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OpenCodeSpace – Disposable Containers for Claude Code in YOLO Mode

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OpenCodeSpace – Disposable Containers for Claude Code in YOLO Mode

Hello HN! I built OpenCodeSpace https://github.com/ngramai/opencodespace — a CLI tool that lets you launch disposable, self-hosted VS Code environments in one command. It’s like a self-hosted Code Spaces, but: - Works locally with Docker or remotely on Fly.io (AWS, GCE coming soon) - Auto-configured with Claude Code, OpenAI, Gemini CLI - Designed for YOLO mode development: temporary, parallel, throwaway sessions As Claude Code and Gemini CLI became core to my dev workflow, I kept needing a way to run them in parallel, in clean, isolated environments in full YOLO mode (`--dangerously-skip-permissions`) without worrying about its effects on my computer. I tired devcontainers, but not all repos have them configured and remote devcontainer setup is a pain. I also tried git work tree + multi window local VSCode, but I felt all the branching and folder replication on my local desktop a bit messy. I wanted something simpler, so I built this. How it works: * Run `opencodespace .` in any folder * It checks for `.opencodespace` (or initializes one) * You choose: run locally with Docker or remotely on Fly.io * Browser-based VS Code opens up with everything ready Install: pip install opencodespace Code: https://github.com/ngramai/opencodespace It’s still early days, but I love using this. I often spin up a few of these on Fly to let Claude code automatically chug away on bugs while I focus on heavier tasks in my local IDE. Would love your feedback and ideas!

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, computer, dock · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Strong signals: para, gemini · Missing: supports, reddit linkedin, podcasting
67%67% 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: 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% 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.

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