claude on S Anand

Things I Learned - 28 Jun 2026

Sun, 28 Jun 2026 00:00:00 +0000

This week, I learned:

Every Substack feed has an RSS feed at https://your.substack.com/feed. Substack help. I used this to scan my browsing history to identify Substacks I visit - and subscribed to Marcus on AI - an AI sceptic AI asked me to read about.
Cloudflare let’s agents create temporary accounts so that they can deploy and test. Enables trial and error - a powerful capability.
“They’re on mobile but this is substantiative enough to warrant length.” I spotted this in Claude’s thinking when prompting on mobile. So, if I ask Claude something on mobile, it will give me shorter responses by default. Clever design - but something to keep in mind. If I want some heavy thinking done by Claude, better to do it on desktop than try to give it conflicting instructions.
Giant Permissive Image Corpus (GPIC) has 100 million Qwen tagged public images. Even as a simple searchable image catalog this has value. Jeff Clark - Import AI
Ethan Mollick had an agent test his book summary against multiple LLMs as readers to find out how they would recommend it - and optimized. This is a great practical use of agents as consumers, and material for my When Data is for Agents, Not Humans workshop.
kage is an easy CLI to clone websites and read offline. For example, kage clone https://simonwillison.net/2026/Jun/ -o ~/tmp/site --scope-prefix /2026/Jun/ --max-depth 1 clones all Jun 2026 articles from Simon Willison’s blog. Then kage serve ~/tmp/site serves it locally. While it’s easy, the only time I need this is on a flight, and in that case, a local RSS feed app works better. I’m using newsboat for that.
To me, the clearest sign of AI writing from the Wikipedia:AI or not quiz was consistent paragraph lengths. I got the first 3/3 wrong, but once I used this heuristic, I got 6/7 right. Updated my LLM Smells.
The files .git/info/exclude and ~/.config/git/ignore are also ignored by git, like .gitignore, but useful if you don’t want to commit them into the .gitignore file. For example, .DS_Store makes sense only for Mac machines, not each repo. .vscode/ makes sense only for VS Code users. Nelson Figueroa
Justin Poehnelt, author of the brilliant Google Workspace CLI gws, was fired for it. There have been no updates for 3 months, but none may be required - it feels perfect. X
Lore is a centralized version control system for large binaries. If you have large binaries (e.g. images, videos, …) that multiple people edit, it’s better than Git LFS or Perforce. ChatGPT
Deno Desktop lets you use JS to build desktop apps. I tried it. It’s easy to install, compact to code, leverages familar web technology, and compiles to multi-platform binary. The binaries are a bit larger than I’d like, though - 80MB for a Hello World on Linux/Windows and ~70MB on Mac.
Codex reported that You have 2 usage limit resets available. Run /usage to use one. This thread has context. After resetting, the next reset might be 7 days after the reset, though (source).
After having a child, fathers are affected biologically, too. Testosterone drops, cortisol & prolactin & estrogen rise, the brain rewires for empathy and threat detection - and of course, there’s less sleep. These sometimes lead to “Paternal Postpartum Depression” - something I didn’t even know was a thing. The havoc kids wreak upon us! 🙂 Gemini
With AI writing more code, formal code proofs are becoming more accessible. You just need to ask a coding agent to prove / disprove a function. You can use:
- Z3 to find/prove whether a counterexample exists. Best default.
- Dafny to prove that code obeys a spec. Best for real algorithmic code.
- Alloy to find loopholes in relational models, schemas, permissions, and workflows. Best for data.
- TLA+ to check whether stateful, concurrent, or agentic systems can evolve into a bad state. Best for systems / workflows.
- .. and there’s a long tail of these.
Python is named after Monty Python, not the snake. I knew this, but forgot!
Python now has multiple cross-platform app paths: PyInstaller and Nuitka for executables, Kivy, Flet, and BeeWare/Briefcase for GUI/mobile/desktop apps, and PyScript/Pyodide for browser/WASM apps - a route that became more serious because Pyodide-compatible WebAssembly wheels can now be published directly to PyPI.
On the one hand, AI is writing code, so there’s no point learning Python. On the other hand, AI is writing code mostly in Python - so THAT’s what you need to learn more. I think we should teach Python using AI, that is, teach how to write and debug Python code using AI. That’ll end up teaching skills people will really need.
Computational thinking = Decomposition + Abstraction + Algorithm design + Pattern recognition. In AI, that translates to = Framing + Context engineering + Orchestration (harness engineering?) + Verification design. Maybe I’d add Assetization / Systems.

Things I Learned - 14 Jun 2026

Sun, 14 Jun 2026 00:00:00 +0000

This week, I learned:

Overheard a journalist saying: “I can tell when humans are lying. There are no tell tale signs of AI lying. At least I don’t have any.”
rdt-cli is a Reddit CLI. It uses a clever trick: it auto-detects installed browsers and extracts cookies (supports Chrome, Firefox, Edge, Brave). So, if you’re logged into Reddit on any browser, uvx --from rdt-cli rdt whoami automatically shows who you are logged in as. (The public-clis repo also lists other useful CLIs like twitter-cli, )
Currently, a $20 Claude Pro gives you ~$400 and a $100 Claude Max gives you ~$2,000 of API usage. For ChatGPT, the numbers are ~$700 and $3,500. SemiAnalysis
When Fable 5 refuses to answer questions, here’s the message that appears: “Fable 5 has safety measures that flag messages on most cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we’re working to refine them. Send feedback or learn more.” I managed to trigger this once while researching an M&A acquisition target. Clicking on “Edit and retry with Fable 5” triggered Opus 5 again, twice.
DNA codons (A, T, C, G) encode proteins in triplets. There are 64 triplets that map to 20 amino acids. Some like Leucine, have 6 codons. Some like Methionine have only one. Why? When creating genes, there’s a wobble, sometimes, at the 3rd codon. THe mapping minimizes that impact: small errors map to similar proteins. The more common proteins have more codons. There’s a lot of fascinating information science going on here. Gemini
ChatGPT now shows a “Check in” button when it’s thinking. Clicking on that gives you a work-in-progress answer while it continues thinking. When done, it replaces the WIP answer with the final answer. A useful feature!

