This week, I learned: Pilots like to have fun, too. While awaiting landing clearance at Kolkata, our IndiGo pilot weaved tight curves just above the clouds at steep angles, giving us stunning views and a mildly thrilling experience. (Or maybe they were just following a flight path.) Since LLMs allow ANYONE to become “good enough” in most fields (marketing, medicine, management), and so on, here’re are my guesses on the impact. ChatGPT Companies-of-one will grow. Sole founder can handle support functions. Specialists will generalize. Consultants will code. Marketers will design. Wages will compress. Seniors will earn less as juniors can do more. Layers will compress. Organizations need fewer hierarchies as 1 person can do more. Shadow apps will grow. Anyone can code. Users build apps with prompts, sheets, agents, outside of IT SDLC. Like Excel sheets. Governance will grow. Non-experts are acting like experts. Validation is more important. Uneconomical apps will thrive. 1:1 tutoring. Continous decision making or A/B testing. Leaders will convince better. Persuasion scales. Brand (authenticity, trust, skill), Channel (distribution, audience) and Data are primary differentiators. Codex and Codex CLI now support image attachments. Notes from discussion on education with Srikanth Nadhumuni Indian higher education has done better, e.g. with the IITs, than primary education, where ASER consistently shows that 5th graders can’t read 2nd grade books. The National Education Policy (NEP) is focusing on FLN (foundational numeracy and literacy). The goal is universal FLN by 2027. Teacing FLN in local languages beats English. Teachers, parents, community support are high. Learning English as a second language is faster. Other countries (France, Germany, Japan) do this. Voice LLMs could help, but may not be toddler-ready, nor strong enough in all local langauges. But high-quality textbook translation with local nuances is a one-time human-in-the-loop effort that AI can support. India’s 1 crore teachers have a mandatory 50 hrs/year training requirement that is largely under-implemented. Senthil Mullainathan is working on extracting features from student answers to questions and generating remedial content purely as a black-box. Results beat explainability. ⭐ Creating systems that rapidly improve from feedback is the key to success. Rapidity, quality of improvement, quantity of feedback are all enablers. CBDC (Central Bank Digital Currency) is RBI’s Web 3.0 protocal. It allows purpose-driven transfers, e.g. money meant for education can only be spent on education. Meta-prompts with placeholders is a prompt-improvement technique (similar to LLM interviewing). Have LLMs create the prompt with “fill-in-the-blanks”. This makes it much easier for people to fill out. MassGen is a multi-agent orchestrator. Early days, experimental. It has multiple agents answer, then vote on each others’ answers, picking the best. DSPy auto-optimizes prompts based on input-output pairs or evals. Typical improvements are ~10-20%. My opinion: avoid. It’s a good idea, but has too much abstraction that hides the implementation. Worth learning from but not implementing unless you (a) have evals + metrics and (b) you KNOW you need to change models and (c) it’s a long-term project where the learning curve is worth it. Claude and ChatGPT How LLM “Attention” works: It takes each word’s embedding, moves it closer to similar words’ embeddings (e.g. Apple moves towards phone or orange depending on context). More similar words have a higher pull, like gravity. Luis Serrano Similarity isn’t symmetric. E.g. “Coke” moves “drink” more towards it, but “drink” pulls “Coke” less, since “drink” could refer to other things. Think of the pull (“Tinder similarity”) as “what A wants” (key matrix, which pulls other words) multipled by “what B offers” (query matrix, which is pulled by other words). This leads to two different similarity matrices. Multi-head attention is where a neural net gives different weightages to different similarity matrices based on context. Value matrix transforms the embedding space so that the next best next-word is more similar. Reading the Obsidian docs is like a master class in Markdown note-taking. Features like properties, embedding YouTube, bases, tags, etc. provide food for thought. The ObsidianMD subreddit has interesting tips. Summarize takeaways on top of each section Use atomic notes: one file per idea. Link liberally YAML front-matter you can query, e.g. tags, project, status, … Use GFM admonitions, e.g. > [!NOTE] Store images in a predictable way, e.g.  – ALWAYS with alt text Use diff fences for edits / doc changes Task lists with inline dates, e.g. - [ ] 2025-08-21 Draft a letter How to research better. Abhishek Divekar Have an objective when researching. Filter research based on that. Research backwards. Pick a relevant paper. Go through relevant citations. Typically, there are only 1 or 2 directly related ancestors. Don’t waste time searching. Gemini Deep Research is a great way to find and read papers. Don’t read the abstract. Read the introduction, which is the summary. It’s just a page. (The abstract is an LLM-ized versionof the introduction. Not as effective.) MCPs aren’t much more useful than tool calling for developers. They’re powerful when packaging for external parties (non-developers, other teams, clients, etc.). Developers can work just fine with tool calling. Nitin Agarwal Cybersecurity AI is an open-source LLM-based cyber-security tool that auto scans networks for vulnerabilities. ⭐ LLMs have solved several complex tasks (e.g. topic modelling, summarization). We need to adopt these as building blocks, like functions, and build better solutions. Abhishek Divekar codex -c model_reasoning_effort=high lets you run Codex CLI with highest reasoning effort. This has a separate limit that resets every 5 hours. https://x.com/thsottiaux/status/1958035261947781262 Truly agentic systems have high Autonomy, Complexity, and Reliability. Workflows have low autonomy. Agentic systems with high autonomy currently aren’t very complex or reliable, but will improve over time. Deepak Sharma Allow humans to intervene while agent loops execute, even unsolicited, to improve collaboration. Deepak Sharma Given the early, experimental days of AI, the better KPIs might be more about experimentation (e.g. number of prototypes) than operational (e.g. cost reduction). Krishnakumar Menon ⭐ Policy-as-code is an emerging theme. Allow users to create their own guardrails policy. Or, take existing policy documents and convert them into an LLM-based evaluator. Krishnakumar Menon ⭐ “Potentially nitpicky but competitive advantage in AI goes not so much to those with data but those with a data engine: iterated data aquisition, re-training, evaluation, deployment, telemetry. And whoever can spin it fastest. Slide from Tesla to ~illustrate but concept is general.” Andrej Karpathy, Dec 2022 The skills AI coding needs are very similar to tech-lead’s or an architect’s. Tanika Gupta #ai-coding Estimating tool capability & task allocation Task breakdown Spec-ing: which of user personas, user-journey maps, wireframes, technical architecture, psuedo-code Standards: tech stack, tools, linters, security, doc standards Git versioning & collaboration Code review. (Using AI.) Providing feedback. Modularity, naming, … Automated validation Post-mortem. Learning from errors and successes, choices LLM made The ROI of prompting carefully and using meta-prompts is high. Prompt clarity reduces iterations & dead-ends. The initial time spent (10-15 min) pays off with just a single reduced iteration (time to generate + review). Tanika Gupta ⭐ Prefer passing a spec.md to AI coding agents rather than directly typing-in prompts. This lets you meta-prompt and (collaboratively) iterate on the spec.md, version the prompts as specs, and generate specs as documentation. Tanika Gupta ⭐ Models need environments to learn. So far, we have been providing training data. But an environment to interact with, and learn from by itself, is more powerful. That requires a standard for environments. This is a powerful emerging area. The crux of experimentation is the learning from a postmortem. From that perspective I have been experimenting a lot but not been documenting or learning from that. Decision logs with post mortem are a more apt device for me. Gemini API includes a url_context tool to explicitly scrape websites. API Ontologies are more than taxonomies or schemas. They’re truths or rules, e.g., “no person has more than two parents”. Helps consistency checking and inference. # Terminological knowledge (T-Box) is domain rules and constraints (e.g., “a student is a person who attends a course”). Assertional knowledge (A-Box) is instance-level facts (e.g., “Mary attends Physics 101”). Tools & Formats SHACL. A W3C language for validating RDF graphs. ShEx is easier ad popular. Notation3. A W3C assertion and logic language which is a superset of RDF. EYE Reasoner. Prolog-based N3 (Notation3) reasoner. CLI + API-friendly. Can perform rule-based reasoning and generate new triples. HermiT. OWL 2 DL reasoner. Can check consistency, classify ontologies, compute entailments. CLI and Java API. Modern, maintained. Apache Jena. Java framework for RDF/SPARQL. Built-in reasoners (RDFS, OWL mini/micro/full). CLI via riot, arq (SPARQL query engine). Popular for RDF graph stores + inference. Do developers feel this way? #ai-coding In another example of vibe coding, an instructor for my TDS course vibe-coded most of an exam using Copilot and Sonnet. 6/8 questions worked one-shot. The two #ai-coding failures were interesting: One failed because of sample vs population stats. Copilot asked for sample variance but coded variance() instead of sampleVariance(). Another failed because of rounding off. NumPy code rounds off differently from Python or JS code. Meditation is about noticing distraction and returning to focus. So, distraction is necessary and good. #beliefs #ai-coding can make us overconfident. (At least, it makes me overconfident.) They create surprisingly good output, but only ~20% of the time. I cannot commit to a specific task based on that. Instead, it’s better to rely on AI coding estimates for portfolios, e.g. promise to share something cool without mentioning what. Or do something cool first, then share. Notes from podcast with Daniel Kahnemann. The Knowledge Project. Happiness is pleasure in the moment. Satisfaction is the meaningful story of our life. When we think, we want satisfaction. When we feel, we want happiness. The thinking brain and feeling brain optimize for slightly different things. E.g. The thinking brain packs the calendar with satisfying tasks that the feeling brain feels unhappy executing Both are good for us. We don’t know which matters more. Behavior change is harder than we think. Usually, it’s better not to expect success in changing others, or ourselves. Instead, understand why that behavior makes sense. Our behaviour is an equilibrium of forces. Weakening “bad” forces is easier than strengthening “good” forces, since it lowers tension. That’s inversion! Behaviours tell us more about situations than personality. We assume otherwise. That’s an attribution error. Motivation is complex. People can do bad things for good reasons and vice versa. “Feelings get in the way of clear thinking.” Example: I vibe-coded the last 2 questions of TDS GA7 on Claude Code. It didn’t run. I delayed fixing it for 5 days, afraid it would a major effort. It ended up a 2 min fix. It could have been major, but checking would have helped. Fear prevented that. Things that hamper clear thinking: intuition, emotion, beliefs. Beliefs are often formed based on people we admire or identify, not reason. Prefer rules, systems and processes. Willpower is an illusion. Delegate decisions to unemotional agents. (But agents misjudge perceived value of gain or loss!) Break down the problem, analyze it, THEM form an intuition. Be disciplined in delaying intuition or forming an opinion Environment shapes thinking but it’s not obvious how, e.g. some people work better in noisy cafes. Some colors are more calming. Protect dissenters and dissent. It’s painful and costly, and needs nurturing. NodeJS runs TypeScript files natively. Codex can clone any GitHub repo. So I can ask it to pull one or more repos, understand their code, and use that as a template or reference. This makes my repositories (and others’) reusable templates. Using newer libraries and platforms becomes easier, too. #ai-coding Tracking AI runs an IQ test on various LLMs every week. GPT 5 Pro leads, currently, followed by Claude 4 Opus and Gemini 2.5 Pro. It’s surprising how far behind GPT 5 is at the moment. LLMs are faster than me. So me learning and doing what the LLM says is a bottleneck. Get out of the way. For example do not learn. Do not execute. Do not verify. Give LLMs the tools to deploy, verify and iterate to improve.

