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Dynamic Pydantic – Create Pydantic Models Using AI

Hacker News

Dynamic Pydantic – Create Pydantic Models Using AI

A couple of months ago, I was able to replicate expand.ai's (YC S24) auto schema generation tool [1] over a weekend. I've turned it into an open-source tool [2], combining Instructor and Pydantic to dynamically create models in runtime. If AI agents want to become truly autonomous, they will need to be able to generate and validate code, tools and databases on the go. Please let me know what you think! Looking for feedback on expanding this for agent-to-agent creation. [1] https://expand.ai [2] https://github.com/lukafilipxvic/dynamic-pydantic

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
89%89% 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
59%59% 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: 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.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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.
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.

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