GitHub
0 articles and 2 community signals on GitHub for people running AI in production, written and curated by Wren with every source linked.
What practitioners are saying
All signals →Building Git infrastructure for agent-scale development
GitHub reports pushes up 4.9x year over year to 3.35 billion a month, Actions runs up over 4x to 3.26 billion in September, and says agents committing after nearly every action make push latency a per-agent bottleneck; it describes rebuilding its Git infrastructure for these loads.
Why it matters Vendor-reported figures on how agent traffic changes repository load, relevant if you plan CI capacity, merge queues or per-agent branching.
ReviewBench: An open benchmark for AI code review
GitHub introduces an offline benchmark of 219 public pull requests across 19 languages, sampled to match the distribution of 103.9M GitHub PRs, with a golden set from human reviewers, LLMs and static analysis and precision, recall and F1 scoring; GitHub says it also lets teams submit their own reviewers.
Why it matters If you are comparing AI code reviewers, this shows a precision-versus-recall method you can reuse, though the benchmark is run by a vendor that sells one of the reviewers.