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This article details the author's efforts to improve the reliability of Claude's code skills activation. After finding that a simple approach only had a 50% success rate, the author built a testing framework to measure different hook configurations. The results show that a "forced eval" hook approach achieved an 84% success rate, while a cheaper "LLM eval" hook had more variable results. The author provides the specific hook scripts and recommendations on which approach to use based on the project's needs and priorities. The article highlights the author's systematic approach to solving this challenge with Claude and provides a useful testing framework for others to try.

This repository presents "Continuous Claude", a tool that runs Claude AI code in a continuous loop to autonomously create pull requests, wait for checks, and merge changes, enabling multi-step projects to be completed without manual intervention. The tool persists context across iterations using a shared Markdown file, allowing the AI to build on previous progress and leave notes for future iterations. The goal is to provide a more robust and self-improving approach to AI-driven development compared to one-off AI code runs.

This webpage describes the author's experience of converting his old course website, made up of RMarkdown documents, into a polished e-book using Quarto, the successor to RMarkdown. It highlights the ease of the process, as the RMarkdown documents "just worked" with Quarto, and the author was able to publish the book on GitHub Pages with minimal effort. The webpage also provides an overview of Quarto's capabilities, including its support for different output formats, interactive code blocks, and new features like Quarto Manuscripts and Quarto Dashboards.

This article discusses the author's experience with using Bash scripts and minimal CLI tools instead of a full-featured MCP (Multi-Command Protocol) server for common browser automation tasks. The author argues that for specific use cases like web frontend development and web scraping, a simple set of CLI tools can be more effective and composable than a feature-rich MCP server. The article provides examples of how the author has implemented a suite of browser automation tools using Puppeteer Core and Bash, demonstrating that this approach can be efficient and easily extensible for an agent.

This webpage provides a detailed guide on how to prevent and manage heart disease. It highlights the author's personal experience of uncovering his own undiagnosed heart disease through advanced testing, despite receiving a clean bill of health from his primary care physician. The key message is that individuals need to take an active role in their heart health by advocating for themselves, getting the right tests, and taking preventative measures, rather than relying solely on their doctors. The article outlines various biomarkers, diagnostic tests, and treatment options that can help people avoid dying from heart disease, which is the leading cause of death globally.

The Nexon CEO believes that it is important to assume that every game company is now using AI in their development processes, as the introduction of AI has greatly improved the efficiency of game production and live-service operations. He emphasizes that the real question is how companies can survive and remain competitive in this AI-driven landscape, suggesting that human creativity and unique strategies are crucial. However, the CEO's stance is contested, with some industry figures arguing that the normalization of AI in game development should not be assumed as a foregone conclusion.

This blog post discusses how Anthropic's Model Context Protocol (MCP) can enable more efficient interaction between AI agents and external tools and data sources. It highlights two key challenges with traditional MCP integration - tool definitions overloading the context window and intermediate tool results consuming additional tokens. The post then explores how code execution with MCP can address these issues by allowing agents to load only the tools they need and process data in the execution environment before passing results back to the model. This approach can result in significant reductions in token usage and latency, while also providing benefits around privacy and control flow.

This article explores the debate around whether current AI systems, exemplified by ChatGPT, are truly intelligent and thinking or merely mimicking and regurgitating information. It highlights the views of proponents who believe these AI systems are demonstrating genuine understanding, as well as the arguments of critics who see them as sophisticated language models without true comprehension. The article delves into the history and technical details of how these AI systems work, while also considering the broader societal and ethical implications of their rapid development and deployment.

This blog post discusses the benefits and risks of running coding agents like Claude in "YOLO mode" with minimal restrictions. The author shares several projects he was able to quickly complete by letting Claude Code figure things out in this unrestricted mode. However, he cautions that this approach is dangerous due to the risk of prompt injection attacks that could leak sensitive data. The post advocates for using sandboxing techniques to safely run coding agents, and provides technical details on how this can be implemented using tools like Apple's sandbox-exec command.

This webpage discusses two new security features introduced in Anthropic's Claude Code: a sandboxed bash tool and Claude Code on the web. The sandboxed bash tool allows Claude to run commands within defined filesystem and network boundaries, reducing the need for permission prompts and increasing security against potential prompt injection attacks. The Claude Code on the web feature executes each session in an isolated cloud sandbox, ensuring sensitive credentials are never exposed to the running code. These new features aim to make Claude Code more secure and autonomous for developers.

This article by Mitchell Hashimoto describes his process of using AI-powered "agentic coding" to develop a non-trivial feature for his Ghostty macOS application - an unobtrusive update notification system that avoids interrupting the user's workflow. Hashimoto provides detailed insights into his approach, including initial planning, prototyping the UI with AI assistance, encountering and resolving challenges, and iteratively improving the codebase. The article highlights Hashimoto's strategic use of AI as a collaborative tool, rather than a replacement for human expertise, and emphasizes the importance of maintaining a deep understanding of the codebase when working with AI-generated solutions.

