Em

Empirical – test framework for JavaScript developers building with LLMs

Hacker News

Empirical – test framework for JavaScript developers building with LLMs

Hi HN! This is Arjun and Saikat, and like other product engineers, we've been excited to build with LLMs. Getting powerful models available as off-the-shelf HTTP endpoints is a huge leap forward to integrate and ship ML to end-users. While building on top of LLMs, we've also experienced the pain of non-deterministic behavior – especially for applications that require smaller models. Iterating through model configuration while ensuring no regressions across hundreds of scenarios is a tricky balance. To make this easier, we built Empirical. Here’s a demo video: https://www.youtube.com/watch?v=p8gSGphcOSU We've focused on: - Fast iteration cycles and interactivity when you need to change the prompt or add a new sample. We wanted to build something that feels like “hot reload” for LLM development - A capable UI that combines objective and subjective evaluation, since eye-balling outputs makes it easier to build intuition around model behavior - Ability to customize which model to test, or how to score it — with JavaScript (or Python, if you really must) - Embedded analytics for evaluation results, powered by DuckDB under the hood (more coming up on this!) You can try Empirical today – with a one line CLI command – locally or on CI/CD. And oh, Empirical is 100% open source – so file an issue and we’d be happy to make it work for your use-case $ npx empiricalrun GitHub: https://github.com/empirical-run/empirical Docs: https://docs.empirical.run/

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, lua, open source · Missing: https docs, just released, exist
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.
TrustMRRLess likely to generate early MRR · Strong signals: ios, video, users · Missing: mobile apps, personal, entrepreneurs
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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
14%14% 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.

Incorrect prediction on native model

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