Vicoa – Code with Claude and Codex Anywhere with Mobile Client

Hacker News

Hey all, Nick here. I am building Vicoa (Vibe Code Anywhere) https://vibecodeanywhere.com , a tool for you to use Claude Code (and Codex) anywhere. With Vicoa you can: - Run a regular Claude Code session from terminal - Pick up the same session on mobile or tablet - Get push notifications when Claude Code needs your input - Keep everything in sync across devices Demo video here: https://www.youtube.com/watch?v=ZBpNzqqLYmg I built this because I kept hitting the same pain point with Claude Code: you have to sit and babysit it. Often Claude stops mid-flow waiting for input. If I stepped away — even just for coffee — I’d come back later to find progress stalled and needs my input. So I built the Vicoa. You can install it with a simple command: `pip install vicoa && vicoa` Download iOS App here: https://apps.apple.com/app/id6751626168 This is an early version. Looking forward to your feedback and comments.

Visit product View on Hacker NewsSep 19, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

84%84% 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: ios · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% 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

25%25% 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.

Correct prediction on native model

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