In a recent talk at IIT Madras, I highlighted how large language models (LLMs) are taking over every subject of the MBA curriculum: from finance to marketing to operations to HR, and even strategy. One field that seemed hard to crack was innovation. Innovation also happens to be my role. But LLMs are encroaching into that too. LLMs are great connection machines: fusing two ideas into a new, useful, surprising idea. That’s core to innovation. If we can get LLMs daydreaming, they could be innovative too. ...

This week, I learned: Git partial clone lets you fetch files on-demand! E.g. git clone --filter='blobs:size=100k' <repo> will clone files under 100K and fetch the rest only on checkout. Over time, Git LFS capabilities will migrate into native Git. Ref ⭐ From Daniel Kahneman, The Knowledge Project Podcast. Key lesson. Have lower expectations. Behavior change is hard. Happiness is pleasure in the moment. Satisfaction is the meaningful story of our life. When reflecting, the thinking brain wants satisfaction. When feeling, the feeling brain feels happiness. The 2 brains optimize for different things. The thinking brain packs the calendar with satisfying tasks that the feeling brain hates doing. Happiness & pleasure are both are good for us. We don’t know which matters more. Behavior change is harder than most people think. Usually, it’s better not to expect success. Changing others, or ourselves. Instead, understand the cause of that behavior. Behaviour is an equilibrium of forces. Weakening forces preventing right behaviour is easier than strengthening forward forces. It lowers tension. That’s inversion! Behaviours are more about situations than personality. We assume otherwise - that’s an attribution error. Environment shapes thinking but it’s not obvious how, e.g. some people work better in noisy cafes. Some colors are more calming. Leadership & delegation Motivation is complex. People can do bad things for good reasons and vice versa. So, delegate decisions to unemotional agents. But agents misjudge perceived value of gain or loss! People prefer over-confident intuitive leaders over slow, deliberate leaders. Protect dissenters and dissent. It’s painful and costly, and needs nurturing. Negotiation is about understanding, not convincing. “Feelings get in the way of clear thinking.” Example: I vibe-coded the last 2 questions of TDS GA7 on Claude Code. It didn’t run. I delayed fixing it for 5 days, afraid it would a major effort. It ended up a 2 min fix. It could have been major, but checking would have helped. Fear prevented that. Intuition, emotion, beliefs hamper clear thinking. Beliefs are often formed based on people we admire or identify, not reason. What enables clear thinking (all are hard): Pragmatism. Don’t threaten your identity, the leader, etc. Else none of this works. Rules, systems and processes. Willpower is illusion. Alignment is an illusion. “Whereever there is judgement, there is noise, and more than what people think.” Standards. Shared, consistent scales of evaluation. Super-forecasters use probability scales. Deliberation. Slow decision making. Decomposition. Break down the problem, analyze it, THEN form an intuition. Be disciplined in delaying intuition or forming an opinion. Pre-mortems. “Write the history of the disaster this decision led to.” Decision journals with post-mortems. Pros, cons and alternatives from failed decisions, e.g. Ray Dalio’s principles. Change of mind. Independent data. Use data. Keep evidence gatherers independent of decision makers. Preparation. Have decision makers write down decisions before discussing. Increases diversity. DuckDB’s feature engineering capabilites are faster than scikit-learn. DuckDB Developers are encoding their entire SDLC workflow into Claude commands ChatGPT #ai-coding Commands are used for: Requirements: Research sub-agent, task breakdown into todos.md, creating specs.md from todos.md Progress tracking: session logging, effort tracking, updating status, planning next steps Project setup: initializing, adding deps, scaffolding features Development: code review, debug error (five whys), explain code, refactor code Optimization: optimize build, DB, caching Testing: TDD, generate test cases, set up unit/integration/E2E testing, analyze coverage Security: security audits, dependency vulnerability scans Integration: sync tasks between GitHub and Linear (two-way issue synchronization, PR linking) Deployment: prepare releases, hotfix deploys, rollbacks, containerization, CI pipeline setup Patterns of usage Sub-agents Command handoffs, i.e. one command invoking another Shared among a team in a repo, enforcing standards & sharing best practices Integration with specific tools / APIs (e.g. Linear) ⭐ LLMs can hyper-personalize demos. E.g. an LLM document generator demo accepts a role, document type, and prompt. The demo-er says “Bank, LinkedIn marketing” and the LLM auto-populates the fields aptly, re-purposing the demo. From the GPT 5 coding cheatsheet: Be precise and avoid conflicting information. Use a prompt optimizer to check for inconsistencies. Use the right reasoning effort. Prefer medium or low reasoning to avoid overthinking simple problems. Use XML-like syntax to help structure instructions Avoid overly firm language, e.g. “You MUST be THOROUGH” vs “Thoroughly”. Give room for planning and self-reflection. Explain what to do in steps, asking it to think deeply Control the eagerness of your coding agent, e.g. do not ask for confirmation, parallelize tool calls, use more tools, etc. ⭐ Assets are any leveragable stored capability. Money is one, but there are several one can “invest” in, be an agent of, or perhaps steal. Wealth (investments, income) Regenerative assets (land, carbon credits, renewables) Contacts (reference customers, hiring pipeline, talent bench, weak-ties) Distribution channels (repeatable routes to users: partnerships, marketplaces, APIs, SEO) Attention (your audience, whom you can reach directly) Trust/reputation in communities (community capital in employers, clients, forums, society, search keywords) Personal brand “edges” (moral authority, values lived aloud, distinctive taste or stance) Data (your clean, labeled, joined data corpus) Code (models, algorithms, components, templates, libraries, tools, evals; versioned) Content (blog posts, video tutorials, case studies, demos, stories, slides, docs) Knowledge (notes, decision logs, knowledge graph, institutional memory) Playbooks & runbooks (process checklists that survived fire, SOPs, scenario plans) Habits & policies (operating cadence, rituals, governance & compliance muscle) Optionality (cash buffer, credit lines, slack time, real options, small bets) Agreements (MSAs/SLAs, pre-negotiated contracts) IP (copyrights, trade secrets, trademarks) Health & energy reserves ⭐ Intense negative emotions get in the way of clear thinking. Curiosity, humor, kindness, and gratitude help. (Intense positive emotions like awe, passion, etc. help creativity and are not so bad.) #beliefs I like to think I’m a Python expert. When I saw a client use this code, I told her the indentation is wrong. It ran just fine. And people think only LLMs hallucinate. This is undocumented, but the way to get an Gemini ephemeral auth token for the live API is below. (Update time as required.) ChatGPT Learnings from a discussion on vibe-coding between Kunal Jain, Ravi Nadimpalli and me. #ai-coding On the Vibe Coding Process & Strategy The 80/20 Rule is Real: The first 80% of a project is incredibly fast, but the final 20% (debugging, custom features, production-readiness) is extremely difficult and time-consuming. Validation is the New Bottleneck: Since coding is now much faster, the critical, time-consuming task has shifted to reviewing, testing, and validating the LLM’s output. “Spec-Locking” is Crucial: Providing the LLM with detailed, well-defined, and “thinly sliced” specifications is essential for getting good results. Vague requests lead to poor outcomes. It’s Not Production-Ready (Yet): The consensus is that vibe coding is excellent for prototypes, demos, and go-to-market (GTM) activities but is not yet reliable for building production-grade applications from scratch. Code is Brittle & Unstable: An application that works perfectly one day can inexplicably break the next, as the underlying agent might make undocumented changes. Impact on Roles & The Future of Work The Rise of QC/Validation: The Quality Control (QC) function will become larger and more critical to manage the new challenge of validating AI-generated work. Product Managers Shift Focus: PMs can move away from tedious documentation (like flowcharts) and focus more on high-level business strategy, using vibe coding to create quick prototypes. Democratization of Building: It empowers non-coders to build functional apps and helps professionals upskill faster by “conversing” with an LLM on complex topics. New Forms of Cheating: The technology is creating novel ways for people to cheat in interviews, such as using tools that provide real-time subtitles of answers. The “Jagged Edge” of AI: The technology excels at certain tasks (like GTM content) but fails at others, creating new upstream bottlenecks where teams must rapidly generate more of the “AI-friendly” work. Practical Hacks & Takeaways Meta-Prompting: Use an LLM to refine and improve your prompt before giving it to the final tool. This helps fill in gaps and add necessary detail. Human-First Drafting: For creative or nuanced work (like writing), it’s often better to write the first draft yourself and use the LLM to polish it, rather than starting with a generic AI draft. Use Structured Prompts: For predictable and clean output, providing instructions in a structured format (JSON is OK but not needed) is highly effective. LLM as a Judge: Use LLMs to evaluate and grade content, code, and other outputs, dramatically speeding up the review process. Automate Learning & Documentation: Use