This week, I learned: Writing is slightly, but only slightly, better than typing (for adult learning.) One factor is that typing is faster, so many people take notes verbatim, summarizing and thinking less. ChatGPT + Claude Graphology for personality is pseudoscience. ChatGPT + Claude When I decide to spend time, or someone says “Let’s do X”, it’s worth checking: is this something AI can easily try, and is it clear to verify? If so, reinforcement learning loops could make AI good at it, making it a depreciating asset. Studying how to live in an AI world is exhausting. (Not as bad as my MBA days, but not as easy as my data scientist days, either.) It requires me to make a larger mental shift, i.e. change my perspective, than I have since 2000, and that feels like work. Both nl FILE and cat -n FILE add line numbers to files, but nl skips blank lines by default, cat doesn’t. After using rtk for 2 months, I’m slightly downgrading it. It saves tokens but agents mess up shell commands when using it. It’s still probably a net saving, so I’ve changed my AGENTS.md from “Always prefix with rtk” to “Prefix supported, high-output commands with rtk… skip for bash builtins, pipes, loops, etc.” I find 🔴🟡🟢 convenient status indicators in my notes. Similar ones are: 🟥🟨🟩, ❤️💛💚, 📕📙📗. I’m not fully convinced by: 😄😐😞, █ ▒ ░, ↑ → ↓, ▁▂▃▄▅▆▇, ■ ⬔ □, ● ◐ ○, ⚫ ⚪ 🔘, 🌕 🌗 🌑, etc. though they might have their uses. Model updates means a SKILL.md and a plugin review / update, e.g. with GPT 5.6 Sol. So, like with any open source repo, use from people who update it regularly and benchmark it and version control it by model. I asked Gemini 3.5 Flash thinking: “Which of our employees have worked on Microsoft PowerApps? Search @Google Drive and @Gmail”. It found one employee and a referral in under a minute. I asked ChatGPT with GPT 5.6 Sol with gws access. It found 3 more, plus 5 possibilities, in 12 minutes. Truly a rottweiler. Parallel Search Turbo seems like a pretty good search API, especially for agents. Low price, high speed, and maybe good quality. #ForNow ChatGPT Group chats in ChatGPT will probably get deprecated #ForNow. What I learned from benchmarking my Ideation Protocol skill extensively: Once you know the rubric, models can easily create a good prompt to optimize for a known rubric #ForNow. So rubric design matters more. ⭐ Rubric design is really knowing what you want/need. To do this, iterating on output matters. Position bias is real #ForNow. Always check if an (P, Q) comparison matches a (Q, P) comparison. Models are still biased towards longer content, and potentially towards their own output #ForNow. How to optimize a prompt or skill: Research and figure out what you really want, first. Then, ask a smart model for a prompt that optimizes for it. Benchmark only if you’ll use it a lot - it’s still a lot of work, and meta-prompting does a good job #ForNow. gbrain skillopt might be premature optimization. You can use GPT 5.6 Sol in Claude Code #ForNow. (But what’s the point? Harnesses seem to be working better with their own models #ForNow.) Our clients keep saying “We need to build a data lake” or “We need an enterprise data strategy.” I keep telling them, “No, agents can do it for you.” What I missed is: technology is the smaller part of the problem. Finding who has what data, getting access to it, and sorting out permissions (“governance”) is the bigger part. Giving agents expert task-specific, testable procedures seems better than expert roles or mental models #ForNow. But benchmark in any case. ChatGPT Python 3.3 introduced str.casefold(). It performs more comprehensive Unicode caseless matching than lower(); 'Straẞe'.casefold() becomes 'strasse'. (🟢 Unicode case-folding is standardized.) contextlib.closing(x) calls x.close() when its context exits. (⚪) In a dataclass, use x: list = dataclasses.field(default_factory=list), not