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PromptFiddle – Open-source WASM-based LLM playground

Hacker News

PromptFiddle – Open-source WASM-based LLM playground

Hey HN, We made an LLM Playground where you can use jinja to render prompts and extract structured data. For example, receipts, invoices, audio snippets, etc. It uses the BAML programming language under the hood. I was tired of not being able to test every model (local + hosted) in the same place. I wanted it work for audio and for images and for text. Plus, I HATE navigating UIs when I can just write code, so the existing non-code first offerings sucked for me. We were inspired a little bit by Markdown Preview and a little bit by Rust. For example, the VSCode version of this has a run-tests buttons right in your IDE cause that is the only reason i started writing tests in rust. What do you think? Code is here: https://github.com/BoundaryML/baml/tree/canary/typescript

Visit productView on Hacker NewsAug 24, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, code, open · 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
79%79% 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: started · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, host · Missing: platform, intuitive, reviews
34%34% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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