My changing AI opinions

Fri, 05 Jun 2026 09:41:33 +0800

I asked Claude about my AI opinions.

Based on my transcripts and blog posts, find the three claims I make most consistently, the three I’ve quietly reversed, and the one assumption I’ve never questioned but everything depends on.

Here are things I’ve changed my opinion on:

THEN: One frontier model will win - not specialization. NOW: Gemini for media, Claude for strategy/style, GPT for rigor. SLMs as tools.
THEN: Carefully curate my course content. NOW: Give students prompts directly.
THEN: Web apps are differentiated artifacts. NOW: HTML is easier to generate than PPT - a signal of slop, not craft.
THEN: Human in the loop. NOW: Human NOT in the loop, bottlenecking it. On-the-loop, etc. is fine.
THEN: Minimal single-agent loop, avoid sub-agents" NOW: Multi-agent, sub-agent, and agent teams.
THEN: Avoid MCP, prefer SKILLS.md. NOW: Use MCP because integrating with Claude / ChatGPT / … is easy.

There are the top contradictions in my opinions.

“Vibe code everything, end-to-end” vs “Don’t commit based on vibe-coding, it’s not fully reliable”.
“Experience is a liability” vs “Domain expertise is an edge”
“Paid plans are fine for private data” vs “Don’t assume paid = private”
“Get out of the way / delegate everything” vs “Verification is the whole job”
“Build, don’t plan” vs “Plan → Correct → Execute”

Some things, I have never questioned.

For numbers, math, or correctness, make AI write and run code – never trust prose arithmetic.
Judge AI against human accuracy, never against perfection – because experts disagree among themselves.
Use AI heavily – reach for it first, high volume.
Don’t build a foundation model from scratch – steer existing general models instead.

Things I usually say, but there are exceptions.

“Always verify”. But blindly trust AI in non-core areas where you are unskilled (such as personal finance).
“AI is improving fast”. But there is a jagged edge, to verify before upgrading.
“Don’t build models”. But go ahead if it’s easy and beats LLMs clearly.
“Models keep getting cheaper”. But not in May 2026.

People skills with AI

Sun, 17 May 2026 22:45:57 +0800

I advise people that people skills are important in the AI era.

Now, I’m using AI to help me with people skills.

This morning, I wrote a script to export my WhatsApp conversations this year. That makes it easy to feed it into AI models.

Then I used my Local MCP connector and asked Claude:

Who are people in my life that most deserve an unreasonable gesture of thanks and what would that be?

It went through my WhatsApp messages – including threads I had not read. Including a group discussing four 90-minute hands-on AI workshops I’m running for IIM alumni in Singapore on Saturday afternoons:

23 May: Context engineering
20 Jun: AI tools & workflows
25 Jul: Agentic analysis
22 Aug: AI strategy

Nayana Jain prepared a series of LinkedIn / WhatsApp posts to promote this workshop series and also created a poster for the workshops - and the best part of it was they were AI-generated. AI didn’t do a great job at the logo, so she asked for and got the IIMPACT logo (which isn’t public) and fed it to the model to re-generate it.

All of this is something I wasn’t even aware of until Claude pointed it out.

I sent a note thanking her.

And, evidently, AI is teaching me how to be human.

How I use Local MCP

Sat, 16 May 2026 22:24:32 +0800

I’d love for Claude or ChatGPT to answer questions like:

What meetings am I not setting up that I really should be?

or:

Based on my activities since 9 May 2026, what should I blog about?

or:

Who in my professional life most deserves an unreasonable gesture?

From data. My files, emails, calendar, contacts, transcripts, blogs, notes, code, browsing history, logs, random Markdown files I forgot I wrote.

Hence, a Local MCP.

My Local MCP server exposes one tool: bash.

@mcp.tool()
async def bash(commands: str, ctx: Context) -> str:
    """Runs multiline bash script."""
    result = subprocess.run(commands, shell=True, executable="/bin/bash")
    return result.stdout + result.stderr

That’s it. No vector database. No UI. No custom connectors. No “AI knowledge platform.”

Just: run shell commands on my machine.

I run this locally, expose it online (which is slightly scary), and give Claude and ChatGPT this prompt fragment:

Local MCP runs bash and exposes:

- ~/code/talks/README.md - talk transcripts, slides
- ~/code/blog/description.md - 20K files, 5K posts. Search for "- llm" for AI-related posts.
... (etc.)

`gws` can access email, calendar, etc.

In one shot, this gives EVERYTHING I have to the agents.

A common use is meeting prep.

You are a brilliant, brutally honest Chief of Staff. You have full access via Local MCP bash tool to calendar, emails, and past transcripts. Produce a briefing card for each substantive external meeting today.

It checks the calendar via gws. It searches my transcripts. My notes. My AI advice. Then gives me a briefing card with everything I need.

I can’t do this by uploading files manually. The context is not one file: it’s scattered all over.

A human assistant could do this. But agents are faster, cheaper, and I trust them more.

Another common use is relationship intelligence.

What meetings am I not setting up that I really should be?

Claude scans transcripts, contacts, emails, and recent activity to find people I should speak.

This is where Local MCP is different from a file upload.

In a file upload, I can ask “Where is X?”.

Here, I’m asking “What am I missing?” and the answer depends on recency, relationship history, frequency, how conversations felt, unresolved actions, and so much more.

A third use is mining my own work.

I used Local MCP to ask what I should blog about. It scanned all my content and found themes I haven’t really thought about, like:

Google Meet captions - a code commit I recently made. I wrote about it.
Agents are the new software - a theme I’ve been talking a lot about. I wrote about it.
Local MCPs - that’s this post

… and half a dozen topics I should be writing about soon.

A fourth use is business research.

I have transcripts from sales calls and client conversations. I don’t attend all of them. But Local MCP can.

I can ask:

Which client needs have we heard repeatedly but not converted into demos?

or:

Which solutions have we pitched to one client that another client has explicitly asked for?

This is beyond a CRM search. A HubSpot search finds what people typed in. This finds what people actually said.

Then an email search finds if they acted on it. Calendar search finds what we spent time on.

Across these, I find opportunities that no single system has.

BUT: this is not safe by default. A bash MCP server can delete my files, run commands, read my browser sessions, send emails gws, and all sorts of risky things.

