AI

AI Agent with Dockerfile-Defined Context

Hacker News

AI Agent with Dockerfile-Defined Context

https://www.syntix.pro/ Syntix is an AI code interpreter. It allows you to ask an AI programming questions, and have it answer back in a Dockerfile-defined container, or in one of 8 environments supported out of the box: NodeJS, NodeTS, Python, C#, Java, Go, Rust, pgSQL. The AI will automatically install any needed dependencies and execute the generated code in its own environment - with full internet access. This gives the AI instant feedback on its code, circumventing hallucinations. You can also upload files and folders to the execution environment for processing. The current size limit is about 7GB. I use fly.io's Fly Machines for the execution environments, as well as for the app's back-end. Here's a demonstration of the Syntix AI agent fetching the top Hacker News post of the day, based on a user request: https://www.youtube.com/watch?v=_wcJ7mTNJ0A And here is a demonstration of using a custom environment on Syntix: https://www.youtube.com/watch?v=JlY0KBnzCzA

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, user · Missing: agents, macos, cursor
80%80% 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 · Missing: supports, reddit linkedin, podcasting
76%76% 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: hacker news, io · Missing: https docs, excited, just released
41%41% 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
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% 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
15%15% 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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