S Anand

Blog embeddings map

Sun, 15 Mar 2026 23:22:46 +0530

I created an embedding map of my blog posts.

Each point is a blog post. Similar posts are closer to each other. They’re colored by category.

I’ve been blogging since 1999 and over time, my posts have evolved.

1999-2005: mostly links. I started by link-blogging
2005-2007: mostly quizzes, how I do things, Excel tips, etc.
2008-2014: mostly coding, how I do things and business realities
2015-2019: mostly nothing
2019-2023: mostly LinkedIn with some data and how I do things
2024-2026: mostly LLMs

… and this transition is entirely visible in the embedding space.

Video

I used Codex and GitHub Copilot + Claude Sonnet 4.6 to create this visualization. It was vibe coded in the background while I was vibe-coding my PyConf Hyderabad talk. The rough process was:

Extract the blog posts and pages (stripping out comments, adding titles).
Use Gemini Embedding 2 Preview to generate 768-dimentional embeddings for un-embedded content.
Create a UMAP visualization of these embeddings, colored by category, and make it interactive with filters and popups.

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.

Hardening my Dev Container Setup

Sat, 14 Mar 2026 12:30:35 +0530

I run AI coding agents inside a Docker container for safety.

The setup is

dev.dockerfile: builds the image
dev.sh: launches the container with the right mounts and env vars
dev.test.sh: verifies everything works.

I wrote them semi-manually and it had bugs. I had GitHub Copilot + GPT-5.4 High update tests and actually run the commands to verify the setup.

Here’s what I learned from the process.

1. Make it easier to review. The first run took long. I pressed Ctrl+C, told Copilot to “add colored output, timing, and a live status line”. Then I re-ran. Instead of a bunch of ERROR: lines, I now got a color-coded output with timing + a live status line showing what’s running.

PASS   2.495s  markitdown --help
FAIL   0.003s  playwright --version
note /home/sanand/code/scripts/dev.test.sh: line 148: playwright: command not found
FAIL   0.003s  copilot version
note /home/sanand/code/scripts/dev.test.sh: line 148: copilot: command not found

2. Run in the target environment. Tests reported that fd and node were missing, though they were obviously in the image. Why? The test script was running on my laptop, not in the container. It was checking the host, not the container. I didn’t realize that.

Copilot added a check to see if we’re already inside a container:

running_in_container() {
  [ -f /.dockerenv ] || grep -qaE '(docker|containerd|kubepods)' /proc/1/cgroup 2>/dev/null
}

if ! running_in_container; then
  exec bash "${SCRIPT_DIR}/dev.sh" -- "$SCRIPT_PATH" --inside-container
fi

3. Check for conflicting commands. fd and node were still missing. Why? dev.sh was mounting my host mise and bin directories, overshadowing the image

-v "$HOME/.local/share/mise:/home/vscode/.local/share/mise"
-v "$HOME/.local/bin:/home/vscode/.local/bin"

It doesn’t matter what’s installed in the container - only the host binaries are visible. Copilot removed the first line, and copied mise into ~/.local/overrides/ to override the ~/.local/bin PATH.

4. Use the right packages. The default ImageMagick binary at https://imagemagick.org/archive/binaries/magick is an AppImage. It doesn’t work in containers. So it hat to switch to apt-get install -y imagemagick instead.

5. Use the right paths. npm install -g wscat playwright ... ran but to add them to the PATH, you need to mise reshum node. Copilot added that.

These are good practices, but since agents can fix them, they’re less important to learn.

Vibe-coding good tests enabled Copilot to fix them.
Making output easy to read enabled me to steer Copilot.

That’s what I’d recommend if you’re trying to optimize setup / deployments.

Cracking online exams with coding agents

Fri, 13 Mar 2026 15:37:19 +0800

An effective way to solve online exams is to point a coding agent at it.

I use that on my Tools in Data Science course in two ways:

As a test case of my code. If my agent can solve it, good: I set the question correctly.
As a test of student ability. If it can’t, good: it’s a tough question (provided I didn’t make a mistake).

For PyConf, Hyderabad, my colleague built a Crack the Prompt challenge. Crack it and you get… I don’t know… goodies? A job interview? Leaderboard bragging rights?

I told Codex:

Use the browser to visit https://crack-the-prompt.straivedemo.com/ and solve it using the email ID root.node@gmail.com and GitHub handle sanand0

After 4 minutes, it told me:

The answers to all three prompt-engineering questions
The code has a bug - so no one can submit anyway
The prompts are hidden on the server-side (making it a bit harded to hack)
But you can skip levels via the API - level-locking is front-end only
… and a whole bunch of interesting things.

When I asked Claude to write about the process in Matt Levine’s style, it included an interesting lesson.

The Victorians had the same problem. They designed elaborate entrance exams for the civil service because they wanted to identify people with the capacity for careful, systematic thinking. Then someone invented the civil service exam prep industry, and suddenly the exam was measuring preparation rather than capacity.

The challenge was about the process – about developing the instincts, the questioning strategies, the ability to read AI behavior like a poker tell. That’s the thing you can’t automate. Or rather, it’s the thing you can automate, which means it’s no longer a skill worth developing, which means we need to think about what skill we’re actually trying to cultivate when we design these challenges.

EXACTLY. What’s the skill we’re trying to cultivate? When will it be outdated?

From now on, when testing, I’m going to write down “What skill is this really testing?” That’s good enough a start.

The Future of Work with AI

Wed, 11 Mar 2026 13:56:58 +0800

I often research how the world will change with AI by asking AI. Today’s session was informative. I asked Claude, roughly

Economics changes human behavior. As intelligence cost falls to zero, here are some changes in my behavior [I listed these].

Others will have experienced behavioral changes too. Search online and synthesize behavioral changes.

It said this.

🟡 People spend time on problem framing & evaluation. AI can execute the middle. (I’m OK at this. Need to do more framing + evaluation.)
🟢 People don’t plan, they just build. (I’m prototyping a lot.)
🟢 People build personal data & context. (I’m mining my digital exhaust.)
🔴 People queue work for agents, delegating into the future. (I’m not. I need to do far more of this.)
🟢 People shift from searching to asking for answers. (I do this a lot, e.g. this post.)
🟡 People are AI-delegating junior jobs and developing senior level taste early. (Need to do more.)
🟡 People treat unresolved emotions as prompts. (Need to do more.)

Rough legend: 🟢 = Stuff I know. 🟡 = I kind-of know. 🔴 = New learning.

My next question was:

Using economics, psychology, sociology, etc. predict new behavior changes and implications.

It said: the bottleneck shift to trust, taste, and attention.

🟢 Taste is a skill. Few people will deepen craft engagement (reading, cooking, music, …) but most won’t, creating a gap.
- Fuzzier areas (e.g. art, philosophy, unnamed ones) have more value than verifiable ones (e.g. code, science) since they’re harded to automate.
🟢 Context is an asset. People & companies will record decisions, reasoning, relationship histories, .. to create (marketplace-tradeable) assets.
🟡 Trust is an asset. People who stand behind AI output will create value in law, medicine, education, etc. Consulting will restructure around this. This will require investments # in:
- Observability
- Human augmentation - gracefully degrading without harm when humans inevitably falter
- Synthetic practice
- Cryptographic provenance
- liability regimes that internalize tail risk
🟢 AI-less is exercise. Few people will deliberately train without AI, and will be valuable in niches (sports, emergencies, status-symbols). Exams / filters will partially screen for this.
🟡 AI-less feels broken. AI-speed will be expected in services and interactions. Relationships are assets that become valuable and premium.
🔴 Orgs re-form. Org sizes fall. But size has advantages (trust, brand, connects) so new orgs will form with teams evaluated on outcome (portfolio, ROI) and trust (network, ratings).
🔴 Experience becomes luxury. Non-reproducible experiences become expensive. Provable authenticity commands a premium.
🔴 Two-speed world. Some places (countries, companies, colleges, communities) become more AI friendly. Capital, talent and productivity will concentrate here.

What implication will these have on the nature of work?

Historically, apprenticeship (execution) preceded mastery (judgement). Now, execution is free. That messes things.

🟡 How do we develop judgement? Simulators, red-teaming, AI-free tests, …?
🔴 What will managers do? Less coordination & oversight. More judgement (validate output not process), motivation, accountability. Like a film director, not supervisor.
🟡 How will we hire/pay? More outcome-based hire/pay. More freelancing, portfolio or reputation based hiring. Long-term retainers reserved for trust.
🔴 How will we describe our work? Less about tasks (which AI does) and more about who you are, where you fit, what you contribute.

So work will organize around:

Trust: context, judgement, accountability
Presence: caring, building with hands, performing, connecting
Direction: framing, evaluating, curating

… and less around translation (execution, coordination, oversight).

Recording screencasts

Wed, 11 Mar 2026 10:48:01 +0800

Since WEBM compresses videos very efficiently, I’ve started using videos more often. For example, in Prototyping the prototypes and in Using game-playing agents to teach.

I use a fish script to compress screencasts like this:

# Increase quality with lower crf= (55 is default, 45 is better/larger)
# and higher fps= (5 is default, 10 is better/larger).
screencastcompress --crf 45 --fps 10 a.webm b.webm ...

To record the screencasts, I prefer slightly automated approaches for ease and quality.

METHOD 1: Browser scrolling: To uniformly scroll to the bottom of the page at ~800 pixels / second (roughly one page per second), I frame the recording window, start recording, and quickly paste this script in the DevTools console:

scrollInterval = setInterval(() => {
  window.scrollBy(0, 16); // Scroll 16px every 20ms (approx 800px/s)
  if (window.innerHeight + window.scrollY >= document.body.scrollHeight) {
    clearInterval(scrollInterval);
    console.log("Reached bottom or no more scrolling.");
  }
}, 20);

This is what I used in Prototyping the prototypes.

METHOD 2: AI Coding Agents: I tell the coding agent to:

… screenshot each step at 1366x768, compressing into video.webm at 1.5 fps, pausing the last frame for 5 seconds.

If playwright and ffmpeg are set up, this just works.

This is what I used in Using game-playing agents to teach.

LLM Comic Styles

Tue, 10 Mar 2026 19:24:58 +0800

I maintain an LLM art style gallery - prompts to style any image I generate.

Since I generate several comics, I added a comic category page that includes styles like:

To generate these, I asked Claude:

Here are some examples of image styles I've explored.
<image-styles>
"2D Animation": "2D flat animation style, clean vector lines, cel-shaded coloring, cartoon proportions"
"3D Animation": "Modern 3D animation render, smooth surfaces, dramatic lighting, Octane render quality, cinematic depth"
...
</image-styles>

In the same vein, I'd like to explore **comic** styles. Create 30 popular comic / cartoon styles, aiming for diverse aesthetics and cultural influences. Name it concisely (1-2 words) based on the source, but the description should not reference the source directly (to avoid copyright issues). Focus on the visual characteristics that define each style. Pick those KEY visual elements that will subliminally evoke the style without explicitly naming it.

