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.” ...
AI image generation still has a long way to go. Here are two images generated by Gemini and ChatGPT from the same prompt: “Create a funny scrabble board of dysfunctional family relationships!” Gemini It’s probably showing off, with coffee stains, and spelling “DYSFUNCTIONAL” right. But “ABLOMY”? “PASSIAVE”? “RGUCT_SVA”? “SORDSP”? Most of the vertical letters are wrong. Some horizontals (“DTENSION”?) are off, too. Also: “Z” has 2 points? “C” has “C” points? “DOUBLE STTER SCORE”? “UUT SCORE SCORE” instead of “TRIPLE WORD SCORE”? ...
GDPval is a benchmark that compares how well AI does (vs experts without AI) on useful real-world tasks. In several areas, the agents outperform experts. For example, AI beats personal financial advisors, but not accountants and auditors. So I used ChatGPT / Claude to decide where to invest, but am having an accountant file my taxes. That’s a high leverage activity, especially since I might not have hired a personal financial advisor by default, and ChatGPT is certainly better than me (I’m not an expert) at personal financial advice. ...
I’m using AI to read books by: Summarizing. This tells me what the books is about, the key points it makes and the main takeaways. It also helps me decide if I want to dig deeper. Fact-checking. I can find mistakes, alternate perspectives, and biases. That’s a huge win! Re-authoring. I can write it in the style of Malcolm Gladwell, Randall Munroe, Richard Feynman, or anyone else I like. Makes dense prose much more enjoyable. So far, I’ve applied this at different levels - and I’m sure there are more possibilities: ...
TL;DR: Join this Google Group to get my blog updates via email. My blog is over 25 years old. At first, people had to visit it to read it. Then I added an RSS feeds. Then email subscriptions. Then via social media, cross-posting on Twitter, now LinkedIn. The RSS feed remains. But I feel it’s time to bring back email subscriptions. It’s the oldest of the technologies, the most robust, and the one I believe will last the longest. ...
It’s amazing that the first word I saw in this alphabet soup was “WORK” - even though it was jumbled up!
My sandals landed me in trouble twice in December. And that’s not uncommon. Mr Krishnan and I were meeting. He suggested: Good morning. Sunday 07/12/25 breakfast at 09:00 am at the Bangalore Club Residency Road (not far from Koramangala)? … but knowing me, he also added: These clubs are peculiar and may insist on no round necked t-shirt or no open sandals. If you have ideological issues with that, no problem and I will suggest another venue. ...
In 2009, I migrated from a self-made Perl static site generator to WordPress because it was slow, WordPress was dynamic and rapidly growing in features, and I wanted to write rather than code. (Also, I had plenty of time in 2009 for such things!) Over the years, problems crept in. Hosting costs ($200/year) for a slow server. No local writing - Windows Live Writer was dead. I wasn’t using most WordPress features. So it was time to migrate back to a static site generator. (Also, I now have plenty of time for such things!) ...
Browsers support SVG favicons as data: URLs. For example, this SVG: <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32"> <circle cx="16" cy="16" r="15" fill="#2563eb"/> <path fill="#fff" d="m16 7 2 7 7 2-7 2-2 7-2-7-7-2 7-2Z"/> </svg> … can be: Compressed via svgomg Converted to a data: URL via svgviewer Inserted into HTML like this: <link rel="icon" type="image/svg+xml" href="data:image/svg+xml,%3Csvg%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%20viewBox%3D%220%200%2032%2032%22%3E%3Ccircle%20cx%3D%2216%22%20cy%3D%2216%22%20r%3D%2215%22%20fill%3D%22%232563eb%22%2F%3E%3Cpath%20fill%3D%22%23fff%22%20d%3D%22m16%207%202%207%207%202-7%202-2%207-2-7-7-2%207-2Z%22%2F%3E%3C%2Fsvg%3E"/> The fun part is that you can use text inside the SVG, styled as you wish: ...
