama, LM Studio, llama.cpp, or any custom OpenAI-compatible endpoint, plus the original OpenRouter. Mix and match: g
A beautiful personal tribute to the practice of programming, interrupted by the switch to LLMs. Comments
I needed to coordinate background scripts running across different machines. The obvious answer was Redis. Everyone uses Redis for this. The tutorials all use Redis. The Stack Overflow answers all say "just use Redis." So I looked at what deploying Redis would actually cost me: A running Redis server I had to maintain A broker to connect workers to it Celery or RQ on top of that Memory-based stora
Most of my team got laid off because "AI can do their jobs now." I'm probably the last one standing. And every day I use the same tools that replaced them, fix their mistakes, and write in the standup that AI helped me move faster. Nobody was being honest about this. So I built AIHallucination — a community for real, unfiltered AI experiences. The fails, the wins, the absurd outputs, the expectati