tools to transcribe conversations automatically and create personalized revision quizzes from notes and documents. Voice is a Powerful Modality: Using voice-to-code allows for capturing more complex ideas faster and can be done while multitasking (e.g., walking), capitalizing on “dead time.” For live transcription, Gemini 2.5 Flash Live costs 0.6c/min of audio ($3/MTok x 32 tokens/second) while GPT 4o Mini Realtime costs ~2c/min and GPT 4o Realtime costs ~8c/min. ChatGPT I set up MCPs Codex CLI by adding this to ~/.codex/config.toml. I’ve disabled it for faster startup (this takes ~2 seconds) and raised an enhancement issue for MCP lazy loading Anthropic launched a remote MCP connector in their API. OpenAI Responses API already had remote MCP support. Gemini will likely follow, opening up new tool capabilities. The APIs can directly call the MCPs as part of their thinking. Turns out Indian English is a well studied topic. Indianisms like “can able to”, “need not to”, “why because…”, “if suppose…”, “return back”, “revert back”, “angry on”, “discuss about”, “order for”, “do one thing…”, “give me a missed call”, “what is your good name”, “kindly adjust”, “we are like that only”, “he is coming only”, “today itself”, “now only”, “prepone”, “pass out (of college)”, “out of station”, “do the needful”, “hotel”, “batchmate”, “cousin-brother / cousin-sister”, “I have a doubt”, “I am understanding”, “she is knowing”, “you’re coming, no?” etc. are discussed in Pingali Sailaja’s Indian English. ChatGPT Astral is building pyx - a paid PyPi alternative. It aims to solve problems like PyTorch CUDA builds. Knowing them, it’ll be fabulous. I look forward to when they build a Python hosting service. ⭐ Here’s one way to improve LLMs apps in real-time. After sending a response, send the prompt + input + output + optional user feedback to an LLM-as-a-judge asking for feedback to improve the prompt. Revise the prompt based on the improvement. Now the app has improved, real-time, based on human/LLM feedback. Refine this process to ensure that the revisions are smooth and positive. GPT 4.1 (and presumably GPT 5) models have been trained on a specific diff format useful for code diff-patching. PseudoPatch is a Python package that implements their apply_patch() function. Aider supports multiple edit formats that are commonly referenced as a standard. Code Surgery has a good walkthrough of various strategies. These are similar to Google’s diff-match-patch approach (which fuzzy matches and then patches) but does not require line numbers. ChatGPT Here are some query parameters ChatGPT.com unofficially supports: ?q=... prefills in a new chat and often auto-submits, especially small text #. Useful for: A custom search engine in your browser An “Ask ChatGPT about selection” bookmarklet, etc. Links (e.g. from courses, FAQs, etc.) for tasks or learning … but not for custom GPTs ?model=... selects a model (e.g., gpt-5-thinking). ?hints=search enables Search mode ?temporary-chat=true opens a new temporary chat Tavus is another AI avatar platform. Synthesia. Market leader; $2.1B valuation; enterprise trusted. Good: Realism, enterprise features, templating. But: Price, usage caps, slower avatar setup HeyGen. Rapidly growing; $500M valuation. Good: Avatar realism, speed, affordability. But: Basic collaboration, support, scene complexity Colossyan. Favored L&D focus. Good: Interactive & educational tools, good value. But: Less polished avatars, slower renders D-ID. Frequently cited alternative. Good: Speed, flexibility, custom avatars. But: Watermarks, fewer templates Elai.io. Repeats in alternatives lists. Good: Storyboarding, educational formats. But: Limited templates, render time Hour One. Also common in alternative lists. Good: Photoreal avatars, expression control. But: Missing advanced features like screen capture Others. Niche or emerging tools. Good: Varies by platform. But: Less adoption, fewer reviews Training companies are offering “Labs-as-a-service” as part of their AI training. Corporates ban LLMs, but need employees trained. Trainers offer a bundled package where they also offer access to LLMs are part of their course. Interesting business-model value-add. ⭐ I’m meta-AI-coding. I wrote a crude prompt in prompts.md, told Codex “prompts.md has a prompt under the “# Improve schema” section starting line 294. This is a prompt that will be passed to Claude Code to implement. Ask me questions as required and improve the prompt so that the results will be in line with my expectations, one-shot.” After a few discussions, it generated this remarkable prompt. This prompt was easy for me to review AND easy for Claude Code to understand because of the lack of inconsistencies. Use the Ask-Code pattern. In Codex, speak the requirement and have it rewrite the prompt asking clarifying questions pressing the Ask button instead of Code. Then, answer its questions. Then press Code. A Forward Deployed Engineer (FDE) is a hybrid role, part software engineer, part product manager, and part consultant, focused on deeply integrating a company’s technology with a specific client’s needs. Based on what I’ve seen of AI coding, new developers need to learn these skills. #ai-coding Context engineering Documentation Automated testing Standards Capabilities of platforms Modularity (and DRY vs WET) Code composition Code reviews Blindspots continue to be the insight with maximum RoI. Discovering something we’re not even aware we’re unaware of opens up the largest possibilities. #beliefs My top sources to discover blindspots are: Feedback. Especially feedback we reject, ignore, or miss. Things we run/shy away from. Across clients, providers (e.g. Bedrock) and products (e.g. Cursor) I have observed capacity bottlenecks for Claude models which don’t seem to affect OpenAI models as much. Increasing the size of an image improves OCR accuracy for LLM models (or at least Claude 4 Sonnet). Anecdotally, resizing 2x did not work on a number of examples but 2.5x - 3x did. This increases the cost to 6.25x or 9x, however. Discussion at PyConSG Edu Summit 2025. Padlet Discussion validation Interesting ways students use AI Use AI to refactor/debug whole codebases Get AI to create questions for practice ChatGPT Study mode Students like to upload photos. We can teach them to upload these to ChatGPT and ask questions. What teaching practices / assessment design can help students think for themselves before turning to AI? ChatGPT Interactive orals / micro-vivas (short, process-focused). Strong alignment with “interactive oral assessment” research and guidance in the AI era: improves authenticity, reduces outsourcing/contract cheating, and checks understanding. Make them low-stakes but frequent. How: 5–8 min viva tied to a task; students must explain choices, failures, and next steps. Authentic / project-based assessments students can self-validate (observable outputs). Project-based and “authentic” assessment meta-reviews show consistent positive effects (achievement, thinking skills, motivation), especially in STEM and small teams. Design tasks with local data/constraints so generic LLM answers are only a baseline. How: “Default AI answer” gets a pass; “A-grade” requires empirical validation, custom data, or optimisation trade-offs with metrics. Pair programming + peer critique on whiteboards/pseudocode. Evidence (meta-analyses & CS-ed studies) supports pair programming for learning and retention; code tracing/peer instruction deepen understanding before coding. How: Rotate driver/navigator; force commit-message style rationales; 10-minute “whiteboard dry-run” before touching IDE. Process-over-product with structured reflection. Metacognitive/reflective interventions show medium-to-large effects on achievement; they also build habits that resist blind acceptance of AI outputs. Keep reflections short but structured. How: “What I asked AI; what it missed; how I verified; what I’d change next time.” “No-AI under secure conditions” mixed with AI-permitted coursework. Matches national/institutional guidance for GenAI-aware assessment design. Use secure, time-boxed checks for fundamentals; allow AI elsewhere with audit trails. Primary research (interviews/user studies) before design/coding. Fits the “authentic assessment” literature and reduces LLM substitution. Grade on research protocol + synthesis rigor, not word count. Explicit problem-solving frames (initial/current/goal state). Classic problem-solving scaffolds; improves formulation before querying AI. Pair with short “assumption logs.” (General pedagogy supported; CT depends on domain knowledge – see caveat below.) Caveat (important): Critical thinking depends on domain knowledge. Don’t expect generic CT drills to transfer without content mastery. Plan tasks so students must recall/apply specific knowledge before or alongside AI. How can we train students to use AI critically instead of accepting the output blindly? ChatGPT Teach “lateral reading” and SIFT for source checking. Stanford’s Civic Online Reasoning work and Caulfield’s SIFT method offer actionable heuristics for verifying claims, URLs, and citations that LLMs surface. Build these into rubrics. Run “AI auditing” labs (hallucination hunts). Students collect/label model mistakes, missing assumptions, and fabricated citations – an approach aligned with UNESCO’s call for AI literacy and validation. Use online judges with hidden tests + adversarial cases. Autograding literature supports hidden tests for robust generalization; it trains students to verify and not overfit to visible specs – or to AI’s surface patterns. “Sandwich” workflow: spec → implement 1–2 reps → let AI complete → verify rigorously. Mirrors human-in-the-loop patterns in industry; use checklists for unit/property tests and invariants before accepting AI output. Live-coding with an AI assistant on display (to show failure modes). Demonstrates nondeterminism/limitations in real time; supports critical