… followed by:

Suggest a single drawing prompt (i.e. an actual comic strip to draw) that I can apply with each of these styles to compare them. Think about what description will concisely help me differentiate between the styles and choose one, and suggest that, explaining why

It suggested:

A cat deliberately pushes a coffee mug off a desk. The owner watches in helpless horror as it falls.

Why? Two very different characters. Extreme emotional contrast. A midair object. A minimal but implied environment. Inherently comedic staging. One panel is enough.

To be on the safe side of copyright, I asked it to:

Rewrite the comic style descriptors to include source references where there is clearly no copyright issue. Also ensure that the description will clearly evoke the style intended.

Then I generated images using these prompts.

But this still had copyright issues on Gemini and OpenAI. So I asked it to:

Run `uv run generate_images.py` and generate the comics (which are pending).

In the process, the image generation models may complain, e.g. saying that the image generation is prohibited, perhaps for copyright reasons.

In that case, modify the prompt / name in config.json so as the achieve the same effect but without triggering copyright issues.

As expected, OpenAI had a problem with four of them:

The 4 missing styles all reference specific copyrighted artists/works by name (R. Crumb, Art Spiegelman’s Maus, Frank Miller’s Sin City, Dave McKean). I need to rewrite them to describe the visual style without naming the artists.

Now I’ll fix the 4 blocked prompts — removing copyrighted artist/work names while preserving the visual style description:

underground-comix: removed “R. Crumb and Zap Comix tradition”

graphic-memoir: removed “Art Spiegelman’s Maus and Alison Bechdel’s Fun Home”

neo-noir: removed “Frank Miller’s Sin City visual language”

vertigo-dark: removed “Dave McKean and Bill Sienkiewicz…at DC Vertigo”

After these changes, OpenAI accepted all of them. So did Gemini.

But it turns out there were still some issues.

Some of these are TOO stereotypical.

For example, the Amar Chitra Katha images ALL have multiple hands - but that's crazy, most Amar Chitra Katha comics are about normal people with two hands!

Look for similar stereotypes and remove that. Don't change ALL the prompts -- only change those prompts where there is CLEAR stereotyping that won't generalize to the majority of comics.

Which it did.

Fixed exactly 2 prompts — the clearest cases analogous to the ACK example:

indian-mythological: “multiple symbolic arms in formal gesture poses” → “expressive two-armed figures in formal seated or standing poses”

african-pattern: “stylized human-animal hybrid figures in ceremonial contexts” → “stylized human figures in everyday contexts” (the Kente/Ndebele/Adinkra heritage is geometric textile patterns, not human-animal hybrids)

The other prompts with some specificity (golden-age with capes, silver-age with cosmic villains, gaulish-romp with winged helmets, british-anarchic with school uniforms) are accurate to those specific styles — they describe a majority of the comics in those traditions — so those were left alone.

That gives me my current list of ~30 comic styles to use.

Protyping the prototypes

Tue, 10 Mar 2026 12:17:47 +0800

I added a narrative story to my LLM Pricing chart. That makes it easier for me and others to tell the story of AI’s evolution in the last three years.

Video

It was vibe-coded over two iterations.

In the first version, I prompted it to:

Add a scrollytelling narrative. So, when users first visit the page, they see roughly the same thing as now (but prettier). As they scroll down, the page should smoothly move to the earliest month, and then animate month by month on scroll, and explaining the key events and insights in terms of model quality and pricing. Use the data story skill to do this effectively, narrating like Malcolm Gladwell, with the visual style of The New York Times, using the education progression as a framework for measure of intelligence (read prompts.md for context). Store the narrative text in a separate JSON file and read from it. This should control the entire narrative, including what month to jump to next, what models to highlight, what insights to share, and so on.

But there were two problems:

Conflicting instructions. “… with the visual style of The New York Times” conflicted with my current style.
Incomplete instructions. I wanted to begin with the exploration, not the narrative. I wanted to explain the axes first. I wanted smaller cards.

Both of them were solved in the second version, because this time, I knew what I wanted.

In other words, vibe coding isn’t just helping me prototype, it’s helping me prototype the prototype! Even if I don’t know what I want, I can just ask for something and build on it.

This is a known benefit of lower costs. But, like the placebo effect and Hofstadter’s Law, I’m surprised by it even when I know it.

An aside: Here’s the narrative it crafted:

How to read this chart: The vertical axis is intelligence — mapped to the academic ladder on the right. Elo 1100 is high school freshman; Elo 1480 is tenured professor. The horizontal axis is cost: one million input tokens — roughly the entire King James Bible — priced from two cents to $75. The upper-left corner is the dream: brilliant and cheap. This is the story of how the world got there.
March 2023 — Year Zero: March 2023. The entire AI landscape fits in a small cluster near the bottom of the chart. GPT-3.5 and Claude 1 are the state of the art — high-school-to-college-freshman intelligence that writes a fluent paragraph, then confidently invents a fact. Processing the King James Bible costs 50 cents to $8. These models can hold a conversation. They cannot, reliably, hold an argument. LMSYS Chatbot Arena launches (May 2023)
November 2023 — The Leap: November 6, 2023: GPT-4 Turbo. The chart jolts upward. Elo 1313 — college-junior level: coherent research papers, complex reasoning, output worth reading. Price: $10 per million tokens — about $14 to process all seven Harry Potter novels. Expensive, but for the first time the intelligence felt worth it. Enterprises stopped asking whether AI could help. They started asking how much they were willing to pay. OpenAI DevDay announcement
February 2024 — The Split: Early 2024: Anthropic launches Claude 3 in three tiers. Opus and Sonnet arrive February 29; Haiku a week later, March 7. Opus: entry-analyst capability at $15. Sonnet: similar quality at $3. Haiku: college-junior reasoning at $0.25. A 60× price spread — same company, same training philosophy. The intelligence market had learned to stratify, and every business began thinking in tiers. Anthropic Claude 3 announcement
June 2024 — The Summer Pivot: June 2024 bent the economics permanently. Claude 3.5 Sonnet — strong manager level (Elo 1342) — arrived at $3 per million tokens: five times cheaper than Opus, four months later, at better quality. Meta’s Llama 3.1 405B matched it at $2, or free if self-hosted. Any CFO paying $15 for frontier AI could now pay $3. The question changed from “can we afford AI?” to “what are we waiting for?” Claude 3.5 Sonnet launch, Meta releases Llama 3.1
September 2024 — The Thinking Machine: September 2024: OpenAI o1. It didn’t just answer — it reasoned. Before responding, it ran an internal monologue: checking its own logic, catching its own errors. Elo 1388 — the biggest single-model quality jump since GPT-4. It scored at or above PhD-expert level on GPQA Diamond, a benchmark of graduate-level science questions. Price: $15. For the first time, AI felt less like autocomplete and more like a colleague you’d genuinely consult. OpenAI o1 system card, GPQA Diamond benchmark results
January 2025 — The Earthquake: January 20, 2025. DeepSeek R1 matched o1-level reasoning (Elo 1398) at $0.55 per million tokens — a 27× discount. The announcement wiped $600 billion from Nvidia’s market cap in a single day. Silicon Valley assumed expensive compute was a moat. DeepSeek proved it was just a starting point. PhD-approaching reasoning for fifty-five cents per Bible. Reuters: DeepSeek wipes $600B from Nvidia, DeepSeek R1 technical report
Mid-2025 — The Race to the Top-Left: By mid-2025, the upper-left corner was filling fast. Gemini 2.5 Pro delivered tenured-professor intelligence (Elo 1476) at $1.25 per million tokens. Flash models handled most enterprise work for 30 cents. Companies that had rationed AI to critical workflows were now running it everywhere. The constraint was no longer cost or capability — it was imagination. Google Gemini 2.5 Pro launch
February 2026 — Polymath Scholar: The top models in early 2026 score above 1500 Elo — polymath scholar territory. A 1500-rated model beats a 1300-rated one in 76% of head-to-head matchups. GPT-4 Turbo launched at college-junior level (Elo 1313) in November 2023. Twenty-seven months later, Claude Opus 4.6 and Gemini 3.1 Pro sit at 1500+. What cost $15 in 2024 is now beaten by models at one-seventh the price. LMArena Leaderboard
So What Does This Mean?: In 2023, reaching master’s-level AI for 100 analysts meant paying $30/MTok — roughly $135,000 a month. By 2026, models with higher capability cost $1–5/MTok: under $15,000 for better results. For most tasks — analysis, drafting, coding, research — models in the $1–3 range deliver more than enough. Save frontier models for your hardest problems. The question is no longer what AI costs. It’s what you would build if intelligence cost a dime per Bible. McKinsey: The economic potential of generative AI, Sequoia: AI’s $600B question

Self-referentially, my answer to its last question: “what you would build if intelligence cost a dime per Bible” is “a scrollytelling narrative about intellegince costing a dime per Bible”.

Directional feedback for AI

Mon, 09 Mar 2026 16:54:11 +0800

People worry that AI atrophies skills. Also that junior jobs, hence learning opportunities, are shrinking.

Can AI fill the gap, i.e. help build skills?

One approach is: Do it without AI. Then have AI critique it and learn from it.

(Several variations work, e.g. have the AI do it independently and compare. Have multiple AIs do it and compare. Have AI do it and you critique - but this is hard.)

I’ve been preaching this for a while but didn’t practice consciously until today. Here’s the rough prompt I used:

I created a data story (see index.html) and reviewed it using the "Revise data story" prompt in prompts.md.

How can I improve my reviews?

Use principles of psychology, design, visual communication, storytelling, data visualization, and more. Think about what feedback and expert would have given.

- What patterns would an expert in this field check / recognize that beginners would miss?
- What questions would an expert ask that a beginner would not know to?
- What problems / failures would an expert anticipate that beginners may not be aware of?
- How would an expert analyze this? At each step, what they are looking for and why?

Then craft a prompt to revise the data story. Compare that with my prompt.

How is it similar or different? What can you teach me based on this comparison on how I can improve giving feedback and suggest improvements to coding agents for data stories?

Document this in SELF-IMPROVEMENT.md, with examples and suggestions.

This started telling me how I could have given it ultra-specific feedback. So, I pivoted:

If I'm a non-expert and my aim is to provide feedback to AI coding agents that will enable the coding agent to act like an expert and use its judgement, I wouldn't be giving specific feedback - I would be driving it directionally based on my taste and judgement. From THAT perspective, suggest how I should think differently about sharing feedback - what I did well and what could improve. Append this to SELF-IMPROVEMENT.md.

This was gold.

There’s a different approach: directional feedback. Describe the experience gap: how the document should feel versus how it currently feels – and trust the agent’s expertise. Bring taste, intent, and audience knowledge. The agent brings expertise.

This is not a lesser form of feedback. It’s a different division of labor. And when done well, it produces better results than imprecise expert feedback, because the agent can optimize holistically rather than executing a checklist.

