o3 on S Anand

Things I Learned - 27 Jul 2025

Sun, 27 Jul 2025 00:00:00 +0000

This week, I learned:

Here are some tech community builders in India. ChatGPT
- Atul Chitnis (Bengaluru) – FOSS.IN and Linux Bangalore
- Dr. Nagarjuna G. (Mumbai) – FSF India and ILUG Bombay
- Rushabh Mehta (Mumbai) – FOSS United & ERPNext Community
- Kiran Jonnalagadda & Zainab Bawa (Bengaluru) – HasGeek Tech Conferences
- Kenneth Gonsalves (Nilgiris/Tamil Nadu) – Indian Python Community (deceased)
- Thejesh GN (Bengaluru) – DataMeet Open Data Community
- Varun Aggarwal (Delhi) – ML-India (Machine Learning Forum)
- Prashant Sahu (Pune) – Pune AI Meetup
- Akshay Dashrath (Bengaluru) – BlrDroid Android Group
- Vikrant Singh (Bangalore) – ReactJS
- Sankarshan Mukhopadhyay – Mozilla India and Wikimedia tech outreach
- Neependra Khare (Bengaluru) – Docker/Kubernetes Meetup
- Atul Jha (Bengaluru/Hyderabad) – OpenStack & CNCF Communities
- Aseem Jakhar & Ajit Hatti (Delhi/Pune) – null Open Security Community
- Rohit Srivastwa (Pune) – ClubHack and Hackerspaces
- Anubha Maneshwar (Nagpur) – GirlScript Developer Network
Digital Public Infrastructure initiatives in India scale if there’s a clear use case and centralized orchestration. Prof R Srinivasan
The distance between the end of the thumb and little finger, when fullet stretched, is ~9 inches. Between the thumb and pointer, when at a right angle, is ~6 inches. I checked this today - and it’s right. A useful rule of thumb for measurement - literally. Vasuki, ~1985
GitHub Sponsors Explore shows you which developers code most of your dependencies. You can sponsor them. I sponsored isaacs who maintains node-tap and sindresorhus who maintains several NodeJS packages for $50/month each.
markmap looks like a promising JS-based interactive mindmap from Markdown. More interactive than Mermaid Mindmap.
- mind-elixir is another option that lets you edit mindmaps and serialize in its own format
- jsmind is yet another but docs are in Chinese
- elkjs seems a good option for laying out nodes in an architecture-style flow diagram
⭐ O3 seems a better data scientist than I am. Based on my Google Searches, I have 3 persona: developer, AI-builder, and India/Singapore geo-culturist. A great example of an analysis from O3 that’s better than anything I could have come up with. ChatGPT
⭐ Fast review of AI be a powerful skill and enabler. I built an Image Editing tool with Codex in ~4 hours, with 11 prompts taking 3.5 - 7.5 minutes each. 3 hours human review, 1 hour LLM coding. I’m 3X slower at reviews while AI will keep improving. ChatGPT: Faster LLM review techniques #ai-coding
- Auditize: citations, rationale, output screens, diffs, test results, risks, unknowns
- Auto validate. Evals, tests
- Prioritize. High z-values, big-useful-surprising areas
At the VizChitra Birds of a Feature session, here’s what people said AI enables:
- Complementary skills enable a team of 1. Non-coders can code. Non-domain people get insights from data
- Solves starting trouble. It offers a first draft
- Generation. New ideas (reduces blind spots), scenarios, non-existent people, new data, new persona for surveys
- Hyper-personalization. Parts of YouTube relevant for THIS asset manager. Implication of data for me
- Automated scaling. Generate 1,000 images. Evaluate 1,000 assignments
- Saves time: debugging, research, validation, documentation, copywriting
- New ways of working. Loading event schedules into my calendar
Qwen-Code is a fork of Gemini CLI and uses qwen3-coder – a model that can also be used with Claude Code and Cline. The model is not anywhere near as good as Claude 4 Sonnet. The app is costlier than using Claude Code directly. #ai-coding
The LLM industry seems to have matured quickly. Early adopters who are open to understand the generic capabilities of LLMs through demos are somewhat saturated. The early majority have come in. They aren’t interested in generic capabilities. They’re looking for solutions that solve their specific problem. Soon the late majority will come in asking for existing solutions that have already solved their problem for many others. ChatGPT: Creating demos for majority
Claude for Financial Services is an agentic version of Claude available on AWS & Google marketplaces tuned for financial services analysis. Video
catbox.moe is a file hosting service that you can upload a file to without any API key. It’s an alternative to 0x0.st. Both can be used for images. Catbox retains files indefinitely and openly publishes costs - might last longer. 0x0 deletes files between 1-12 months based on size.
Agents face 3 problems: compounding errors, quadratic costs, and poorly designed tools. Start with small scope & strong reviews while you solve these problems. Betting Against Agents
Leadership and vision will matter more. LLMs iterate fast. They can think for longer. So tasks where people need to work longer independently than LLMs can are what humans will be needed for. That requires understanding the objective. So leadership and specifically vision transfer will become more valuable. You need to be able to tell people what to do well enough that they can work independently for weeks.
Having LLMs go through engineering drawings, floor plans, etc. and understand them, find problems, etc. is an emerging use case. People are using Veo 3 to convert a floor plan into a 3D walk through too.
Digital adoption is slow partly because of a skill gap. “Old-timers” are slow to let go of traditional approaches.
Video recordings are used in manufacturing to evaluate quality (e.g. wafer inspection, assembly inspection, component presence) using AI. An interesting by-product of this data is that they can also measure productivity, task time.
“Common sense is a specialization”. That’s something I said accidentally when seeing that some schools/colleges tend to produce more broad, sensible thinkers (e.g. Naval College @ Goa) while others produce more narrow-thinking specialists (e.g. engineering colleges).
Three groups control the financial economy. To sell sustainability services, you need to have sold to one of them. via Sundeep
1. Banks, who will sell a loan against anything they can insure, and look to insurers for long-term thought leadership.
2. Insurers, who will insure anything they can re-insure, and re-insurers, who look at real-estate trends as a stable long-term asset
3. REITs who own the majority of the world’s real-estate
We could think of a copilot as an (agentic) LLM chat interface for an artifact. E.g. Code pilot (Claude Code. Cursor.). Data analysis copilot (Google Colab, sort-of. ChatGPT). That allows us to imagine tools that will create/edit artifacts. Here are some I’ve encountered as a demand.
- Documents. E.g. Docsearch, GPTs, Microsoft Copilot, Gemini
- Slides. E.g. Microsoft Copilot, Gemini
- Sheets. E.g. Microsoft Copilot, Gemini
- Code. E.g. Cursor, Claude Code
- Database. Create DB schema, ER diagrams, synthetic data, ingestion scripts, etc.
- Data (analysis). E.g. Datachat, Google Colab, Marimo
- Posters. E.g. Postgen
- Shell. E.g. Warp
- Topic modeling. E.g. classify
- Surveys. E.g. Personagen
- APIs. E.g. apiagent
- Drug regulatory submissions.
- Contracts (risk).
- Manufacturing SOPs.
- Curriculum.
- Data quality.
- Support tickets.
- Dashboards.
- IaaC / DevOps.
- Video campaigns.
- Resumes.
- Patents.
CLI optimization for LLMs will likely emerge. More CLIs (and wrappers / hooks in the shell) will improve output and error contexts for LLMs, e.g. printing current directory, caching slow outputs, suggesting alternate commands, etc. Ref
Frequent commits with linting & building seems like a good AI coding strategy, especially for Claude Code. Ref #ai-coding

