How to use AI skills—reusable packages of instructions and files—to automate repetitive data science workflows. By moving beyond simple prompting into structured skills, users can maintain shorter context windows while ensuring consistent, high-quality outputs for complex tasks like data visualization or metric investigation.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
Schematik is a new AI-driven program designed to democratize hardware engineering by allowing users to "vibe code" physical devices. Much like Cursor has revolutionized software development through AI assistance, Schematik helps non-experts design electronics, suggests necessary components, and provides links for purchasing parts. The tool aims to lower the barrier to entry for makers while ensuring safety through low-voltage constraints.
Key points:
* Schematik functions as an assistant that guides users from concept to physical assembly.
* The startup recently secured $4.6 million in funding from Lightspeed Venture Partners.
* Anthropic has signaled interest by releasing a Bluetooth API for makers to connect hardware with Claude.
* The tool focuses on low-voltage architecture to prevent dangerous electrical failures during the learning process.
Key points:
* Schematik functions as an assistant that guides users from concept to physical assembly.
* The startup recently secured $4.6 million in funding from Lightspeed Venture Partners.
* Anthropic has signaled interest by releasing a Bluetooth API for makers to connect hardware with Claude.
* The tool focuses on low-voltage architecture to prevent dangerous electrical failures during the learning process.
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
This article explores the "Ralph" technique, a method for using Large Language Models (LLMs) to automate software engineering through continuous, autonomous loops. Rather than seeking a perfect prompt, the author advocates for a "monolithic" approach where a single process performs one task per loop, guided by strict specifications and technical standard libraries. The author demonstrates this by using the technique to build "CURSED," a brand-new programming language, even in the absence of training data for that specific language. By managing context windows through subagents and implementing robust backpressure via testing and static analysis, the "Ralph" technique aims to significantly automate greenfield software development projects.
Rohan, a developer, analyzed the 30MB TypeScript source code of Anthropic’s Claude Code, a terminal-based AI coding agent. While praising the tool’s impressive engineering in areas like its query loop and concurrency system, he identified several architectural choices that appear problematic, particularly given Anthropic’s substantial funding. These issues include a massive single React component, extensive use of feature flags and environment variables, circular dependencies, and convoluted type handling – all indicative of a codebase that grew rapidly without sufficient architectural foresight. Despite these concerns, the tool functions well and is widely used, highlighting the prioritization of functionality over pristine code quality.
* **Giant React Component:** The main interface is a single 5,005-line React component with 227 hook calls, making it difficult to test and maintain.
* **Feature Flag Overload:** 89 feature flags are scattered throughout the code, suggesting a lack of clear product direction and increasing complexity.
* **Circular Dependencies:** 61 files contain workarounds for circular dependencies, revealing a poorly designed module structure.
* **Verbose Type Casting:** A specific type name appears 1,193 times as a cast to ensure safe logging of analytics data, creating unnecessary noise.
* **Conditional Requires & Growth:** Many issues stem from rapid growth; features were added quickly, leading to architectural debt and workarounds like conditional `require()` statements.
* **Giant React Component:** The main interface is a single 5,005-line React component with 227 hook calls, making it difficult to test and maintain.
* **Feature Flag Overload:** 89 feature flags are scattered throughout the code, suggesting a lack of clear product direction and increasing complexity.
* **Circular Dependencies:** 61 files contain workarounds for circular dependencies, revealing a poorly designed module structure.
* **Verbose Type Casting:** A specific type name appears 1,193 times as a cast to ensure safe logging of analytics data, creating unnecessary noise.
* **Conditional Requires & Growth:** Many issues stem from rapid growth; features were added quickly, leading to architectural debt and workarounds like conditional `require()` statements.
This repository contains the leaked source code of Anthropic's Claude Code CLI, which occurred on March 31, 2026, due to a .map file exposure in their npm registry. Claude Code is a terminal-based tool for software engineering tasks, including file editing, command execution, codebase searching, and Git workflow management.
The codebase is written in TypeScript and runs on Bun, utilizing React and Ink for its terminal UI. It features a robust tool system, command system, service layer, bridge system for IDE integration, and a permission system. The project incorporates several design patterns like parallel prefetching and lazy loading to optimize performance.
The codebase is written in TypeScript and runs on Bun, utilizing React and Ink for its terminal UI. It features a robust tool system, command system, service layer, bridge system for IDE integration, and a permission system. The project incorporates several design patterns like parallel prefetching and lazy loading to optimize performance.
This repository focuses on the concept of an "agent" as a trained model, not just a framework or prompt chain. It emphasizes building a "harness" – the tools, knowledge, and interfaces that allow the model to function effectively in a specific domain. The core idea is that the model *is* the agent, and the engineer’s role is to create the environment it needs to succeed.
The content details a 12-session learning path, reverse-engineering the architecture of Claude Code to understand how to build robust and scalable agent harnesses. It highlights the importance of separating the agent (model) from the harness, and provides resources for extending this knowledge into practical applications.
The content details a 12-session learning path, reverse-engineering the architecture of Claude Code to understand how to build robust and scalable agent harnesses. It highlights the importance of separating the agent (model) from the harness, and provides resources for extending this knowledge into practical applications.
Meta is heavily investing in AI integration, demonstrated through "AI Week" – intensive training sessions for employees. These weeks involve hackathons, demos, and hands-on experimentation with tools like Anthropic's Claude Code. The goal is to foster AI adoption across all job functions and seniority levels, with a focus on AI agents capable of automating tasks like coding and report generation.
Meta is also restructuring teams into AI-native "pods" and setting specific AI adoption targets. CEO Mark Zuckerberg believes 2026 will see a significant impact of AI on the way Meta employees work, despite recent layoffs and the delayed launch of its own AI model.
Meta is also restructuring teams into AI-native "pods" and setting specific AI adoption targets. CEO Mark Zuckerberg believes 2026 will see a significant impact of AI on the way Meta employees work, despite recent layoffs and the delayed launch of its own AI model.
Starlette 1.0 has been released, and Simon Willison explores its new features by leveraging Claude’s skill‑building capabilities. He demonstrates how Claude can clone the Starlette repository, generate a comprehensive skill document with code examples, and even create a fully functional task‑management app complete with database, API endpoints, and Jinja2 templates—all generated and tested by Claude itself. The article highlights the practical benefits of integrating an LLM as a coding agent, showcases the new lifespan mechanism, and reflects on the growing popularity of Starlette as the foundation of FastAPI.
Infinite Monitor is an AI-powered dashboard builder that allows users to describe the widget they want in plain English, and an AI agent will write, build, and deploy it in real time. Each widget is a full React app running in an isolated iframe, offering flexibility and customization. Users can drag, resize, and organize these widgets on an infinite canvas for various applications like cybersecurity, OSINT, trading, and prediction markets.
The project supports multiple AI providers and offers features like dashboard awareness, live web search, and a widget marketplace. It prioritizes security with local-first storage and threat scanning.
The project supports multiple AI providers and offers features like dashboard awareness, live web search, and a widget marketplace. It prioritizes security with local-first storage and threat scanning.