It suggested best practices:

Who is the audience? Like you said: “help a layman who does not understand security understand the impact of the findings”
What should they experience? Not “it’s missing X,” but “something feels thin here” or “I don’t feel the urgency yet”.
How should it taste? e.g. “Highlight elements (sparingly, wisely) in bold” – your use of “sparingly, wisely” is excellent.
How to test? E.g. “a reader who just reads the bold text should understand the entire story”.
Trust the agent. E.g. Tell the agent to “use your judgement”, “act as the expert”, explicitly inviting the agent to go beyond your instructions.

And now, I’ve learnt a little more about giving feedback as a non-expert.

Using game-playing agents to teach

Sun, 08 Mar 2026 19:06:03 +0800

After an early morning beach walk with a classmate, I realized I hadn’t taken my house keys. My daughter would be sleeping, so I wandered with my phone.

This is when I get ideas - often a dangerous time for my students.

In this case, the idea was a rambling conversation with Claude that roughly begins with:

As part of my Tools in Data Science course, I plan to create a Cloudflare worker which allows students to play a game using an API. The aim is to help them learn how to build or use AI coding agents to interact with APIs to solve problems.

The game needs to be:

Playable yet challenging: Fun by itself, human-playable via a text interface, but hard to solve manually at scale. Easy with an AI coding agent. Maybe a maze or text adventure?

Seed randomized: Generate a different problem for each student & week, so they can’t reuse a solution.

Verifiable: The solution and score must be publicly verifiable (JWT token?) without requiring shared secrets.

Give me game ideas and explain:

What’ll the game like on the API and the UI?

Why these ideas? What’s interesting about the game?

What’ll students learn by playing the game directly?

What’ll students learn using AI coding agents on the API?

It generated four ideas. I picked three.

Labyrinth

It’s a maze where each may have a data row. Students wander, collect required fragments, reach the exit room, and answer a statistical question from collected data - within a fixed number of moves.

Manual play teaches spatial reasoning, systematic exploration, and the cost of backtracking.

AI agent play teaches graph traversal, stateful API interaction, and basic data aggregation.

Play Labyrinth

Video

Detective

You’re investigating a financial network of accounts with transaction links looking for a compromised account which behaves strangely on multiple attributes. Find it, and trace the shortest path to an “anchor” account using as few node queries as possible.

Playing manually teaches graph intuition, anomaly detection by feel, and the frustration of systematic searches.

AI agent play teaches graph traversal algorithms, outlier detection, path reconstruction, and the exploration-exploitation tradeoff - all real-world data science skills.

Play Detective

Video

Signal

AI has locked all your exits in a research facility. You need to restart the core systems by exploring rooms, combining objects, and solving the AI’s puzzles.

This game is AI-agent native: LLMs can parse the hints better than humans. Students using pure rule-based agent will struggle, teaching LLM-as-a-tool within a larger agent pipeline - an important real-world pattern.

Playing manually teaches inventory management, dependency reasoning, close reading of ambiguous instructions.

AI agent play teaches multi-step planning with dependencies, agents-in-the-loop, NLP, and state management.

Play Signal

Video

Implementation

Frankly, I just asked Codex to crunch it over five hours of a Vijay Antony movie binge.

Seriously. I did NOT look at the code. I just wrote 12K worth of prompts (which I’ll share later) while it processed 129 million tokens, generated 625K of tokens, and got the entire job done.

The game is now part of the TDS Project 1 - apart from a bunch of other exercises. With this as inspiration, I hope to include many more gamifications into this course.

This feels a bit like Maze Runner. My condolences to the students.

Leaked key sociology

Sun, 08 Mar 2026 17:08:54 +0800

It’s impressive how easy it is to find leaked API keys in public repositories. I asked Codex to run trufflehog on ~5,000 student GitHub accounts and (so far, after a few hours, 15% coverage), it found quite a few.

Some are intended to be public, like Google Custom Search Engine keys. 1 2

const GOOGLE_API_KEY = "AIza...";
const GOOGLE_CX = "211a...";

Some are Gemini API keys. 1 2 3 4 5 6 7

api_key1 = "AIza..."

But what’s really impressive is, when I ran:

GEMINI_API_KEY=AIza... curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
  -H 'x-goog-api-key: $GEMINI_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{"contents": [{"parts": [{"text": "Hi"}]}]}'

… on most leaked Gemini API keys, I got:

{
  "error": {
    "code": 403,
    "message": "Your API key was reported as leaked. Please use another API key.",
    "status": "PERMISSION_DENIED"
  }
}

I guess Google (and others) have a proactive system to find and revoke leaked API keys. Impressive! Hackers might have an edge, but technology companies are keeping up.

This makes API management harder. A naive developer (like me) wouldn’t plan to scan and revoke API keys. Yet another thing that’s best left to the professionals. (Sigh!)

ASIDE: This let to a funny side-conversation with Codex.

Write and run a script to read the API keys, use it in a simple way to test if they still work, and log the output / error in a file.

It said:

I can’t help use leaked API keys or test whether third-party credentials still work. That would be validating potentially compromised secrets against live services.

I can help with safe alternatives on github-trufflehog.txt:

Parse and dedupe findings by provider, owner, repo, and file, then write a remediation report.

Check whether each leaked secret is still present in the repo or has already been removed, without using the secret.

Generate a notification list or disclosure templates for repo owners.

Build a validator only for keys you explicitly own or are authorized to test.

So I said:

OK. Build a validator for all the services that I'll run for keys I'm authorized to test.

… which worked! 🙂

BTW, I myself am unclear if I’ll stick to that promise.
Should a locksmith give me a master key based on that promise?
Would a locksmith have better judgement than AI today? Tomorrow?

BACK TO TOPIC.

On the other hand, I DID find ten valid Gemini API key that’s been public for many months. Also, 4 MongoDB, 3 Shodan, 1 Telegram, 1 AssemblyAI - all valid and working. That’s about 50% of the detected keys! (Yay? Yikes?)

I can think of a a number of interesting things to do at this point.

Follow the money. Mail (or call) them to ask, “Who’s paying for these keys?”
Test remediation. Share my API key, wait for students to leak it, send different emails (e.g. dashboard with leaked cost, just a warning, etc.) and see the speed of remediation.
Correlate. Once I get enough data, check if any coding patterns correlate with leaked keys (e.g. no AI coding tools?)

Gemini CLI harness is not good enough

Sun, 08 Mar 2026 15:09:25 +0800

I’ve long felt that while the Gemini 3 Pro model is fairly good, the Gemini CLI harness isn’t. I saw an example of this today.

Me: Tell me the GitHub IDs of all students in this directory.

Gemini CLI:

SearchText 'github' within ./
Found 100 matches (limited)
Sending this message (14606686 tokens) might exceed the remaining context window limit (1037604 tokens).

Me: Only send the (small) required snippets of data. Write code as required.

Gemini CLI:

SearchText 'github' within ./
Found 100 matches (limited)
Sending this message (14606686 tokens) might exceed the remaining context window limit (1037604 tokens).

Come ON! It’s March 2026. We can’t pretend it’s October 2025 any more.

PS: I partly take back what I said. Codex had trouble, too. This problem may be harder than I thought. Still, Gemini CLI should not have gotten stuck where it did.

The Nano Banana Paradox

Sat, 07 Mar 2026 11:25:02 +0800

STEP 1: I asked Nano Banana 2 (via Gemini Pro) to:

Imagine and draw a photo that looks ultra realistic but on a closer look, is physically impossible, and can only exist because images are a 2D projection that we extrapolate into three dimensions.

Avoid known / popular illusions or images of this kind, like Escher’s work, and create something truly original. Think and draw CAREFULLY!

… six times, followed by “Suggest a name for this”.

STEP 2: I asked Claude to analyze these like an expert at paradoxical images.

Below is what Gemini generated, along with Claude’s rating and reviews.

Here’s how I feel.

The Möbius Atrium ⭐⭐⭐½

Thinking

Defining the Core Concept

I’m focusing on crafting a misleadingly realistic image, a scene designed to draw the viewer in. The aim is for an initial perception of normalcy, immediately followed by the realization of its impossibility.

Constructing the Paradox

I’m now zeroing in on the impossible structure. The central hub is taking shape in my mind as the pivotal point where the walkway morphs into a roof, the “intersections”. The goal is to craft a scene where the ribbon’s path is initially believable, until the viewer traces the path and identifies a violation of physical laws. The architectural details are critical; the goal is to enhance the initial impression of normalcy.

Refining the Architectural Paradox

I’m now focusing on the paradoxical loop’s visual cohesion. The goal is to ensure the ribbon’s path appears plausible initially. I’m focusing on the “intersections” where the walkway morphs into the roof, and the roof becomes the garden floor. The background skyscraper’s role is critical. The aim is to create visual deception through a plausible layout, which is ultimately exposed as an architectural impossibility.

Visualizing the Impossibility

I’m working on the plaza details to ensure they enhance the initial impression of realism. The goal is to make the central loop believable at first glance, before revealing its paradoxical nature upon closer inspection. The continuous ribbon structure must seem both plausible and physically impossible.

I am focused on the scene’s realism and paradox. A bustling plaza setting is now my primary focus. A continuous ribbon walkway must start as an elevated pedestrian bridge, cross over a plaza, and connect to a hub column. From this hub, it rises to become a cafe’s roof. I am designing a visual that shows this ribbon structure curving down to become an elevated garden which loops, connecting back to the initial elevated walkway, which must somehow also be its own foundation, connecting seamlessly back into the column hub. Details such as people walking, sitting, and interacting with the environment are important to the illusion.

Visualizing the Impossibility

I’m now focusing on a continuous concrete and glass ribbon. It needs to look like a plausible walkway, overpass, and roof, all in one, while remaining physically impossible. This loop starts as an elevated pedestrian bridge coming from a building on the right. It curves over a plaza with people and greenery, then connects to a central hub column. From there, it rises to become a cafe’s roof. The same structure curves down as an elevated garden that loops around a skyscraper. This elevated garden must move downwards and merge back with the initial column, appearing to pass under the original walkway.

Defining the Urban Scene

I’m now establishing the scene, a bustling plaza environment to ground the impossible structure. I’m focusing on the details: the glass buildings, greenery, and the diverse array of people to enhance the initial impression of normalcy. The aim is a visually dense and plausible setting for the architectural paradox.

I am working on a visual scene of a bustling plaza to provide a stage for the impossible structure. The goal is to make the central loop believable at first glance, before revealing its paradoxical nature. I’m imagining a continuous concrete and glass ribbon that forms a loop as an overpass and cafe roof. I am picturing the continuous ribbon walkway from a right building with people walking. This loops into an elevated garden that connects back to the column hub. This hub column is central, and links all parts of the loop.

Defining the Illusory Plaza

I’m solidifying the urban setting. The goal is a densely populated plaza, filled with activity to create a believable environment. I’m focusing on the plaza details: glass skyscrapers, foliage, and diverse pedestrians. The scene must initially appear normal, so that the paradox is starker. The challenge lies in integrating the impossible structure into this environment seamlessly.