To keep Claude Code in line on my project, I’ve relied heavily on linters, build scripts, formatters, and git commit hooks. It’s pretty easy to get Claude Code to commit often by including it in your CLAUDE.md, but it often likes to ignore other commands like “make sure the build doesn’t fail” and “fix any failing tests”. All my projects have a .git/hooks/pre-commit script that enforces project standards. The hook works really well to keep things in line.
Google Apps Scripts are actually a web apps platform in JavaScript more than a macros equivalent. Ref
⭐ DuckDB supports joins based on embedding similarity and even hybrid similarity! Ref
Human persuasion techniques like Cialdini’s work well with LLMs Ethan Mollick
gitwatch is a clean way of auto-committing & pushing files into GitHub. It effectively converts GitHub into a Dropbox-like service.
Adding ?udm=14 to Google Search URLs removes AI mode and other clutter. Ref
⭐ Never embed LLM‑generated summaries without a disclaimer, source links, and flag‑as‑wrong feedback button. Build a fast appeal/edit pipeline before release. via Death By AI

Sun, 29 Jun 2025 04:10:05 +0000

How long have you made ChatGPT think? My highest was 6m 50s, with the question: Here are vehicle telematics stats for 2 months. Unzip it and take a look. Find interesting insights from this data. Look hard until you find at least 5 surprising insights from this.

The next largest thinking block (5m 42s) was where I asked: I would like to explore parallels to the current phenomenon where intelligence is becoming too cheap to meter. Historically, both in recent history as well as over ancient history, what technologies have made what kind of tasks so cheap that they are too cheap to meter? Give me a wide range of examples

Completing long tasks is one measure of intelligence. Working independently for long is another. O3 is at ~6 minutes. While it works, I’m practicing Bubble Shooter in 6 minutes!