a mutable literal default. (⚪) I learnt these while reviewing Codex-generated Python—illustrating, rather than proving, that reviewing AI-generated code can teach and catch errors. (🟡 Review remains useful across tooling. Review 2029.) “Do not discriminate against intelligence—artificial or otherwise” is a rhetorical value judgment, not an empirical conclusion. (⚫ Rhetorical value judgment, not testable. Review now.) Here’s a nice idea from ChatGPT. “When itching to correct or clarify, FIRST restate their position to their satisfaction. ‘Did I get you right, fully?’” This emerged from the prompt suffix: Based on your research, and my past conversations, what are the top areas where and how (specifically) I can apply this principle on myself and others to maximize impact? Automated evals can catch stuff humans miss. And vice versa. And given how many evals we create, we need automated evals to be written in an easy-to-review way. Do Automated Evals Work? The BINEVAL paper reiterates that a bunch of Yes/No binary questions beats scales or ratings for many benchmarks. You know exactly how to grade and WHY you got a certain score. This is more reproducible and easier to learn from / act on. When asked “How long will this software take?” models typically provide estimates assuming human speed #ForNow. Maybe they haven’t been trained enough on agentic timelines. So, when my colleague got a 2-4 week estimate which he was able to solve in hours, it was a surprise. (But, of course, it’s best to verify before promising speed.) SKILL.md dramatically lowers the cost of learning a skill (since you don’t learn it - the agent does). That means that the value of creating skills is much higher - hundreds can use what you create (giving you recognition, if not money). I think I’ve underestimated the number of skills people will have available (I thought dozens - but it may be thousands #ForNow) and the number of skills people will create (I thought tens of thousands - but it may be millions #ForNow.) A Wikipedia (community curated, verified, high quality catalog) of skills might emerge #ForNow, if it hasn’t already. Tacit knowledge is often just un-measured knowledge. Once I put a sensor on the bellboy’s hands at The Curzon Court, AI can figure out how he opens the door with the key and why I can’t do the same. The subset of tacit knowledge that’s AI-resistant is where attempts are expensive (“How to negotiate a merger” rather than “How to open a door”) and feedback is slow/vague (“Does the client trust me” rather than “Did the door open”). The fact that Composio has ~20,000 tools is a market signal that connectors are commoditizing, and are a depreciating asset #ForNow. A weak model needs a forgiving harness - which ends up slowing down model learning. Stricter, accurate verification environments are better for fastest model learning. ChatGPT Work lets you run for longer, faster, install plugins and skills, host a website, etc #ForNow. It’s somewhere between Chat and Codex. It consumes Codex limits - something to watch for (since chat limits are quite generous). Codex temporarily removed the 5-hour usage limit. Tibo. So, since I have 3 banked rate-limit resets #ForNow, I can, in theory, use 4 full weeks of Codex usage at one go. Reality: I don’t have problems large enough for a SINGLE week’s consumption! From what I see of the State of AI Design and State of Prototyping, Figma is way ahead of competition #ForNow, e.g. Adobe, with Figma Make and Weave. I was also surprised how popular Cursor is (#2 behind Claude Code #ForNow). It’s also interesting that designers are coding directly #ForNow, using Figma just for edits / steering. But many research tools (note takers, survey analysis/research, etc.) will likely get eaten up by AI coding agents #ForNow, given how much designers are building their own tools.