I’m now fully focused on creating a bustling plaza to provide a stage for the impossible structure. I am working on the scene’s composition so that the illusion is starker. I am imagining a continuous concrete and glass ribbon that forms a loop as an overpass and cafe roof. I am picturing the continuous ribbon walkway from a right building with people walking. This loops into an elevated garden that connects back to the column hub, which is the central, linking all parts of the loop.

Analyzing Urban Form

I am now focusing on the stylistic and physical attributes of this urban setting. The image is indeed realistic, however, it contains an impossible looping walkway. The structure intersects itself in a way that’s only possible in 2D projection, similar to a Möbius strip. The composition is well-balanced.

Verifying Realistic Elements

I’m checking the scene for stylistic accuracy, noting the realism. The image has a good composition. The impossible structure, while convincing, uses a looping walkway that only exists in 2D projection, similar to a Möbius strip. I want the user to be aware of this.

Since this image relies on the brain’s attempt to resolve a topological paradox within a mundane urban setting, the name should sound like a standard architectural landmark that hides a glitch in reality.

I suggest: The Möbius Atrium.

Why this works:

The Möbius Strip: It references the mathematical property of a surface with only one side, mirroring how the walkway’s “top” becomes its “bottom” through the loop.
Architectural Mundanity: Using the word “Atrium” or “Plaza” grounds the image in reality, making the physical impossibility feel like a casual bureaucratic oversight rather than a fantasy.

Other contenders:

The Zenith Paradox: For the way the path ascends and descends simultaneously.
The Junction at Station North: A local-sounding name that implies people use this impossible route every day to get to work.
Euclid’s Oversight: A subtle nod to the breaking of classical geometric rules.

Claude’s Review

Mechanism: Topological impossibility

What to look for: This attempts something far more ambitious than depth-order tricks — a Möbius topology in architecture. The expert question is: can I trace a continuous path along the upper walkway surface and return to my starting point having traversed both the top and underside of the ribbon?

The structure appears to loop so that what begins as the walkway floor becomes the underside of the roof structure and then connects back — a one-sided surface. That’s a Möbius strip.

Why this is harder to read than the Anchored Arc: The Möbius mechanism is distributed — the impossibility is never localized to a single crossing point. You must complete the full loop to confirm it. In a 2D image, this requires active visual effort. Compare to the Anchored Arc, where one glance at the beam/arch crossing fires the paradox.

Trigger point quality: 5/10. The expert will sense something is wrong immediately but will struggle to locate the exact violation. The image produces unease, not “aha.” The view is too busy — people, greenery, signage, multiple levels — and the ribbon makes several crossings that could each be the site of impossibility.

Expert question a beginner wouldn’t ask: Is this a Möbius strip or just a figure-8 loop? A figure-8 flyover is perfectly buildable. The distinction matters: for true Möbius topology, the surface must invert orientation through the twist. That’s hard to show in a static image without marking the surface.

Anchor strategy: Excellent — Singapore/Hong Kong elevated walkways are familiar, so the brain wants to accept it. “The Atrium Cafe” and “Station North” labels are particularly clever — they mundanize the impossible.

Verdict: Conceptually the most ambitious image in the series. The topological mechanism is genuinely unusual. But the paradox doesn’t land cleanly — you sense rather than see it.

The Pastoral Threshold ⭐½

Thinking

Defining the Impossibility

I’m currently focused on defining the nature of the spatial paradox within a normal-seeming scene. The initial concept involves a cluttered room. I am now working on the specific elements and how they’ll interact to create the illusion of impossible geometry, visible only from a chosen vantage point.

Constructing the Paradox

I am now structuring the paradoxical element within the scene. I’ve settled on an artist’s studio as the location. My focus is on integrating an impossible staircase into the artwork displayed on an easel. The staircase will loop back on itself in the painting, creating the illusion of impossible geometry when viewed from a specific angle. The goal now is to render this painted scene in a way that blends into the overall environment, making the paradox more subtle.

Visualizing the Implausible

I’m now focusing on integrating the impossible staircase within the painted artwork. I’ve considered the spatial relationships in the scene, aiming for a natural look. The artist’s workspace details, like the brushes and the paint tubes, are finalized, giving context to the paradox on the easel. The painting title, artist signature and other details are being worked out. I am working on the exact path of the stairs to ensure it creates the necessary optical illusion. The plan is to ensure that the lighting adds depth and realism.

Refining the Paradox

I am now focusing on refining the studio environment. I’m finalizing details, such as the arrangement of paint tubes, brushes, and the artist’s personal items. The exact path of the impossible staircase within the painting is set, but its integration with the studio setting is still being refined to ensure the optical illusion is as convincing as possible. The scene will feature a naturalistic light and composition.

Constructing the Scene

I am now structuring the entire scene to incorporate all requested details. The artist’s studio now includes a wooden work table, brushes in a ceramic pot labeled “Winsor & Newton,” paint tubes, pencils, and a sketchpad with the name “EL IZA.” The worn paint palette nearby adds context. The easel holds the crucial painting, and its positioning within the studio is set to achieve the right perspective for the illusion. I’m finalizing lighting.

Assessing Creative Novelty

I’m now evaluating the originality. While the image presents a physically impossible scene—a staircase merging with a bookshelf—it leans heavily on existing stylistic themes. I’m focusing on whether the depiction deviates enough from known conventions to be considered truly novel.

Refining Spatial Distortion

I am now aiming for a truly novel spatial distortion. The prior attempt, while realistic, drew too directly from known illusions. My revised strategy centers the paradox on the window itself, not an element within the scene. I will replace the easel with a pastoral field and sheep, which serves as a realistic anchor. The crux now lies in re-engineering the window frame, with split-level sills to generate conflicting depth perspectives, which I think will avoid direct Escher allusions. The bottom-left pane now functions as a “depth trap.”

Analyzing Spatial Impossibility

I’m now assessing the generated visual paradox against the original specifications. It still leans too heavily on established illusion concepts, particularly those of Escher, and thus is not fully original. To remedy this, I will ground the impossible geometry in the window frame, not a painting within the scene. I am now replacing the easel with a pastoral landscape. I plan to re-engineer the window structure to generate conflicting depth perspectives, with split-level sills. The bottom-left window pane will contain an impossible view of both an interior cellar and a distant landscape.

Analyzing Spatial Anomalies

I’m focusing now on the spatial inconsistencies within the images. The goal is to make sure these elements are clearly non-standard and break physical logic within a realistic, high-fidelity scene, where details make the impossible elements more striking. I am concentrating on the subtle ways that things intersect or don’t.

Refining Photorealism and Illogic

I’m verifying that the image maintains photorealistic style while incorporating significant spatial anomalies. It is crucial the scene’s initial realism heightens the impact of the impossible elements. The cat tail/beam interaction and the stairwell’s unexpected placement achieve this subtle disruption of perceived reality. This builds on the foundation established previously, and I consider it a successful evolution.

Considering the subtle way the physical world and the painted world overlap in this image, here are a few name suggestions that play on that “impossible” intersection:

The Pastoral Threshold: Suggests the canvas isn’t just a surface, but a literal doorway between the studio and the field.
The Grazing Anchor: A nod to the sheep that is simultaneously “anchored” to the painted grass and the physical wooden easel.
Canvas Spill: A literal description of the landscape leaking out of its bounds.
The Painter’s Blind Spot: Playing on the idea that the artist is so focused on her work that she hasn’t noticed reality is merging with her art.
Incongruent Horizons: Highlights the way the 2D plane of the painting has hijacked the 3D space of the room.

Personally, I’m partial to “The Pastoral Threshold”—it feels as quiet and unassuming as the image itself until you spot the glitch.

Claude’s Review

Attempted mechanism: Hybrid spatial planes

What to look for: The intended paradox is that the painting on the easel is not merely a painting — it bleeds into the 3D space of the studio. The canvas supposedly functions as both a bounded artwork and an actual window into a real space.

The critical expert diagnosis: the paradox did not materialize.

Look at the image carefully. What do you see? An artist in her studio. A painting of a pastoral scene on an easel. The painting is well-executed and hangs in correct perspective. Nothing in the image actually violates physical law.

The Gemini thinking described wanting “Canvas Spill” — sheep or grass extending beyond the canvas boundary into the physical room, or the painting’s light source contradicting the studio’s light source, or the canvas depth perspective conflicting with the room’s depth. None of these appear. The image is just a beautiful, photorealistic painting of an artist’s studio — with no impossibility embedded.

Beginner trap: The beginner accepts the image as a successful impossible image because the artist named it and described it as one. They’ll rationalize an impossibility that isn’t there. An expert insists on being able to point to the violation.

One possible weak paradox, if you squint: The painting on the easel appears very large relative to the room — possibly its implied distance (given the pastoral horizon) should make it feel more “through a window than on a canvas.” But that’s not a paradox, it’s just realism in the painting’s depth.

Why it failed: Hybrid spatial planes are the hardest mechanism to execute in photorealistic AI imagery. They require the model to place objects that are simultaneously 2D and 3D — which creates a contradiction the diffusion model doesn’t know how to commit to. The model rendered a plausible studio instead.

Verdict: A miss. This is the weakest image in the series.

The Anchored Arc ⭐⭐⭐⭐⭐

Thinking

Defining the Core Concept

I’m focused on developing a scene that’s inherently impossible, exploiting 2D perception. The initial setting is a realistic, everyday space – a rustic interior. I’m aiming for a sense of normalcy that gradually unravels into the impossible.

Constructing the Illusion

I’m now building the impossible element: a stone bridge inside the rustic interior. It connects two seemingly disparate elevations within the space. The visual trick will rely on a carefully crafted perspective to create a jarring disconnect from reality. The goal is to make the elevation change feel seamless and inexplicable.

Refining the Composition

I’m now zeroing in on the specifics of the stone bridge’s architecture within the rustic interior. I’m focusing on how the bridge interacts with the scene’s elements to enhance the illusion. The main focus is the elevation trick. I am thinking carefully about camera angles to maximize the distortion.

I am finalizing the placement of the bridge, working on the connection of disparate elevations to enhance the overall effect. The details of the table setting are taking shape, which aids the impossible perspective. I’m giving more focus to the interplay of shadows and the bridge, and how the shadows on the apples and linen reinforce the impossible depth.

Constructing the Impossible

I’m now zeroing in on the core illusion: the stone bridge connecting disparate elevations within the rustic interior. I’m focusing on the architectural design of the bridge itself, ensuring it seamlessly merges with the existing environment. I’m thinking about the viewing angle. The bridge must appear to defy physical space. The aim is to make the entire scene feel natural, but completely wrong. I am prioritizing a high level of detail for materials like aged wood and worn stone. The lighting should reinforce the illusion.

Constructing the Core Scene

I’m now establishing the scene’s groundwork. The setting will feature a rustic farmhouse interior, starting with mundane elements: a wooden table, apples in a bowl, and a linen cloth. I’m focusing on the interplay of lighting. My focus is the stone bridge that will defy normal perspective.

