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A ChatGPT TUI with custom bots

Hacker News

A ChatGPT TUI with custom bots

Hi HN! We just shipped a full-featured TUI (Text User Interface) for chatting with your Marvin bots, powered by GPT 4 or GPT-3.5. Like all of Marvin, it's fully open-source and we hope you find it useful. To launch it, upgrade and run `marvin chat`. The TUI is built with Textual ( https://github.com/textualize/textual/ ) and uses some of its newest features including background workers and modals. We've made base TUIs before but this is the first one that's a true "app" with many screens and coordinated global state. Happy to answer any questions about working with Textual - once it "clicked" it was surprisingly similar to building a traditional front end! Small note: Terminal.app on MacOS isn't great for TUIs, so while it'll work, we suggest an alternative terminal. One of our goals with the TUI was to integrate Marvin's bots into the familiar chat UX. Bots can have distinct personalities, instructions, and use plugins, so each one is like a mini "conversational application." You might know about Marvin because of AI Functions, but at its core Marvin is a library for building and deploying bots (in fact, AI functions are actually a bot!). We started building the TUI as a way to quickly explore and assess our bots' capabilities. It quickly became so useful that we decided to make it a first-class experience. We've preloaded several bots, including one that can guide you through an RPG and another that is obsessed with explaining regex, and will add many more. You can even create your own bots just by asking the default bot (Marvin) to help you. We hope the TUI is a fun way to quickly interact with your bots and it was a great way for us to learn Textual. Please check out the code and let us know what enhancements we can add!

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126points
51comments
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, user · Missing: agents, agent, cursor
91%91% 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 · Strong signals: started, including · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
33%33% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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