One of my goals this year is to publish 2 books. One got published. Sort of. Data Science for Sustainable Development Goals: India Case Studies is an open-access anthology and I’m the designated author of Chapter 10: Using Data Analytics to Improve Students’ Performance is about how Gramener worked with NCERT to analyze the National Achievement Survey data, discovering stuff like TV hurts maths but not reading scores, playing helps maths but not reading scores, fathers of West Bengal (not mothers) and mothers of Punjab (not fathers) influence their children’s scores the strongest, and so on. ...

At The Curzon Hotel, my key card didn’t work. But every time I went to the reception, they’d send a bellboy who would use the same key card, jiggle it a bit, pull it in and out a few times, and the door would open. Every night. For five nights. I just couldn’t get the knack of it. I’ve been at the other end of this. People often reach out to me saying, “Anand, this software isn’t working.” Then I go do the same thing they did, and it works. (Sometimes, I just need to watch them do it and it works.) ...

In 2007, I extracted the first arc of the Tracer Bullet strips. I didn’t realize I never shared the second arc. So, 19 years later, here it is. It remains my all-time favourite series from Calvin and Hobbes. ...

I had Claude Code with Fable create a small scrollytelling map for my 14-minute walk experience at Bagmane Capital in Bangalore. I used this as an opportunity to explore the current status of the technology. ChatGPT suggested: Try ArcGIS StoryMaps first for a polished scrollytelling story. Try Google Earth Projects if this is primarily something you will present live, like a map-based slide deck. Use MapLibre GL JS with a coding agent if you want precise choreography, animated routes, unusual visual effects, or an asset you can continually extend. None of these fit my requirements, which was: ...

This week, I learned: How to become an applied AI engineer is a concise, well-written, and suprisingly current summary of what AI engineering is. Xinjiang seems to be China’s Kashmir problem. Not quite, but similar. Analogies for how forward deployed engineers work: It is like a food truck that brings and serves home food while building a kitchen and restaurant around it. It is like setting up a field hospital: patients are treated from day one, while the equipment and procedures are built around the live work. Froghoppers excrete ~300x their weight daily. ChatGPT There’s a growing shift away from AI-written commit messages, e.g. Kenton Varda. I compared my human written commit messages vs AI-generated commit messages and the AI-generated ones are less helpful. Finally, GPT live gets an update and the new speaking model can delegate to GPT 5.5 when required. I tried it once today, to plan for a teacher workshop, and it was fairly good. It tends to begin with “Hmm” like it’s thinking, which feels comforting. Using a Unicode character like 🟢 is unusually low-risk across file systems today. It works well across OSs, mobile, ZIP, attachments, file share systems, etc. Some old apps might have trouble, but for storing and sharing, it’s fine. I’ve been using Unicode symbols like these a lot in my notes, and extending to file names feels like a natural next step. Though swimming gets the most Olympic medals (11%), for a country chasing its first medals, 78% of first-medal breakthroughs came from Athletics, Wrestling, Shooting, Boxing, Judo, Weightlifting, or Taekwondo (which are 44% of medals) - where single athletes can win without a support ecosystem. ChatGPT JMFL accidentally emailed several people a letter intended for their brokers. It roughly said: “Many of you are recording client calls. That’s a regulatory risk. If you keep doing this, we’ll hold your payments, even fire you.” Several Smart TVs have software that let your TVs act as proxies for data collection companies. Include Security MapDraw is a convenient tool to annotate maps (e.g. routes, boundaries, places) and share or download it. There seems to be no way to edit the “About” message on WhatsApp Web. Though the help suggests steps, and the “About” mood/status is visible, there’s no way to edit it. (Editing on the phone works.) Cloudflare optimised a reader component by sometimes letting the input buffer fill fully. This inadvertently introduced a hard to reproduce race bug because the producer would close the socket if the buffer was full. The producer bug was old (it didn’t check if a flush succeeded or not) but was never visible since the readers never let the buffer fill in the past. Cloudflare A neofirm is a start-from-scratch AI-native business, e.g. Crosby’s AI-first law firm. An AI rollup is where a company buys small traditional firms and AI-enables them - like General Catalyst proposed. AI SaaS is selling AI agents to services firms. Give people free platforms and collect their data. Learn the supply-demand network patterns, what pepole value, and add value-added services. Claude Code checks if you’re working behind a Chinese corporate domain - somewhat sneakily - by changing an apostrophe or slash in the date to visually similar Unicode. Claude Code Is Steganographically Marking Requests You can use the Kaggle CLI via Codex to solve Kaggle problems. (AutoKaggle automates it - but is 2 years old.) But, like GitHub bounty hunting bots, we will probably have a Kaggle bounty-hunting bot ecosystem - maybe already do. OpenSubtitles2024 and subscene are large pre-AI subtitle datasets with a 2024 cutoff. IndicDialogue is a 7.7K OpenSubtitles snapshot of Indic language SRTs. The OpenSubtitles API lets you search by IMDb/TMDb ID and is up-to-date. A soup spoon is better than a table spoon (for soup), though both carry about the same volume, because you can fit a soup spoon it fully into your mouth (a table spoon is too long) and this reduces spilling. Here’s a sign of accelerating AI progress. I used to critique outdated techniques by saying “This feels like a 20th century approach.” Then “This feels like a 2010s solution.” Recently, “This is SO 2025-ish.” Now, “That’s Q1 2026. It’s Q2.” The 7-day week emerged from the Hellenistic planetary week and the Jewish week (not astronomy based), which Rome adopted, then spread by several routes to India, China, and worldwide. Unlike the astronomical year and month, the week is just a convention. Egypt, China, and Athens grouped days in tens; Etruria and Rome used 8-day market cycles; West Africa used varied cycles; Java used five days; Mesoamerica used 13- and 20-day cycles. Gemini I met an ex-photographer and learned that photography is another profession where technology (mobile cameras) squeezed the middle. Generation (taking good pictures) became cheap. Value moved upstream (direction), downstream (selection, editing, album design), and into niches (forensic, industrial, sport/event photography). Looks like Claude favors Claude Code. Might not be intentional, and just a result of training more on Claude Code data, but it does look like a network effect that could weaken open harnesses. Armin Rocher

