AI

AI sandbox that runs on your homelab

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AI sandbox that runs on your homelab

Pixels provisions Incus containers onto your TrueNAS server using their websocket API. I took inspiration from https://sprites.dev/ . I have been doing a bit of vibe coding recently, but I didn't want to pay for a sandbox product. It's supposed to be simple to startup a container and get into a console. The most popular agent CLI's that I could think of are already installed if you provision with `devtools = true`. I used Claude Code extensively but this is NOT vibe coded! I review every line of code. I might have missed some corkers when developing this at midnight. It is using a bit of trickery to support checkpoints of the Incus containers. The main reason for this was so that you can spin up a base container, install everything you want, and then create a new container from that. I did try to make the sandbox secure. I think I can do a better job there. Opus is actually pretty good at escaping a sandbox if you ask it to. Read the details in SECURITY.md. In future I would like to provision the base container as part of the setup, to help speed up subsequent creates. I'm not totally sold on the name. It was just the first thing I thought of. I don't know if this is even something others would want to use, but it scratches an itch for me. --- A bit of backstory: Over the last couple of months I have been working on a Terraform provider for TrueNAS SCALE [0] and it occurred to me that all this work could be extracted into a client library [1]. I wanted to play around with vaxis [2], so I created a basic TUI that you can use to monitor your TrueNAS server [3]. [0]: https://github.com/deevus/terraform-provider-truenas [1]: https://github.com/deevus/truenas-go [2]: https://github.com/rockorager/vaxis [3]: https://github.com/deevus/truenas-tui

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, new · Missing: mac, agents, macos
88%88% 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: created · Missing: supports, reddit linkedin, podcasting
80%80% 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
44%44% 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 · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
26%26% 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
16%16% 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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