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Polyfire – Javascript SDK to build AI apps without a backend

Hacker News

Polyfire – Javascript SDK to build AI apps without a backend

Victor, Lancelot and Kevin here - we are building Polyfire, it allows you to build AI apps in your frontend without having to worry about deploying any backend or infrastructure, it’s a Firebase style product but for AI apps. Right now, it’s a bit like Vercel AI + LangChain + Pinecone in one Javascript SDK. The repo is https://github.com/polyfire-ai/polyfire-js , our home page is https://polyfire.com . Last June we were working on AI-generated docs but we didn’t quite understand how to make it work. So in July, we decided to open-source everything and focus on the underlying infrastructure. Before this startup, I built at least 20 different apps with Firebase. So I thought it could be really cool to build something like Firebase but to make AI apps. Therefore, Polyfire’s goal is to be simple. Setup takes a couple of lines of code, and then you can call text and image models from your Javascript frontend. It also includes a vector store so you can easily add semantic context to your calls with embeddings. We have more things in the SDK Library like a Chat abstraction with automatic long term memory, DataLoader (e.g. to load text or audio files) and a system to turn prompt in environment variables. We tried to detail as much as possible in our docs: https://docs.polyfire.com . We want to add many more things, your feature requests are welcomed! Our goal right now is to make the best tool to build projects during hackathons: making it super easy to build and experiment with LLMs by adding more integrations, models, and features so hackers have many options. We think we can make something great if we can integrate in one experience the top 10-15 tools people need building AI apps. Give it a look: https://github.com/polyfire-ai/polyfire-js . Let us know what you think!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, models · Missing: mac, agents, macos
94%94% 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
92%92% 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 · Missing: https docs, excited, just released
75%75% 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: apps · 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 · Strong signals: calls · Missing: plus, platform, intuitive
31%31% 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
17%17% 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, audio · Missing: web3, crypto, cryptocurrency
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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