Completing long tasks: https://www.lesswrong.com/posts/deesrjitvXM4xYGZd/metr-measuring-ai-ability-to-complete-long-tasks

You can do try this on your history. If you managed to beat 7 minutes, could you please share your prompt?

How to export ChatGPT history: https://help.openai.com/en/articles/7260999-how-do-i-export-my-chatgpt-history-and-data
How to analyze thinking time: https://www.npmjs.com/package/chatgpt-to-markdown

… or run:

npx -p chatgpt-to-markdown thinktime conversations.json

How long can I make ChatGPT think?

Sun, 29 Jun 2025 04:08:32 +0000

Jason Clarke’s Import AI 414 shares a Tech Tale about a game called “Go Think”:

… we’d take turns asking questions and then we’d see how long the machine had to think for and whoever asked the question that took the longest won.

I prompted Claude Code to write a library for this. (Cost: $2.30).

(FYI, this takes 2.3 seconds in NodeJS and 4.2 seconds in Python. A clear gap for JSON parsing.)

The top 3 thinking blocks took 6m 50s, 6m 21s, and 6m 1s each, all in roughly the same conversation thread. Here were my questions:

Here are vehicle telematics stats for 2 months. Unzip it and take a look. Find interesting insights from this data. Look hard until you find at least 5 surprising insights from this.

Double check these with the data

Run the analyses and tell me what factors drive the maintenance schedule. Give me insights validated with tables and charts.

The next largest thinking block (5m 42s) was on what happens when intelligence becomes cheap:

I would like to explore parallels to the current phenomenon where intelligence is becoming too cheap to meter. Historically, both in recent history as well as over ancient history, what technologies have made what kind of tasks so cheap that they are too cheap to meter? Give me a wide range of examples

Completing long tasks is a measure of intelligence. Working independently for long is another. O3 is at about 6 minutes on this. While it works, I’m practicing Bubble Shooter in 6 minutes.

You can run by exporting your ChatGPT history, extracting conversations.json and running:

npx -p chatgpt-to-markdown thinktime conversations.json

How to Create a Data Visualization Without Coding

Sun, 27 Apr 2025 09:45:21 +0000

After seeing David McCandless’ post “Which country is across the ocean?” I was curious which country you would reach if you tunneled below in a straight line (the antipode).

This is a popular visualization, but I wanted to see if I could get the newer OpenAI models to create the visual without me 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 any code (i.e. I just want the answer.) After a couple of iterations, O3 did a great job with this prompt:

𝙱𝚞𝚒𝚕𝚍 𝚊 _𝚜𝚒𝚗𝚐𝚕𝚎_ 𝙶𝚎𝚘𝙹𝚂𝙾𝙽 (𝙴𝙿𝚂𝙶:𝟺𝟹𝟸𝟼) 𝚝𝚑𝚊𝚝 𝚜𝚑𝚘𝚠𝚜, 𝚏𝚘𝚛 𝚎𝚊𝚌𝚑 𝚌𝚘𝚞𝚗𝚝𝚛𝚢, 𝚘𝚗𝚕𝚢 𝚝𝚑𝚎 𝚙𝚊𝚛𝚝𝚜 𝚘𝚏 𝚒𝚝𝚜 𝚊𝚗𝚝𝚒𝚙𝚘𝚍𝚎 𝚝𝚑𝚊𝚝 𝚕𝚒𝚎 𝚘𝚟𝚎𝚛 𝚘𝚌𝚎𝚊𝚗. 𝙲𝚊𝚛𝚎𝚏𝚞𝚕𝚕𝚢 𝚑𝚊𝚗𝚍𝚕𝚎 𝚌𝚘𝚞𝚗𝚝𝚛𝚒𝚎𝚜 𝚝𝚑𝚊𝚝 𝚜𝚝𝚛𝚊𝚍𝚍𝚕𝚎 𝚝𝚑𝚎 𝚙𝚛𝚒𝚖𝚎 𝚖𝚎𝚛𝚒𝚍𝚒𝚊𝚗 - 𝚄𝙺, 𝙵𝚛𝚊𝚗𝚌𝚎, 𝙰𝚕𝚐𝚎𝚛𝚒𝚊, 𝚎𝚝𝚌.

Here is the output and here is the ChatGPT conversation that generated it.

I learnt a few things:

Ask for the output, not the code. Models like O3 and O4 Mini can run code while thinking. Let’s stop asking for code to run. Just ask for the output directly. Let it figure out how.
Edge cases are everywhere. I had a problem with UK, France, Algeria, etc. straddling the prime meridian. If all goes well, you get AI-speed results. But it never does, and fixing it takes an expert and human-speed results. Programmers under-estimate edge cases, so compensate for this.