So I monitor the commands like a hawk, and give it fairly controlled access, and only when I’m actually running one of these use-cases.

I tried OAuth but setting up Auth0, dynamic client registration, callback URLs, scopes, ChatGPT connector errors, … I gave up.

For now, supervised local usage gives me most of the value.

BUT #2: Claude and ChatGPT use Local MCP differently.

Claude uses it beautifully. Smooth. No mistakes. References memory.

ChatGPT is more restrictive. No chat memory accessed, nor saved. Keeps asking for permissions.

So I use ChatGPT less for Local MCP-heavy tasks. But ChatGPT is rigorous. When I want structured analysis, exhaustive lists, or better verification discipline, it is useful.

Local MCP is powerful because it lets AI use all systems I have access to:

local files - across Dropbox, Google Drive, my notes, blog posts, transcripts, slides, …
code - not just reading, but running, rewriting, and generatig
email, calendar, contacts
browser history
shell tools - which can be used to access even more system

Local MCP invites Claude / ChatGPT as a real assistant into my laptop.

And into my 2,700-line TODO archive.

You probably shouldn’t expose a bash tool to an AI. But note the direction I’m going with this:

If your work and transactions are agent-readable, your past work compounds.
If they are trapped in apps, screenshots, and memory, your AI has amnesia.

Unresolved questions across disciplines

Thu, 07 May 2026 20:45:58 +0800

I asked Claude: “What are the most effective and impactful ways you can help me?” One of its ideas was to ask it:

What are the three questions this field has not resolved, where the disagreement is substantive and not just semantic? Who represents each position most forcefully?

So I posed this question about several subjects. This is a great way to discover the frontiers of knowledge in a field.

Mathematics
- Are mathematical objects (numbers, sets, complex numbers) real, independent of human minds? Or useful fiction? Do we discover or invent them?
- Is there one true set theory or a multiverse of equally valid set theories, e.g. where the continuum hypothesis is true vs false?
- Are theorems true or false, even if we can’t prove them?
Biology
- Does natural selection operate at a gene, individual, or group level?
- Does evolution proceed through gene mutation + selection, or do we need to add epigenetics, niche construction, and developmental bias?
- Which came first - RNA or mitochondria?
Physics
- What actually happens when a quantum system is measured?
- When a black hole evaporates, is the information about what fell in preserved (and how) in the Hawking radiation, or destroyed?
- Why does the universe have a strong entropy increase toward the future but not the past?
Computer science
- Is P really equal to NP?
- Can fault-tolerant quantum computers actually be built?
- Can we really achieve AGI with computation?
Finance
- Do asset prices track underlying value or market narrative / noise?
- Why is the historical US equity premium (~6%) so high that it implies a ~30–40x risk aversion?
- Is financial instability inevitable in capitalism, or is it due to external shocks?
Psychology
- Are emotions hard-wired (hence discovered) or constructed from more primitive ingredients?
- Does trauma hide the memory that we recover later, or are these recovered pieces fabricated?
- In psychotherapy, do specific techniques work? Or is it just the relationship?
History
- Were the atomic bombings of Japan militarily necessary?
- Was the Holocaust planned from the start, or did it emerge from bureaucratic chaos?
- Did the Industrial Revolution raise or lower living standards for ordinary workers?
Geography
- Does physical geography cause big changes to human outcomes in the long run?
- Are places about a geographic location or about the flow of capital, people, and power that flow through?
- Are local, regional, national, … really nested ways of organizing society, or an ineffective way of thinking about geography?

AI Palmistry

Sun, 15 Mar 2026 23:08:55 +0530

I shared a photo of my right hand with popular AI agents and asked for a detailed palmistry reading.

Apply all the principles of palmistry and read my hand. Be exhaustive and cross-check against the different schools of palmistry. Tell me what they consistently agree on and what they are differing on.

I was more interested in how much they agree with each other than with reality. So I shared all three readings and asked Claude:

What are things all three agents clearly agree on? List these.
What are things they differ on? List as a comparison table.

Here’s what they agree on. The first is probably right. I’m not sure about the rest.

✅ You think in patterns and connections, not just logic. Your mind works by synthesizing ideas across domains rather than following straight lines. (Oh, yes. My mind’s drunk.)
❓ You have strong physical energy and bounce back well from illness or setbacks. (Do I? In 2023, I spent a year “bouncing back”. And I still haven’t bounced back from 26 Oct 2014.)
❌ You feel deeply, but don’t show it loudly. Your emotions are genuine and loyal — just not dramatic. (I’m actually shallow. I show what I feel and it’s not much.)
❓ You built your own path. No preset direction was handed to you; your sense of purpose developed through your own choices. (Um… this is partly true for everyone?)
❓ You’re not a blunt-action person. You process experience through interpretation and nuance before acting. (Maybe? Sometimes I’m impulsive. Sometimes not.)
❓ There’s more going on inside than people around you typically see. (Um… this is partly true for everyone?)

Clearly, AI’s analysis of my chats is much better than of my palm.

You can read the full readings here.

Extracting AI advice

Mon, 09 Feb 2026 09:52:19 +0800

This weekend, two people asked me, roughly “How do I use AI better?”

This is a frequently asked questions. I document my FAQs, e.g. time management, career advice, etc. and it was time to add AI advice to this list.

I often record online calls and transcribe them. I asked Gemini, Claude and ChatGPT for the best way to summarize 400 transcripts of ~40K each.

Claude’s suggestion was the best:

Use Gemini Flash (1M context, dirt cheap) to process calls in batches of 20-25
Each batch → extract advice themes
Aggregate batch results with Claude Sonnet for final synthesis

But I ignored it because it was too much work. (See my AI advice: “Ask for easier output”)

Instead, I listed all my transcripts and used Simon Willison’s llm CLI tool to run:

llm -m gemini-3-flash-preview --system "Summarize ALL AI-related advice from Anand this call transcript into 1-sentence bullets" -f file-1.md > "extract/ai/file-1.md"
llm -m gemini-3-flash-preview --system "Summarize ALL AI-related advice from Anand this call transcript into 1-sentence bullets" -f file-2.md > "extract/ai/file-2.md"
...

(A for-loop is smarter. Copy-paste is easier. I optimize for ease.)