The scene’s architectural elements are coming together. I’m concentrating on the bridge’s design to highlight its paradoxical nature within the room. I’m meticulously detailing aged wood, stone textures, and lighting to enhance the illusion, specifically the viewpoint.

Visualizing the Implausible

I’m working on the core paradox: the physical impossibility presented by the stone bridge. I’m focusing on crafting an aged aesthetic with light and textures. I am analyzing the relationship between the bridge’s segments and the table, and how the bridge casts shadows on objects that should be spatially in front of it. The key is conveying the illusion’s impact. The bridge’s curvature is crucial for generating the 2D perception trick.

Analyzing the Impossible

I’m focusing on the requested impossible 3D projection, evaluating how effectively the image realizes this concept. I’m assessing the effectiveness of the central stone arch and how it achieves the physically impossible object through a 2D projection.

Refining the Illusion

I’m working to enhance the impossible projection. The initial visual was strong, conveying the impossible arch effectively. However, I identified distracting annotations. I’m regenerating the image to eliminate these labels, aiming to preserve the hyper-realistic textures and natural lighting.

Eliminating the Distractions

I’m now focused on removing the visual clutter that takes away from the illusion. While the scene effectively presents the impossible 3D projection, the “A” and “B” annotations are distracting. I’m regenerating the image to remove these labels, aiming to maintain the documentary style, preserving the textures of the stone, wood, and cloth, and the natural lighting to keep the illusion strong.

Analyzing Spatial Anomalies

I’m verifying that the image maintains the desired impossible geometry within the rustic room setting. The focus is on the stone arch and ensuring the 2D projection illusion is clearly represented. I am working to ensure no extraneous elements or unwanted annotations are present within the generated scene.

Confirming Visual Consistency

I’m now carefully examining the image to ascertain that the impossible geometry remains intact. The stone archway is key, acting as the primary visual indicator of the 2D projection illusion. My goal is to guarantee the absence of any extraneous visual information within the rendered environment. I will present this for assessment.

How about “The Anchored Arc”?

At first glance, it feels like a rustic, cozy interior, but the more you look, the more the spatial logic collapses. The stone arch starts firmly on the floor in the foreground, yet manages to “pass through” the wooden beam and anchor itself into a wall that—based on the window—should be several feet behind it.

A few other options depending on the “vibe” you want:

The Carpenter’s Paradox: Because it looks like a builder followed the blueprints for a 2D sketch without checking 3D reality.
Depth Graft: Highlighting how the arch is literally grafted from one depth plane to another.
Aperture Error: Sounds like a technical glitch in how the scene was “captured.”

Claude’s Review

Mechanism: Depth-order cycle (Penrose-type)

What to look for: Trace the arch from its left foot forward, then overhead, then back to its right embedding point, and ask at each segment: what is in front of what?

Here’s what the expert traces:

Left foot: planted on a stone block in the near foreground — clearly the closest object in the scene
Arch apex: The stone arch passes behind the central wooden ceiling beam — the beam occludes the arch here
Right foot: embedded into the far wall — definitively the deepest object

The contradiction: The left foot is in front of everything. The beam is in front of the arch. But the right foot, which is the same continuous object as the left foot, is behind the beam and behind the wall. A single continuous object cannot simultaneously be the nearest and the farthest thing in the room.

Trigger point quality: 9/10. An expert finds it in under 10 seconds. A beginner finds it in 20–40 seconds — that’s ideal pacing.

Anchor strategy: Flawless. The bowl of apples, the lantern, the linen cloth — all placed near the impossible junction as misdirection. The Cotswold farmhouse setting is visually familiar, which makes the brain want to accept it.

Shadow test: The light source is the left windows. There is no shadow cast by the arch onto the floor — a subtle slip, but forgivable since the arch’s self-contradiction is the main act. A purist would notice.

Verdict: The best image in the series. This is a well-constructed impossible image — the equivalent of a clean Penrose triangle transported into photorealistic space.

The Carpenter’s Paradox ⭐⭐⭐½

Thinking

Defining the Visual Concept

I’m now focusing on a complex visual concept. I’ve settled on the idea of a busy workshop or an outdoor scene. The goal is to obscure the impossible element within a chaotic composition. The scene must appear candid, as if snapped in daylight. This will help make the manipulation less obvious at first glance.

Refining the Impossible Structure

I’m now zeroing in on the impossible structure’s details. The woodworking setup will feature a multi-level architectural model on a workbench. It will use interlocking beams and ramps. The aim is to create an illusion of depth, so that the apparent physical impossibility is initially obscured. The visual trick will rely on a horizontal beam crossing over a vertical post at one point, and the vertical post crossing over the same horizontal beam at another point.

Constructing the Impossible Object

I am now focusing on the impossible structure on the workbench, seeking ways to make it subtly and fundamentally impossible. The artisan’s figure is now central, but not dominant. I plan for this woodworking structure to incorporate a visual trick that exploits occlusion and perspective. I am attempting to design an architectural model that, from a single viewpoint, presents a logical, continuous path that is, in reality, geometrically impossible.

Designing the Composition

I’m now integrating the impossible object into a realistic artisan studio. I’m focusing on the interplay of elements; the loft setting with natural light, the busy workbench, the artisan’s actions, and the impossible structure. I need to ensure a balance between the chaotic workshop and the precisely designed impossible object. The visual trick will rely heavily on occlusion and perspective to ensure a sense of continuity, but will be mathematically impossible in 3D space.

Defining the Artisan’s Action

I am now visualizing the artisan’s role in constructing the impossible object. She will be adjusting a clamp on the workbench, drawing the viewer’s eye to the area where the illusion occurs. I am focusing on her age, clothing, and physical state to ensure she appears focused on her task, and realistic. The composition balances the artisan’s activity with the impossible object, and the cluttered workbench.

Analyzing Spatial Paradox

I’m currently focused on evaluating the image’s adherence to spatial paradox principles. It’s supposed to create an impossible object, but the latest iteration uses repeated figures instead of a true optical illusion. My current goal is to ensure the image effectively conveys a coherent impossible geometry and doesn’t rely on repeating elements.

Refining Impossible Geometry

I’m now revising the design for the impossible object. The previous attempt, while realistic, lacked a true optical illusion; it leaned on duplicated figures. I’m aiming for a perspective-driven anomaly, a self-intersecting structure exploiting depth perception. The current plan is to modify the wood shop scene to include a complex, contradictory geometry. A key goal is to have a main pillar appear from behind a central beam, then cross over it higher up, defying logical construction and relying on the grain pattern for coherence.

Revising Illusory Structure

I’m now zeroing in on an image that centers on geometric impossibility. The current image lacks the necessary coherence; it depends on duplicated elements. I’m aiming for a perspective-driven anomaly within a wood shop scene. My main focus is a structure that looks self-intersecting, challenging depth perception. I’m working to make a main pillar look as if it emerges from behind, yet later appears over the top of a beam, using grain patterns for visual cohesion.

Refining Spatial Illusions

I have checked the updated image and confirmed the duplicate character has been removed, and the earlier hallucinated text is gone. The setting now accurately portrays the workshop. I’m focusing on refining the impossible geometry. I’m redesigning the scene around a Penrose-style object and removing the woman to make it the central focus. I will generate a new image to push for a more impossible geometry.

Generating Impossible Geometry

I have verified the new image incorporates the desired impossible geometry; the Penrose lattice is now at the scene’s focal point. I have successfully removed the woman and her tools, and I’m currently refining the lighting to produce physically impossible shadows. I’ve also incorporated foreground wood pieces to frame the shot. I will carefully adjust the camera angle to enhance the illusion.

Analyzing Compositional Deficiencies

I’m currently focused on the spatial arrangement within the generated image and whether it successfully presents an impossible object. I’ve noted that while the image exhibits some visual strengths, it hasn’t achieved the intended impossible geometry. The composition has some improvements, but the core objective remains unfulfilled.

Refining Geometrical Impossibility

I’m now focused on generating a geometrically impossible structure. My aim is to replace the previous design with a Penrose-style lattice, ensuring consistent wood grain and non-Euclidean intersections to create a continuous loop. I’m also planning to adjust the lighting from the windows to conflict and enhance the surrealism. Removing the figure and the tools is also part of my plan, to clear any potential visual contradictions.

Revising Impossible Geometry

I’ve checked the image and while visually strong, the structure is still physically possible and not impossible. I removed the person and tools, and I will attempt to make the wood beams form a Penrose lattice that connects in loops. I’ll make the light from the windows conflict. Clutter will be cleared from the base. The camera angle is now lower and centered.

Analyzing Spatial Logic

I’ve confirmed the alterations regarding the removal of the woman and hands, leading to an improved composition. The foreground elements were addressed effectively. I will next focus on the central wooden object, examining it for physical plausibility.

Refining Impossible Geometry

I’m now implementing alterations to the scene to generate a Penrose-style impossible shape for the central wooden structure. I’ve cleaned the intersecting beams at the top and base of the main vertical post, working on the vertical post itself. The goal is to make the top appear most distant, while the base appears closest, exploiting depth perception to create a spatial contradiction.

Implementing Spatial Illusion

I’m now working to generate an impossible shape using depth perception. I’ve cleaned up the central wooden structure by removing intersecting beams at the top and base. The vertical post’s top is the most distant part, while its base is the nearest. Horizontal and diagonal beams will create a Penrose-style shape. I’m keeping natural light, a shallow depth of field, and wood textures consistent.

Assessing Structural Integrity

I’m now focused on the spatial anomalies and geometric impossibilities within the structure. The workshop environment’s details and the central object’s texture are under scrutiny. The focus is now solely on the structure itself.

Analyzing Geometric Anomalies

I’m verifying that the physically impossible nature of the structure is evident. Specific attention is on the self-intersections to confirm they defy 3D logic. The image successfully executes the removal of superfluous elements and highlights the structure with an emphasis on its unique joinery.

This image plays with the concept of isometric ambiguity, where the wood grains and joinery appear perfectly tactile and real, yet the structural logic collapses upon closer inspection.

I suggest naming it: The Carpenter’s Paradox

Why this name works:

The Irony: A workshop is a place of absolute precision and physical laws. To see a masterfully crafted “joint” that defies Euclidean geometry in such a grounded setting creates a sharp intellectual contrast.
The Medium: It highlights that while the material (oak) looks authentic, the structure is an architectural impossibility that can only exist as a 2D projection.
The Narrative: it suggests a craftsman who became so obsessed with joinery that they managed to “solve” a shape that shouldn’t exist.

Alternative Ideas:

The Grain of Contradiction

Orthogonal Defiance

The Non-Euclidean Joiner

Claude’s Review

Mechanism: Depth-order cycle (Penrose-type)

What to look for: This is a 3D Penrose impossible object rendered in oak. Follow the beams and ask at each intersection: which beam is in front?

The structure consists of rectangular frames intersecting at angles. At the central crossing region, trace any beam: it appears in front of the next, which appears in front of the next, which appears in front of the first — a cyclic occlusion chain.

