Paseo, an open source environment for coding agents

Hacker News

Hey HN, I'm Mo. I've been building Paseo, an open source environment for running Claude Code, Codex, and OpenCode across desktop, mobile, web, and CLI. It started last September as a push-to-talk voice interface for Claude Code. I wanted to talk to an agent while going on walks. Then I wanted to see what it was doing. Then text it when I couldn't talk. Then review diffs, run multiple agents, and manage work across machines. After a lot of iteration, it turned into a broader environment. The basic model is: - A daemon runs on your machine (MacBook, desktop, VPS, etc.). - Clients connect to it over WebSocket. - There's an optional built-in E2EE relay for easy remote/mobile access. A few things I care about: - It works with the first-party agent CLIs, not by calling inference APIs directly or extracting OAuth tokens. - It's multi-provider instead of locking you to one model. - It supports local voice, git worktrees, split panes, terminals, and running agents across different hosts. - No forced account, no telemetry, no tracking. The daemon is written in TypeScript. The app uses Expo for native mobile and web, and the desktop app is Electron. I actually started with Tauri and had to migrate due to platform specific problems. The relay is built on Cloudflare Durable Objects. I use Paseo every day, but one thing I’m increasingly excited about is the CLI. It's turning into a primitive for more advanced orchestration, loops, and agent teams, which feels like a bigger direction than the app alone. Repo: https://github.com/getpaseo/paseo Homepage: https://paseo.sh/ Discord: https://discord.gg/jz8T2uahpH Happy to answer questions about the product, architecture, or where I think this is going.

Visit product View on Hacker NewsMar 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, apple

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, interface · Missing: plus, intuitive, reviews

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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