This took a little over an hour and a dollar. Then, I combined all the extract/ai/*.md files into one big file:

uvx files-to-prompt *.md | xclip -selection clipboard

… and pasted it into Gemini 3 Pro, which could comfortably handle the 750KB of context and prompted it:

Here is the summary of AI related advice I provided in discussions to various people.

What is the most common advice I provide to people that is relevant to people looking to use AI for personal use and productivity?

The result was a good list along the lines of:

### 1. The "50 Conversations a Day" Rule (The Core Habit)

You frequently tell individuals that they cannot learn AI through theory. To build intuition, they must force themselves to use it for everything.

- **The Advice:** Aim for a volume target of **50 interactions per day**.
- **Why:** High-frequency usage forces you to find "micro-use cases" you would otherwise ignore. It moves you past the "novelty" phase into the "utility" phase where you instinctively know which tasks the AI can handle.
- **Application:** Don't save AI for big projects. Use it to fix grammar in a text, explain a joke, plan a menu, or debug a single line of code.

… which I condensed into:

- **Have 50 conversations a day with AI**. High-usage forces you to find tiny use-cases you'd otherwise ignore.

Here’s my current AI Advice.

Another advantage of creating an extract/ai/ folder is that I can pull out technical AI advice, governance-related AI advice, etc. later.

In fact, this “map-reduce” style pattern is clearly powerful. For $2 and a little time, I get very useful synthesis. I plan to use it more.

The Periodic Table by Primo Levi and Randall Munroe

Mon, 12 Jan 2026 15:32:26 +0800

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.

It began wonderfully.
It began humorously.
It was enjoyable.
It was fun.

And then it turned darker.
No, not darker. Grayer. Something else.

Something to learn from.
Something to cry on.

Something that sticks. (I’m writing in its style.)
Something that drains. (Yet, somehow, enriches?)

I could not have read this book without the Randall Munroe rewrite.
I did not read the original, so I cannot compare it with the original.
I probably would NOT have read the original, so maybe it does not matter.

I wanted to write about the process:
Using AI to narrate each chapter in someone else’s style.
This is a useful technique.

But it feels like so small compared to the book itself.
Or the author. Or his experience.

I can read 30 book summaries in the time this took.
That probably has more utility.
But …

Some posts are meant to for reading.
Some are meant for the author, just to write.
This is for me. Not to read. Just to write.

Self-discover LLM capabilities

Sat, 10 Jan 2026 11:40:00 +0800

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.

For example, I didn’t know that ffmpeg (which ChatGPT, Gemini, Claude, etc. can run) can visualize audio using filters. They could create a bunch of stunning visualizations as a video compilation.

So, I told Claude:

I did not know ffmpeg could visualize audio via filters…
You have a container environment with a set of tools installed and you can run commands.
Identify creative ways in which the tools you have access to can be used…
…
Fact-check by cursorily verifying the command options…
But no need to implement any of these…
BLOW MY MIND!!

It gave me 125 ideas from drum patterns of log timestamps, directory structures as artistic graphs, frequency domains of images via Fourier transforms, morphological image erosion/dilation effects, and a whole bunch of things I’ve never heard of.

It was too much, so I didn’t bother. (I’ll read later.)

Implement the most visually impressive among these.

And the result was a stunning video compilation:

It generated these 10 purely algorithmic (no external assets) visualizations:

Mandelbrot Set via FFmpeg’s mandelbrot filter: Deep zoom into the famous seahorse valley, revealing infinite complexity from z² + c
Sierpinski Carpet via FFmpeg’s sierpinski filter: Recursive self-similar fractal pattern that animates through chaos game iterations
Game of Life via FFmpeg’s life filter: Conway’s cellular automaton with glowing cells and mold trails showing emergent complexity from 4 simple rules
Rule 30 via FFmpeg’s cellauto filter: Wolfram’s elementary cellular automaton that generates apparent randomness from deterministic rules
Domain Coloring via Python + NumPy: Complex function visualization where hue represents angle and brightness represents magnitude, morphing through z², z³, and rational functions
L-Systems via Python + PIL: Three fractal trees grow algorithmically using Lindenmayer system grammar rules - pure mathematical botany
Barnsley Fern via Python chaos game: 500,000 points plotted using an Iterated Function System, emerging from randomness into a perfect fern
Julia Set via Python + NumPy: Dancing fractals as the complex parameter c traces a wobbling circle, continuously morphing fractal boundaries
Plasma Effect via FFmpeg’s geq expression filter: Real-time interference patterns using layered sine waves in RGB channels
Gradient Spiral via FFmpeg’s gradients filter: Six-color rotating spiral with 8x speed, creating hypnotic color field animation

… with cinematic title cards (fade transitions, credits) in a 1080×1080 square format perfect for social media.

I learnt at least a few things from this:

The tool side. I now know that ffmpeg has built-in fractal capability. Fractals have fascinated me since I was 12. This is something to explore.
The technique side. I’m learning new terms like “temporal slit-scan photography” - used to create time-slice effects like bullet time in The Matrix, using ffmpeg. Or “music chord visualization” using neato, or capturing packet flow data using tcpdump to visualize network traffic, etc.

I would never have thought of these, but the capabilities are in my hands.

I think there’s benefit in just spending time with LLMs, asking them (in different ways) what they can do, and what would help, interest, or even amuse us.

PS: ChatGPT’s response to this was a bunch of good ideas and a tiny 0.5 second Mandelbrot video. Gemini shared a tiny list of 10 ideas (read them all) but made up with this brilliant Veo-generated video.

Creating data stories in different styles

Fri, 09 Jan 2026 17:20:00 +0800

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.

But you’re not an expert in everything.

Instead, “style transfer” experts.

LLMs are trained on the styles of experts across the world. Tell them to adopt an expert’s style. That’s a shortcut to improve output quality. It won’t be as good as that expert, but likely better than you.

For example, Linux Foundation leaderboards evaluates open source projects - are they active, who’s behind it, do they follow security best practices, what’s their popularity, etc.

