Underscore – Code with AI in the browser, zero setup required

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I built Underscore a browser-based coding environment that runs entirely in the cloud. There’s no local setup, CLI, or Docker—open the site and start coding with AI assistance from any device. Each project runs in its own persistent cloud container (Cloudflare) with common languages and tools preinstalled (Node.js, Python, Go, Rust, Bun, etc.). It’s built on top of opencode ( https://opencode.ai ), an open-source AI coding agent. Features: Cloud containers with persistent storage Access from any browser 75+ AI models (Claude Sonnet 4.5, GPT-5.2 Codex, DeepSeek R1, etc.) Project templates (AI agents, serverless functions, web apps, APIs, E2E tests) GitHub integration (clone repos, push changes back) MCP directory with one-click setup for 20+ services (GitHub, Linear, AWS, Sentry, PostgreSQL, etc.) Automatic public preview URLs for dev servers Built-in terminal and file browser (code editor coming soon) Status: Beta. Core workflows work, but there are rough edges. I’d love feedback from HN on what’s useful, what’s missing, and where this fits (or doesn’t) in your workflow. Happy to answer questions about the architecture or AI integration.

Visit product View on Hacker NewsJan 30, 2026

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

97%97% 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 · Missing: supports, reddit linkedin, podcasting

71%71% 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: io · Missing: https docs, excited, just released

42%42% 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: soon · Missing: plus, platform, intuitive

37%37% 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.

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

25%25% predicted probability of success on TrustMRR, 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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