AI/ML

Customize and extend Claude Code's behavior by registering shell commands

Multiple parallel Claude-Code sessions power a self-improving AI factory where agents write, review, and refine code.

A short demo of using Claude Code to add a new feature to one of my GitHub repositories by implementing a GitHub Actions workflow for me.Full code written by...

A topic-organized collection of 200+ LLM research papers from 2025

We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks...

Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls, according to Gartner #GartnerSYM #GartnerIT

This new paper by 11 authors from organizations including IBM, Invariant Labs, ETH Zurich, Google and Microsoft is an excellent addition to the literature on prompt injection and LLM security. …

Feature: More fiction than science

Image generators are designed to mimic their training data, so where does their apparent creativity come from? A recent study suggests that it’s an inevitable by-product of their architecture.

LLM failures to reason, as documented in Apple’s Illusion of Thinking paper, are really only part of a much deeper problem

LLMs were invented in four major developments... all of which were datasets

Notes from a talk I delivered at the 2025 Data + AI Summit, detailing the problem with prompts in your code and how DSPy can make everything better.

Reviewing the few changes in the Claude 4.0 system prompts, we get a sense for how system prompts program chatbot applications.

Claude Is my favorite AI (even with ChatGPT, Gemini and DeepSeek in the mix)

6 tactics for fixing your context and shipping better agents. As Karpathy says, building LLM-powered apps means learning to ‘pack the context windows just right’—smartly deploying tools, managing information, and maintaining context hygiene.

Drew Breunig has been publishing some very detailed notes on context engineering recently. In How Long Contexts Fail he described four common patterns for context rot, which he summarizes like …

A talk about what I have learned about programming with agents over the last few months.

Armin Ronacher delivers a 37 minute YouTube talk describing his adventures so far with Claude Code and agentic coding methods. A friend called Claude Code catnip for programmers and it …

Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density…

A pragmatic take on AI coding with practical insights

Current recommendations of agentic coding.

Contribute to NomenAK/SuperClaude development by creating an account on GitHub.

On vision and how to understand deep learning.

35% off our evals course: https://bit.ly/evals-ai Vincent introduces Marimo, a reactive notebook environment. He walks us through the features of Marimo, including interactive and reactive charts and widget integration. Vincent demonstrates how you can use these components to build annotation apps for evals. Vincent also highlights differences between Marimo and traditional Jupyter Notebooks. Links: 1. Repo w/notebook: https://github.com/koaning/molabel 2. Vincen'ts drawing pad: https://www.amazon.com/Inspiroy-H640P-Graphics-Battery-Free-Sensitivity/dp/B075T6MTJX 3. Vincent's sites: https://koaning.io , and https://calmcode.io/ 00:00 Introduction to Data Science Journey 00:27 Exploring the Chick Weight Dataset 00:57 Interactive Data Analysis with Marimo 02:04 Importance of Looking at Data 03:32 Advanced Data Visualization Techniques 05:14 Introduction to Marimo's Unique Features 06:44 Reactive Programming in Marmo 12:50 AI Integration and Custom Rules 15:30 Marimo's Storage and Export Options 27:16 Advanced Visualization and Annotation 37:10 Introduction to Any Widget 37:45 Building Custom Widgets 38:56 Showcasing the Scatter Widget 40:29 Defining Widgets with Any Widget 45:58 Annotation Widgets and Their Uses 52:14 Exploring More Widget Capabilities 01:01:32 Marimo's App Mode and Deployment 01:03:37 Final Thoughts and Future Directions 01:04:45 Q&A and Closing Remarks

VS Code Copilot Memory-Bank Instructions. GitHub Gist: instantly share code, notes, and snippets.

To create reliable agents, AI companies had to go beyond predicting the next token.

Introducing a new, unifying DNA sequence model that advances regulatory variant-effect prediction and promises to shed new light on genome function — now available via API.

We let Claude run a small shop in the Anthropic office. Here's what happened.

In "what could possibly go wrong?" news, Anthropic and Andon Labs wired Claude 3.7 Sonnet up to a small vending machine in the Anthropic office, named it Claudius and told …