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Beanbox a microVM built for LLMs and humans

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Beanbox a microVM built for LLMs and humans

Beanbox is a microVM built for LLMs and humans. The core idea is to allow LLMs to control a computer in isolation and in a safe way. Additionally, the interaction with the LLM should be like pair coding. You can write a few commands and the LLM can continue from there. It runs on the libkrun virtualisation library and the rootfs is an unpacked ubuntu docker image. On VM startup a forked version of gotty is started so you can access the VMs terminal on localhost:3002 (port may vary). I use GPT-4 + selenium + the browser terminal to have GPT-4 execute code in VM. Essentially creating a pair coding session. A lot here is hacky, but the idea is to demonstrate & explore what a more integrated coding interaction would look like.

Visit productView on Hacker NewsMar 26, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: computer, dock, coding · Missing: mac, agents, macos
95%95% 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: ide, io · Missing: https docs, excited, just released
79%79% 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: started · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · 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
12%12% 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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