I made a simple agentic framework in JavaScript

Hacker News

Well, I wouldn't call it a framework, but rather an easy way to define tools and agents, allowing the agent to think and do the work. - Currently supports Open AI, and DeepSeek models. I tried building an HN title recommender agent with this framework that analyzes top-performing posts on HN and suggests titles for HN posts. And this was the agent's reply after inspecting 10 best posts: Having analyzed the top 10 performing posts on Hacker News, we've observed that successful titles are often concise, intriguing, and sometimes provocative or impactful. They tend to evoke curiosity, highlight significant issues, or reveal unexpected information. Based on these insights and considering the unique nature of your GitHub repo, which introduces an agentic framework in JavaScript, here's a suggested title and message for your Hacker News post: Title: 'EnvoyJS: A New Frontier in JavaScript - Introducing Agentic Frameworks' Lol :P Still a lot of work to do!

Visit product View on Hacker NewsJan 22, 2025

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

86%86% 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 HackersIH features products with proven revenue · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created

46%46% 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: hacker news, ide, io · Missing: https docs, excited, just released

38%38% 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: ios, way · Missing: mobile apps, personal, entrepreneurs

38%38% 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

35%35% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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