Abra Actions – Plug-in AI assistant that executes front end functions

Hacker News

Hey HN! I built abra-actions, an open-source TypeScript SDK that lets you add a natural language assistant to your app that actually runs real functions you define. You register frontend functions, and Abra connects them to an assistant powered by an LLM. When a user types something, Abra figures out the right function to call and parameters to attach, just like clicking a button and it runs on your own infrastructure with your existing logging, security, and permissions. Setup takes about 5 minutes. Works best with TypeScript + React. Useful for internal tools, admin dashboards, or anywhere you’ve got scattered UI and want to simplify it. Would love to hear thoughts or feedback especially if you’re building internal dashboards, complex UX flows, or anything you think this would useful for! https://abra-actions.com GitHub: https://github.com/abra-actions/abra

Visit product View on Hacker NewsApr 30, 2025

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

95%95% 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: para · Missing: supports, reddit linkedin, podcasting

64%64% 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: exist, existing, io · Missing: https docs, excited, just released

46%46% 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: para · 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 · Missing: plus, platform, intuitive

30%30% 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.

Correct prediction on native model

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