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What's the cheapest way to eval a RAG pipeline nightly without burning a ton of tokens? We change chunking/prompts weekly and keep finding regressions late.
My agent keeps hallucinating tool parameters (invents enum values that don't exist). Temperature is at 0.7 — is that the problem or do I need stricter schemas?
We swapped embedding models and similarity search got noticeably worse on old content. Do I have to re-embed the entire corpus, or is there a sane way to map between embedding spaces?
Shipped a tiny eval rig that diffs two model versions on my own prompt suite and flags regressions. Writeup soon.
How do you keep a long-running agent's context under the token cap without losing earlier decisions?
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Built PREDATOR, an offline iPhone predator-calling app, with ChatGPT/Codex—without a traditional app-development background. I'm a hunter/firefighter. The idea started because YouTube ads kept interrupting coyote stands. The app runs offline and plays through the Bluetooth speaker hunters already carry. Now I'm recruiting real coyote hunters to field test it and try to break it before release: https://sites.google.com/view/predatorfieldtesting/home I'd especially value feedback on the AI-assisted build process and what matters in an offline field tool.
Fast demos, lo-fi launches, and dirty prototypes worth stealing from.
A website that screams when you scroll too fast. That's it. That's the whole app. It is perfect.
Shipped QRJam — make an animated QR code that still scans. One file. Surprisingly hard to keep it scannable.
Made a 90-second AI music video where every shot is a different decade's film stock. The prompt was longer than the script.
Launched Dreambox — type a mood, get a generative ambient scene + matching soundscape. Built in a weekend.
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