inspect is now our digital coworker. 75%+ code at ramp now comes from inspect.
what we've invested in:
1. repo setup across every main repo: is every dep installed, every command able to run and is performant, every skill token efficient and installed. are all the tools available - are the unused tools removed? which mcps are bloated? what can we precompute and snapshot in the sandbox fs (mypy, uv deps, bytecode caching, git clone). every additional source of feedback improved the final PR quality: for infra we added terraform plans, for web we added a parallel browser testing agent, for backend we added mypy's new experimental parallel mode, fast enough to run in a sandbox
2. ui polish and closing the loop outside of github - human reviews, ai reviews, diffs, child sessions, stats, fast voice inputs with realtime gpt
3. insane performance obsession (mostly @_dylanga), chasing down every ms across sandbox boot, new prompts, db queries, network round trips
4. try to match or exceed local agent performance across all axes (tools, skills, performance, repo set up)
5. robust api - used for many other automations internally
6. cost optimization: tool bloat, flex api, picking the right model, reasoning level across models
7. making sure prompts are always declarative, and almost never imperative. fix this ✅ implement this feature ✅ use the datadog mcp to fix this ❌ use the db migration skill from this link ❌
it's a moving target but a unending emphasis on speed, defaults, repo setup has allowed us to get as close as we can to "just works" on any knowledge work tasks at ramp - eng, product, data, support, sales, uxr
Dylan Garcia (@_dylanga)
Article
Inspect at Scale
Since our original blog post, Inspect's adoption has skyrocketed, the product has matured, and the industry has moved just as fast. What started as an engineer’s tool for writing code is becoming
— https://nitter.net/_dylanga/status/2067999346780524753#m