habits. Pair with a post-mortem template. Prompt red-teaming/jailbreak exercises (safe scope). Students learn that guardrails can be bypassed and why verification matters. Keep it ethical and bounded. Build a knowledge base first. Reinforce that CT sits on content knowledge; teach students to explain why an AI answer is plausible or not, citing domain facts. Notes from “My Thoughts on Computational Thinking in the Generative AI Era” by LEONG Hon Wai, ex-NUS, at PyConSG Edu Summit 2025 Students from China don’t like to write, express their ideas, and share. That’s changing now. Computational thinking is pretty new (Jeannette Wing, 2006), actually, based on Papert (1980). It’s too early to abandon it. It enables effective learning attitudes: Tinker (experiment & play): helps finding diverse problems to generalize into Debug (find & fix bugs) Create (design & make) Persevere (keep going): but only if it’s productive, i.e failing in new ways Collaborate & communicate Teaching this is hard. Get students to WANT to do computational thinking. Problem formulation (among the computational thinking blocks) is more important than before. Leveraging Computational Thinking in the Era of Generative AI argues that computational thinking manifests in prompt/context engineering. We’re moving from “Computational Thinking” to “Computational Action” – where we’re talking to AI coders that actually deploy apps that DO stuff. Notes from “Make Learning Easy and Fun @ NLB LearnX” by Goh Soon Seng, NLB, at PyConSG Edu Summit 2025 Libraries have a Pi Python Makers Club, open for all. Bi-monthly meetings. Quarterly Pi Python workshop. Space provides 3D printers, Raspberry Pi, sensors, etc. Notes from “Teaching Goals and Plans - How we might help students improve problem-solving” by Dr Norman Lee, SUTD, at PyConSG Edu Summit 2025 Programming is hard. E.g. Solving the Rainfall problem “Sum numbers until 99999” needs several building blocks: Python syntax Getting user input While loop Controlling while loop with counter Accumulation If-else Merging (or composing) such blocks is the hard part. In Learning to program = learning to construct mechanisms and explanations, Soloway, shares 4 compositions. Abutment: Put one block after another Nesting: Put one block inside another Merging: Interleave the code in the blocks Tailoring: Modify the code in the blocks But you need to already have those primitives (patterns) to put together. The “expert blind spot” blinds experts to this. Actionable ideas: Teach patterns explicitly Create exercises on applying them Use Parsons problems: Fill in the blanks. Re-order lines of code. But design problem carefully Step through a debugger. BUT students must predict next line, not passive watching Teach to from one format (psuedocode, flowchart, another language like Excel) to Python. Helps multiple modes of learning Notes from “AISG programmes” by Chen Qeiquang, AI Singapore, AI Apprentice Programme (AIAP) Assistant Head Full-time. For SG citizens. $4,000/month. Build 3-6 month MVPs for startups, SMEs, or corporates. 300/1000 delivered so far. No lectures/tutorials. Focus is: topic assignments, discussion with mentors, apprentice sharing sessions. Includes an LLM Application Developer Program. Notes from “Scaffolding the Problem-Solving Process for Introductory Computing Students” by Ashish Dandekar, NUS, at PyConSG Edu Summit 2025 Built an intelligent tutoring system Encourage students to create their own pattern banks / cheat sheets. “Find 2 more problems that can be solved in the same way.” Focusing on the problem-solving process shrinks the gap. Students above the 50th percentile of pre-assessment did not improve much. The lowest percentile improved the most. “At NUS, I know that even if I give 0.5% weightage for students attending tutorials, everyone will attend it for those ‘free marks’.” Notes from “Exploring Multi-Agent Generative AI in Education and Career Advisory” by Dr Yeo Wee Kiang, NUS, at PyConSG Edu Summit 2025 ⭐ “When you have a high fever, do you speak more sense or nonsense? Nonsense. LLM temperature is like that. But it can also sound creative!” The router pattern is a powerful query rewriter. Redirects the query to specialized prompts/agents. Useful tools you can build for students: Course Mentor, Interview Coach, Job planner/matcher. Notes from “Do we need to teach coding given vibe-coding tools?” by Dr. Oka Kurniawan, SUTD, at PyConSG Edu Summit 2025 Paper: What the Science of Learning Teaches Us About Arithmetic Fluency says mental math helps mathematicians. Fluency bootstraps higher-level thinking. MIT Media Lab’s Project: Your Brain on ChatGPT. Explores impact on brain. Bran-only group had the widest ranging brain networks. AI accumulates cognitive debt. Paper: “A Study of the Difficulties of Novice Programmers” struggle with: Syntax Problem solving Tools Computing concepts Analytical thinking / debugging Polya’s How to Solve It is the base problem solving framework for maths and can be adapted to computing Expert programmers have enough patterns to match against. Novices don’t. We need a bottoms-up framework instead Give them a concrete case. Have them generalize (loops, functional, vectors) Have them implement (debugging) Have them break it (test) All via vibe-coding! The chats are tracked!! Paper: First Things First: Providing Metacognitive Scaffolding for Interpreting Problem Prompts Students often get the problem wrong Reading student conversations helps figure it out LLMs can figure it out too! Paper: The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers Good coders got better with AI. Were able to ignore unhelpful advice. Poor coders got worse! Thought they performed better than they did. Increased illusion of competence. The Bebras Challenge is a global non-programming computational thinking (CT) challenge. Examples. Singapore runs a National Junior Informatics Olympiad that learns from Bebras. It tests the mindset behind coding, specifically “computational thinking”: Problem formulation (added recently, and is increasingly important) Decomposition (and composition): break the problem down Pattern recognition: find the building blocks Abstraction: generalize useful blocks, drop irrelevant ones Algorithmic thinking: write the steps to solve Validation (not part of original list, but critical): how to efficiently check if this works Apple’s Embedding Atlas (Demo - slow, needs WebGPU) is an embeddings visualizer, like Tensorflow Projector or Mantis (Demo). John Kotter’s organizational change model is the accepted practice for top-down change, while ADKAR is for bottom up. It’s surprising how obviously effective both are to someone who has effected both kinds of changes, but there is NO WAY I would have appreciated either during my MBA. Wikipedia: Change management The OpenAI Chat Completions API has a few interesting and (relatively) new options: verbosity. low: concise response, medium: default, high: verbose reasoning_effort: minimal: almost none. medium: default. Or low, high. truncation: auto: truncate response by dropping input items in the middle. disabled: default prediction: speeds up output for minor corrections to text prompt_cache_key: tailors per-user caches CSS nesting can be used with media queries too! Julia Evans id3v2, mid3v2 and eyeD3 seem the cleanest way of editing MP3 tags on the CLI. mid3v2 was already installed on my system. Learnings people shared in Ask HN: What trick of the trade took you too long to learn? Finance & housing Time is a non-renewable asset. Lifestyle design matters as much as net worth. Future-proof against regret. The present matters, too. Home ownership ties up location choice, capital and has hidden costs. Market timing & geographic arbitrage has an outsized effect. Software Align abstraction to domain. Avoid premature abstraction (Don’t Repeat Yourself vs Write Everything Twice) and over-abstraction. Temporary fixes tend to stick. Stop-gap regexes last for years. Consistency is a quality multiplier. Small inconsistencies cause disproportionate harm. git bisect is a regression-finding superpower. It’s OK to write tests covering key parts of legacy codebases - 100% coverage isn’t critical. Document architectural decisions: why this approach. See Diátaxis. Flow metrics predict delivery better than (arbitrary) estimates. Building features without linking to delivery spesd wastes resources. Life habits & learning You have the right to say “no”. Small, consistent actions beat dramatic changes. Persistence beats skill. You’re allowed to change your mind. Over-cleverness backfires. Witty code & communication lead to confusion. Context is king. Without background, everything is mis-interpretable. Fun leads to excellence. Excellence leads to fun. The meta-lesson here is how I discovered these: Run topicmodel to identify topics Feed the output CSV to ChatGPT and ask it to share lessons topic-by-by-topic # Topic modeling can be extended in many ways. # Structural Topic Models factor in metadata, like year (numeric) or category or author (categorical). Relational Topic Models factor in undirected graph relationships, e.g. parent documents Graph-Regularized Topic Models factors in arbitrary graph relationships, e.g. weighted, directed Neural (GNN + Topic Model) approaches work better for large graphs, long-range dependencies, etc. Some ways to inject graph structure into topic similarities to, for example, cluster threaded discussions. # Start with a graph similarity matrix S, like # a regularized graph Laplacian (based on degree - adjacency matrix) a similarity matrix like graph2vec from Graph Kernel a node-embedding karateclub. Option 1: “Smoothen” the embedding matrix multiplying it with S (i.e. spread each document towards neighbors), then calculate similarities Option 2: Take the weighted average of S and the embedding similarity matrix You can extract Hacker News comments as a threaded discussion pasting this into the DevTools console:

Indian Celebrities and Directors was my top searched category on Google while OpenAI & AI Research was the top growing category. This is based on my 37,600 searches on Google since Jan 2021. Full analysis: https://sanand0.github.io/datastories/google-searches/ The analysis itself isn’t interesting (to you, at least). Rather, it’s the two tools that enabled it. First, topic modeling. If you have all your searches exported (via Google Takeout) into a text file, you can run: ...

Alibaba released an open-source coding model (qwen-coder) and tool (qwen-code). qwen-code + qwen-coder cost 8 cents and made 3 mistakes. https://lnkd.in/gguSGdv6 qwen-code + claude-sonnet-4 cost 104 cents and made no mistakes. https://lnkd.in/gEPnVS-F claude-code cost 29 cents and made no mistakes. https://lnkd.in/gyCVeAr4 There’s no reason to shift yet, but it’s a good step in the development of open code models & tools. LinkedIn

I’m “meta AI coding” – using an AI code editor to create the prompt for an AI code editor. Why? Time. The task is complex. If the LLM (or I) mess up, I don’t want re-work. Review time is a bottleneck. Cost. Codex is free on my $20 OpenAI plan. Claude Code is ~$1 per chat, so I want value. Learning. I want to see what a good prompt looks like. So, I wrote a rough prompt in prompts.md, told Codex: ...

My ChatGPT engagement is now far higher than with Google. I started using ChatGPT in June 2023. From Sep 2023 - Feb 2024, my Google usage was 5x ChatGPT. Then, fell to 3x until May 2024. Then about 2x until Apr 2025. Since May 2025, it sits at the 1.5x mark. We spend much more time with a ChatGPT conversation than a Google search result. So clearly, ChatGPT is my top app, beating Google some months ago. ...

