Reading the repo — stats and the shipped feed land in about ten seconds…
Monarch refactored its cast routing layer to use `CastNode` and `CastHop` representations, improving how sender hops and routing tiles are modeled while stabilizing domain materialization with `ValueMesh<CastDestination>`. In parallel, the team expanded the `returns_future` async utility with…
Get this in your inbox every Monday →A deterministic 0–100 hygiene score — README, license, CI, tests, docs, and freshness.
Who ships this repo — author concentration and the bus factor across the last 300 mainline commits.
How welcoming this repo is to contributors — issue throughput, close time, responsiveness, and good-first-issue count.
What this project is built on — dependency count by ecosystem, the license mix, and anything worth a legal look before you adopt it.
Whether this project's CI can be trusted — pass rate, run times, flaky runs, and which workflow is the weak link.
Grounded in monarch's README, structure, and recent commits — answers won't invent code they haven't seen.
A Monday email with what shipped, in plain English — no account needed.
Showing raw commit titles for the newest commits. Sign in to generate AI summaries.
Run `__supervise__` on the actor's event loop instead of blocking the actor (#4999)
Return Vec<CastSubtree> instead of Vec<CastRoute> for CastHop.next_hops (#4985)
A floor, not a guess: counts only commits whose author, co-author trailer, or message explicitly credits an AI tool (Claude, Copilot, Cursor, aider, Codex…). Based on 30 mainline commits. Unattributed AI code isn't counted here — the full audit estimates that separately.