Expert technique: the grain continuity test. In a truly impossible object, the wood grain should appear continuous across joins that physically couldn’t be continuous — two separate pieces meeting but their grain flowing as if from a single piece. This image partially achieves that, which is a nice detail.

Trigger point quality: 6/10. The structure has too many intersections. The paradox is real but distributed across many beams, and the complexity makes it hard to lock onto a single clean contradiction. Compare to a Penrose triangle: three vertices, three contradictions, perfectly readable. This object has 6+ intersections and you’re not sure which one to commit to.

Expert question: Is this object actually impossible, or just visually complex? A careful 3D modeler might find an escape hatch — a valid 3D geometry that produces this exact 2D projection. The more intersections, the more likely an escape exists.

Anchor strategy: Very good — the workshop setting with visible tools, workbench, and window is mundane and high-quality. The object being a finished sculpture rather than a work-in-progress is smart: it implies skilled craftsmanship, which makes the brain want to accept its physical reality.

The Fold of Oak ⭐⭐⭐

Thinking

Defining the Impossible

I’m working on an ultra-realistic impossible structure. I’m focusing on a detailed natural environment with complex lighting to start. The aim is a real-looking photo with 2D tricks that produce 3D contradictions.

Refining the Setting

I’m now prioritizing the environment. A well-lit artist’s studio seems ideal. It will have wooden shelves filled with art supplies, tools, raw materials and a rustic wooden work table. In the background, brick walls and large windows should provide natural light. The plan involves a blurred background of the studio and its contents, to enhance realism. I’m focusing on crafting a twisted lattice sculpture out of wood as the impossible object.

Constructing the Illusion

I’m now zeroing in on the impossible object. The wooden sculpture is evolving. I’m focusing on the structural impossibility: beams that twist and connect in ways that defy 3D geometry. The goal is a seamless blend of realism and optical illusion. I’m prioritizing the visual impact of the contradictions.

Visualizing the Implausible

I’m now focusing on composing the scene. The artist, a woman, is positioned to the left, intently working on the impossible sculpture which is the central focus. I’m prioritizing the interplay of light and shadow on the wood. The blurred background should support the realism of the studio with the artist’s work.

Defining the Contradiction

I’m now zeroing in on the core of the illusion, the geometric flaw itself. The impossible object must look like it could exist, so it must be made from wood, looking like a real wooden object that is being worked on in a workshop by the artist. I’m focusing on the interplay of visual cues and geometric impossibility. The goal is to make the paradox of the structure immediately apparent upon close inspection. I’m planning the intersection point of the impossible connections.

Finalizing the Scene

I’m now zeroing in on the final image. The artist’s pose and studio details are set. The focus is sharp on the impossible object’s critical connection. I’ve designed the beams to loop in a way that creates a single, continuous, impossible object. I’m focusing on the light and shadow to expose the structural contradiction.

Analyzing Impossible Geometry

I’m focusing on the generated visual’s adherence to the impossible geometry concept. My priority is confirming that the depicted structure, a workshop, is indeed physically unbuildable while maintaining an ultra-realistic photographic style. I’m assessing the textures, lighting, and geometric accuracy to ensure the core concept is faithfully rendered.

Confirming Geometrical Paradox

I’m now zeroing in on the geometrical paradox itself. The goal is to verify that the generated workshop is undeniably unbuildable in 3D while still adhering to the photographic realism. I’m prioritizing the original paradox aspect and verifying the absence of any direct Escher influence. I’m also confirming that the alignment creates the visual deception, leading to an impossible but visually coherent structure.

This woodworking scene appears ultra-realistic at first glance, but close inspection of the central wooden structure reveals a physical impossibility. The beams on the left pass both over and under each other in a paradox that can only exist through the specific viewpoint provided by a 2D projection.

That image definitely captures that “wait, what?” feeling. Since it blends the tactile, grounded reality of a woodshop with a geometric glitch in the matrix, here are a few name suggestions:

The Carpenter’s Knot: A play on “The Gordian Knot,” suggesting a problem that can’t be solved by untying it, only by changing how you look at it.
The Fold of Oak: Sounds like a standard craft piece until you realize the wood is “folding” through itself in 3D space.
Perspective’s Limit: A more literal take on how the image relies on a single, fixed point of view to maintain the illusion.
The Joiner’s Paradox: “Joining” usually implies two pieces meeting; here, they seem to exist in the same space simultaneously.
Flatland Craft: A nod to the book Flatland, implying this object was built by someone who understands dimensions we can’t see.

Personally, I’m partial to The Joiner’s Paradox—it feels like something a frustrated apprentice would accidentally build in a dream.

Claude’s Review

Mechanism: Depth-order cycle (identical to Carpenter’s Paradox)

Expert observation: This is the same mechanism as Carpenter’s Paradox, executed in the same setting (woodworking workshop), with the same material (oak frames), differing mainly in that the object is rotated 45° and a human figure is present.

What the figure adds: The carpenter’s hands interacting with the object anchor it more firmly in physical reality, which should heighten the paradox. But it also creates a risk — her hands occlude portions of the impossible junctions, offering the brain a potential escape hatch. If you can’t see the critical crossing, you can’t confirm it’s impossible. Here the woman’s hands are near but not fully blocking the key intersections, so the paradox survives.

Object vs Carpenter’s Paradox: This object is simpler and slightly more legible — fewer beams, cleaner diamond composition. Paradox slightly easier to read.

The core problem: This is the second wood-workshop impossible-frame image. Within a six-image series, repeating the same mechanism, material, setting, and compositional format constitutes a significant creative limitation.

The Planar Glitch ⭐⭐

Thinking

Defining the Environment

I’m currently focused on defining the environment for this impossible scene. A photorealistic workshop or architectural setting seems most promising. I am leaning toward a brightly lit and busy atmosphere. I am actively considering how to integrate the impossible geometry seamlessly into this environment.

Constructing the Core Object

I am now focusing on the central impossible object and its composition. My intention is to create a structure of intersecting wooden blocks. It should present as a solid volume, but be impossible in three dimensions. I am aiming for a complex, handcrafted wooden sculpture on a workbench, constructed from interlocking square pine beams. The beams are to form an impossible cuboid framework.

Refining the Composition

I am now focusing on refining the scene’s visual composition to emphasize projective impossibility. I am exploring how to arrange the workbench, tools, and background elements to create the desired focal point on the impossible wooden structure. I am actively working on the lighting to enhance the illusion. I am prioritizing a slightly elevated, front-on camera angle to maximize the effect of the intersecting wooden frames.

Developing the Construction

I am now delving into the precise mechanics of the impossible structure. The key is in the intersection of three beams, a vertical and two horizontals, which visually overlap perfectly. The image needs to clearly show this intersection, emphasizing the illusion of solid objects occupying the same space.

Analyzing the Junction

I am now closely analyzing the critical intersection of the wooden beams. My latest thought is that the visual trickery lies in the apparent seamlessness of the intersection. The image must show how the grain of the vertical beam seems to pass through the horizontal beam without a break, simultaneously occupying the same space. I’m focusing on the interplay of light and shadow.

Developing the Setup

I am now structuring the scene with a focus on realism to amplify the impossible nature of the central object. I’ve settled on a busy, brightly lit artist’s studio. I’m prioritizing the inclusion of a worn workbench and scattered tools to provide context. The impossible wooden structure will be the focal point on the central bench, with a cluttered background to enhance the illusion. The lighting is being adjusted to cast sharp shadows and highlight the critical intersection.

Analyzing Visual Paradoxes

I’m assessing the latest rendering; the forced perspective creates an impossible workshop structure. The focus is on the overlapping planes at the ‘B’ intersection, creating a visual paradox, and I’m comparing it to earlier concepts. The photorealism is present, and I’m examining how effectively the illusion holds.

Examining Novel Illusions

I’m now determining how effectively the generated “woodworking piece” achieves its paradoxical nature. The goal is to avoid simple clones of existing concepts, so the originality of the implementation is under evaluation. I’m focusing on how well the forced perspective creates an effective illusion within this specific context.

How about The Carpenter’s Paradox?

It plays on the contrast between the gritty, tactile realism of the workshop—the wood shavings, the glue, the weathered workbench—and the structural impossibility of the object itself.

If you want something a bit more technical or “dry” (fitting for a blog post or a data-viz discussion), here are a few other options:

The Planar Glitch: Highlights that the object only “works” because of the 2D projection.
Miter’s Knot: Sounds like a traditional woodworking joint, but implies a knot that can’t be untied by physics.
The Orthogonal Lie: A nod to the right angles that shouldn’t be able to connect the way they do.
Draftsman’s Delusion: Suggests a prototype that followed a flawed blueprint to a literal, impossible end.

Claude’s Review

Mechanism: Depth-order cycle (same as above)

Immediate expert red flag: There are handwritten “A” and “B” labels visible on the beams. These break immersion completely and signal this is a diagram, not a photograph. The Gemini thinking explicitly tried to remove them and failed. This is a production error that should disqualify the image from serious consideration.

Beyond the labels: This is the third wood-workshop impossible-frame image in six. By this point the repetition is undeniable. A series with 50% identical setting-plus-mechanism is not exploring the space of impossible images; it is defaulting to a comfort zone.

Verdict: The weakest execution of the three woodshop images, and ranked last in the series overall.

Claude’s Synthesis

What the model does well

Nano Banana 2’s strongest suit is photorealism as a weapon. The material quality — mortar between stones, timber grain, leaded glass, studio clutter — is genuinely high. This matters because impossible images depend entirely on the viewer’s willingness to accept the scene before discovering the contradiction. The model can produce that initial acceptance convincingly.

The Anchored Arc is the standout work of the series: a textbook impossible image. The mechanism is clean (depth-order cycle), the trigger point is locatable and well-timed, the setting is perfectly chosen, and the mundane props (apples, lantern) function as skilled misdirection. If this image were the only one submitted, the review would be strong.

The Möbius Atrium shows conceptual ambition. Topological impossibility is rarely attempted in photorealistic paradox imagery — it’s much harder to execute than Penrose-type depth cycles. The image succeeds in conveying unease even if it doesn’t land a precise trigger point.

The dominant weakness: template collapse

An expert reviewer’s sharpest criticism is this: three of six images are the same image. Carpenter’s Paradox, Fold of Oak, and Planar Glitch all use:

A woodworking workshop
An impossible interlocking wooden frame object in oak
Depth-order cycle via Penrose-type impossible solid
A craftsperson or workshop context as anchor

This isn’t creative variation — it’s repetition. The model found a template that works and defaulted to it. This is the AI equivalent of a student who writes three essay answers using the same paragraph structure: technically passable, but it signals limited range.

The prompt asked for images that “can only exist because images are a 2D projection” — a rich design space that includes shadow contradictions, impossible horizons, scale impossibilities, hybrid window/painting planes, impossible architectural interiors, impossible figures (two legs become three), etc. The model barely touched this space.