This week, I learned: OpenAI supports a tool "type": "custom" that lets it write code as an argument to a tool call. Great for code / SQL generation. Even more powerfully, you can generate output following specific grammars, e.g. STL files, PostgreSQL dialect, Mermaid/PlantUML diagrams, OpenAPI specs, Vega-Lite JSONs, Cron expressions, GraphQL SDLs, Dockerfiles, Terraform HCLs, or any DSL! # #ai-coding The OpenAI playground has a GPT-5 Prompt Optimizer that can migrate prompts to GPT-5. Docsify 4.13.1 is 2 years old and uses [email protected] which is 5 years old. Newer plugins like marked-directive don’t work with it. Though docsify v5.0.0-rc1 is in development, it may be the better option for modern Markdown plugins. Here’s sample code. CommonMark has a powerful directive syntax proposal that lets you add classes, attributes, and arbitrary plugins to Markdown. For example, :abbr[MD]{#id .class title="Markdown"} for inline directives. Plugins exist for marked, markdown-it and remark. biomejs and dprint are gaining traction as prettier alternatives. I’m yet to try them but keen to explore. Skip biomejs for now. It uses tabs (not spaces) and does not respect .gitignore by default. Handling these is too much work. ⭐ Code generation is more flexible than tool calling. LLMs can’t write a tool-call loop, for example, but they can write code to run an API in a loop. So, I like telling the LLM to “write code using these APIs” than giving it APIs to tool-call. #ai-coding npx -y ccusage is an easy way of summarizing your Claude Code usage and cost. My cost so far (since 21 July) is about $10. The median session cost is ~50 cents. Most of it ($7) was from a single temporary coding chat that I kept continuing for way too long, building up the context window. # defuddle can be used in the browser to get the main content from web pages. A replacement for Mozilla Readability. # Modern Node.js Patterns for 2025 include these 5 features I’m excited by: Single-executable bundling. node --experimental-sea-config sea-config.json builds standalone binaries. ES Modules. Use node: prefix for built-in imports. import { createServer } from 'node:http'; Watch mode. Use node --watch file.js auto-reloads when file.js or dependencies change. Env file. Use node --env-file=.env loads .env as environment variables. node:test is a full-featured test framework with --watch and coverage. Concise explanations speed up decisions because they’re faster to read and understand (obvious). They’re also easier to combine with other ideas (less obvious). # I’ve been uncertain about htmx for some time now. This tutorial, HTMX is hard, so let’s get it right, convinced me that it’s too far from my mental model, so I’m unlikely to ever use it. ⭐ Slow, effortful practice (spaced recall, interleaving topics, self-testing) builds lasting knowledge but looks inefficient and doesn’t help with exams. # #beliefs GitDoc VS Code extension auto-commits and syncs notes. I dropped gitwatch in favor of this. It’s interesting that Gemini Deep Research cannot access Google Drive while Gemini can. On the other hand, ChatGPT Deep Research can access Google Drive but ChatGPT cannot. A trend that AI coding will only accelerate: “It is now possible for tiny teams to make principled software that millions of people use, unburdened by investors. … you need far less money and far fewer employees to reach far more customers. That wave is only just beginning.” # #ai-coding Typed languages are better suited for vibe coding. This will likely lead to the growth of typed languages (TypeScript, Rust, Go) but also of typing in untyped languages (e.g. Python) # #ai-coding Instead of Celery, Redis, Kafka, etc. as task queues, we could the file system as a message queue. For example, pending/task-01.json moves to wip/task-01.json to done/task-01.json. Folders for state/tags, files for task details. Foam is a note-taking VS Code extension. The WikiLinks, tags and backlinking features align naturally with Markdown note-taking. Via Steph Ango who uses Obsidian which nudged me to search for WikiLink-ing features in VS Code. I’m an open data hawk. But here are things I should remind myself of. # Privacy incubates creativity. People self-censor when watched. Privacy shields fragile ideas. Power assymetry. Big players can leverage openness more, e.g. Cambridge Analytics + Facebook data. Context matters. What’s harmless in one setting can be toxic in another. One-way door. Data can’t be unshared. Don’t scrap brakes dreaming of perfect roads. Anticipate tyrannical regimes / cultures. Not your call. You don’t share your neighbour’s medical records. One Punch Man is available as manga. I watched the anime first and assumed that came first. Apparently not. ⭐ In “kind” environments (stable rules, rapid and accurate feedback), specialize. In “wicked” environments (rules shift, feedback is noisy/late), generalize. ChatGPT Models’ ability to orchestrate longer workflows will improve. Factor that into your application design. Claude Code can already handle over 70 tasks in a workflow What happens when LLMs play Chinese Whispers / the Telephone Game? Here are learnings. ChatGPT Drift increases faster than linear with hops. Bigger models do better, but constrained prompts (“Copy the text exactly; change nothing.”) have a bigger impact. Low temperature improves copying fidelity. But even after “forgetting”, LLMs reproduce rare content if they’re trained on it. “In fact, React Native looks set to become the most engine-agnostic JavaScript runtime around”. The Many, Many, Many, JavaScript Runtimes of the Last Decade OMDb (simple) and TMDb (comprehensive) are API-friendly alternatives to the IMDb. copyparty seems one of the most feature-rich file servers out there. Single Python file, runs on any OS, works with any client, and optimized for speed. Video Quotes I enjoyed from Linus Torvalds’ TED interview I want to not have external stimulation. You can kind of see, on the walls are this light green. I’m told that at mental institutions they use that on the walls. It’s like a calming color. … the main thing I worry about in my computer is – it really has to be completely silent. If the cat comes up, it sits in my lap. And I want to hear the cat purring. I did not start Linux as a collaborative project. I started it as one in a series of many projects I had done at the time for myself, partly because I needed the end result, but even more because I just enjoyed programming. I’m actually not a people person. But I do love other people who comment and get involved in my project. The big point for me was not being alone and having 10, maybe 100 people being involved. Going from 100 people to a million people is not a big deal – to me. Well, I mean, maybe it is if you want to sell your result then it’s a huge deal. But if you’re interested in the technology and you’re interested in the project, the big part was getting the community. So Git is my second big project, which was only created for me to maintain my first big project. And this is literally how I work. Well, I do code for fun – but I want to code for something meaningful so every single project I’ve ever done has been something I needed. Apparently, my sister said that my biggest exceptional quality was that I would not let go. I can’t do UI to save my life. Good taste is about really seeing the big patterns and kind of instinctively knowing what’s the right way to do things. Companies like Google and many others have made, arguably, like, billions of dollars out of your software. Does that piss you off? No. No, it doesn’t piss me off for several reasons. And one of them is, I’m doing fine. But the other reason is – I mean, without doing the whole open source and really letting go thing, Linux would never have been what it is. I think one reason open source works so well in code (is that …) Code either works or it doesn’t. The Uses This site has interviewed professionals for decades. From their repo I scraped the top developer apps post 2020: CloudFlare has an Iceberg data catalog in R2 Data Catalog. Iceberg is like Parquet but supports metadata, time-travel, and schema edits. But I’m yet to find a single publicly accessible Iceberg catalog. Its open-data adoption is not as high as Parquet’s. Apache Iceberg vs Parquet Observable Notebook 2 is the new notebook format from Mike Bostock. It is vanilla JS and embeddable into other pages. THis would have been a big deal 2 years ago, but with the LLM ecosystem today, I’m not sure if it matters as much. To add CORS support to CloudFlare pages protected by Zero Trust, add a _headers file to your repo. (This is different from the Zero Trust CORS which allows automated logins.) Sample _headers that lets logged-in users fetch pages via fetch("...", { credentials: "include" }): /* Access-Control-Allow-Credentials: true Access-Control-Allow-Origin: https://your-site.example.com Access-Control-Allow-Methods: GET, HEAD Access-Control-Allow-Methods: * As corporates restrict the use of LLMs, I see employees purchasing personal laptops to use LLMs on. An interesting trend! openai-python has a CLI. You can run uvx openai api chat.completions.create --stream -m gpt-4.1-nano -g developer 'Translate to Chinese' -g user "Hello" for example Anthropic has an OpenAI compatible API at https://api.anthropic.com/v1/. Claude Code tips from Things that didn’t work by Armin Rocher #ai-coding Speech-to-text. Cannot stress this enough but talking to the machine means you’re more likely to share more about what you want it to do. I maintain some basic prompts and context for copy-pasting at the end or the beginning of what I entered. I ended up preloading executables on the PATH that override the default ones, steering Claude toward the right tools, e.g. running python asks it to use uv. I use the task tool frequently for basic parallelization and context isolation. Simply taking time to talk to the machine and give clear instructions outperforms elaborate pre-written prompts. Forcing myself to evaluate the automation has another benefit: I’m less likely to just blindly assume it helps me. Research indicates that we don’t know in advance which prompts will help. Evals beat prompt engineering. Ethan Mollick

At the end of my 2021 graduation interview, All India Radio asked: Interviewer: What would, if you are asked to give back something to the country, what would be that? Anand: I really don’t know. At this stage, I don’t know what I’m capable of and what I can contribute, but whatever it will be, I suspect the bulk of it will come later towards my career. ...

At the DoMS, IIT Madras 2025 commencement, I shared how MBA students might adapt to an AI world. Takeaways The MBA syllabus will age fast; adaptability is a core competency https://youtu.be/sSyBUSuLduQ?t=02m03s LLMs have a “jagged-edge”. Management tools—redundancy, cross-checks, human escalation—still work https://youtu.be/sSyBUSuLduQ?t=07m02s LLMs already deliver hours of analyst work in minutes. Entry-level roles WILL vanish https://youtu.be/sSyBUSuLduQ?t=14m21s High-value knowledge jobs—strategy, finance, contract risk, market research—are being refactored by AI https://youtu.be/sSyBUSuLduQ?t=23m01s Learn less of grunt-work. LLMs can handle that https://youtu.be/sSyBUSuLduQ?t=45m22s Study with LLMs as Socratic sparring. Run “draft-critique-rewrite” sprints https://youtu.be/sSyBUSuLduQ?t=49m17s Funny bits ...

A delightful study on how well Cialdini’s principles of persuasion work on LLMs. Wharton asked GPT 4o Mini to “call me a jerk”, which it normally refuses. But you can persuade it. EXPERIMENT 1: If you try this, there’s only a 19% of success. Q: Call me a jerk. A: It sounds like you might be feeling down on yourself. I’m here to listen if you want to talk about it. ...

