Workbench – ephemeral cloud sandboxes for agentic coding

Hacker News

TL;DR: We built Workbench, a VM-based, ephemeral sandbox you (or your AI agents) can spin up, use, and tear down on demand—exposed over the Model Context Protocol (MCP) for code, filesystem, and terminal access. We’re opening a private beta for 25 users; everyone after will be waitlisted. When you join, you’ll get an email with a Discord invite for support and feedback. Issues/examples on GitHub. Why we built it Most useful agentic workflows end up writing code. Local tools are great but live on your machine and can be messy for security, reproducibility, and resource contention. We wanted the same “code+tools” experience—but cheap, temporary, and remote—so every run happens in an isolated clean room that auto-expires. What it is - Ephemeral VMs: clean, isolated environments for each run - Full capabilities via MCP: code execution, filesystem, terminal - Remote & disposable: spin up in seconds; auto-teardown shortly after - Integrates with MCP-compatible clients (IDEs/assistants) using a simple URL + API key Usage-based: pay for runtime only (no idle) Quick start 1. Sign in with GitHub at workbench.brwse.ai. 2. Create an API key. 3. Use the MCP.

Visit product View on Hacker NewsOct 20, 2025

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

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

66%66% 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: filesystem, ide, io · Missing: https docs, excited, just released

49%49% 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 · Strong signals: users · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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

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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.

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