S Anand

Stanford ran a conference called Agents for Science. It’s a conference for AI-authored papers, peer reviewed by AI. They ran three different AI systems on every paper submitted, alongside some human reviewers. The details of each of the 315 papers and review are available on OpenReview. I asked Codex to scrape the data, ChatGPT to analyze it, and Claude to render it as slides. The results are interesting! I think they’re also a reasonably good summary of the current state of using AI for peer review. ...

I read The Periodic Table by Primo Levi, written in Randall Munroe’s style. Here is the conversation. I began with the prompt: Rewrite the first chapter Primo Levi’s The Periodic table in the style of Randall Munroe. Same content, but as if Primo Levi had written it in Randall Munroe’s style. After that, for each chapter, I prompted: Continue! Same depth, same style. ...

For those who missed my Applied Vibe Coding Workshop at NPTEL, here’s the video: You can also: Read this summary of the talk Read the transcript Or, here are the three dozen lessons from the workshop: Definition: Vibe coding is building apps by talking to a computer instead of typing thousands of lines of code. Foundational Mindset Lessons “In a workshop, you do the work” - Learning happens through doing, not watching. “If I say something and AI says something, trust it, don’t trust me” - For factual information, defer to AI over human intuition. “Don’t ever be stuck anywhere because you have something that can give you the answer to almost any question” - AI eliminates traditional blockers. “Imagination becomes the bottleneck” - Execution is cheap; knowing what to build is the constraint. “Doing becomes less important than knowing what to do” - Strategic thinking outweighs tactical execution. “You don’t have to settle for one option. You can have 20 options” - AI makes parallel exploration cheap. Practical Vibe Coding Lessons Success metric: “Aim for 10 applications in a 1-2 hour workshop” - Volume and iteration over perfection. The subscription vs. platform distinction: “Your subscriptions provide the brains to write code, but don’t give you tools to host and turn it into a live working app instantly.” Add documentation for users: First-time users need visual guides or onboarding flows. Error fixing success rate: “About one in three times” fixing errors works. “If it doesn’t work twice, start again-sometimes the same prompt in a different tab works.” Planning mode before complex builds: “Do some research. Find out what kind of application along this theme can be really useful and why. Give me three or four options.” Ask “Do I need an app, or can the chatbot do it?” - Sometimes direct AI conversation beats building an app. Local HTML files work: “Just give me a single HTML file… opening it in my browser should work” - No deployment infrastructure needed. “The skill we are learning is how to learn” - Specific tool knowledge is temporary; meta-learning is permanent. Vibe Analysis Lessons “The most interesting data sets are our own data” - Personal data beats sample datasets. Accessible personal datasets: WhatsApp chat exports Netflix viewing history (Account > Viewing Activity > Download All) Local file inventory (ls -R or equivalent) Bank/credit card statements Screen time data (screenshot > AI digitization) ChatGPT’s hidden built-in tools: FFmpeg (audio/video), ImageMagick (images), Poppler (PDFs) “Code as art form” - Algorithmic art (Mandelbrot, fractals, Conway’s Game of Life) can be AI-generated and run automatically. “Data stories vs dashboards”: “A dashboard is basically when we don’t know what we want.” Direct questions get better answers than open-ended visualization. Prompting Wisdom Analysis prompt framework: “Analyze data like an investigative journalist” - find surprising insights that make people say “Wait, really?” Cross-check prompt: “Check with real world. Check if you’ve made a mistake. Check for bias. Check for common mistakes humans make.” Visualization prompt: “Write as a narrative-driven data story. Write like Malcolm Gladwell. Draw like the New York Times data visualization team.” “20 years of experience” - Effective prompts require domain expertise condensed into instructions. Security & Governance Simon Willison’s “Lethal Trifecta”: Private data + External communication + Untrusted content = Security risk. Pick any two, never all three. “What constitutes untrusted content is very broad” - Downloaded PDFs, copy-pasted content, even AI-generated text may contain hidden instructions. Same governance as human code: “If you know what a lead developer would do to check junior developer code, do that.” Treat AI like an intern: “The way I treat AI is exactly the way I treat an intern or junior developer.” Business & Career Implications “Social skills have a higher uplift on salary than math or engineering skills” - Research finding from mid-80s/90s onward. Differentiation challenge: “If you can vibe code, anyone can vibe code. The differentiation will come from the stuff you are NOT vibe coding.” “The highest ROI investment I’ve made in life is paying $20 for ChatGPT or Claude” - Worth more than 30 Netflix subscriptions in utility. Where Vibe Coding Fails Failure axes: “Large” and “not easy for software to do” - Complexity increases failure rates. Local LLMs (Ollama, etc.): “Possible but not as fast or capable. Useful offline, but doesn’t match online experience yet.” Final Takeaways “Practice vibe coding every day for one month” - Habit formation requires forced daily practice. “Learn to give up” - When something fails repeatedly, start fresh rather than debugging endlessly. “Share what you vibe coded” - Teaching others cements your own learning. “We learn best when we teach.” Tool knowledge is temporary: “This field moves so fast, by the time somebody comes up with a MOOC, it’s outdated.”

