A loader with level of detail
Borrowed from 3D rendering: level of detail. The loader shows more as it grows and strips back as it shrinks, so it reads cleanly at any size.
We are a team of researchers and engineers at GitHub, exploring things beyond the adjacent possible. We prototype tools and technologies that will change our craft. We identify new approaches to building healthy, productive software engineering teams.
Borrowed from 3D rendering: level of detail. The loader shows more as it grows and strips back as it shrinks, so it reads cleanly at any size.
Canary puts a small, auditable gate in front of agentic workflows so untrusted artifacts are classified before powerful agents act on them.
https://github.com/githubnext/repo-assist-impact/blob/main/report.md
What happens when a proactive AI repository agent is deployed across 13 open source repositories? 578 issues closed, median 8x increase in issue closure velocity, and 10x in PR merge velocity — transforming largely dormant projects into actively maintained ones. The single most important factor? The rate at which human maintainers decide to act.

A practical mental model for agents, workflows, and human-machine systems in agentic engineering.
Agents can power robust workflows by intelligently reacting to unexpected conditions, creating a new form of flexible resilience.
https://dsyme.net/2026/05/05/understanding-repositories-as-human-agent-knowledge-factories-%f0%9f%9a%80/
How do you maintain team velocity when AI-generated code needs cleanup? You have two choices: slow everyone down with more review hurdles, or let automated agentic processes clean things up after the fact. The second path is the key to velocity — and it’s now practical with repository automation.
tsb is a from-scratch TypeScript port of pandas, being built almost entirely by Autoloop — one iterative improvement at a time.