Varun use GitHub Copilot with GPT-5 mini to scrape the data Then, he had Claude Opus 4.5 create data visualizations in five different styles:

A Wall Street Journal style. Prompt
Malcolm Gladwell + NYT style, i.e. written in Malcolm Gladwell’s voice (who writes for the New Yorker), but with the New York Times’ visual style. This ability to remix is powerful. Prompt
The Polygraph / The Pudding style. We aren’t specifying a single publication here, but providing multiple publications, allowing it to mix and match from those. Prompt
From Shirley Wu, who is a data artist, allowing us to go to the style of a specific individual. Prompt
An “open source data adventure”. That’s not a publication or a person, but a theme. Prompt

Same input. Five different styles.

For example, while The New York Times comes up with transitional scatter plots (which are great for rich interactive explorations):

… Shirley Wu comes up with these hidden gems, focusing on the smaller projects that have a remarkably diverse contributor base.

Or, while The Wall Street Journal opens with the state of the economy:

The open-source software that underpins trillions of dollars in global commerce is showing signs of strain.

… Malcolm Gladwell opens with perspective:

In the spring of 2023, a small project called CBT Tape caught my attention. Three contributors. That’s it. Yet they had pushed 3,414 commits in twelve months—a rate of 1,138 commits per person. To put that in perspective: a “normal” project sees perhaps 20-30 commits per contributor annually.

At least for the next few years, the ROI is less from expertise. It’s more from style.

Try out different styles. Learn to guide AI towards your preferences. Pick what works best.

And share!

AI agents to hire

Tue, 06 Jan 2026 17:26:39 +0800

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.

Financial management is just one aspect of life. There are several. I don’t hire professionals in many areas where I’m not an expert, so hiring AI agents here is almost a no-brainer.

Doctor: Symptom triage, report analysis, drug check, health planning.
Financial advisor: Budgeting, investing, tax saving.
Lawyer: Contract review (rental, employment, …), disputes (claims, employment, property), planning (will, power of attorney), legal compliance.
Real-estate broker: Property search, lease negotiation, regulatory compliance.
Editor: Presentations, documents, emails, code.
Investigator: Client, vendor, partner, competitor, consultant, product.
Teacher: Skill development, test prep, concept learning, project guidance.
Counselor: Mental health, relationship, career, life coaching.

But these are the more obvious ones. I had Claude list what AI agents can be “hired” of some of the less obvious but high-leverage “hires” and what I could ask them:

Philosopher.
- Why does this feel uncomfortable?
- What do these choices reveal about my implicit values?
- AI may cut jobs but also improve lives. Help me resolve this.
- Play devil’s advocate from three ethical frameworks.
Historian.
- Generate structured questions to ask parents/elders.
- Here’s a transcript. Extract themes and suggest follow-up questions.
- Connect this family story to its broader historical context.
- Turn these fragmented stories into a coherent narrative.
Relationship architect.
- Brief me on my history with this person before I meet them.
- I haven’t contacted X in months. Draft a warm reconnection message.
- Who in my network could help me reach [goal]? What’s the warmest path?
- What patterns do you notice in the relationships I find energizing?
Scenario planner.
- Create three 2030 scenarios for [domain]. Stress-test my current strategy against each.
- What robust moves work across all scenarios?
- What early warning indicators should I watch for?
- I’m choosing between X and Y. What would I need to believe for each to be correct?
Epistemologist.
- What are my load-bearing beliefs? Which haven’t I stress-tested recently?
- Steelman the strongest case against my view on [topic].
- Here’s a prediction I made. Track it. What’s my calibration like?
- Where might I have galaxy-brained myself into an unusual position?
Diplomat.
- I’m meeting [context]. What unwritten expectations might I miss?
- Review this email. Am I being appropriately [direct/indirect] for this culture?
- What mistakes do Indians typically make in [context]?
- The meeting felt off. Here’s what happened. What did I miss?
Taste curator.
- I liked [X, Y, Z] but not [A, B]. What does this reveal about my aesthetic?
- What’s adjacent to my current taste that would stretch me without losing me?
- I want to develop taste in [domain]. What’s the learning path? What do I experience first?
- My taste feels derivative. What would make it more authentically mine?
- Recommend [books/films/music] that would give me vocabulary for [aesthetic/emotion/idea].
- I’m designing [space/event/gift]. What references should I draw from given my taste?
- My taste in [domain] is developed. My taste in [other domain] is naive. Bridge them.
Rhetorician.
- Analyze this transcript. What’s my default argumentation style? Its blind spots?
- I need to convince [skeptical audience] of X. What’s the optimal structure?
- Help me turn this observation into a memorable, quotable framework.
- Steelman their likely objections and give me responses.
Archivist.
- What have I previously thought about [topic]?
- This new idea connects to something—find the link in my past work.
- I’ve written about X and Z separately. Synthesize them.
- What gaps exist in my thinking on [domain]?
Liturgist.
- Design a family ritual for [transition] that fits our values.
- Create a weekly reflection practice for our family.
- We lost [person]. Design a remembrance practice that feels genuine.
- I want to mark [milestone] meaningfully, not performatively. How?
Activist.
- I care about [issue]. Map the power structure. Who actually decides?
- What’s the smallest intervention with the largest leverage on this system?
- Who are unlikely allies? What would make opponents neutral?
- I have [resources/reach]. What’s my highest-impact move?
- Craft a narrative frame that makes [change] feel inevitable, not radical.
- What’s the history of successful change in similar domains? What worked?
Intelligence agent.
- What weak signals should I monitor for [risk/opportunity]?
- Here’s what [competitor/market] did this quarter. What does it reveal about their strategy?
- What am I not seeing because of my position? Where are my blind spots?
- This seems like noise. Is there a pattern I’m missing?
- Red team this: How could [scenario] hurt me? What would I not see coming?
- Verify this claim. What would make it false? What’s the source quality?
Bodyguard.
- Audit my digital footprint. What’s publicly available that shouldn’t be?
- I’m traveling to [place]. What’s the threat profile? What precautions matter?
- Review this [email/message/request]. Is this social engineering?
- What’s my current weakest security link—physical, digital, financial?
- Someone determined wants to harm me. What’s their easiest path? How do I close it?
- Design a security protocol for [context: travel/home/data] that I’ll actually follow.
Lab assistant.
- I want to test whether [intervention] affects [outcome]. Design an N=1 experiment.
- Here’s two weeks of data. What’s the signal? What’s noise?
- I think X causes Y in my life. What confounders should I control for?
- This experiment failed. Was it the hypothesis or the method?
- What’s the minimum viable test before I commit to [major change]?
- I’ve tried [interventions]. Synthesize: what actually works for me?

