🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory.
AutoOED: Automated Optimal Experimental Design Platform
A GUI-forward Python framework for automating experimental setups.
Autonomous ML research on rental GPUs — LLM-driven hypothesis generation and fleet orchestration on RunPod
Experiment / job runner with automatic GPU assignment, task queuing, and web UI.
An operating system for autonomous research — from literature to manuscript inside a governed, checkpointed loop.
An abstraction layer for pulse sequence scripting with National Instruments hardware.
InfiEpisteme 📜 — Talk to Claude Code, get a research paper. 19 Markdown skills orchestrate the full pipeline: literature survey → ideation → experiments → writing → cross-model review. No framework, no lock-in — pure .md skills runnable by any LLM agent.
TraceOS standardizes AI experiments into reproducible, searchable, and comparable assets. One command runs experiments, generates reports, and produces structured analysis: capability vectors, failure taxonomy, and recommendations. Every run is tracked, traceable, and comparable. Built on ABC-130K (amazon-far/abc). Apache 2.0.
Autonomous experiment loops for Claude Code. Let AI run 100 experiments while you sleep. Works on any codebase.
Experimental research framework for running AI benchmarks at scale
The ‘whisper_chunk_transcribe’ repository offers a Python script that utilizes OpenAI’s Whisper model to transcribe audio files in segments, enhancing accuracy and efficiency for lengthy recordings. It supports various audio formats and allows customization of chunk duration and overlap settings to optimize performance.
Autonomous experiment loop skill for Claude Code. Give it a codebase and a metric — it experiments forever. Generalized from Karpathy's autoresearch.
Search Google Scholar, enrich papers with OpenAlex, and build structured literature reviews locally.
Light project, only for autosettings of lock-in amplifier and generator. Single-thread. Very light project
A framework for the comparative training and evaluation of statistical and deep learning models for multi-feature categorical sequence modeling, utilizing feature fusion and automated with MLflow and Optuna integration.
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