Vivek published an Indian commodity export/import dataset on 31 Dec 2025. Codex and Claude increased their rate limits for the holiday season, so I had: Codex analyze the data (OpenAI models are a bit more rigorous) and create an ANALYSIS.md file. Claude create a visual story based on the analysis. (Claude narrates and visualizes better). Here is the data story. Here are the prompts used. Analyze I downloaded export-import.parquet from https://github.com/Vonter/india-export-import which has data sourced from the Indian [Foreign Trade Data Dissemination Portal](https://ftddp.dgciskol.gov.in/dgcis/principalcommditysearch.html) Each row in the dataset represents a trade entry for a single commodity, country, port, year, month, and type (import or export). - `Commodity` string: Name of the commodity - `Country` string: Name of the foreign country - `Port` string: Name of the port in India - `Year` int32: Year for the import/export activity - `Month` int32: Month for the import/export activity - `Type` category: Type of trade (Import or Export) - `Quantity` int64: Quantity of the commodity - `Unit` string: Unit for the quantity - `INR Value` int64: Value of the commodity in INR - `USD Value` int64: Value of the commodity in USD Analyze data like an investigative journalist hunting for stories that make smart readers lean forward and say "wait, really?" - Understand the Data: Identify dimensions & measures, types, granularity, ranges, completeness, distribution, trends. Map extractable features, derived metrics, and what sophisticated analyses might serve the story (statistical, geospatial, network, NLP, time series, cohort analysis, etc.). - Define What Matters: List audiences and their key questions. What problems matter? What's actually actionable? What would contradict conventional wisdom or reveal hidden patterns? - Hunt for Signal: Analyze extreme/unexpected distributions, breaks in patterns, surprising correlations. Look for stories that either confirm something suspected but never proven, or overturn something everyone assumes is true. Connect dots that seem unrelated at first glance. - Segment & Discover: Cluster/classify/segment to find unusual, extreme, high-variance groups. Where are the hidden populations? What patterns emerge when you slice the data differently? - Find Leverage Points: Hypothesize small changes yielding big effects. Look for underutilization, phase transitions, tipping points. What actions would move the needle? - Verify & Stress-Test: - **Cross-check externally**: Find evidence from the outside world that supports, refines, or contradicts your findings - **Test robustness**: Alternative model specs, thresholds, sub-samples, placebo tests - **Check for errors/bias**: Examine provenance, definitions, methodology; control for confounders, base rates, uncertainty (The Data Detective lens) - **Check for fallacies**: Correlation vs. causation, selection/survivorship Bias (what is missing?), incentives & Goodhart’s Law (is the metric gamed?), Simpson's paradox (segmentation flips trend), Occam’s Razor (simpler is more likely), inversion (try to disprove) regression to mean (extreme values naturally revert), second-order effects (beyond immediate impact), ... - **Consider limitations**: Data coverage, biases, ambiguities, and what cannot be concluded - Prioritize & Package: Select insights that are: - **High-impact** (not incremental) - meaningful effect sizes vs. base rates - **Actionable** (not impractical) - specific, implementable - **Surprising** (not obvious) - challenges assumptions, reveals hidden patterns - **Defensible** (statistically sound) - robust under scrutiny Save your findings in ANALYSIS.md with supporting datasets and code. This will be taken up by another coding agent to create reports, data stories, visualizations, dashboards, presentations, articles, blog posts, etc. Ensure that ANALYSIS.md is documented well enough so that all assets are clear, the approach, intent and implications are understandable. Visualize I downloaded export-import.parquet from https://github.com/Vonter/india-export-import which has data sourced from the Indian [Foreign Trade Data Dissemination Portal](https://ftddp.dgciskol.gov.in/dgcis/principalcommditysearch.html) Each row in the dataset represents a trade entry for a single commodity, country, port, year, month, and type (import or export). - `Commodity` string: Name of the commodity - `Country` string: Name of the foreign country - `Port` string: Name of the port in India - `Year` int32: Year for the import/export activity - `Month` int32: Month for the import/export activity - `Type` category: Type of trade (Import or Export) - `Quantity` int64: Quantity of the commodity - `Unit` string: Unit for the quantity - `INR Value` int64: Value of the commodity in INR - `USD Value` int64: Value of the commodity in USD Then I had Codex analyze it. The analysis is in ANALYSIS.md. Find the most intesting insights from ANALYSIS.md and create a data story with supporting visualizations. Write as a **Narrative-driven Data Story**. Write like Malcolm Gladwell. Think like a detective who must defend findings under scrutiny. - **Compelling hook**: Start with a human angle, tension, or mystery that draws readers in - **Story arc**: Build the narrative through discovery, revealing insights progressively - **Integrated visualizations**: Beautiful, interactive charts/maps that are revelatory and advance the story (not decorative) - **Concrete examples**: Make abstract patterns tangible through specific cases - **Evidence woven in**: Data points, statistics, and supporting details flow naturally within the prose - **"Wait, really?" moments**: Position surprising findings for maximum impact - **So what?**: Clear implications and actions embedded in the narrative - **Honest caveats**: Acknowledge limitations without undermining the story Visualize like The New York Times Interactives. Ensure that all visualizations interactive and provide revelatory insights as well as some kind of delightful experience. Follow the typography, color & theme, backgrounds, interaction patterns, and animation principles of The Verge's frontends. Generate a single page index.html + script.js.
I use a tiny SVG favicon.svg. <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32"> <circle cx="16" cy="16" r="15" fill="#2563eb"/> <path fill="#fff" d="m16 7 2 7 7 2-7 2-2 7-2-7-7-2 7-2Z"/> </svg> It’s small enough that I usually inline it in HTML: <link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 32 32'><circle cx='16' cy='16' r='15' fill='%232563eb'/><path fill='%23fff' d='m16 7 2 7 7 2-7 2-2 7-2-7-7-2 7-2Z'/></svg>"> But sometimes I need a /favicon.ico because I don’t want to change the HTML (e.g. generated content, others’ code, too many files to change) and /favicon.ico is the default browsers look for. ...