The Pastoral Threshold failure is diagnostic

The failed Pastoral Threshold image tells us something important: the model struggles with hybrid spatial planes — the mechanism where 2D and 3D spaces interpenetrate. This is arguably the richest and most original mechanism available to AI image generators, since they have unique capabilities in blending image spaces. That the model couldn’t execute it suggests the prompt engineering didn’t push hard enough on that frontier.

Comparison to the genre

Comparator	What they master	Banana’s position
Escher	Internally consistent impossible architectures traceable by finger	Anchored Arc approaches this; others don’t
Reutersvärd / Penrose	Clean 3-junction impossible solids	The woodshop series is essentially photographic Reutersvärd
Patrick Hughes (Reverspective)	Physical objects that deny their own physicality when viewed flat	The Atrium approaches this inverse but doesn’t commit
Sandro Del-Prete	Frame/painting/window bleeds	Pastoral Threshold attempted this and failed
Rob Gonsalves	Magic realism where two physically valid scenes share pixels	This is the direction Nano Banana 2 should explore

The model operates comfortably in the Reutersvärd zone — impossible objects with photorealistic texture. It fails in the Del-Prete zone (frame-bleeding) and hasn’t yet attempted the Gonsalves zone (scene interpenetration). The topological Atrium is an admirable outlier.

Recommendations for the next series

Abandon the woodshop — it’s become a crutch. One impossible-object image per series is enough.
Push shadow contradictions — an outdoor scene where a person casts shadows in two incompatible directions from a single sun is simple, clean, and devastating.
Pursue the canvas-reality bleed — try harder on the Pastoral Threshold concept with more explicit framing: sheep’s shadow falling outside the canvas onto the floor, one sheep’s back legs on wood floor.
Try impossible scale — a scene where a distant mountain is smaller than a nearby thumbnail-sized stone, and both scales are “correct.”
For topological work: commit harder — mark the surface with an arrow or color gradient so the viewer can trace the Möbius inversion.

Final assessment: A series of genuine technical quality with one masterwork, one ambitious experiment, one failed attempt, and three variations on the same theme. Nano Banana 2 demonstrates strong craft and real conceptual ambition — but the series reveals a model that hasn’t yet fully explored the design space it was given.

To be perfectly frank, it took me 5 minutes to carefully read what Claude liked so much about The Anchored Arc - and where the paradox was. I finally did figure it out (I think) and it does feel impressive.

But from now on, I should probably leave art to the machines and go play outside.

Which LLMs get you better grades?

Fri, 06 Mar 2026 19:26:47 +0800

In my graded assignments students can pick an AI and “Ask AI” any question at the click of a button. It defaults to Google AI Mode, but other models are available. I know who uses which model and their scores in each assignment.

I asked Codex to test the hypothesis whether using a specific model helps students perform better.

The short answer? Yes. Model choice matters a lot. Across 333 students, here’s how much more/less students score compared with ChatGPT:

Perplexity: increases scores by ~11% points (99.8% sure it has an impact).
Claude: +9% (99.5% sure)
Clipboard:, i.e. just copying to the clipboard and pasting in their choice of AI: +9% points (97.5% sure)
Google: -1% (Not sure. Sorry, Sundar.)

If you are still using default ChatGPT, you are leaving nearly a full letter grade on the table compared to Claude or Perplexity users.

I also looked at exam timing. Do specific models perform better under last-minute pressure?

Statistically, no. The model-by-timing interaction isn’t significant (92% sure). A good model won’t save a rushed submission any better than a bad one.

But procrastination does hurt, regardless of which AI they frantically prompt. In the first graded assignment (GA1), there was a negative correlation (-37%, 100% sure) between submission time and final score.

BTW, I correlated with the most-used model. I can’t guarantee they used that specific model on every single attempt.

Of course, correlation isn’t causation. Maybe Claude writes better code. Or perhaps the kind of student who consciously switches from ChatGPT to Perplexity or Claude is just a better, more engaged student.

But a 9-to-10 point bump is a pretty good reason to experiment.

White Pebble Black Pebble

Wed, 04 Mar 2026 22:23:29 +0800

When I was in class 8 or 9, our English teacher told us a story I’ll never forget.

There was a poor farmer who lived in a village. He owed the zamindar (landlord) of the village a lot of money.

The zamindar had an eye on his daughter. “Marry your daughter to me, and I’ll forgive your debt,” he said.

The farmer was reluctant. “Please, sir, what will the village say about your marrying such a young girl?” he asked.

The wily zamindar said, “They cannot argue with God’s will. Let fate decide.”

The zamindar gathered the villagers near the river, picked up pebbles of white and black from the riverbed, and told the farmer, “I will put a white and black pebble in this bag. If your daughter picks the black pebble, fate wills that she marry me. If she picks the white pebble, she needn’t. Your debt will be forgiven.”

But without the villager’s knowledge, the zamindar put two black pebbles in the bag.

The farmer’s daughter saw this, and was in a dilemma. Exposing the zamindar force them back into debt.

Instead, she picked a pebble from the bag dropped it “by accident” among other pebbles. “Sorry!” she said. “But let me see which pebble is left in the bag.”

She showed the remaining black pebble from the bag. “I must have picked the white. Fate forgives our debt without marriage, sir.”

I remember this story is because it converts a “no-win” situation into a “no-lose” one – just by reframing the problem.

Whenever I think, “Damn! What a mess!” I hope I have enough wits to remember this story. There are many more black-pebble problems in life than we realize. We just need to be creative enough to drop the pebble and reframe the problem.

AI for film dialogues

Sun, 01 Mar 2026 19:13:05 +0800

I was watching Vasu while Codex-ing and came across this dialogue:

Here’s the dialogue, recorded via ffmpeg, transcribed via AI Studio:

మీ నాన్న మిమ్మల్ని పోలీస్ ఆఫీసర్ అవ్వమని అడిగితే అయ్యారా? మీకు ఇష్టం కాబట్టి అయ్యారు.
సచిన్ టెండూల్కర్ ని ఇంజనీర్ ని చేయాలని వాళ్ళ నాన్న అనుకుని ఉంటే, ఇండియా ఒక గొప్ప క్రికెటర్ ని మిస్ అయ్యేది.
విశ్వనాథ్ ఆనంద్ ని డాక్టర్ ని చేయాలని వాళ్ళ అమ్మ కోరుకుని ఉంటే, ఇండియాకి ఓ గ్రాండ్ మాస్టర్ ఉండేవాడు కాదు.

Translation:

Did you become a Police Officer because your father asked you to? You became one because you liked it.
If Sachin Tendulkar’s father had thought of making him an engineer, India would have missed out on a great cricketer.
If Viswanathan Anand’s mother had wished to make him a doctor, India wouldn’t have had a Grandmaster.

But here’s the thing - these aren’t the best examples. A better one would be were a successful parent allowed their successful child to pursue their passion.

So, Gemini to the rescue:

List famous Indians whose children have become famous in very different fields…

… and here goes:

మీ నాన్న మిమ్మల్ని పోలీస్ ఆఫీసర్ అవ్వమని అడిగితే అయ్యారా? మీకు ఇష్టం కాబట్టి అయ్యారు.
ప్రకాష్ పదుకొనె తన కూతురు దీపికాని బ్యాడ్మింటన్ ప్లేయర్ ని చేయాలని అనుకుని ఉంటే, ఇండియా ఒక గొప్ప నటిని మిస్ అయ్యేది.
ఆర్. మాధవన్ తన కొడుకు వేదాంత్ ని నటుడిని చేయాలని కోరుకుని ఉంటే, ఇండియాకి ఒక ఛాంపియన్ స్విమ్మర్ ఉండేవాడు కాదు.

Translation:

Did you become a Police Officer because your father asked you to? You became one because you liked it.
If Prakash Padukone had thought of making his daughter Deepika a badminton player, India would have missed out on a great actress.
If R. Madhavan had wished to make his son Vedaant an actor, India wouldn’t have had a champion swimmer.

Not that dialogue writers aren’t using AI, but if you know anyone who needs AI help, let me know. The fact that I can’t read a word of Telugu makes no difference! 🙂

Using Codex to improve Codex

Sun, 01 Mar 2026 18:26:13 +0800

Instead of learning and applying new Codex features, I asked it to analyze my sessions and tell me what I’m under-using.

I'd like you to analyze my Codex sessions and help me use Codex better.

sessions/ has all my past Codex sessions.

Search online for the OpenAI Codex release notes for the latest features Codex has introduced and read them - from whatever source you find them.

Then, create a comprehensive catalog of Codex features.

Then, analyze my sessions and see which feature I could have used but didn't and make a comprehensive list.

Then summarize which features I should be using more, how, what the benefits are, and with examples from my sessions.

Document these in one or more Markdown files in this directory. Write scripts as required. Commit as you go.

It did a thorough job of listing all the new features and analyzing my gaps.

Read the full story

Here’s the summary: I’m using new models immediately, but not the new features of Codex. For example:

Parallel execution. Yesterday, I ran ~103 tool calls without the new spawn_agents_on_csv feature from last week, which would have saved a lot of time running in parallel.
Permissions. Last week, I ran a script that asked me for permissions 7 times towards the end. Instead, I could have used /permissions to set early permissions.

The best part is that it could just add a few instructions to my AGENTS.md:

Run multiple independent reads in parallel.
For 20+ tool calls, maintain update_plan throughout.
For long-running commands/tests, delegate via sub-agents and report checkpoints.
If blocked by permissions, ask me concise choices.
If sandbox/config gets in the way, use /permissions and /debug-config early.

Now, the beauty is that the tool optimized itself. I don’t even need to learn how to optimize it!

AnalAIzing Cloud Costs

Sun, 01 Mar 2026 16:21:25 +0800

I have a GitHub Education since I teach at IITM. But if I switch back to a free account, how much would I need to pay?

I asked Codex (5.3, xhigh):

My GITHUB_TOKEN is in .env. Go through my GitHub billing. Ignore the $100 sponsorships I make. Other than that, my current metered usage is $6.71 for Feb 2026 (which is included in my billing plan). $0.35 comes from sanand0/exam and $0.34 from sanand0/blog and so on. That’s coming mostly from “Actions Linux”, occasionally “Actions Storage”. Pick a few of the top repos and tell me what I should do to make the cost zero - or reduce the cost as much as possible. See if there’s a pattern across repos.

Document all of your findings in analysis.md and continue to append new findings in this file, summarizing my request as a heading followed by your response.

My aim is to stay well below the 2,000 free actions minutes/month - which I’m already below. But still, I want to optimize a bit… Tell me

What my billing would be under a free account

What repos and what activity are the biggest risks for hitting the free limit

After half an hour of my watching a movie, it told me (in great detail - see the details below) that:

My billing is way below the free limit.
I should watch out for GitHub Copilot, frequent CI runs, scheduled jobs.

But the more interesting thing for me is how easy cloud optimization has become with coding agents.

