I built a tiny table that learns your local LLM's classification answers and serves the repeats in microseconds [D]
It only answers where it agrees with your model 95%+ of the time on held-out answers; everything else still goes to the model.
Proof1 community thread
Key points
- Happy to answer anything, and I'd love to hear where it breaks.
Sources (1)
- [1]I built a tiny table that learns your local LLM's classification answers and serves the repeats in microseconds [D]r/MachineLearning (top, daily) · Oct 12, 07:33 AM
It only answers where it agrees with your model 95%+ of the time on held-out answers; everything else still goes to the model.
Happy to answer anything, and I'd love to hear where it breaks.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 12, 2026Open-sourced my image-dataset prep tool — quality scoring, dedupe, leak-safe splits, Kohya export (pure-Python, MIT)
- Oct 12, 2026AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers
- Oct 11, 2026onehumanbeing/esp32-iphone-agent: Let an AI model use a real iPhone with a $7 ESP32: AirPlay eyes, USB HID hands, any OpenAI-compatible vision model. No Mac, no developer acc
- Oct 11, 2026SonarQube + OpenAI: Agentic Development — Killian Carlsen-Phelan, Sonar
- Oct 11, 2026openai/openai-python v3.29.0
- Oct 9, 2026New Math from OpenAI
