Hivemind runs AI coding agents side by side. Each one owns a defined set of files. Reach outside that list and the change is stopped before it lands — nothing touches your branch until you say ship.
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SCOPE, LIVErules onTHE PROBLEM
Most agents read, write, delegate, review, and remember inside one long conversation. Soon the code and logs fill the room. Context gets compressed, decisions go missing, and long jobs start making up for what they forgot. A bigger window only buys you more time.
“Review every change” sounds responsible. It also falls apart when you’re tired, busy, or staring at a 400-line diff. At that point, it’s a promise you’re hoping to keep.
Hivemind doesn’t ask you to be more careful. It gives carelessness nowhere to go.
WHY MORE AGENTS FORGET
Ultra mode and “spin up subagents” give one model more hands, not a better memory. It still has to track every worker, remember the original ask, and decide whether the result actually works.
One model plans, reads, writes, spawns workers, checks the output, and keeps the whole run in the same thread. Code and logs push the reasoning out. When the context compacts, old constraints come back as a summary — and a decision from five turns ago can disappear.
Hivemind gives each worker a smaller job and a clean context. Less to remember. Fewer ways to drift.An agent can build the shape of a feature, leave the real behavior missing, then write tests for the shape it made. Every self-written check passes. The result still does not do the job you asked for.
Check the original request before trusting the green tests.Wrong work can look finished. Other tasks build on it, and by the time someone spots the missing behavior, the repair has a much bigger blast radius.
Stop the bad result before it becomes everyone else’s starting point.HOW IT WORKS
01
It turns your request into tasks and keeps the state on disk instead of asking one model to carry your whole repo. The old version still asked a model what came next at every step — 178,121 tokens spent choosing moves that were already obvious. Taking the lock and opening a worktree are rules, not opinions, so that work is plain code now. The model plans, hears you mid-run, and steps in only when something breaks. On that two-task fixture, the new path projects 11 fewer model calls and 52.9% fewer tokens. That is a projection from the accepted run, not a new paid benchmark.
02
Every worker gets its own checkout and a short list of files. That sounds simple until a lock can be stolen. The first version treated “permission denied” like “no such process” and took files from a live worker — three different ways, all bad. Now any ambiguous signal means stop and leave ownership alone. One two-task run had 55 seconds of real overlap, the same token cost, and a third less waiting.
03
For a while, the button said Merged when nothing had merged. Shadow verification built a rehearsal on a throwaway branch, deleted it, and the UI treated the rehearsal as a real merge. My main branch never moved. Now the label appears only after the branch moves and git confirms it. The UI reports what the repo says, not what it wishes had happened.
INSIDE
These are real captures from the current build.
WHAT IT PREVENTS
One of my agents got a simple assignment: build a command-line tool with an --input flag. It built something else, wrote tests for that wrong version, and passed all nine. Every check was green.
This happens far more often than people realize. An LLM can make a convincing skeleton, test the skeleton, and leave the missing behavior for someone else to discover after the work has spread. Hivemind checks the original spec before it runs the agent’s own tests. The agent can prove its code is self-consistent; it cannot quietly change the assignment.
The worst failures are the ones that look finished.
FAQ