Nimbalyst open-source visual workspace for ClaudeCode, Codex, OpenCode

Hacker News

We're open sourcing Nimbalyst, a multi-agent, visual workspace for building with Claude Code, Codex, and Opencode (alpha). It pairs parallel session management with WYSIWYG editors so you and your agents can work on the same files at the same time. The core idea: agents edit files, you edit files, and Nimbalyst makes that collaboration legible. Built-in WYSIWYG editors cover markdown, mockups, Excalidraw diagrams, data models, spreadsheets, and code (Monaco). Every edit is tracked back to the session that made it, with red/green WYSIWYG markdown diffs for review and approval. A session Kanban lets you search, resume, and manage parallel runs, and link sessions to files (and back). A lightweight tracker (alpha) keeps plans, bugs, tasks, and custom items in the same workspace the agent is working in — agents create, update, and execute them, and you see the same view they do. The extension system makes any file type pluggable; current extensions include Excalidraw, mind maps, CSV spreadsheets, a data model designer, mockups, PDF viewing, and a SQLite browser, and they're exposed to the agents too. Worktrees, workstreams, visual git management, and agent-driven commit proposals round out the developer side, and an iOS app handles remote session management (Android in development). Currently supports Claude Code, Codex, Opencode (alpha), and Copilot (alpha), with more planned. It's local-first: files stay on your filesystem in open formats (markdown, excalidraw, html). The local app is MIT licensed and free; team collaboration features are AGPL and will be paid. Tech stack: Electron, React, Jotai, PGLite (Postgres in WASM), Lexical, Monaco, Playwright; collaboration runs on Cloudflare Workers with Durable Objects. https://github.com/Nimbalyst/nimbalyst https://nimbalyst.com/ We'd love your feedback and contributions.

Visit product View on Hacker NewsApr 30, 2026

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

98%98% 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.

Hacker NewsStrong engagement from HN community · Strong signals: filesystem, ide, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para, ios · Missing: reddit linkedin, podcasting, created

56%56% 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, para · Missing: mobile apps, personal, entrepreneurs

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

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

22%22% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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