ML/AI in the wild
🌱 notes (masquerading as a post) 🌱
Developers’ ML workflows
Building personal software with Claude - Nelson Elhage
Nicholas Carlini: How I Use "AI"
Kevin Lynagh: Inventory software, useful LLMs, haunted stm32, casual modeling, minimalist workouts
Erik Schluntz: Replacing my Right Hand with AI
Armin Ronacher: “Sometimes AI is good. I just took the PR description, some hints on the bad functions and cursor generated a functional fix without my involvement.”; GitHub issue: Fix lstrip_blocks being too eager by mitsuhiko · Pull Request #674 · mitsuhiko/minijinja
They all use it - by Thorsten Ball - Register Spill
How I write code using Cursor: A review - Tom Yedwab
Prompts!
Contemplative reasoning response style for LLMs like Claude and GPT-4o
Dev tools
🔪 JAX - The Sharp Bits 🔪 — JAX documentation
LangWatch - Monitor, Evaluate and Optimize your LLM-apps
Build Compound AI Systems Faster with Databricks Mosaic AI | Databricks Blog
Modal: Serverless cloud infrastructure for AI, ML, and data
thebes on X: “SAEs don’t just mean interpretable text models…”
Bio tools
Through a Glass Darkly | Markov Bio
A Future History of Biomedical Progress | Markov Bio
[2301.08559] The Lost Art of Mathematical Modelling
Neural nets in nature
Data and its (dis)content
From The “it” in AI models is the dataset. – Non_Interactive – Software & ML:
Sufficiently large diffusion conv-unets produce the same images as ViT generators. AR sampling produces the same images as diffusion. This is a surprising observation! It implies that model behavior is not determined by architecture, hyperparameters, or optimizer choices. It’s determined by your dataset, nothing else. Everything else is a means to an end in efficiently delivery compute to approximating that dataset. Then, when you refer to “Lambda”, “ChatGPT”, “Bard”, or “Claude” then, it’s not the model weights that you are referring to. It’s the dataset.
ML researchers and SWEs
Research Code – Non_Interactive – Software & ML
Indie ML orgs
Practical ML
Why I bet on DSPy | Isaac Miller's Blog
Making Decisions With Classifiers – Colin Fraser
Estimating how many there are of something when you can’t see them all perfectly – Colin Fraser
AI papers - aggregations sites
Agents
Thread by @egrefen on Thread Reader App – Thread Reader App