autolabhq / autolab

What is autolab?

Autolab runs AI agents through 23 realistic optimization challenges, from speeding up AES encryption in C to fine-tuning vision-language models on multi-source math datasets with GRPO. It gives each agent a starting codebase, strict compute budgets (1-12 hours on CPU or L40S GPU), and clear metrics like throughput or accuracy, scoring iterative improvements against baselines and human references. Developers plug in models via Harbor sandboxes to benchmark agent skills on frontier tasks like flash attention tiling or data selection for IFEval.

Why is it gaining traction?

Unlike toy coding benchmarks, autolab stresses real research workflows—diagnosing bottlenecks, hypothesizing fixes, and iterating under timeouts—mirroring how humans optimize systems or train models. Tasks span C/Go/Rust kernels, Python ML pipelines with Unsloth/LLaMA-Factory, and even reversible sorting networks, with log-scaled rewards that reward big speedups. GPU support and Harbor integration make it dead simple to spin up evals without local setup hassles.

Who should use this?

AI researchers tuning agents for scientific progress, like evaluating earthsea-style exploration or pedestrian prediction benchmarks. Framework builders testing models on GPU-heavy fine-tuning (Qwen2.5-VL, multilingual OCR) or low-level opts (BM25 search, BVH raytracing). Skip if you're not into agent evals—it's for those chasing evaluating benchmark quality beyond GitHub Copilot hype.

Verdict

Grab it if agent benchmarking is your jam; the 23 diverse tasks deliver actionable scores fast. With just 19 stars and 1.0% credibility, it's raw—docs are README-only, no broad tests yet—but the Harbor backbone and reference solutions make it viable for experiments today. Worth starring for the long game.

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