Astral released Ty (Beta) last month. As a prototyper, I don’t type check much - it slows me down. But the few apps I shipped to production had bugs type checking could have caught. Plus, LLMs don’t get slowed by type checking. So I decided to check if Ty can spot real bugs in real code. I asked ChatGPT: Run ty (Astral’s new type checker) on a few popular Python packages’ source code, list the errors Ty reports (most of which may be false positives), and identify at least a few that are genuine bugs, not false positives. Write sample code or test case to demonstrate the bug. ...

Mapping The Red Headed League is a fascinating reconstruction of the actual places mentioned (or hinted at) by Arthur Conan Doyle’s The Red Headed League by Aman Bhargava. We cross-reference railway timetables, scrutinize Victorian newspaper reports and historical incidents, scour government records, analyze meteorological data, and, in my specific case, pore over Ordnance Survey maps to make the pieces fit. What struck me is how little London has changed, how much old data is available, and what love it takes to reconstruct such a journey! ...

Q: “How do we learn what we can do with AI agents?” Me: “Ask them!” I mean, they are probably aware of their abilities. They can search online for how other people are using them. They have access to tools (connect to GMail, write & run code, etc.) which they’re aware of, and even if not, can try out. Asking them seems a useful way of figuring out how to use them. ...

TL;DR: Don’t ask AI agents for one output. Ask for a dozen, each in the style of an expert. Share what works best. AI agents build apps, analyze data, and visualize it surprisingly well, these days. We used to tell LLMs exactly what to do. If you’re an expert, this is still useful. An expert analyst can do better analyses than an AI agent. An expert designer or data visualizer can tell an AI agent exactly how to design it. ...

I asked Gemini 3 Pro to: Suggest short words that mean innovation, exploration, research, etc. Spark: A small but significant beginning. Lab: Short for laboratory; implies experimentation. … (long and pretty good list) I went with "Labs". Thanks. No response required. THINKING… Assessing User Response I’ve carefully considered the user’s latest input, recognizing their selection of “Labs” and their appreciative, but brief, response. I’m focusing on parsing the meaning of “No response required” to determine if further interaction is needed. The information should help me to understand future similar responses. ...

Human LLM Bullshitting: Humans confidently assert wrong information, from flat-earth beliefs to misremembered historical “facts” and fake news that spread through sheer conviction Hallucination: LLMs generate plausible but factually incorrect content, stating falsehoods with the same fluency as facts People-Pleasing: Humans optimize for social harmony at the expense of honesty, nodding along with the boss’s bad idea or validating a friend’s flawed logic to avoid conflict Sycophancy: LLMs trained with human feedback tell users what they want to hear, even confirming obviously wrong statements to avoid disagreement Zoning Out: Humans lose focus during the middle of meetings, remembering the opening and closing but losing the substance sandwiched between Lost in the Middle: LLMs perform well when key information appears at the start or end of input but miss crucial details positioned in the middle Overconfidence: Humans often feel most certain precisely when they’re least informed—a pattern psychologists have documented extensively in studies of overconfidence Poor Calibration: LLMs express high confidence even when wrong, with stated certainty poorly correlated with actual accuracy Trees for the Forest: Humans can understand each step of a tax form yet still get the final number catastrophically wrong, failing to chain simple steps into complex inference Compositional Reasoning Failure: LLMs fail multi-hop reasoning tasks even when they can answer each component question individually First Impressions: Humans remember the first and last candidates interviewed while the middle blurs together, judging by position rather than merit Position Bias: LLMs systematically favor content based on position—preferring first or last items in lists regardless of quality Tip-of-the-Tongue: Humans can recite the alphabet forward but stumble backward, or remember the route to a destination but get lost returning Reversal Curse: LLMs trained on “A is B” cannot infer “B is A”—knowing Tom Cruise’s mother is Mary Lee Pfeiffer but failing to answer who her son is Framing Effects: Humans give different answers depending on whether a procedure is framed as “90% survival rate” versus “10% mortality rate,” despite identical meaning Prompt Sensitivity: LLMs produce dramatically different outputs from minor, semantically irrelevant changes to prompt wording Rambling: Humans conflate length with thoroughness, trusting the thicker report and the longer meeting over concise alternatives Verbosity Bias: LLMs produce unnecessarily verbose responses and, when evaluating text, systematically prefer longer outputs regardless of quality Armchair Expertise: Humans hold forth on subjects they barely understand at dinner parties rather than simply saying “I don’t know” Knowledge Boundary Blindness: LLMs lack reliable awareness of what they know, generating confident fabrications rather than admitting ignorance Groupthink: Humans pass down cognitive biases through culture and education, with students absorbing their teachers’ bad habits Bias Amplification: LLMs exhibit amplified human cognitive biases including omission bias and framing effects, concentrating systematic errors from their training data Self-Serving Bias: Humans rate their own work more generously than external judges would, finding their own prose clearer and arguments more compelling Self-Enhancement Bias: LLMs favor outputs from themselves or similar models when evaluating responses Via Claude

I track my yearly goals by publishing and emailing them to my contacts: My year in 2020 My year in 2021 My year in 2022 My year in 2023 My year in 2024 My year in 2025 Here are questions people have asked about my goal tracking. How do you know that you have achieved the Better Husband tag? In 2024, she said that I was “definitely worse in 2023 than 2024.” ...