The Jamnagar Chokepoint - Data Story

Thu, 01 Jan 2026 03:14:11 +0000

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 count AI summarized books as "Read"

Sat, 27 Dec 2025 02:54:41 +0000

I have this nagging feeling (maybe you do too?) that it’s cheating and I’m not really learning if it’s so easy. The same voice makes me feel guilty when using coding agents to code or ChatGPT in meetings.

I’m telling that voice to relax.

I upload books to Claude and ask it to “Comprehensively and engagingly summarize and fact-check, writing in Malcolm Gladwell’s style, the book …”. I can read it in an hour instead of twelve. Four bullet points instead of forty. With (this surprised me) roughly the same number of insights I actually do something with.

I read books for pleasure, learning, and implementing.

That last bit is key, but the bottleneck isn’t the book. It’s me: how much I can absorb, retain, and act on. From what I can tell, between reading 300 pages vs 10, there’s not much difference.

The pleasure is intact

I worried I’d lose the emotional jolt – those mind-blown moments that make reading feel worth it. For science and self-help books, I haven’t.

For example, take this summary excerpt of How Minds Change

The irony is almost too perfect. To change someone’s mind, you have to stop trying to change their mind. You have to start by trying to understand it.
And when you do that—really do that—something unexpected happens. Sometimes their mind changes. And sometimes, if you’re honest with yourself, yours does too.

That blew my mind for a good 10 minutes.

Or, from Ants:

Think of a leafcutter ant colony as a high-tech Fungus Farm. There are four main players in this system: two are partners, one is a bodyguard, and one is a villain.

I loved the villain part!

There’s a learning bonus: fact-checking

When I ask Claude to fact-check the book while summarizing, I learn beyond the book. For example, I learnt that, among these longevity books:

Lifespan is largely rubbish.
The Telomere Effect has a decent core but builds fancy castles on shaky foundations.
Outlive is more careful with its claims, though less ambitious.

I learned this during the reading, not months later when some podcast corrected me.

You can also just… ask questions.

While reading Ants, “OK, aren’t most species superorganisms then? If yes, what’s the big deal?”
While reading The Cancer Code, “Why are free radicals produced? Give me examples. Are they always oxygen molecules that lack electrons? ELI15.”

The book doesn’t do that. The summary can. (I’m loving the ELI15 responses, BTW.)

I can read inaccessible books

I can now read Kamba Ramayanam in Tamil (a script I read quite slowly). Or Gitanjali, The Iliad, Dream of the Red Chamber, or more.

It’s not just language. I can read boring or intimidating books. On Growth and Form. The Road to Reality. The Structure of Evolutionary Theory. Brilliant but unapproachable.

Heck, I managed to read Ants after 30 years!

Sometimes the very first line of a summary hooks me so hard I can’t stop reading. That’s a strange thing to admit about a summary. But there it is.

I haven’t figured it out fully

I would read Brandon Sanderson or Jeffrey Archer this way yet. The point is the experience itself: the slow unfolding, the surprise, the voice in your ear. They write in a style I like. Why summarize that?

Biographies are trickier. The Choice’s summary made me cry. So, I do want to read The Choice in original - and the summary helped me prioritize it.

Histories? I’m not sure. Manga? No way.

I’m logging these on Goodreads as “read”. I don’t know if that’s kosher. The voice in my head has opinions. But I’m learning things I wouldn’t have learned otherwise, from books I wouldn’t have finished or started, with the same pleasure and actionability.

Maybe that’s cheating. But maybe the rules of our game have changed, and I haven’t learned them yet.

Update: 31 Dec 2025. Here are the books I read this year via AI summaries, along with links to the Claude summaries.

Sex at Dawn by Christopher Ryan, Cacilda Jethá Claude
The Molecule of More by Daniel Z. Lieberman and Dopamine Nation by Anna Lembke Claude
Noise by Daniel Kahnemann Claude
How to Change by Katy Milkman Claude
Lifespan by David Sinclair and The Telomere EFfect by Elizabeth Blackburn and Outlive by Peter Attia. Claude
How Minds Change by David McRaney Claude
The Obesity Code by Jason Fung Claude
The Diabetes Code by Jason Fung Claude
The Cancer Code by Jason Fung Claude
The Ants by Bert Hölldobler, Edward O. Wilson Claude
Price of the Modi Years by Aakar Patel Claude
Behave by Robert Sapolsky Claude
The Choice by Edith Eva Eger Claude
How Not To Die by Michael Greger Claude
Why We Sleep by Matthew Walker Claude
The Slight Edge by Jeff Olson Claude
The Origin of Consciousness in the Breakdown of the Bicameral Mind by Julian Jaynes Claude
Immune: A Journey Into the Mysterious System That Keeps You Alive by Philipp Dettmer Claude Gemini
The Gene: An Intimate History by Siddhartha Mukherjee Claude
The Structure of Evolutionary Theory by Stephen Jay Gould Claude
The Extended Phenotype by Richard Dawkins Claude
The Body Keeps the Score by Bessel van der Kolk Claude
The Culture Map by Erin Meyer Claude
Outcomes over Output by Josh Seiden Claude
The Song of the Cell: An Exploration of Medicine and the New Human by Siddhartha Mukherjee Claude
The Emperor of All Maladies: A Biography of Cancer by Siddhartha Mukherjee Claude
Reality is Not What it Seems: The Journey to Quantum Gravity by Carlo Rovelli Claude
The Happiness Hypothesis by Jonathan Haidt Claude
Why We Die: The New Science of Aging and the Quest for Immortality by Venki Ramakrishnan Claude
Same as Ever: A Guide to What Never Changes by Morgan Housel Claude
Build: An Unorthodox Guide to Making Things Worth Making by Tony Fadell Claude
Metabolical: The Lure and the Lies of Processed Food, Nutrition, and Modern Medicine by Robert H. Lustig Claude
The Language Instinct: How the Mind Creates Language by Steven Pinker Claude
How the Mind Works by Steven Pinker Claude
The Blank Slate: The Modern Denial of Human Nature by Steven Pinker Claude
The Stuff of Thought: Language as a Window into Human Nature by Steven Pinker Claude
The Demon-Haunted World: Science as a Candle in the Dark by Carl Sagan Claude
Entangled Life: How Fungi Make Our Worlds, Change Our Minds & Shape Our Futures Claude
Phantoms in the Brain: Probing the Mysteries of the Human Mind by V.S. Ramachandran Claude
Small Wins Every Day: 100 Powerful Ways to Transform Your Life and Health by Luke Coutinho Claude
The Tell-Tale Brain: A Neuroscientist’s Quest for What Makes Us Human by V.S. Ramachandran Claude

Update: 11 Apr 2026. I started adding this follow-up prompt:

Based on what you know of me, what are the things I should learn from these books? What might I do differently? Why? How?

