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Litterbox – Defend Against Supply Chain Attacks

Hacker News

Litterbox – Defend Against Supply Chain Attacks

Are you worried about a supply chain attack (or even a rogue AI agent perhaps) compromising your entire development system? To minimise damage in such a scenario, I've built https://litterbox.work/ ( https://github.com/Gerharddc/litterbox ). Litterbox leverages Podman on Linux to create reproducible and somewhat isolated development environments (these environments are isolated from each other and from your host machine). These are similar to VSCode's DevContainers but take the concept a step further by putting the editor itself inside the container too. This helps to protect against exploits inside the editor (from rogue extensions perhaps) but more importantly, it eliminates the need for editor integration (i.e. the editor needs no knowledge of or support for Litterbox). Furthermore, Litterbox comes with a specialised SSH agent for exposing SSH keys in a more secure way where each request to the agent needs to be approved in a pop-up dialog. This project is still in the very early stages with plenty of rough edges so any contributions or suggestions would be greatly appreciated!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, code · Missing: agents, macos, cursor
68%68% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
44%44% 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
18%18% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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