typerassistant – turn any Typer app in to a function-calling assistant

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I'm working on an automation assistant for a customized development workflow, and I built this quick and easy wrapper to instantly build in a function-calling assistant to any Typer CLI. For now, it injects an 'ask' command in to your typer CLI, which you can use to perform standard model queries via the OpenAI assistant API. These queries have the full typer CLI exported to them as functions. There are examples provided of how the assistant can chain these calls together for you, and do some very cool things. Additionally, the underlying assistant objects are exposed. This part needs a lot of work still to get it to where I think it needs to be - there is a roadmap in the README.md. I'm paying particular attention to the lifecycle of assistants and doing things like intelligent incremental updates when the underlying assistant definition changes, but that's all a bit rough right now. Again, details in the README.md. This is my first PyPI published package and I would very much appreciate any helpful tips on how to make this project a success; I think it can do a lot more than it's currently doing, and I would like to commit to growing it. (Also: I am looking for full-time remote work in backend, tooling, or devops!)

Visit product View on Hacker NewsNov 19, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, openai, open · Missing: mac, agents, macos

79%79% 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

74%74% 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

45%45% 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 · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

40%40% 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

17%17% 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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