Nimbus – Browser with Claude Code UX

Hacker News

Hi HN, I'm Anil. Nimbus is a desktop browser with an AI agent built into it. The UX is shamelessly inspired by Claude Code: a chat bar at the bottom, an agent log above it, and the webpage itself when its needed. This is mainly a UX experiment for me. And also the reason it isn't a Chrome extension: once you have a chat bar that understands intent, the URL field is redundant. You shouldn't have two places to tell the browser what you want. I didn't want to bolt an agent onto an existing browser's chrome and end up with duplicated controls everywhere — I wanted full freedom to redesign the shell from scratch, decide what stays, what goes, and what a browser even looks like when the agent is the primary interface. Download for macOS: https://usenimbus.app Launch video: https://youtu.be/dj23-XIiB1o

Visit product View on Hacker NewsApr 24, 2026

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

96%96% 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.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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 · Strong signals: interface · 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

27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

25%25% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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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