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Create AI Agents with React

Hacker News

Create AI Agents with React

For the last couple of months, we've been exploring how to make agentic AI development more approachable for web developers. Here's what we have, so far: import { Agent } from "react-agents"; const NoteTaker = () => { return ( <Agent> <Prompt> You are a note-taking assistant that helps organize and summarize information. </Prompt> <Memory type="vectorstore" /> </Agent> ) } It's somewhat inspired by [Priompt]( https://github.com/anysphere/priompt ): instead of rendering a user interface, our framework renders a prompt + action interface for AI agents. The renderer is backed by a stateful agentic runtime loop, with a library of components you can import to add capabilities like TTS, database and social media integration. We want the experience to feel nice and webby like Vercel, but for AI agents. For those interested in trying it out: npm install usdk -g usdk create We're particularly curious about: - Does this approach feel natural to React developers? Does it help you transition to agentic AI? - DX roadblocks we should fix for our next release - Integrations you'd like us to add (We already have Discord, X, and we're working on Twitter Spaces) - Different environments this can integrate with (we've tested local, electron, browser, and server) The source is open and we're actively working on improvements. Links: - [1] Homepage: https://upstreet.ai - [2] Documentation: https://docs.upstreet.ai - [3] Open-source GitHub repository: https://github.com/UpstreetAI/upstreet-core

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor
97%97% 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
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

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