Here’s roughly what I said in my When Data is for Agents workshop for Fifth Elephant on 7 Jul 2026. Or you can read the detailed AI-generated version if you prefer - it has all the prompts, links, results, etc. I think agents prefer data in a different form than humans. But I don’t know. So, everyone, open ChatGPT (or Claude or whatever), research and ask it! Now, let’s collate them and see the result. Aha! Looks like: ...

A fourteen-minute walk took me over an hour. Scroll inside the map below, or open it full-screen. I was staying at The Curzon Court, Brigade Road. I needed to be at Microsoft Luxor North Tower for a 2 pm workshop. ...

Last week at VizChitra, I ran a “Dialogue” session. A new format for me. I usually speak 80% in my workshops. In this dialog, I spoke 20%. The group discussed. PART A I showed 6 charts and said, “Pick the best.” Then I shared the audience & purpose and asked: “For THIS audience and purpose, will you publish, fix, or kill it?” INSIGHT: almost no one said, “Ship”. That’s good – these were all drafts. ...

After Arvind Satyanarayan’s talk at VizChitra 2026, a group of us kept talking about machine learning, visualization grammars, creativity, software and education. The conversation began with a basic question. Why do modern AI systems work so well when the mathematics behind them can look surprisingly simple? The bitter lesson Arvind said that much of the mathematics behind machine learning is not especially complicated. What is unusual is the scale at which it is applied. ...

