Sun, Jul 27, 2025 | Anand S - Things 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

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. #github

markmap looks like a promising JS-based interactive mindmap from Markdown. More interactive than Mermaid Mindmap. #html #markdown
- 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

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. #cloud #hosting

"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

Google Apps Scripts are actually a web apps platform in JavaScript more than a macros equivalent. Ref #automation

⭐ DuckDB supports joins based on embedding similarity and even hybrid similarity! Ref #embeddings

Human persuasion techniques like Cialdini's work well with LLMs Ethan Mollick #llm-ops

gitwatch is a clean way of auto-committing & pushing files into GitHub. It effectively converts GitHub into a Dropbox-like service. #github

Adding ?udm=14 to Google Search URLs removes AI mode and other clutter. Ref

⭐ 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 #automation #code-agents #github #prompt-engineering
- 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: #ai-coding #automation
- 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 #code-agents #github

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 #llm-ops

Claude for Financial Services is an agentic version of Claude available on AWS & Google marketplaces tuned for financial services analysis. Video #code-agents

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 #code-agents #optimization

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. #future #llm-ops

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. #future #llm-ops #models

Digital adoption is slow partly because of a skill gap. "Old-timers" are slow to let go of traditional approaches. #future

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. #ai-coding #future

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. #ai-coding #automation #chatgpt #code-agents #llm-ops #prompt-engineering
- 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 #llm-ops #optimization

Frequent commits with linting & building seems like a good AI coding strategy, especially for Claude Code. Ref #ai-coding #code-agents #github #markdown

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.