ML/AI in the wild
🌱 notes 🌱
Developers’ ML workflows
How I write code using Cursor: A review
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
Dev tools
🔪 JAX - The Sharp Bits 🔪 — JAX documentation
Build Compound AI Systems Faster with Databricks Mosaic AI | Databricks Blog
Modal: Serverless cloud infrastructure for AI, ML, and data
Bio tools
Through a Glass Darkly | Markov Bio
A Future History of Biomedical Progress | Markov Bio
thebes on X: “SAEs don’t just mean interpretable text models…”
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
Making Decisions With Classifiers – Colin Fraser
Estimating how many there are of something when you can’t see them all perfectly – Colin Fraser