AI/ML

OpenAI researchers recently claimed a major math breakthrough on X, but quickly walked it back after criticism from the community, including Deepmind CEO Demis Hassabis, who called out the sloppy communication.

An Experimental Digital Garden

I used to write more

When I played doctor with the chatbot, the simulated patient confessed problems that are real—and that should worry all of us.

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization. - google/langextract

Max Mynter - Full Stack Machine Learning Engineer. Explore my blog and portfolio. Multiple years of experience in data science, software engineering, and machine learning.

Contribute to apple/ml-simplefold development by creating an account on GitHub.

It turns out we've all been using MCP wrong. Most agents today use MCP by exposing the "tools" directly to the LLM. We tried something different: Convert the MCP tools into a TypeScript API, and then ask an LLM to write code that calls that API. The results are striking.

Richard Sutton is the father of reinforcement learning, winner of the 2024 Turing Award, and author of The Bitter Lesson. And he thinks LLMs are a dead end. After interviewing him, my steel man of Richard’s position is this: LLMs aren’t capable of learning on-the-job, so no matter how much we scale, we’ll need *some* new architecture to enable continual learning. And once we have it, we won’t need a special training phase — the agent will just learn on-the-fly, like all humans, and indeed, like all animals. This new paradigm will render our current approach with LLMs obsolete. In our interview, I did my best to represent the view that LLMs might function as the foundation on which experiential learning can happen… Some sparks flew. A big thanks to the Alberta Machine Intelligence Institute for inviting me up to Edmonton and for letting me use their studio and equipment. Enjoy! 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkesh.com/p/richard-sutton * Apple Podcasts: https://podcasts.apple.com/us/podcast/richard-sutton-father-of-rl-thinks-llms-are-a-dead-end/id1516093381?i=1000728584744 * Spotify: https://open.spotify.com/episode/3zAXRCFrHPShU4MuuIx4V5?si=c9f4bf24fb4c43e3 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 * Labelbox makes it possible to train AI agents in hyperrealistic RL environments. With an experienced team of applied researchers and a massive network of subject-matter experts, Labelbox ensures your training reflects important, real-world nuance. Turn your demo projects into working systems at https://labelbox.com/dwarkesh * Gemini Deep Research is designed for thorough exploration of hard topics. For this episode, it helped me trace reinforcement learning from early policy gradients up to current-day methods, combining clear explanations with curated examples. Try it out yourself at https://gemini.google.com/ * Hudson River Trading doesn’t silo their teams. Instead, HRT researchers openly trade ideas and share strategy code in a mono-repo. This means you’re able to learn at incredible speed and your contributions have impact across the entire firm. Find open roles at https://hudsonrivertrading.com/dwarkesh To sponsor a future episode, visit https://dwarkesh.com/advertise 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 – Are LLMs a dead end? 00:13:51 – Do humans do imitation learning? 00:23:57 – The Era of Experience 00:34:25 – Current architectures generalize poorly out of distribution 00:42:17 – Surprises in the AI field 00:47:28 – Will The Bitter Lesson still apply after AGI? 00:54:35 – Succession to AI

Radiology combines digital images, clear benchmarks, and repeatable tasks. But demand for human radiologists is ay an all-time high.

Contribute to humanlayer/advanced-context-engineering-for-coding-agents development by creating an account on GitHub.

Safe, Open, High-Performance — PDF for AI. Contribute to opendataloader-project/opendataloader-pdf development by creating an account on GitHub.

☁️ Convert any site to clean markdown & llms.txt. Boost your site's AI discoverability or generate LLM context for a project you're working with. - harlan-zw/mdream

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024) - hiyouga/LLaMA-Factory

Across the world, marriages are being destroyed as spouses use AI like OpenAI's ChatGPT to attack their partners.

Verifying magic on the jagged frontier

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A curated collection of fun and creative examples generated with Nano Banana🍌, Gemini-2.5-flash-image based model. This repository showcases diverse AI-generated visuals and prompts, highlighting t...

For Richard, a crazy idea was an opportunity to either prove it wrong or prove it right.

Reproducibility is a bedrock of scientific progress. However, it’s remarkably difficult to get reproducible results out of large language models. For example, you might observe that asking ChatGPT the same question multiple times provides different results. This by itself is not surprising, since getting a result from a language model involves “sampling”, a process that converts the language model’s output into a probability distribution and probabilistically selects a token. What might be more surprising is that even when we adjust the temperature down to 0This means that the LLM always chooses the highest probability token, which is called greedy sampling. (thus making the sampling theoretically deterministic), LLM APIs are still not deterministic in practice (see past discussions here, here, or here). Even when running inference on your own hardware with an OSS inference library like vLLM or SGLang, sampling still isn’t deterministic (see here or here).

A very common question I see about LLMs concerns why they can't be made to deliver the same response to the same prompt by setting a fixed random number seed. …

AWS S3 Vectors aims for 90% cost savings for vector storage. But will it kill vectordbs like Milvus? A deep dive into costs, limits, and the future of tiered storage.

A Blog post by Stefano Fiorucci on Hugging Face

This methodology provides a structured approach for collaborating with AI systems on software development projects. It addresses common issues like code bloat, architectural drift, and context dilu...

My notes now talk back and it’s terrifyingly useful.

“Don’t use chatbots as search engines” was great advice for several years... until it wasn’t. I wrote about how good OpenAI’s o3 was at using its Bing-backed search tool back …

A big nerd debate with bigger implications for the future of work, technology, and the economy

Part 1 of 6 in the series “LLMs Need Knowledge Graphs. Use RDF or End Up Rebuilding It.”