llm-experiments

An experiment-driven laboratory for understanding LLM sampling strategies. Explore how Temperature, Top-k, and Top-p influence token probabilities and generation diversity through reproducible simulations, visualizations, and real Groq API experiments.

experiment in storing face embeddings in sqlite and searching the filesystem for face matches

Give your coding agent a short break after real work, then measure what happens next. Universal agent skill & experiment harness.

This repository documents a seven-round investigation into whether alternative computational geometries can improve upon the standard dot-product attention mechanism used in transformer language models.

Clean-room safe Rust implementation of unencrypted Access 97 / Jet 3 MDB. Built as a test of current LLM capabilities.

Orchestrator-worker multi-agent pattern: one planner, three independent workers with identical prompts but different sampling configs (temperature/top_p). Isolates what sampling alone changes in model behavior — conservative vs balanced vs creative brainstorming.

This project explores the use of a Large Language Model (LLM) to evaluate image sharpness through a paired comparison experiment. The goal is to establish Just Noticeable Difference (JND) levels for sharpness perception

Experiments in Non-Euclidean Geometry for Language Models

Pi extension: plan mode, subagent delegation, and Exa web research

Can Large Language Models detect hidden deterministic patterns without being told they exist?

A hands-on Generative AI learning environment featuring practical experiments and projects for building AI-powered applications.

Code for Randomized PipeDream experiments comparing PipeDream-style pipeline training with randomized stale block-gradient methods on simple objectives and small LLM benchmarks.