Anthropic Claude Code Leak: AI Giant Exposes Source Code in 'Simple Mistake'; What Was Revealed & Why Experts Are Concerned? - The Sunday Guardian
<a href="https://news.google.com/rss/articles/CBMi_wFBVV95cUxQYWxDMFhhOWlUdUJJM042aVZyVEZDYzRSb3hoOUFEM3hTaGRrODNWUU1vZXk2Y3g2TFJha1hlcWR4bGpVeG5IbWY1bXJRRzhjUWlCc0pfMnFaOXE1Wi1VUXZLd0VNUWtNdzRGWS1fRFQzaXl2bHpzZlZZQTdndE8tTUxha0FsdWdUakptd1dDODhBRVRlUVpBQzFYSTB5dlZJTU9uaFJET2g0ZlFhMFM5bmw3blpVUmVhOGdOQ2I2Y19pVFlhVjdmN1hqallxdDhtNGV0d2hUMW1MWDhPQko2WVM4TEljNVA4dEdvYXNsRTFSQVR0Y2t1R2hDTWNvbUU?oc=5" target="_blank">Anthropic Claude Code Leak: AI Giant Exposes Source Code in 'Simple Mistake'; What Was Revealed & Why Experts Are Concerned?</a> <font color="#6f6f6f">The Sunday Guardian</font>
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My most common research advice: do quick sanity checks
Written quickly as part of the Inkhaven Residency . 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
I Built an MCP Server So Claude Can Answer Questions About Its Own Usage
Here's something that didn't exist until recently: you can ask Claude how much Claude Code you've been using , and get a real answer backed by your actual data. You: "How much have I used Claude Code this month, and is my streak going to survive?" Claude: "You've logged 47.3h interactive + 83.1h AI sub-agent work in March, for 130.4h total. You're on a 36-day streak with 22 Ghost Days. Based on your last 14 days, your streak is likely to survive — you've been active 100% of days this month." That's cc-mcp . An MCP server that gives Claude real-time access to your Claude Code usage stats. The problem with analytics tools I've built 26 other Claude Code analytics tools. You run them, they print stats, you close the terminal. The knowledge doesn't go anywhere useful. What I wanted was for Cla
Using GPT-4 and Claude to Extract Structured Data From Any Webpage in 2026
Using GPT-4 and Claude to Extract Structured Data From Any Webpage in 2026 Traditional web scraping breaks when sites change their HTML structure. LLM-based extraction doesn't — you describe what you want in plain English, and the model finds it regardless of how the page is structured. Here's when this approach beats traditional scraping, and the complete implementation. The Core Idea Traditional scraping: price = soup . find ( ' span ' , class_ = ' product-price ' ). text # Breaks if class changes LLM extraction: price = llm_extract ( " What is the product price on this page? " , page_html ) # Works even if the structure changes completely The trade-off: LLM extraction costs money and is slower. Traditional scraping is free and fast. Use LLMs when: Structure changes frequently (news site
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My most common research advice: do quick sanity checks
Written quickly as part of the Inkhaven Residency . 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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