Kiln – AI Boilerplate with Evals, Fine-Tuning, Synthetic Data, and Git

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I noticed there weren't boilerplates for AI projects like there were for web apps, so I built one. Same idea - everything you need to get a project up and running quickly. However, instead of web-framework/CSS/DB, it's tools for AI projects: evals, synthetic data gen, fine-tuning, and more. Kiln is a free, open tool that gives you everything most AI projects need in one integrated package: - Eval system: including LLM-as-judge evals, eval data generation, human baselines - Fine-tuning: proxy to many fine-tuning providers like Fireworks/Together/OpenAI/Unsloth - Synthetic data generation: deeply integrated into evals and fine-tuning - Model routing: 12 providers including Ollama, OpenRouter, and more - Git-based collaboration: projects are designed to be synced through your own git server The key insight is that these tools work much better when they're integrated. For example, the synthetic data generator knows whether you're creating data for evals vs. fine-tuning (which have very different data needs), and evals can automatically test different prompt/model/fine-tune combinations. It runs entirely locally - your project data stays in local files, and you control your own git repos. No external services required (though it integrates with them if you want). Main project GitHub: https://github.com/Kiln-AI/Kiln Demo GitHub where I use it to build a 'natural language to ffmpeg command' demo with evals, fine-tunes, and synthetic data (including demo video): https://github.com/Kiln-AI/demos/blob/main/end_to_end_projec...

Visit product View on Hacker NewsJul 28, 2025

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

89%89% 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, apps, openai · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, ide, io · Missing: https docs, excited, just released

49%49% 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, video · 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

15%15% 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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