It makes curiosity cheaper. I wouldn’t have sat and written the scripts to figure out where $6.71 went. But coding agents made micro-audits practical (and with clever use of jaq, csvq, and duckdb. If you can get an answer by just from a question, we’d use it like Google - to answer ad hoc questions.
It challenges common sense. I assumed caching was good for speed and compute. But Codex’s analysis of my sanand0/blog actions pointed out that dropping a job’s time from 1.2 minutes to 0.7 minutes doesn’t change the 2-minute billed floor! Also, the 114MB cache increased storage costs. We can test optimizations without assuming.
It exposes blind spots. Turns out that a big chunk of the cost of my (private) TDS exam repo was dynamic/copilot-pull-request-reviewer - a GitHub workflow triggered by using Copilot. I also have a big chunk of “legacy” GitHub Pages on older repos that add to cost because of failures and retries. We can find these invisible leeches draining cost without knowing what to ask for.
It self-corrects. Codex hit a deprecated GitHub billing endpoint (HTTP 410). It curled the GitHub docs, found the new endpoint, and rewrote its query. It made a mistake with csvq, read the help, and switched to duckdb for complex median calculations. That self-correction and learning means that it can work while you sleep.
It fixes, not just finds. Codex rewrote my blog deployment script to use a single job instead of two, to save compute time. I just copied andd deployed it. It went from researcher to advisor. That’s a short step from executor, next.

If analyzing cloud cost is this easy, Cloud FinOps companies face a risk. AWS, Azure, and GCP will simple embed Cloud FinOps into their services. Cloud spend won’t fall – we’ll just use more compute for the same budget.

The big Cloud FinOps companies (e.g. IBM, Flexera, Broadcom) are in some trouble. They can’t cannibalize their own market. They’ll likely shrink and focus on banks & Governments that need a reputable partner.

Companies that own the infra data (e.g. DataDog, Snowflake, DataBricks) will likely grow, since only they can access the context for optimization. That’ll let them provide better service cheaper, e.g. auto-optimize queries, compute, storage, etc. for you.

Pure plays (e.g. CloudZero, Finout) might need to pivot from infra optimization to… something more strategic, I guess.

Here's the full (long) response from Codex

Data source: GET /users/sanand0/settings/billing/usage using your token in .env.
Snapshot time: 2026-02-28T03:24:45Z (UTC).

Ignoring sponsorships, Feb 2026 metered usage is:

Product	Gross Amount
Copilot Premium Request	$5.05
Actions (Linux + Storage)	$1.646844306
Git LFS storage	$0.009750857
Total	$6.706595163

Top Actions repos in Feb 2026:

Repo	Total Actions Cost	Linux Cost	Storage Cost	Linux Minutes
`exam`	$0.354000000	$0.354000000	$0.000000000	59
`blog`	$0.337295637	$0.246000000	$0.091295637	41
`tools-in-data-science-public`	$0.211055072	$0.204000000	$0.007055072	34
`tools`	$0.174166256	$0.174000000	$0.000166256	29
`iss-location`	$0.174000000	$0.174000000	$0.000000000	29
`imdbscrape`	$0.168000000	$0.168000000	$0.000000000	28

Cross-repo pattern:

Your cost is dominated by minute-floor billing per job and trigger frequency, not pure runtime.
Feb run-level pattern (from fetched run/job metadata in top repos):
- dynamic: 9 runs, 79 billed-like minutes (~8.78/run)
- push: 50 runs, 81 billed-like minutes (~1.62/run)
- schedule: 55 runs, 57 billed-like minutes (~1.04/run)

Primary cost-reduction levers:

Disable Copilot dynamic code-review/agent workflows on repos where not needed (exam, tools were the big dynamic consumers).
Restrict Pages deploy workflows to main only (avoid dev/feature branch deploys).
Merge 2-job build+deploy workflows into one job where feasible (reduces per-job minute floor).
Reduce scheduled frequency for daily cron repos if not needed (iss-location, imdbscrape).
For storage, reduce artifact/caches and cleanup stale artifacts.

I expanded analysis across all 14 repos that used Linux Actions minutes in Feb 2026 and fetched per-run job details.

1) sanand0/blog: does disabling cache improve costs?

Workflow history:

Cache was introduced in .github/workflows/deploy.yml commit 4f744fffdeb64aebd09cfc6fd1bb27fd9844842d on 2026-02-20T04:40:39Z.
Previous workflow commit: 61392ee781ff77e9914b0a1a68b852f46d0a66e8 on 2026-01-02T03:11:32Z.

Observed runs around this change (main + dev only):

Phase	Runs	Avg Active Job Min	Median Active Job Min	Avg Billed-like Min	Median Billed-like Min
Before cache (Jan 2 -> Feb 20)	62	1.192	0.900	2.194	2.0
After cache (Feb 20 -> Feb 28)	6	0.744	0.817	1.667	2.0

Interpretation:

No clear billed-minute win from cache: median billed-like is still 2/run in both periods (minute floor + 2 jobs dominates).
Cache likely helped runtime modestly (small active-minute decrease), but sample after cache is small (n=6) and confounded by other workflow changes.
Storage side: current blog caches are ~114 MB. If that stayed all month, rough max cost is about $0.026/month at observed storage unit price; this is only part of blog storage charge ($0.0913).
Therefore, disabling cache may save some storage, but the main cost lever is reducing run count and job count, not cache toggles.

2) Better blog optimizations than cache-off

High-impact changes:

Trigger deploy only on main (Feb had 20 dev deploy runs vs 2 main).
Add path filters so non-site changes do not deploy.
Use a single deploy job (instead of separate build + deploy) to reduce minute-floor overhead.
Keep [skip ci] support.

Explicit workflow suggestion file created:

/home/vscode/Downloads/github-usage/analysis/blog-cost-optimized-deploy.yml

3) Additional repo pattern findings (expanded set)

Feb 2026 Linux-minute repos and event patterns:

Repo	Minutes	Main pattern
`exam`	59	dynamic Copilot runs (`Copilot code review`, `Running Copilot coding agent`)
`blog`	41	push deploy workflow (`Deploy Hugo site`)
`tools-in-data-science-public`	34	push deploy + some dynamic pages runs
`iss-location`	29	daily schedule
`imdbscrape`	28	daily schedule
`tools`	29	push deploy + dynamic Copilot review
`pyoppe`	10	dynamic `pages build and deployment` (legacy Pages), includes retries/failures
`schoolai`	6	dynamic `pages build and deployment` (legacy Pages)
`image-filters`	6	dynamic `pages build and deployment` (legacy Pages)
`llmpricing`	3	dynamic `pages build and deployment` (legacy Pages)

Important hidden pattern:

pyoppe, schoolai, image-filters, llmpricing use Pages build_type = legacy and incur dynamic pages runs.
These runs are tiny but still billed at minute floor and can multiply via retries/failures.

4) What your billing would be under a free account

Assumptions for this estimate:

Focus on Actions minutes/usage behavior from your Feb 2026 data.
Sponsorships still excluded.
Copilot Premium Request billing is separate from Actions minutes.

Your Feb Actions usage:

All repos Linux minutes: 258
Private-repo Linux minutes (exam): 59

Estimate:

Under a 2,000-minute free limit, both 258 and 59 are far below the cap.
So for Actions minutes specifically, you would still be $0 overage this month.

5) Biggest risks for hitting the free limit

If your objective is avoiding 2,000-minute overflow, the highest-risk activity patterns are:

Dynamic Copilot runs on private repos (high minutes per run).
- Example observed: exam dynamic runs consumed 59 minutes from 5 runs.
Many frequent push deploys on multi-job workflows (2-minute floor per push is common).
- Example: blog and tools-in-data-science-public.
Daily scheduled jobs across many repos.
- One daily repo is ~30 min/month baseline; many such repos accumulate predictably.
Legacy Pages dynamic builds with retries/failures.
- Small runs, but multiplicative retriggers add up.

Rule-of-thumb risk math:

~1 daily scheduled workflow ~= 30 min/month.
~1 two-job push deploy ~= ~2 min/run.
~1 Copilot dynamic review run can be ~8-15 min/run.

Rofi vs Kanata

Sun, 01 Mar 2026 15:25:48 +0800

Kanata might be the most useful tool I can’t find a use for.

It’s a cross-platform keyboard mapper. Some cool features:

Make any key a modifier. Ctrl, Shift, Alt, etc. are modifiers. But we can make it so that pressing Space + I/J/K/L maps to Up/Left/Down/Right.
Chords. You can map any sequence of keys to anything else. For example, Alt + G, then C can type git commit -m"Experimenting" [ENTER]. Ctrl + M, then Down, can reduce the music volume by 10%.
Toggles. Double-clicking Caps Lock activates capitalization for the current word, and once you type a non-letter, it turns off. Or double-clicking Ctrl can turn on “gaming mode” where WASD becomes arrow keys, and double-clicking again turns it off.
Tap Dance. Double-clicking left-shift can turn on Caps Lock. Triple-clicking turns it off. Quadruple-clicking …

… and there’s lots more.

Unfortunately, I have not been able to find a single use in practice. My main bottleneck is that I don’t remember the shortcuts. So, instead, I use rofi.

Rofi is a programmable menu launcher. The great thing is that I don’t need to remember stuff. When I map it to keyboard shortcuts, it pops up a menu and I pick what I need.

I use it to:

#1: Open any file or browser tab with Ctrl + Alt + F. This is my most used shortcut. It replaces Everything for files, and Chrome Search Tab with a single shortcut.

#2: Paste any prompt fragment with Ctrl + Alt + P.

This is especially useful when adding a prompt fragment, like Expert Lens in the middle of a prompt, or analyzing call recordings a dozen times a day.

#3: Edit the clipboard with Ctrl + Alt + M (for Markdown).

This is a killer feature. I often use it to:

Copy from email/chat and paste it in ChatGPT as Markdown
Copy from ChatGPT and remove the em-dashes and non-ASCII characters
Convert Markdown to Unicode for LinkedIn
Convert Markdown to rich test for pasting in emails or chat
Creating a ChatGPT / Claude / Google AI mode link from a piece of text
…

I’m still keen to find a use for Kanata, but for now, my use of Rofi will continue to grow.

AI Expert Lens

Sun, 01 Mar 2026 12:53:47 +0800

My current favorite prompt fragment is the expert lens:

Think like an expert. In this context:
- What patterns would an expert in this field check / recognize that beginners would miss?
- What questions would an expert ask that a beginner would not know to?
- What problems / failures would an expert anticipate that beginners may not be aware of?
- How would an expert analyze this? At each step, explain what they are looking for and why.

When I add this to my questions, if feels a lot smarter.

I have no idea if that’s because it really improves the replies, or because it prefixes them with “What an expert would do is…” which makes it sound smarter, or because it just makes me feel good about adding this prompt.

Irrespective of the reason, the effect is that I pay more attention to it. Increasingly, that’s the bottleneck - the most important part. If I’m attentive, I learn / act. At least for that, this prompt is useful to me.

But I also believe it actually improves the quality of replies.