Based on past conversations, AI knows me. For example, here were Claude’s thoughts:

Anand wants personalized life lessons from these books, filtered through what I know about him. Let me think about what I know:

Chief Innovation Officer at Straive, AI strategy, enterprise consulting

Faculty at IIT Madras, teaching Tools in Data Science to large cohort

Based in Singapore, Indian origins

Blogs daily at s-anand.net about AI experiments

Interests: data visualization, behavioral science, cross-domain pattern recognition, design

IIM Bangalore alumnus

Regular speaker at conferences

Core intellectual commitments: judgment and taste as scarce resource; verifiable/auditable AI outputs; Jevons Paradox applied to knowledge work; production costs collapsing shifts bottleneck to curation

Thinks in systems, economic, and forecasting frameworks with superforecaster-style probabilistic reasoning

Identifies as “LLM Psychologist”

Teaching an AI workshop for senior IAS officers at LBSNAA

That leads to high quality ideas. For example, it told me:

… you’re surrounded by people optimizing for the marathon — the visible, bounded deliverable. But Pheidippides’ actual run — the slow, unglamorous, unsexy one that actually won the war — was the one nobody watched.

What’s the Spartathlon version of your work? Probably: the long, patient work of changing how a client organization thinks, not just what tools it uses.

… make sure you’re spending some deliberate effort on it, even when there’s no one watching and no milestone presentation scheduled.

That led me to review transcripts for AI adoption impact. Answer: workshops help AI adoption most. I’m keeping an AI experiments list handy for impromptu workshops.

That’s real impact. In less time than I’d have taken to read a book.

Sure, I didn’t get what the books had to offer. I didn’t struggle. But I did get personalized value.

Tue, 23 Dec 2025 05:36:05 +0000

AI agents are messing up software tool learning.

Normally, we need to pass stages of competence:

KNOW what you can do
LEARN how to do it
EXECUTE it.

Excel: KNOW you can summarize by category, LEARN pivot tables, EXECUTE an Insert → PivotTable → select data range → drag …

Photoshop: KNOW you can erase objects, LEARN Content-Aware Fill, EXECUTE Lasso tool → select → Edit → Content-Aware Fill → …

AI agents collapse these stages. Especially with command line tools.

For example, I did not KNOW ffmpeg could visualize audio, nor LEARN the complex filters, nor EXECUTE it on my laptop.

Instead, I told Claude to Visualize this audio clip creatively in multiple ways. It generated a dozen stunning visualizations of this Why This Kolaveri clip: https://lnkd.in/g7vfcPEs

I didn’t KNOW ⮕ LEARN ⮕ EXECUTE. I did an EXECUTE ⮕ KNOW ⮕ don’t bother to LEARN.

Since that’s possible, my prompts are now like: “I don’t know what I want. Guess my desires. Fulfill them. Show me. Then maybe I’ll know what I want.”

Which is fantastic! But on the other hand, this TOTALLY messes up how I teach software tools…

Tue, 23 Dec 2025 05:36:05 +0000

I used to be a data visualization expert. I’m not sure I still am.

When Anthropic published an article about how AI is transforming their engineers’ work, I ran this prompt:

Suggest how the following engineer productivity patterns can be illustrated using interactive animated charts, graphs, or infographics.

Be diverse. Xenographics are welcome. Novel animation* / *interaction styles, artistry, xenographics, and diverse chart types are encouraged.

Be intuitive. A single glance should tell them exactly what insight we are trying to convey.

Its suggestions were better than mine.

Gemini 3 Pro was the best, followed by Claude (Opus/Sonnet 4.5) and then ChatGPT (GPT 5.2). They came up with:

a Throughput Flow showing commits as particles, going from a trickle to a stream
a Knowledge Neural Net where an engineer’s mental model transforms from isolated clusters into a dense mesh
a Dark Matter Radar detecting invisible tasks, a Feedback Pulse, a polyomino task stacking
… and more.

I have ~20 years of professional data visualization experience. I teach it. And these AIs just casually suggested more creative visualization ideas in 10-30 minutes than I probably would have come up with in a day.

This cost less than $2. The new per-day-rate for my expertise, I guess.

Luckily: my expertise has two parts.

Part 1: Ideating innovative visualizations.
Part 2: Selecting the right one for the job.

AI’s better at ideation. It’s fast and can generate creative ideas faster than I can sketch on a whiteboard. It knows xenographics and every visualization ever made and can recombine them in novel ways.

I’m better at selection. For now. Knowing which visualization requires knowing the audience, context, and intent. It requires taste (accumulated wisdom from showing the wrong chart to the wrong audience enough times).

But still, since AI can ideate, anyone can create innovative visualizations. No 20-years of experience, reading Edward Tufte’s books, or comparing a violin plot and a beeswarm. Just tell the story and AI will suggest ten creative ways to tell it. Take your pick.

I’m no longer a bottleneck.

Naveen Gattu - you asked me to clone myself. 10 years later, here’re the recipe: https://sanand0.github.io/datastories/anthropic-work/

Video

Tue, 16 Dec 2025 05:36:05 +0000

Style transfer is my newly discovered AI super-power: having AI rewrite in someone’s style.

EXAMPLE 1: Kalama Sutta. I asked Claude to “Rewrite this Kalama Sutta translation. Pick an author whose style is modern, thoughtful, and VERY readable. Mention the author and rewrite in their style.”

