Hivemind orchestrates multiple AI coding agents in parallel. Each one is locked to specific files. Anything outside its scope gets rejected before it lands — and nothing reaches your branch until you click ship.
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THE PROBLEM
Agents that read your code, write your code, and decide what to do next all share one context window. The code crowds out the reasoning and long jobs fall apart. Bigger context windows just delay it.
“Review every change” is a promise, not a mechanism. It works until you’re tired, or busy, or the diff is 400 lines. Then it doesn’t.
Hivemind doesn’t ask you to be careful. It makes carelessness harmless.
WHY MORE AGENTS FORGET
Ultra mode and “spin up subagents” give one AI more hands. The same overloaded AI still has to remember what every worker is doing, keep the original request straight, and decide whether the result actually works.
One AI plans, reads code, spawns workers, reviews output, and keeps every result in one conversation. Code and logs crowd out the reasoning. Compaction shortens earlier constraints into summaries, so it can lose decisions it made only a few turns ago.
Hivemind gives each worker a smaller task context instead of one conversation that has to remember everything.Wrong work can look finished, so more tasks build on top of it. By the time a human discovers the missing behavior, other changes depend on the mistake and the repair is much larger.
Hivemind stops failed work before it becomes the next task’s foundation.HOW IT WORKS
01
It breaks your request into tasks and tracks everything on disk, so it isn’t holding your codebase in its head. But it was still asking a model what to do next at every step — 178,000 tokens a run to pick moves that were already determined. Take the lock, open a worktree; there’s one legal next step and no judgment in it. That part is plain code now. The model only plans, listens to you mid-run, and decides what to do when something breaks. 11 fewer model calls, 50%+ fewer tokens.
02
Each agent gets its own copy of the repo and a list of files it owns. Simple — except the first version of that lock could be stolen. If the operating system said ‘permission denied’ instead of ‘no such process,’ the code assumed the other agent was dead and took its files. Three separate ways that could happen. Now anything ambiguous means the other agent is alive and nothing gets taken. 55 seconds of real overlap on a two-task run, same token cost, a third less waiting.
03
For a while the button lied. It said Merged. Nothing had merged — shadow verification builds your changes on a throwaway branch and deletes it, and the app was reporting the rehearsal as the real thing. My main branch sat at the same commit while the interface told me the work had landed. Now Merged appears only after the branch has moved and the app has re-read git to confirm it. What it tells you is checked against the repository, not against what it hoped happened.
INSIDE
Every screenshot below is the build that runs today.
WHAT IT PREVENTS
One of my agents was told to build a command-line tool that takes an --input flag. It built something else entirely, wrote its own tests for its own wrong version, and passed all nine. Every check was green.
This happens more often than anyone realizes: an LLM can build a convincing skeleton, test the skeleton, and only reveal the missing behavior after other work depends on it. Hivemind checks the original spec independently before the agent’s own tests run. An agent can prove its implementation is internally consistent; it cannot quietly redefine what success meant.
The dangerous failures are the ones that look done.
FAQ