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Repowire
Peer-to-peer mesh for AI coding agents
Real projects span multiple repos, multiple runtimes, multiple machines. Today every agent operates in a sealed box: no shared context, no live channel, no way to ask the agent next door. Repowire is a live mesh for your agents. Local-first. Runtime-agnostic
About Repowire on Product Hunt
“Peer-to-peer mesh for AI coding agents”
Repowire was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #69 on the daily leaderboard. Real projects span multiple repos, multiple runtimes, multiple machines. Today every agent operates in a sealed box: no shared context, no live channel, no way to ask the agent next door. Repowire is a live mesh for your agents. Local-first. Runtime-agnostic
On the analytics side, Repowire competes within Open Source, Developer Tools and GitHub — topics that collectively have 621.3k followers on Product Hunt. The dashboard above tracks how Repowire performed against the three products that launched closest to it on the same day.
Who hunted Repowire?
Repowire was hunted by Prass. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Repowire including community comment highlights and product details, visit the product overview.
When working across features spanning multiple repositories, I noticed myself often copy pasting the output from one agent into another agent ("What is the API contract like?", "What terraform do I need for a feature shaped like X?", etc.). I called this the human bottleneck.
So, I set out to remove the human-in-the-middle to see whether the output quality of the model improves and/or the speed. It did. Started off simple, added a tmux mcp to my claude code. As I started using it more, I created Repowire.
Today, Repowire connects your agents into a live mesh. Any agent can query, notify, or broadcast to any other. You manage the mesh from a dashboard, Telegram, or Slack. It's local-first, works across agent runtimes, and scales from 2 peers to 20+. Currently supports Claude Code, Codex, Gemini, Opencode. And because we support multiple kinds of agents, we end up observing quite interesting patterns, such as
Multi-repo coordination: Agents in different repos ask each other live questions like ask_peer("project-b", "what endpoints do you expose?") instead of relying on stale docs.
Cross-agent review: One peer builds, another reviews for code quality, security, and simplification. Works especially well across different agent runtimes.
Orchestrator: A dedicated coordinator peer dispatches tasks, tracks progress, and runs review cycles, making 10+ agent meshes manageable.
Worktree isolation: Spawn peers on git worktrees so each works on its own branch and PR in parallel, with no merge conflicts during development.
Mobile mesh management: A Telegram bot lets you dispatch work, check peer status, and message any peer from your phone.
Infrastructure-as-peer: A dedicated infra peer (k8s, DNS, cloud config) that project peers coordinate with directly for namespaces, deploys, and the like.
Overnight autonomy: Hand peers long-running tasks (migrations, refactors, test suites) and disconnect and they report back via Telegram or dashboard when you return.
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I hope you enjoy the tool,
Prass