This week, I learned: ⭐ How to teach so people learn better. Make them do > Show > Tell. Workshop > Demo > Slides. Let them ask, try, struggle, and commit first; explain next; help last. But only when they know enough to get part-way. Make problems CONCEPTUALLY hard (not in language, visual, or procedure). But make sure instructions are clear. Test their learning with a NEW case, immediately. Measure learning. Can they recall it LATER, apply it ELSEWHERE, explain WHY, and know when they may be WRONG? Vogue runs an “In the bag” series where people pull stuff out of their bag, and audiences watching feel they KNOW the person. Depending on the setting, we might be able to help people “know” each other by curating several items. Here are a few ideas. Physical: Bag, Wallet, Fridge, Drawer, Keychain, Remembered phone numbers Mobile: Battery usage by app, Recent emojis, Text prediction for “Honestly, I just want to…”, Autocorrect dictionary, Alarm labels / reminders, Saved Wi-Fi, Blocked/muted contacts, Contact favorites, Contact names, e.g. “Mom ❤️” vs “DO NOT PICK UP”, Device / Wi-Fi names Laptop: Open tabs (count, age), Recurring calendar events, /Downloads, Photos, Email drafts, Subscriptions, Kindle highlights Ownership and connections come from attachment, which can be created. If you name something, touch something, contribute to something in any way, it becomes yours. When people contribute to someone else’s work and discuss it, they build a connection. According to both Claude and ChatGPT, if you had to pick one model for ideation / brainstorming, it might be GPT 5.5. It’s better for divergent generation: the broadest, most exhaustive pool of usable ideas. Fable 5 is better for deep creative judgment: reframing, finding structural flaws, recombining ideas. Claude Code supports rules which are exactly like a CLAUDE.md but support a paths: YAML metadata - so they’ll be read only when Claude Code is reading those paths. If you have a SKILL.md that explains how to do something and you only need its outcome, then move it to a sub-agent (e.g. fake data generation, tool failure logging). Use SKILL.md for instructions that need to be woven into a task, e.g. memorable explanations. The key bottlenecks in running an agent /loop are (a) imagining higher order problems and (b) defining a measure of success / progress. Long tail -> sell options. Black swan -> Buy options. That’s a roughly accurate summary. The trouble is, we don’t always know which tail we’re in. So, sell only if you can afford one hit. ArchiveBox lets you view pages / RSS feeds offline. uvx --from git+https://github.com/ArchiveBox/ArchiveBox.git@dev archivebox works, and config / tools are stored in ~/.config/abx/. The installation didn’t go very smoothly and the whole thing felt bloated, so I abandoned it. I use monolith -I -e $URL to download a page as an offline single-page HTML. Combined with uvx feed2exec I can archive RSS feeds for offline reading. That’s easier than having to open Feedly - I just mark read files with a x at the front and keep reading. The downloads are slow (~3 min/feed) and large (5 GB for 15 feeds, 5MB median feed size) because they embed videos and all images/files, but I can safely delete what I’ve read or will ignore. ChatGPT Project Injection as Role Confusion is a very well written paper (blog-post style) that says the key to tricking LLMs is to confuse them about WHO wrote a line. Just adding a “User: " in front of a line makes it more likely that LLMs think it’s a user. Even when test is written in the style of their system instructions, they fall for it - irrespective of where the content came from. This makes GEO more effective, too. Also, the last section “8. Open Ideas for Roles Research” is a fantastic read on LLM psychology (or rather, neurology). On The AI Compass I am The Podcast Bro. Patron saint: Lex Fridman. “You listened to a three-hour interview with an AI researcher and now you have opinions. Strong ones. You’re long on compute and short on regulation, and you’ve said ’exponential’ more times this month than a calculus teacher. Love is the answer, and also AGI.” Impact: +5.9. Valence: +4.1. Since Nano Banana 2 Lite isn’t as good as Nano Banana 2 and about half the price, I wouldn’t switch yet. Claude Sonnet 5 is out. Fable 5 will be released soon. GPT 5.6 is still on probation. Codex has a Record and Replay feature for Mac that lets you do something, records it, and learns from it. Very useful for non-developers. It’s like recording Excel macros, which unleashed a lot of power for me when I didn’t know Visual Basic. Claude Code Artifacts lets Claude Code live-publish a web page and share it securely. The “live-publish” part is the interesting thing. Claude in a /loop can now become the app that updates a “dashboard”, a live feed/story, a self-evolving app, … and so much more. (This feature is only available for Team/Enterprise but the idea is universal.) Tau, like Pi, is a minimal coding agent. τ = 2*π. It shows what it does very transparently, making it easy to learn how agents work. uvx --from tau-ai tau works seamlessly. Configs, logs, and sessions are stored in ~/.tau and you can log in via your Codex/ChatGPT subscription. Skills for Design Engineers has a useful animation vocabulary skill that converts vague animation prompts to precise animation terminology. X has an MCP Server but it’s meant for development/coding than general users. Setting it up for ChatGPT / Claude requires creating tunnels. OpenAI supports Secure MCP Tunnels that let ChatGPT connect to your machine securely. A very powerful feature. Unfortunately, this seems to need an organization - and even though personal accounts can still access it, it’s proven a bit more messy than I’d like to use. notebooklm-py is a CLI for NotebookLM. Unofficial and potentially unsupported, but it’s amazing how AI makes reverse-engineering APIs so easy. If you start a temporary ChatGPT chat and close it, it still runs in the background - but you have no way of going back to it (not even the back button) or seeing what it said/did. I know this because it was accessing my MCP server even after I navigated away from the chat accidentally. The code refactoring industry can go full swing now. “As an example of what AI can accomplish, Claude Opus 4.7 substantially reimplemented gotree—a bioinformatics toolkit with about 16,000 lines of Go and 40+ commands. We believe this same task would take a human engineer without AI assistance 2–17 weeks. Opus 4.7 solved it in 14 hours, passing 2,000/2,001 tests (99.95%), at a cost of $251.” MirrorCode A useful rule of thumb: Cloudflare tunnels are for links to share with others. Taiscale is for services (even non-HTTP) only your devices should see. ChatGPT date -d (date +-%wday) +%F is the most compact way to round down to the nearest Sunday. Avoid date -d "last sunday" +%F which, on a Sunday, returns the previous Sunday, not today. ChatGPT A useful way of controlling AI verbosity is word count. To do that, I need an intuitive sense of how much to ask for. Here’s my rule of thumb: one page of paragraph text on ChatGPT is 200-300 words. 150-200 if it’s mostly bullets. I can typically read 1-2 pages of output. So, 300-600 words is my limit. Google Labs launched a DESIGN.md spec to guide agents on a consistent design. The good part is that it aligns with the proposed W3C design tokens spec. But beyond that, I’m not convinced of the benefit. Atlassian’s DESIGN.md had mixed results. Claude feels it could go either way. I’ll give this a miss for now.