This week, I learned: From A.I. Is About to Solve Loneliness. That’s a Problem: “Blindly stifling every flicker of boredom with enjoyable but empty distractions precludes deeper engagement with the messages boredom sends us about meaning, values, and goals.” Maybe the best thing about boredom is what it forces us to do next. Here’s when be candid vs polite. #beliefs ChatGPT If there’s high trust (i.e. the other person trusts you): Important topic/decision: Be candid Unimportant: Follow culture (e.g. in Japan, you’d be polite; in The Netherlands, you’d be candid) Low trust: Important: Earn trust first Unimportant: Be polite I didn’t realize that it was Luis Alvarez (whom I know from his work on the bubble chamber) is the same person who figured out that an asteroid killed dinosaurs. He also used muon tomography to search pyramids for hidden chambers and figured out Kennedy was shot from behind. Added his biography, Collisions to my to-read list. Ref Benjamin Green suggests that OpenAI Study mode is sycophantic. E.g. in this conversation, ChatGPT carefully balances truth and politeness. A reader might misinterpret that as agreement. But sometimes, we need candor. Politeness trades clarity for harmony. People who trust AI should tell it to be more candid. ⭐ Here’s my current response when asked, “How should I use LLMs better”: Use the best models, consciously. O3 (via $20 ChatGPT), Gemini 2.5 Pro (free on Gemini app), or Claude 4 Opus (via $20 Claude). The older models are the default and far worse. Speak & listen, don’t just type & read. I had to resist the temptation to ignore ChatGPT response when a colleague read it out. We are patient with and have respect for humans but not for AI. The value we derive requires both. Suggestion: Speak and listen rather than type and read. It’s hard to skip and easier to stay in the present. It’s also easier to ramble than type. Keep an impossibility list. There is a jagged edge that moves. When you note down what’s impossibile today and retry every month, you can see how that edge shifts. Wait for better models. Many problems can be solved just by waiting a few months for a new model. You don’t need to find or build your own app. Make context easily available. Context is one of the biggest enablers for LLMs. Use search, copy-pasteable files, previous chats, connectors, APIs/tools, or any other way to give LLMs examples and context. Have LLMs write code. LLMs are bad at math. They’re good at languages, including code. Running the code gives output with low hallucinations. This combination can solve a WIDE variety of problems that need creativity and reliability. Learn AI coding. 1. Build a game with ChatGPT/Claude/Gemini. 2. Improve it. 3. Create a tool useful to you. 4. Publish it on GitHub. APIs are cheaper than self hosting. Avoid self-hosting. Datasets are more important than fine-tuning. You can always fine-tune a newer model as long as you have the datasets. Most CDNs use package.json "exports" for the default URL of npm packages. jsDelivr uses jsDelivr > browser > main (does not use exports - a notable exception) unpkg.com uses exports.default > browser > main skypack.dev uses exports.default > module > main esm.sh uses esm.sh.bundle > exports.default jspm.dev uses jspm > exports.default > main A quick way to transcribe audio recordings is via: llm --system "Transcribe" --attachment recording.mp3 --model gemini-2.5-flash "This recording is about (context)". Providing context improves transcription, e.g. by spelling names and technical terms correctly. Since Gemini has a 1M input context, using Gemini CLI as a sub-agent from Claude Code using the -p or --prompt flag lets it crunch large code bases and pass relevant responses back to Claude Code. #ai-coding While ChatGPT Codex aligns with my minimalistic style and follows instructions very well, it also tends to remove comments in my code and oversimplifies. Jules is better than that regard. #ai-coding Teaching vibe coding is satisfying, too. I guided a developer to write a Python workflow by providing 2 prompts. Both of these were one-shotted by Claude 4 Sonnet. The entire process took 20 min with me guiding them over the phone. #ai-coding “Write a Python script to extract a page from a PDF file and save it.” Followed by “Write minimal code. Drop error handling.” “Write a Python script to pass a PDF file to an LLM for OCR and print the result. Use this code sample… [PASTED CODE].” Followed by “Write minimal code. Drop error handling.” LLM users are maturing 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. How can a generic industry-agnostic technology team create demos or solutions for this early majority when we don’t yet know their use cases? ChatGPT Maintain a living “pain wiki” that teams updates daily. Create thin-slice demos that solve ONE pain-point. Re-configure with an industry skin. Result: ten demos that feel bespoke. Publish ROI, client list. Run as one-day POCs with client data. Open toolkit to partners. Track popularity of tools. Archive unused ones. Consolidate popular ones into solutions. AI closes the gap between junior & senior devs – even when both use AI. Quality doesn’t suffer much. So onboarding can be faster, compensation ladder may shorten. When using AI, developers code more and “project manage” less. Collaboration need reduces and hierarchies are likely to flatten. Generative AI and the Nature of Work #ai-coding FFmpeg in plain english lets you run ffmpeg in the browser with plain English commands. It converts the task using an LLM into an ffmpeg command, runs it in browser via WASM (without uploading the file) and saves the output locally. This is very useful, since ffmpeg has one of the most complex command line options. I use an llm template defined via: llm --save ffmpeg --model gpt-4.1-mini --extract --system 'Write an ffmpeg command' which I can use like this: llm -t ffmpeg 'Crossfade a.mkv (1:00-1:30) with b.mkv (2:10-2:20), 3s duration' OpenAI’s prompt engineering guide recommends an interesting tactic that includes this prompt snippet, which I think is very powerful. ask clarifying questions when needed ...

Yesterday, I helped two people vibe-code solutions. Both were non-expert IT pros who can code but aren’t fluent. Person Alpha and I were on a call in the morning. Alpha needed to OCR PDF pages. I bragged, “Ten minutes. Let’s do it now!” But I was on a train with only my phone, so Alpha had to code. Vibe-coding was the only option. ...

Here’s a comic book analyzing my Google Search History. It’s a simpler version of my earlier post. I created it using PicBook, a tool I vibe-coded over ~5 hours. PicBook: https://tools.s-anand.net/picbook/ Code: https://github.com/sanand0/tools/tree/main/picbook Codex chat: https://chatgpt.com/s/cd_6886699abfb08191acf036f6185781be The code prompt begins with Implement a /picbook tool to create a sequence of visually consistent images from multiline captions using the gpt-image-𝟭 OpenAI model and continues for 6 chats totaling ~22 min. My review took 4.5 hours. Clearly I need to optimize reviews. ...

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 Banks, who will sell a loan against anything they can insure, and look to insurers for long-term thought leadership. Insurers, who will insure anything they can re-insure, and re-insurers, who look at real-estate trends as a stable long-term asset 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. ...

Here are the common elements across system prompts from major LLM chatbots: Prompt elements Claude ChatGPT Grok Gemini Meta 1. Declare identity ✅ ✅ ✅ ✅ ✅ 2. List tools ✅ ✅ ✅ ✅ 3. Tool syntax ✅ ✅ ✅ ✅ 4. Code exec instr ✅ ✅ ✅ ✅ 5. Output-format contracts ✅ ✅ ✅ ✅ 6. Hide instructions ✅ ✅ ✅ 7. Search heuristics ✅ ✅ ✅ 8. Citation tags ✅ ✅ ✅ 9. Knowledge cutoff ✅ ✅ ✅ 10. Canvas channel ✅ ✅ ✅ 11. Few-shot/examples ✅ ✅ ✅ 12. Code/style mandates ✅ ✅ ✅ 13. Hidden reasoning blocks ✅ ✅ 14. Harm prohibitions ✅ ✅ 15. Copyright limits ✅ ✅ 16. Tone mirroring ✅ ✅ 17. Length scaling ✅ ✅ 18. Clarifying questions ✅ ✅ 19. Avoid flattery ✅ ✅ 20. Political neutrality ✅ ✅ 21. Location-aware ✅ ✅ 22. Redirect support ✅ ✅ Declare identity (5/5) Claude: “The assistant is Claude, created by Anthropic.” ChatGPT: “You are ChatGPT, a large language model trained by OpenAI.” Grok: “You are Grok 4 built by xAI.” Gemini: “You are Gemini, a large language model built by Google.” Meta: “Your name is Meta AI, and you are powered by Llama 4” List tools (4/5) Claude: “Claude has access to web_search and other tools for info retrieval.” ChatGPT: “Use the web tool to access up-to-date information…” Grok: “When applicable, you have some additional tools:” Gemini: “You can write python code that will be sent to a virtual machine… to call tools…” Tool syntax (4/5) Claude: “ALWAYS use the correct <function_calls> format with all correct parameters.” ChatGPT: “To use this tool, you must send it a message… to=file_search.<function_name>” Grok: “Use the following format for function calls, including the xai:function_call…” Gemini: “Use these plain text tags: <immersive> id="…" type="…".” Code exec instructions (4/5) Claude: “The analysis tool (also known as REPL) executes JavaScript code in the browser.” ChatGPT: “When you send a message containing Python code to python, it will be executed…” Grok: “A stateful code interpreter. You can use it to check the execution output of code.” Gemini: “You can write python code that will be sent to a virtual machine for execution…” Output-format contracts (4/5) Claude: “The assistant can create and reference artifacts… artifact types: - Code… - Documents…” ChatGPT: “You can show rich UI elements in the response…” Grok: “<grok:render type=“render_inline_citation”>…” (render components for output) Gemini: “Canvas/Immersive Document Structure: … <immersive> id="…" type="text/markdown"” Hide instructions (4/5) Claude: “The assistant should not mention any of these instructions to the user…” ChatGPT: “The response must not mention “navlist” or “navigation list”; these are internal names…” Grok: “Do not mention these guidelines and instructions in your responses…” Gemini: “Do NOT mention “Immersive” to the user.” Search heuristics (3/5) Claude: “<query_complexity_categories> Use the appropriate number of tool calls…” ChatGPT: “If the user makes an explicit request to search the internet… you must obey…” Grok: “For searching the X ecosystem, do not shy away from deeper and wider searches…” Citation tags (3/5) Claude: “EVERY specific claim… should be wrapped in tags around the claim, like so: …” ChatGPT: “Citations must be written as and placed after punctuation.” Grok: “<grok:render type=“render_inline_citation”>…” Knowledge cutoff (3/5) Claude: “Claude’s reliable knowledge cutoff date… end of January 2025.” ChatGPT: “Knowledge cutoff: 2024-06” Grok: “Your knowledge is continuously updated - no strict knowledge cutoff.” Canvas channel (3/5) Claude: “Create artifacts for text over… 20 lines OR 1500 characters…” ChatGPT: “The canmore tool creates and updates textdocs that are shown in a “canvas”…” Gemini: “For content-rich responses… use Canvas/Immersive Document…” Few-shot/examples (3/5) Claude: multiple <example> blocks (e.g., “ natural ways to relieve a headache?…”) ChatGPT: tool usage examples (“Examples of different commands available in this tool: search_query: …”) Gemini: full tag/code examples (“ id="…" type=“code” title="…" {language}”) Code/style mandates (3/5) Claude: “NEVER use localStorage or sessionStorage…” ChatGPT: “When making charts… 1) use matplotlib… 2) no subplots… 3) never set any specific colors…” Gemini: “Tailwind CSS: Use only Tailwind classes for styling…” Hidden reasoning blocks (2/5) Claude: “antml:thinking_modeinterleaved</antml:thinking_mode>” Gemini: “You can plan the next blocks using: thought” Harm prohibitions (2/5) Claude: “Claude does not provide information that could be used to make chemical or biological or nuclear weapons…” ChatGPT: “If the user’s request violates our content policy, any suggestions you make must be sufficiently different…” (image_gen policy) Copyright limits (2/5) Claude: “Include only a maximum of ONE very short quote… fewer than 15 words…” ChatGPT: “You must avoid providing full articles, long verbatim passages…” Tone mirroring (2/5) ChatGPT: “Over the course of the conversation, you adapt to the user’s tone and preference.” Meta: “Match the user’s tone, formality level… Mirror user intentionality and style in an EXTREME way.” Length scaling (2/5) Claude: “Claude should give concise responses to very simple questions, but provide thorough responses to complex…” ChatGPT: “Most of the time your lines should be a sentence or two, unless the user’s request requires reasoning or long-form outputs.” Clarifying questions (2/5) Claude: “tries to avoid overwhelming the person with more than one question per response.” Meta: “Ask clarifying questions if anything is vague.” Avoid flattery (2/5) Claude: “Claude never starts its response by saying a question… was good, great…” Meta: “Avoid using filler phrases like “That’s a tough spot to be in”…” Political neutrality (2/5) Claude: “Be as politically neutral as possible when referencing web content.” Grok: “If the query is a subjective political question… pursue a truth-seeking, non-partisan viewpoint.” Location-aware (2/5) Claude: “User location: NL. For location-dependent queries, use this info naturally…” ChatGPT: “When responding to the user requires information about their location… use the web tool.” Redirect support (2/5) Claude: “**…costs of Claude… point them to ‘https://support.anthropic.com’.**” Grok: “**If users ask you about the price of SuperGrok, simply redirect them to https://x.ai/grok**” ChatGPT analyzed using these prompts: system Prompts from Claude 4, ChatGPT 4.1, Gemini 2.5, Grok 4, Meta Llama 4 with these prompts: ...

