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Middleware for running autonomous AI coding agents in sandboxes

Hacker News

Middleware for running autonomous AI coding agents in sandboxes

We've built what we believe is the missing piece in AI development: an agent-agnostic middleware infrastructure that lets AI coding assistants run in parallel sandboxed environments. Key technical features: - Dynamic workspace provisioning with parallel sandbox environments - Connect to the workspace with VS Code - Full system interaction capabilities (shell, Git, LSP) - Resource-optimized cloud infrastructure - Enterprise-grade security and access controls Why we put together this proof-of-concept: Current AI coding agents are limited by working in single files or environments. We have a vision to enable agents to: - Spin up multiple sandboxes to test different solutions simultaneously - Access full development environments, not just single files - Run real-time tests - Scale compute resources efficiently Our implementation addresses the core issues outlined in our CEO's recent analysis of AI coding infrastructure ( https://go.daytona.io/ai-coding ). This is a proof of concept demonstrating how AI agents can work with proper infrastructure support. We're looking for feedback from the HN community, especially: - Developers building AI coding assistants - Teams working on development environment tooling - Anyone interested in AI agent infrastructure standards The code is open source and we welcome contributions. Happy to answer any technical questions! --- Built at Daytona - we make dev environment tools

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, single · 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: para, efficiently · 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: open source, io · Missing: https docs, excited, just released
37%37% 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: para · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
21%21% 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
19%19% 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.

Correct prediction on native model

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