Ggml.js – Serverless AI Inference on Browser with Web Assembly

Hacker News

What is ggml.js? Run any ggml ported ML models directly on your web browser with ggml.js This project provides JavaScript bindings on ggml models, so that you can embed GGML models to your web apps to build serverless AI inferencing. Currently the framework supports following model types: - Dolly v2 - GPT2 - GPT J - GPT NEO X - MPT - Replit - StarCoder You can find more details about the framework: https://rahuldshetty.github.io/ggml.js/ For live demo examples, you can visit here: https://rahuldshetty.github.io/ggml.js-examples/ Source Code: https://github.com/rahuldshetty/ggml.js Please do provide any feedback/questions/suggestions in the comments. I'll be happy to take it up :)

Visit product View on Hacker NewsAug 8, 2023

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

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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, 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 · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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