This article provides a critical overview of the history and current state of artificial intelligence (AI) development. It highlights the limitations and hype surrounding AI, noting that while there have been genuine advances, particularly in machine learning and generative AI, there is also significant confusion and misrepresentation of the technology's capabilities. The article cautions against the inflated claims and misunderstandings propagated by "AI prophets" and discusses the need for more realistic and nuanced discussions about the current state and future potential of AI.

This article discusses the author's experience of integrating the AI assistant Claude with the note-taking app Obsidian. The author found that this integration revolutionized their workflow, allowing them to automate tasks like tracking pitch ideas, managing reading lists, and generating flashcards for studying. While the author encountered some limitations with Claude's usage restrictions, they believe the benefits of the integration, such as the email integration and the ability to offload administrative tasks, more than make up for these drawbacks. The article suggests that integrating Obsidian with an AI tool can greatly enhance the platform's capabilities for power users.

This article presents 28 ideas for AI-powered tools that the author wishes existed, covering a wide range of applications from photo editing and writing assistance to specialized agents for tasks like decompiling code and building personalized curriculum. The author highlights the current capabilities of AI models and expresses a desire for more user-friendly, task-specific tools that could enhance various aspects of daily life and work. The article suggests that the author is interested in exploring the potential of AI to streamline and augment human activities across various domains.

The webpage announces the launch of a public preview for the new Chrome DevTools Model Context Protocol (MCP) server, which allows AI coding assistants to debug web pages directly in Chrome and benefit from DevTools debugging capabilities. This improves the accuracy of AI agents when identifying and fixing issues in web development. The article provides details on what MCP is, how it can be used for various debugging and performance tasks, and how to get started with the Chrome DevTools MCP server.

Google has announced the Agent Payments Protocol (AP2), an open protocol developed with leading payments and technology companies to facilitate secure and trusted agent-led payments across platforms. AP2 aims to establish a common framework for users, merchants, and payment providers to transact with confidence, addressing key challenges like authorization, authenticity, and accountability in AI-driven commerce. The protocol leverages mandates and verifiable credentials to create a non-repudiable audit trail, enabling new commerce experiences like smarter shopping, personalized offers, and coordinated tasks. AP2 is designed to support a variety of payment methods, including cryptocurrencies, and Google is inviting the broader payments and technology community to collaborate on its evolution.

This article discusses how the role of software developers is evolving with the rise of AI, shifting from "typers" to "thinkers" focused on architecture, abstraction, and high-level design. The author shares their personal experience of using AI to handle the implementation details, allowing them to focus on the core aspects of software development like system design, naming, and communication. The article argues that the true value of developers lies in these higher-level, architectural tasks, rather than just coding, and encourages readers to think of themselves as architects rather than just coders.

This webpage discusses how the AI coding agent Claude Code from Anthropic has shown surprising capabilities in interactive theorem proving (ITP), an area typically considered very challenging for AI. The article explores how Claude Code can assist with various aspects of proof engineering, such as conceptual reasoning, translating ideas into formal languages, decomposing theorems, and debugging proof failures - tasks that often require significant human expertise. The author suggests that Claude Code points to a future where ITP tools can be more accessible to a wider audience, not just expert mathematicians and computer scientists.

Cerebras is launching two new plans, Cerebras Code Pro and Cerebras Code Max, that provide access to Qwen3-Coder, a powerful open-weight coding model capable of generating code at up to 2,000 tokens per second with a large context window. These plans aim to make AI-powered code generation faster and more accessible, allowing developers to integrate the model into their preferred IDEs and workflows.

This GitHub repository presents Claude-Flow, a leading agent orchestration platform for the Claude AI system. It allows users to deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. The platform features enterprise-grade architecture, distributed swarm intelligence, integration with RAG (Robust Agent Governance), and native support for Claude Code via the MCP (Modular Coordination Protocol) protocol. Claude-Flow is ranked as the #1 agent-based framework and provides a comprehensive set of tools and capabilities for developing advanced AI-powered applications.

Taskmaster AI is a platform that aims to be the "project manager" for your AI agent. It provides tools and services to help developers manage, monitor, and optimize their AI systems, ensuring they operate effectively and efficiently. This service could be useful for organizations and teams working on complex AI projects that require close monitoring and coordination.

This article discusses the limitations of using Claude Code sub-agents for coding tasks, despite their advantages for research and analysis. Sub-agents suffer from context isolation, leading to significantly higher token consumption and the inability to share context between tasks, which often results in contradictory code outputs. The article presents alternative approaches, such as the "Service Expert Method" and the "Claude Flow Framework," which aim to overcome these limitations by managing context more effectively. It suggests that the future of AI-assisted development lies in understanding the strengths and weaknesses of sub-agents and adopting appropriate context management strategies, rather than forcing sub-agents to handle tasks they are not designed for.

This webpage provides a detailed guide on how to use the AGENTS.md file to improve the output of AI-generated code. The author shares their best practices for creating an effective AGENTS.md, including specifying version requirements, preferred coding patterns, project structure, and API documentation references. The goal is to give AI agents clear guidelines and context to produce higher-quality and more consistent code that aligns with the project's standards. The author also emphasizes the importance of providing concrete examples and a PR checklist to ensure the generated code meets the project's requirements.

