Rick Lamers

@ricklamers.bsky.social

Tinkering with ML systems at Groq 🧙‍♂️

You probably haven't heard of PydanticAI yet, but I'm sure you have heard of Pydantic. Check it out, it's a VERY neat way to build agentic LLM-backed programs using all the goodness types and structured outputs give you. The agent framework you won't hate? Groq + PydanticAI = 🚀

add Groq client support by ricklamers · Pull Request #84 · pydantic/pydantic-ai

Added GroqModel class to interact with Groq API. Implemented structured and text-based responses for Groq models. Added example and tests for Groq model integration. Updated dependencies to include...

github.com

Evals are "too damn expensive" until you: • can't migrate underlying models safely • can't add new features with confidence • can't ship without HITL evals, which takes >100x longer • product development and iteration grinds to a halt • lose customer trust due to poor user experience

This meme holds surprisingly well. AWS ushered in an era where infra was no longer a differentiating factor, all startups built on cloud and moats continued to exist (network effects, momentum, proprietary data, etc.). AI APIs = new cloud layer.

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Great example of why mathematical understanding of eg matrix decompositions is key for optimizing existing methods. You might not need math to utilize other people’s work in machine learning but be prepared to bring a mathematical toolkit if you’re looking to innovate.

Parameter-Efficient LLM Finetuning With Low-Rank Adaptation (LoRA) - Lightning AI

In the rapidly evolving field of AI, using large language models in an efficient and effective manner is becoming more and more important. In this article, you will learn how to tune an LLM with Low-Rank Adaptation (LoRA) computationally efficiently!

lightning.ai