No growth hacks. No "10x your productivity." Just what I built, what broke, and what I learned.
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CEOs say AI isn't moving the needle on productivity. I've been running AI in production for months. Here's why they're both right and wrong — and what the actual measurement problem is.
A post about autonomous AI agents hit the top of HN this morning. I actually run them. Here's what I've learned about where they work, where they fail, and the 6 rules I've settled on.
Moonshine AI posted their open-weights speech-to-text toolkit on Hacker News this morning. 252 points in 13 hours. The top-line claim: higher accuracy than Whisper Large V3, with a tiny 26MB
r/selfhosted just banned AI-vibe-coded projects except on Fridays. After running 100+ containers in production and building two SaaS products with AI assistance, here's where I actually land.
Three crashes, two NaN catastrophes, and one breakthrough: fine-tuning a 33B model on Mac Studio to learn my coding patterns. Real configs, real mistakes, real lessons.
I built a full church management platform in 48 hours using AI agents. 80+ Claude runs, 12,000 lines of code, zero team. Here's the playbook.
I fine-tuned a 20-billion parameter model on my Mac Studio M3 Ultra and took validation loss from 1.85 to 0.79. Here's what I learned about running serious AI workloads on Apple Silicon.
I've been running large language models locally for a few months now, and honestly? I'm not going back to API-only workflows. The Cost Math Cloud AI