If you want to run this yourself, the code is at https://github.com/sanand0/antipodes

Sun, 27 Apr 2025 09:36:39 +0000

This is my decision tree for which model to use on #ChatGPT right now.

O𝟯: Use by **default.
O**𝟰-mini-high: Use when **coding.
GPT** 𝟰o: Use for a quick response or to create image.

O3 Is Now My Personalized Learning Coach

Sun, 20 Apr 2025 12:10:53 +0000

I use Deep Research to explore topics. For example:

Text To Speech Engines. Tortoise TTS leads the open source TTS.
Open-Source HTTP Servers. Caddy wins.
Public API-Based Data Storage Options. Supabase wins.
etc.

But these reports are very long. With O3 and O4 Mini supporting thinking with search, we can do quick research, instead of deep research. One minute, not ten. One page, not ten.

For example, I asked O3 to automate my weekly tech learning process:

From the links at https://www.hntoplinks.com/week pick those that would be of interest to me.
Read the articles and explain the key insights – what’s non-obvious and how can I use it.
When sharing how I can use it, research alternate approaches and suggest which approach is best.

This brings two new capabilties I love.

Memory. I said, “… that would be of interest to me”. Given my past chats and context, it really knows me. For example, the reasoning trace shows:

I’m narrowing down the list for Anand, focusing on his interests like generative AI, open-source tools, and minimal libraries.

That’s exactly right!

Long reasoning. It reads each site step by step, unsupervised. Here’s a brief reasoning trace:

Searched the web
Got the Unsure Calculator article. Now, onto the next one: the Hackable AI assistant with SQLite cron jobs. I’ll open it and dive into the details. I think this will be a great fit for Anand, considering the focus on AI tools and development. It should provide some interesting insights!
Searched the web
Now that we’ve moved through a couple of articles, it’s time to tackle the Gemini 2.5 Flash AI model. I think this one will be particularly interesting for exploring advancements in AI. Let me grab it and dive into the details so I can distill the insights into something relevant.
Searched the web
Next up: I’ll open the OpenAI blog article on GPT-4.1 in API. This could bring in some cool technical
…

The result is fantastic. While on a trouser shopping trip, I learnt more than when I browse Hacker News:

“Unsure” is a tool to calculate uncertainty formulas, like the uncertainties Python package.
Responses API adds features like linkable history, mid‑chat truncation, flexible reasoning detail, and more.
Augment Code is an AI code editor that’s growing popular on Reddit.
GPT 4.1’s 75% discounted prompt caching (instead of 50%) gives them an edge on repetitive tasks. OpenAI
Nix flakes are a reliable alternative to DevContainers that don’t need Docker - but don’t work on Windows.
TLS certificates will expire in 47 days from 15 Mar 2020. Automated domain renewals are a must. Digicert
… and a bunch of other things.

Here are Hacker News summaries for a month or specific days:

Mar 2025: Mistral OCR is a dedicated OCR, not an LLM. DuckDB UI has notebooks!
13 Apr 2025: Learnt about Signalbloom which automates Q1 summaries and this MCP Primer
15 Apr 2025: 12-factor agents shares agent lessons. Send JSX instead of JSON. AI as Normal Technology.

This enables:

Personalized learning, i.e. it tells me what I don’t know, and how I can apply it. This is powerful.
Learning on the go, e.g. via voice while cycling or walking.
Learning from untapped sources. This includes: GitHub repos, research papers, open data registries, patent filings, earnings transcripts, subreddits, judgements or acts, open data repositories or collections, and many more.

Take GitHub as an untapped source. I asked O3:

Go through the OpenAI Codex CLI repo on GitHub.
Teach me innovative, new, and surprising techniques or approaches or libraries I might not know about.
For each, explain what is interesting, how I might use it, and how this approach contrasts with alternatives.

This was a treasure trove, too! I learn about:

Ink: React for CLI!
Zod: schema validation
Creating firewalls
Fuzzy path matching
… and a bunch of other things

I tried O4 Mini High and saw similar results. I felt O3 still gave me more personalized suggestions.

Let’s see what we can learn from pull requests on Codex.

Go through all the pull requests that have been merged into OpenAI’s codecs repository on GitHub. Pick the ones that would be most interesting to me. You can group a few if they are very related and give me the top 10 most interesting PRs that would be relevant for me. Also explain why these are relevant to me, how I might use them, and any interesting details about the way in which the PRs were written.

Soon, we won’t just follow a lesson plan – we’ll have lessons built just for us. AI will track how we learn and adapt in real time. It’ll feel like having a personal coach in your back pocket. That future starts now.

This also opens a door to endless curiosity. There’s no limit to what we can explore. Curiosity is the competitive advantage, now.