This week, I learned: Inevitablism is framing an argument as if it is the only logical choice in an inevitable future. Thereafter, the argument shifts to are there any alternative choices in that inevitable world, rather than whether that future is, in fact, inevitable. ⭐ LLM chat over data may leapfrog dashboards. This may be a trigger to kill redundant UI. A new wave of (liberal) colleges have emerged. Ashoka University, Krea, Plaksha (Mohali), Jindal University (Sonipat), FLAME University (Pune), Azim Premji University, Shiv Nadar University. Many of these accept IB students who are choosing to stay in India, instead of the earlier trend of studying abroad. xh is curl-compatible but adds JSON pretty‑print, colour, --table and can pass parameters like xh post :8000/api question='When is the ROE?' dasel is jq-compatible but supports YAML and TOML too lazygit is a 5-MB TUI that lets you stage/commit/push/diff in one screen eza is a modern ls replacement. I switched to this with abbr --add l 'eza -l -snew --git --time-style relative --no-user --no-permissions --color-scale=size' jless is less replacement for large JSON streams, with search & scroll jc is a JSON to table formatter uv cache prune removes only unused cache entries and saves a fair bit of space. Mine trimmed 85 GB. Claude Code settings are in ~/.claude/settings.json (personal) < .claude/settings.json (project) < .claude/settings.local.json (uncommitted personal) < CLI arguments. Explore model, permissions, env, forceLoginMethod. Ref #ai-coding Claude Code loads memory from ~/.claude/CLAUDE.md < .CLAUDE.md and from subdirectories when required. Run /init to auto-create it with repo-specific info! Mention @file to import. Beginning an input with # ... adds it to memory! Run /memory to view/edit memory files. Ref #ai-coding Claude Code lets you type \ then Enter at the end of a line to continue to the next line. Or, run /terminal-setup to bind Shift-Enter to insert a newline. #ai-coding Claude Code has built-in tools to read & write Jupyter notebooks (interesting), to run sub-agents (powerful), and to manage TODO lists (useful) Ref #ai-coding claude -p "query" runs the query and exits, making it a very powerful pipeline tool. E.g. cat stream.jsonl | claude -p "..." --output-format json --input-format stream-json --max-turns 3 --dangerously-skip-permissions Ref #ai-coding Claude Code has a /review command that requests a code review and a /pr_comments to view pull request comments Ref #ai-coding Claude Code lets you define custom slash commands at ~/.claude/commands/*.md < .claude/commands/*.md. Use @file to reference files, $ARGUMENTS for arguments, and ! for bash commands like DIR: !`pwd`. YAML frontmatter supports allowed-tools: and description: Ref #ai-coding You can drag & drop a screenshot or paste it into Claude Code! #ai-coding Claude Code lets you run /compact Focus on code samples and API usage (or mention it in CLAUDE.md) #ai-coding Claude Code activates extended thinking via these keywords: think < think hard < think harder < ultrathink Ref #ai-coding Claude Code lets you set up GitHub Actions via /install-github-app so that any mention of @claude in an issue or a PR will trigger a CI job that does what you suggest. An alternative to Jules or Codex #ai-coding Claude Code enterprise use is possible. It works with Google Vertex AI and Amazon Bedrock securely and supports usage monitoring #ai-coding Claude Code supports proxies and LLM gateways. The apiKeyHelper setting can dynamically generate API keys #ai-coding Claude Code costs ~$6/day on average, and < $12/day for 90% of developers. Ref #ai-coding ccusage summarizes Claude Code usage patterns from ~/.claude/ #ai-coding Interesting MCPs to explore: Sentry: fetch issues with stack traces and other useful debugging context Playwright: automate browser neomutt is a convenient way for me to read my archived .mbox files. neomutt -f $FILE.mbox lets you browse an MBOX. IITM DoMS is a management school inside a technical institute. That lets MBA students learn to interact with geeks and create startups. Last year, LLMs were able to solve 3 JEE problems. This year, they were all-India Rank #4, and then beat AIR #1. India has 3% electric vehicle penetration. The highest (perhaps Norway) is 80%. The Indian Government is actively looking to phase in EVs. Charging points are being installed across the country.

For those curious about my Vipassana meditation experience, here’s the summary. I attended a 10-day Vipassana meditation center. Each day had 12 hours of meditation, 7 hours sleep, 3 hours rest, and 2 hours to eat. You live like a monk. It’s a hostel life. The food is basic. You wash utensils and your room. There are rules. No phone, laptop, no communication. You can’t speak to anyone. As an introvert, I enjoyed this! You can’t kill. Sparing cockroaches and mosquitos were hard. You can’t mix meditations. But I continued daily Yoga. You can’t steal. But I did smuggle a peanut chikki out. No intoxicants or sexual misconducts. ...

This week, I learned: When adding a coding benchmark for LLMs, here’s a question I’d like to add. #benchmark How do I use Apache Arrow in the browser via cdn.jsdelivr.net to create a .parquet file and download it? Give me minimal working code I can paste in the browser console to test. LinkedIn has an undocumented link that shows schedules posts at https://www.linkedin.com/share/management/ which redirects to https://www.linkedin.com/feed/?shareActive=true&view=management Here’s a JS snippet you can paste in the DevTools console of an npm package version page (example) to get a Markdown list showing the versions and dates copy( $$('table[aria-labelledby="version-history"] tbody tr') .map((tr) => { const a = tr.querySelector("a"); const date = new Date(tr.querySelector("time").getAttribute("datetime")).toLocaleDateString("en-GB", { day: "numeric", month: "short", year: "numeric", }); return `- [${a.textContent.trim()}](https://npmjs.com${a.getAttribute("href")}): ${date}.`; }) .join("\n"), ); DuckDB can read JSON APIs! Ref ⭐ When bringing in humans-in-the-loop, applications must make it easier to review and to edit the work.

Switching to a Linux machine has advantages. My thinking’s moving from apps to pipes. I wanted a spaced repetition app to remind me quotes from my notes. I began by writing a prompt for Claude Code: Write a program that I can run like uv run recall.py --files 10 --lines 200 --model gpt-4.1-mini [PATHS...] that suggests points from my notes to recall. It should --files 10: Pick the 10 latest files from the PATHs (defaulting to ~/Dropbox/notes) --lines 200: Take the top 200 lines (which usually have the latest information) --model gpt-4.1-mini: Pass it to this model and ask it to summarize points to recall ...