The original sounds like this: https://lnkd.in/gQhi8CBY

“It is proper for you, Kalamas, to doubt, to be uncertain; uncertainty has arisen in you about what is doubtful. Come, Kalamas. Do not go upon what has been acquired by repeated hearing; nor upon tradition; nor upon rumor…”

Here it is in the style of Pico Iyer, an essayist who writes the way good conversation feels: https://lnkd.in/gMFpc74y

“Your confusion makes sense. When things are genuinely doubtful, doubt is the appropriate response. So here is what I suggest: Don’t accept something just because you’ve heard it repeated many times. Don’t accept it just because it’s tradition, or rumor, …”

EXAMPLE 2: AI Personhood. Here’s a fascinating paper by Google: https://lnkd.in/gikMnUzj

“Assuming some essence of personhood is “out there” waiting to be discovered, or a metaphysical fact about what AIs or persons “really are” that can settle our practical questions seems to us, unlikely to prove helpful. We propose treating personhood not as something entities possess by virtue of their nature, but as a contingent vocabulary developed for coping with social life in a biophysical world.”

In Tim Urban’s style, it becomes this one-liner: https://lnkd.in/g8t8fG4g

“Given this mess in front of us… what vocabulary actually works?”

Both articles have amazing content. But now they’re accessible to me, thanks to style transfer.

PROMPT: Here’s the prompt fragment I’m using:

Think about whose style of writing would be the most engaging and informative to write the following content.List options, mentioning their style, why they’re suitable, and pick the best, with reason.Then rewrite it in their style.

Claude has better style. ChatGPT has higher rigor. Pick what works for you!

PS: Image by Nano Banana Pro. “Draw Keanu Reeves as Buddha but wearing jeans, addressing a crowd of villagers. He is clearly modern. The village and villagers are clearly ancient. The image is very realistic, like from a modern digital camera.”

Tue, 09 Dec 2025 05:36:05 +0000

When my father mentioned that Virat Kohli scored a century (again) against South Africa, I wondered how he compared to the likes of Tendulkar and Gavaskar.

I asked ChatGPT:

If you had to evaluate the quality of Indian batsmen over time, what single metric (possibly composite) would you use?

Evaluate the top Indian batsmen in history on this metric. Plot them over their active years (X-axis) along with the metric (Y-axis), labelled with the player names, on a beautiful visualization.

It picked a simple metric: Batting Average * log(Total Runs).

Claude and Gemini, too, preferred simple metrics that favored the batting average or total runs. Strike rate, relative to era, win rate impact, etc. were all excluded.

(Sadly, any metric that misses Kapil Dev feels a bit… incomplete.)

Data story: https://sanand0.github.io/datastories/indian-batting-greats/

Is all AI content slop?

Wed, 05 Nov 2025 00:00:00 +0000

Is all AI content slop?

I asked Claude to:

Analyze this thread. Then explain it like a Malcolm Gladwell New Yorker article. https://news.ycombinator.com/item?id=45820872

It gave me a beautiful, engaging and insightful essay about a 300+ message debate about AI vs humans on routine tasks. https://claude.ai/share/60c5810f-5c81-4970-8026-a24bf89c3392

Is this slop?

One phrase stood out:

There’s an irony here that the commenter doesn’t quite state but implies beautifully: we’ve spent so long celebrating automation because humans are imperfect that we’ve forgotten we also value humans because they’re imperfect.

Note: it’s something “the commenter doesn’t quite state” and yet Claude inferred it. “Beautifully”.

Is this slop?

Another paper, The Hidden Curriculum, is quite readable as papers go. https://mgcuna.github.io/website/JMP_latest.pdf

But look at how it begins:

Despite dramatically expanded access to selective U.S. colleges, first-generation students persistently trail continuing-generation peers in GPA, internship attainment, and early-career outcomes.

Here’s how Claude’s Malcolm Gladwell essay begins: https://claude.ai/share/81d7ff3c-db89-4c9f-8423-82358fb660a3

When Sara Martinez arrived at UC Berkeley as a freshman, she did everything right. She showed up to every class. She completed every reading assignment. She took detailed notes. She studied harder than most of her peers. By every visible measure, she was the model student.

I read every word of the essay. The paper has value, but the essay has accessibility.

Is this slop?

History repeats this pattern.

The printing press displaced calligraphers.
But typography emerged as an art.
Film displaced theater but gave us montage, screenplay, and editing.
CAD displaced drafting but birthed parametric design.
3D printers are displacing workshops, creating toolpath sculpture and generative objects.

Maybe AI slop is the start of new arts?

Vibe-Coding for Interesting Data Stories

Mon, 06 Oct 2025 09:03:35 +0000

Last weekend, I fed Codex my browser history and said “explore.” It found a pattern I call rabbit holes – three ways we browse:

Linear spiral - one page > next page > next. E.g. filing income tax, clicking “next” on the PyCon schedule.
Hub & spoke - hub > open tabs > back to hub. E.g. exploring DHH’s Ubuntu setup, checking Firebase config.
Wide survey - source > many, many pages. E.g. clearing inbox, scanning news.

Then Claude Code built this lovely data story.

My goal? Find challenges in vibe-coding interesting data stories. I found several.

A. I don’t know what I want.

Solution? Ask for multiple options. More options = more ideas. Codex proposed two I hadn’t planned: rabbit holes and search funnels.

B. I don’t know if it’ll turn out well.

Solution? Build them all. Don’t pre-judge. I did not expect rabbit holes to be interesting - a clear prediction error.

C. Reviewing is the bottleneck. It’s slow and painful.

Solution? Make reviews easy.

Ask for review-friendly output. E.g. A table/heatmap comparing options.
Use LLMs to pre-review. E.g. Pick top 3 with reasons.
Review output, not code. E.g. Have it build a working demo, then review.

D. Model / tool strengths vary.

Solution? Align with strengths. For example:

Use GPT-5 for planning. It’s better than GPT-5-Codex or Claude 4.5 Sonnet.
Code UI with Claude 4.5 Sonnet. It’s better than most models.

Check out the prompts & process.

Try this: Pick one messy dataset you have. Ask an LLM for five ways to explore it. Build them all. One will surprise you.