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Paseo – Open-source coding agent interface (desktop, mobile, CLI)

Hacker News

Paseo – Open-source coding agent interface (desktop, mobile, CLI)

Hey HN, I'm Mo. I'm building Paseo, a multi-platform interface for running Claude Code, Codex and OpenCode. The daemon runs on any machine (your Macbook, a VPS, whatever) and clients (web, mobile, desktop, CLI) connect over WebSocket (there's a built-in E2EE relay for convenience, but you can opt-out). I started working on Paseo last September as a push-to-talk voice interface for Claude Code. I wanted to bounce ideas hands-free while going on walks, after a while I wanted to see what the agent was doing, then I wanted to text it when I couldn't talk, then I wanted to see diffs and run multiple agents. I kept fixing rough edges and adding features, and slowly it became what it is today. What it does: - Run multiple providers through the same UI - Works on macOS, Linux, Windows, iOS, Android, and web - Manage agents in different machines from the same UI - E2EE Relay for mobile connectivity - Local voice chat and dictation (NVIDIA Parakeet + Kokoro + Sherpa ONNX) - Split panes to work with agents, files and terminals side by side - Git panel to review diffs and do common actions (commit, push, create PR etc.) - Git worktree management so agents don't step on each other - Docker-style CLI to run agents - No telemetry, no tracking, no login Paseo does not call inference APIs directly or extract your OAuth tokens. It wraps your first-party agent CLIs and runs them exactly as you would in your terminal. Your sessions, your system prompts, your tools, nothing is intercepted or modified. Stack: The daemon is written in Typescript. The app uses Expo and compiles to both native mobile apps and web. The desktop app is in Electron (I started with Tauri and had to migrate). Sharing the same codebase across different form factors was challenging but I'd say that with discipline it's doable an the result has been worth it, as most features I build automatically work in all clients. I did have to implement some platform specific stuff, especially around gestures, audio and scroll behavior. The relay is built on top of Cloudflare DO, so far it's holding up quite well. I love using the app, but I am even more excited about the possibilities of the CLI, as it become a primitive for more advanced agent orchestration, it has much better ergonomics than existing harnesses, and I'm already using it to experiment with loops and agent teams, although it's still new territory. How Paseo compares to similar apps: Anthropic and OpenAI already do some of what Paseo does (Claude Code Remote Control, Codex app, etc.), but with mixed quality and you're locked onto their models. Most other alternatives I know about found are either closed source or not flexible enough for my needs. The license is AGPL-3.0. The desktop app ships with a daemon so that's all you need. But you can also `npm install -g @getpaseo/cli` for headless mode and connect via any client. I mainly use Mac, so Linux and Windows has mostly been tested by a small group of early adopters. If you run into issues, I’d appreciate bug reports on GitHub! Repo: https://github.com/getpaseo/paseo Homepage: https://paseo.sh/ Discord: https://discord.gg/jz8T2uahpH Happy to answer questions about the product, architecture or whatever else!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, apple, agentic
100%100% 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: started, para, ios · Missing: supports, reddit linkedin, podcasting
90%90% 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, exist, existing · Missing: https docs, just released, lua
70%70% 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 · Strong signals: mobile apps, ios, apps · Missing: personal, entrepreneurs, video
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews
51%51% 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
32%32% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, audio · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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