Polpo – Control Claude Code (and other agents) from your phone

Hacker News

Polpo is an open-source mobile controller for AI coding agents. It runs a lightweight server on your machine and gives you a phone-friendly dashboard to manage sessions, send prompts, approve tool calls, and review plans. We just released v1.1.0 with support for 5 agents (Claude Code, Codex, Gemini, OpenCode, Pi), skills management from the phone (browse/install/remove skills from skills.sh), and the ability to start new sessions without touching the terminal. The idea started because we wanted to kick off coding tasks from the couch and check on them from the phone. It grew from there. Built with Node.js, no framework on the frontend, WebSocket for real-time updates. Works on LAN or remotely via tunnel (cloudflared, localtunnel, ngrok, SSH). Built by PugliaTechs, a non-profit association from Puglia, Italy.

Visit product View on Hacker NewsFeb 28, 2026

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

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: started, gemini · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: just released, ide, io · Missing: https docs, excited, exist

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, calls · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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