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I've been hard at work on Kiln (3k Github stars, ~10k users):

- Gihub: https://github.com/Kiln-AI/Kiln

- Demo Video: https://getkiln.ai#demo

It's goal is to makes AI model+product development processes better in a bunch of ways:

- Truly intuitive desktop apps, on top of our open-source python library

- Rapid prototyping: Quickly try a range of state of the art models+techniques (no code needed)

- Task specific evals: How does the system perform on your goals (not just global benchmarks)

- Iterate with confidence: update to better+faster models/techniques, knowing it works on all past evals/tests

- Team collaboration: get the entire team (PM/QA/Eng/Design/DS) engaged early and often. Shared datasets+evals you can sync with Git.

Features include:

- Fine tune dozens of models

- Interactive synthetic data generation to build train/eval datasets

- Powerful custom evals: LLM as Judge and G-Eval

- Collaboration: share with Git or shared drives

- Runs any LLM: locally or remote

- Private: it's local software

- Open Source Python library

- Completely free

- Much more: dataset management, ratings, prompt generators, JSON support, LLM repairs, reasoning models...

Happy to answer any questions!

Comments URL: https://news.ycombinator.com/item?id=43525610

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Comments URL: https://news.ycombinator.com/item?id=43525233

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Comments URL: https://news.ycombinator.com/item?id=43525181

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I have some experience with Nutrisense Continuous Glucose Monitors (CGM) , and the experience is very user-friendly through the app. The problem is it costs $225/mo, and most of that cost is in the software.

I think you can get Libre CGMs for $40/mo but they don't come with any software or insights.

There has to be an open source solution, right? On google I saw a few people built their own projects, but I'm wondering if anyone here is familiar with the range of open source in this area and what the best app is.

Comments URL: https://news.ycombinator.com/item?id=43525163

Points: 3

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PromptRepository is an open-source framework (Typescript) that illustrates an approach to creating more reliable LLM-based applications through systematic prompt validation, and testing.

Key features:

- Simple JSON schema for externalized prompts with versioning and parameter validation - Lightweight approach inspired by Anthropic's guidance to "keep it simple" with LLM applications - 'Unit tests for Prompts' inspired by Chris Benson/Practical AI's recommendations from episode 295. - Bundled prompts to generate unit tests and evaluations for new prompts built with the framework.

The framework demonstrates a novel approach: bundle tested prompts that help generate code to use the framework.

Repo: https://github.com/jonverrier/PromptRespository

Take a look.

Comments URL: https://news.ycombinator.com/item?id=43525158

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Article URL: https://www.johnirons.com/pdfs/shadowleguin.pdf

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Article URL: https://old.reddit.com/r/reddit/comments/1jf1bxy/private_messages_will_be_replaced_with_reddit/

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Article URL: https://om.co/2025/03/29/the-mediocrity-of-modern-google/

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Article URL: https://thesociologicalreview.org/projects/undisciplining/talks-discussions-and-debates/on-neoreaction/

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