I built a simple CLI helper integrating with GPT-3

Hacker News

Hi all! I don’t think CHATGPT has gone unnoticed by anyone here on Hackernews. I’ve myself played around a bit with it, but felt it was annoying to always have to log into their GUI to ask the questions. To scratch my own itch and at the same time learn more about how to write my own command line interface, I created 'askai'. 'askai' is a simple CLI integration with OpenAI’s GPT3 models. I’ve primarily used it to get quick answers to technical questions, like: > askai "How to mock user input when writing a Python pytest test?" > askai "How do I remove a conda environment?" As I've found it quite helpful, I decided to spend some time to package it in a nicer way to share it with all of you. I've also uploaded it to PyPI to simplify the installation process. 'askai' enables you to: * Ask questions and get the answers straight into your terminal * Configure which model and model parameters you want to use * Overwrite saved configurations when you ask questions Currently, it only supports OpenAI’s models, but my plan is to integrate more endpoints as soon as new capable NLP endpoints are popping up. I hope some of you find it useful :)

Visit product View on Hacker NewsDec 27, 2022

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

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created, para · Missing: reddit linkedin, podcasting, latex

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: answers, way, para · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: interface, soon · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, 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.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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