This article discusses the concept of "context engineering" for building better "agentic RAG (Retrieval-Augmented Generation) systems". The author, Jason Liu, shares insights from his experience helping companies build these systems and studying coding agents from various providers. The series covers topics such as designing tool responses and interaction patterns to give agents better situational awareness, using faceted search and metadata to provide navigational context, and strategies for managing context pollution and agent compaction. The overall goal is to enable agents to effectively explore and navigate complex information spaces, beyond just consuming data chunks. The article provides a roadmap for how engineering teams, product leaders, and researchers can apply these context engineering principles to their own agent-based systems.

This webpage provides a comprehensive guide and documentation for Claude, an AI assistant developed by Anthropic, and Claude Code, a coding tool that integrates with the user's development environment. It covers the key features and capabilities of Claude and Claude Code, as well as the author's personal experiences and insights on using and optimizing the technology. The content is aimed at providing practical, community-tested techniques and best practices for getting the most value out of Claude Code in real-world development scenarios.

This article discusses the decline of traditional search engine optimization (SEO) and the rise of a new approach called "generative-engine optimization" (GEO) or "answer-engine optimization." It explains how the emergence of AI chatbots like ChatGPT, which can directly provide answers to queries instead of just linking to websites, is disrupting the SEO industry. The article outlines strategies for adapting to this change, such as creating content that is easy for chatbots to summarize and cite, and using AI tools to generate content optimized for these new search paradigms. The article suggests that the future of online visibility will involve a shift towards creating content that is helpful and informative for both human users and AI systems.

This blog post discusses the author's recommendations and practices for "agentic coding" - using AI language models like Claude Code to assist with programming tasks. Key points include: using the cheaper Sonnet model, optimizing for token efficiency, assigning tasks to an AI agent, leveraging tools and languages (like Go) that are well-suited for agentic coding, and ensuring tools are fast, user-friendly, and provide good observability. The author shares their specific workflows and experiences to help others navigate this rapidly evolving field.

The webpage provides an overview of the SWE-bench leaderboards, which track the performance of various models on different benchmarks for software engineering tasks. It includes information on the SWE-bench Verified, Lite, and Multimodal datasets, as well as recent news and updates on the project, including the development of the mini-SWE-agent and the SWE-smith paper. The page also acknowledges the support of several institutions that have contributed to the project.

This article by Martin Fowler provides some thought-provoking insights on the impact of large language models (LLMs) on software development. Fowler cautions that surveys on the effects of AI may be misleading, as they often fail to account for the different ways developers are using LLMs, such as direct code editing rather than just autocomplete. He also expresses uncertainty about the future of programming and the potential impact of LLMs, encouraging experimentation and sharing of experiences. Additionally, Fowler discusses the inherent risks of LLMs, such as their tendency to hallucinate and the increased attack surface they create for software systems, particularly in browser-based applications.

The Interactive Handbook on Data Structures and Algorithms is an interactive and engaging resource that allows readers to visualize and experiment with various data structures and algorithms. It provides concise explanations, interactive visualizations, customizable code snippets, and a wide range of practice problems, making it a valuable tool for students, self-learners, and professionals preparing for technical interviews or seeking a comprehensive reference on the subject. The book prioritizes active learning and offers a unique approach to understanding fundamental computer science concepts.

This blog post discusses the author's experience developing a terminal-based code editor using a language model (LLM) like Claude. The key points are: 1. The author used an iterative workflow of writing a plan, prompting the LLM to complete tasks, and then reviewing and refining the work, rather than relying on the LLM to generate a complete solution. 2. The author found that LLMs excel at "whiteboarding" - generating initial solutions, but struggle with iterative improvement based on subjective quality metrics. Providing a clear specification and test suite helped guide the LLM's development. 3. The author shares lessons learned about architecting the project for testability, using a "snapshot" function to easily regenerate the test suite, and the trade-offs of maintaining a custom tool built with an LLM. The post provides practical insights into using LLMs for coding tasks and highlights the importance of engineering the right development workflow and tooling to leverage their strengths.

This article describes the author's experience of "vibe coding" - using AI coding assistants to co-develop a software project implementing algorithms to solve the Tower of Hanoi puzzle. The author, an experienced programmer with a PhD in AI, was impressed by the AI assistants' programming capabilities and ability to reason about the problem. The article compares the performance of different AI coding assistants and discusses the collaborative nature of the development process, which involved over 300 exchanges between the author and the AI. Overall, the article provides an insightful look at the current state of AI-assisted software development.

FEX-Emu is a fast Linux usermode emulator that allows users to run x86 and x86-64 applications on ARM64 Linux devices, similar to QEMU-user and Box64. It offers broad compatibility with both 32-bit and 64-bit binaries, supports forwarding API calls to host system libraries to reduce emulation overhead, and features an advanced binary recompiler with support for modern x86(-64) instruction set extensions. The emulator also includes a user-friendly configuration system and can be used alongside Wine/Proton to play Windows games.