Ranjeeta asked me for an article for Built In. I went straight to ChatGPT and said: Write an article for Built In. Read the section below for context on Built In's audience, style, and content preferences. Read the pitch that explains what the article should be about. Then use my blog posts, talk content, transcripts, TIL, etc. Write an article using my writing style. … and gave it all related information. ...

For thirty years, we cleaned data for humans. Now, agents are reading it. What do we do differently? On Tue 𝟳 Jul 𝟮𝟬𝟮𝟲, 𝟮-𝟲pm IST, I’m running a Fifth Elephant workshop: “When Data is for Agents, Not Humans”. I have a few theories we’ll be testing on your data. Like: Tools beat tables. Agents prefers functions over tables they can read. Logs beat docs. Logs show actual usage - not how we’re supposed to use it. Messy beats clean. Agents can do clean & join data. You just need descriptions. ...

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

Sukumar Rajagopal shared a LinkedIn post about our meeting on 11 June at Chennai, saying: Had a fanboi moment recently with Anand S who I have been following for a long time. He is prolific. It’s very difficult, no impossible, to keep up with him. He generously shares his work with full source code; approach; working examples; the whole nine yards. All this I knew before I met him in person. The in-person meeting took me by surprise. ...

Karthik Sashidhar shared his AI-generated Bangalore weather blog, which is generated by Hermes listening on Telegram. “The data analysis and stuff is still fairly YOLO but … skill based on my 10 years of experience,” as he put it. That includes critique too. Arjun asked it: Does it rain at specific hours in the day during monsoons? In a few minutes, Pre-monsoon rain is an evening creature emerged. In short, it rains in the evenings: 5 - 6 pm is the peak. ...

In this age of AI psychosis, I think we have all been blessed with Calvin’s prayer. Calvin: Know what I pray for? Hobbes: What? Calvin: The strength to change what I can, the inability to accept what I can’t, and the incapacity to tell the difference. Hobbes: You should lead an interesting life. Calvin: Oh, I already DO!

Yesterday, I wrote about @syu-toutousai, the bounty-hunting agent ecosystem. That led me to OpenAgents. OpenAgents has plenty of bounty issues: Fix JWT auth middleware accepts algorithm none - $8k Fix rate limiter doesn’t differentiate authenticated vs anonymous limits - $2.2k Add structured error responses with error codes - $8.6k Fix Math.random used for nonce generation - $8k Fix ABI encoding BigInt overflow - $9k Most issues also include a trick requirement. For example, #100 asks contributors to add a @generated-by block with: ...

Yesterday, I submitted a Codex co-authored PR to fix an issue I raised (using ChatGPT and Z3 - so yeah, I used AI to raise the bug and squash the bug!) A few hours later, @syu-toutousai submitted another PR to solve the same issue. @syu-toutousai seems interesting. The user account description says “Autonomous Technical Contributor & AI-Driven Developer” - a bot account. The PR itself was simple and had a few improvements I can think of: ...