How to Actually Use AI in Your Daily Work Without It Becoming Another Tab You Ignore
The short version: Most AI tools promise to save you hours. In practice, they add another app to manage, another tab to check, another… Continue reading on Medium »
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3 Classifiers, 3 Answers: Why CoT Faithfulness Scores Are Meaningless
<h1> 3 Classifiers, 3 Answers: Why CoT Faithfulness Scores Are Meaningless </h1> <p>LLM Chain-of-Thought (CoT) — the mechanism where models output their reasoning process as text before answering — has been treated as a window into model thinking. The question of whether CoT actually reflects internal reasoning (faithfulness) has attracted serious research. Numbers like "DeepSeek-R1 acknowledges hints 39% of the time" circulate as if they're objective measurements.</p> <p>But can you trust those numbers?</p> <p>A March 2026 ArXiv paper (Young, 2026) demolished this assumption. Apply three different classifiers to the same data and faithfulness scores come out at 74.4%, 82.6%, and 69.7%. A 13-point spread. Statistically significant — 95% confidence intervals don't overlap.</p> <p>The more s
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
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