Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Autoresearch belongs on RepoRadar because it ships a real, runnable autonomous-research primitive rather than a chat wrapper or skill collection. The agent edits the actual training code (train.py) -- architecture, optimizer, hyperparameters, batch size, everything -- not a config file or a program.md wrapper. The 5-minute wall-clock budget per experiment makes the loop tractable on a single
Where this stands now
karpathy/autoresearch ranks #2 of 13 tracked AI tooling items by composite score (8.5 against a section median of 7.8). The section currently carries 7 Gold, 6 Silver. RepoRadar has retained observations for this record since 2026-07-12 (90 days in the current window). Signal extremes versus the section: momentum at the 96th percentile; novelty at the 88th percentile.
Who should use it
Who should skip it
Skip karpathy/autoresearch unless the captured evidence suggests it solves a problem you are actively working on.
About this signal
karpathy/autoresearch is tracked by RepoRadar as a code repository in the AI tooling section. First seen 2026-07-12; the source record was last checked on 2026-07-12. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. Across RepoRadar's eight signals, karpathy/autoresearch is strongest on workflow potential (9.6) and momentum (9.5) and weakest on setup ease (6.4) — a profile worth weighing against your own priorities. This page summarizes the public evidence on the linked source page and states where additional review is still needed.
How this item is evaluated
The karpathy/autoresearch record combines a 8.5/10 composite score with separate popularity (100.0), risk (low), and setup (moderate) signals. See the scoring methodology for the current weights and evidence definitions.
Putting this into practice? Read How to read AI benchmarks without getting fooled for the checklist behind this score.
Risk explanation
Last commit was 2026-03-26 (~3.5 months ago) -- under the cycle 141 4-month stale threshold but approaching it; the cycle-script chose the pick on the strength of the README's authoritative MIT declaration and Karpathy's MIT-by-convention track record (nanochat, llm.c, minbpe are all MIT); No LICENSE file at the repo root (raw main/LICENSE returns 404); the MIT declaration lives only in the README's ## License / MIT section -- authoritative for the maintainer's intent but weaker than a dedicated LICENSE file for downstream legal review; Single-machine scope: the README targets a single H100 (or comparable) GPU; multi-GPU / cluster workflows require the maintainer's separate nanochat project (56,175★) or the active community forks; Default-branch master is unusual for a Python project in 2026 (most Python projects default to main); not a risk per se but worth noting for tooling that branches off.