Anthropic Scrambles to Address Leak of Claude Code Source Code - Bloomberg.com
<a href="https://news.google.com/rss/articles/CBMiswFBVV95cUxOUzRiZnEtRmlmNkhGRktyclVYemV4T1A2el9LdDEwQmtKbE9OVV9sMzhhdDNMaExDSVNaYnl5WU8ydDFXTXloWUpuSjZPejY4U2VfSmZmeUZZeTl2Um5ja2NRc05wb1F0NFdQR1VaeFBNWGo3UzhBVHVLRnRHTkxIMzMtSURHbUFDUW45Rm5tXzVvVndad2ZuZFI1dGw4UXZWVXBseTktd0Y5ZTRLdFN3aFAzRQ?oc=5" target="_blank">Anthropic Scrambles to Address Leak of Claude Code Source Code</a> <font color="#6f6f6f">Bloomberg.com</font>
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My most common advice for junior researchers
Written quickly as part of the Inkhaven Fellowship . At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece. Doing quick sanity checks Research is hard (almost by definition) and people are often wrong. Every researcher has wasted countless hours
Open Source Project of the Day (Part 27): Awesome AI Coding - A One-Stop AI Programming Resource Navigator
<h2> Introduction </h2> <blockquote> <p>"AI coding tools and resources are scattered everywhere. A topically organized, searchable, contributable list can save enormous amounts of search time."</p> </blockquote> <p>This is Part 27 of the "Open Source Project of the Day" series. Today we explore <strong>Awesome AI Coding</strong> (<a href="https://github.com/chendongqi/awesome-ai-coding" rel="noopener noreferrer">GitHub</a>).</p> <p>When doing AI-assisted programming, you'll face questions like: which editor or terminal tool should I use? For multi-agent frameworks, should I pick MetaGPT or CrewAI? What RAG frameworks and vector databases are available? Where do I find MCP servers? What ready-made templates are there for Claude Code Rules and Skills? <strong>Awesome AI Coding</strong> is ex
Claude Code Architecture Explained: Agent Loop, Tool System, and Permission Model (Rust Rewrite Analysis)
<h2> Claude Code Deep Dive (Part 1): Architecture Overview and the Core Agent Loop </h2> <p>Claude Code’s leaked source code weighs in at over <strong>510,000 lines of TypeScript</strong>—far too large to analyze directly.</p> <p>Interestingly, a community-driven Rust rewrite reduced that complexity to around <strong>20,000 lines</strong>, while still preserving the core functionality.</p> <p>Starting from this simplified version makes one thing much clearer:</p> <blockquote> <p>What does an AI agent system <em>actually need</em> to work?</p> </blockquote> <h2> Why Start with the Rust Rewrite? </h2> <p>On March 31, 2026, Claude Code’s full source was unintentionally exposed due to an npm packaging mistake.</p> <p>The package <code>@anthropic-ai/claude-code v2.1.88</code> included a <strong
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My most common advice for junior researchers
Written quickly as part of the Inkhaven Fellowship . At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece. Doing quick sanity checks Research is hard (almost by definition) and people are often wrong. Every researcher has wasted countless hours
Parameter Count Is the Worst Way to Pick a Model on 8GB VRAM
<h1> Parameter Count Is the Worst Way to Pick a Model on 8GB VRAM </h1> <p>I've been running local LLMs on an RTX 4060 8GB for six months. Qwen2.5-32B, Qwen3.5-9B/27B/35B-A3B, BGE-M3 — all crammed through Q4_K_M quantization. One thing I can say with certainty:</p> <p><strong>Parameter count is the worst metric for model selection.</strong></p> <p>Online comparisons rank models by size — "32B gives this quality," "7B gives that." Benchmarks like MMLU and HumanEval publish rankings by parameter count. But those assume abundant VRAM. On 8GB, parameter count fails to predict the actual experience.</p> <p>This article covers three rules I derived from real measurements, plus a decision framework for 8GB VRAM model selection. All data comes from <a href="https://qiita.com/plasmon" rel="noopener
My most common advice for junior researchers
Written quickly as part of the Inkhaven Fellowship . At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece. Doing quick sanity checks Research is hard (almost by definition) and people are often wrong. Every researcher has wasted countless hours
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