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.

Questions I was asked

Week ending 28 Jun 2026

  • Question: Why is building invoice generation the wrong next step after finding reconciliation errors?
    Answer: Fix the wrong number inside the existing workflow. Don’t replace a working accounting system with new software just because you can generate a PDF.
  • Question: In enterprise AI delivery, should the reusable accelerator be the large part and client customization the small part?
    Answer: No. The common layer is usually thin; customization is the larger part because client environments and workflows vary.
  • Question: Why not automatically send every prompt to ChatGPT and Claude in parallel?
    Answer: Don’t remove all friction. Prompting is cheap; reading is the bottleneck. A tiny copy-paste cost stops me from generating outputs I will never review.
  • Question: What are the most common corporate pushbacks to implementing AI?
    Answer: Security, hallucinations, and cost—in that order.
  • Question: Do forward deployed engineers need to be onsite?
    Answer: No. They need access to the client environment and stakeholders; physical location is secondary.
  • Question: Why ask the agent to solve the problem directly instead of first researching existing models?
    Answer: Start direct. If it fails, break it into chunks. It saves time and tests whether the model has become smart enough to handle broader delegation.
  • Question: What best practices should non-coders follow when vibe-coding enterprise products?
    Answer: Don’t optimize the old software workflow. State the business goal, let the agent build whatever is needed, and review the output hard.
  • Question: For a high-stakes EMS, should we deliver software or the decisions it produces?
    Answer: Deliver decisions. Software, agent, and human together are the stack; price the outcome, not the code, tokens, or FTE effort.
  • Question: How can we guarantee decisions if humans cannot be right every 15 minutes?
    Answer: Treat it like a warranty. If the decision is wrong, don’t pay me; if it is right, pay X. Price in the error margin.
  • Question: Should a high-stakes EMS use a deterministic mathematical model with an agentic layer on top?
    Answer: Yes. What you know for sure goes into the program; what you don’t know stays with the agent or human. Benchmark both on the outcome.
  • Question: How do you get yourself out of the loop instead of becoming the bottleneck?
    Answer: Keep an AI bottleneck log. Every time I am stuck, I ask AI how to remove that bottleneck; “interview me” and “assetize this” are surprisingly effective.
  • Question: In AI hiring, who should we hire when specific skills keep getting commoditized?
    Answer: Hire flexibility, not fixed skill. The “best” data scientist, engineer, or product manager can become legacy in months.
  • Question: How do we train sales and delivery leaders to speak credibly about AI solutions?
    Answer: Don’t run classroom AI training. Run live solution labs where leaders use AI on a real workflow, build the first output, and draft what they will take to the client.
  • Question: Can AI help me structure client pitches without depending on internal experts?
    Answer: Yes. Feed it the messy conversation, files, links, and prior context. It won’t be identical to an expert, but it can produce above-average analyst output at scale.
  • Question: What attitude helps people start using AI every day?
    Answer: Don’t take AI too seriously. Its job is to serve you; give rough instructions, ask it to interview you when you’re unclear, and iterate.
  • Question: Is clicking “Ask AI” a good signal that students are struggling?
    Answer: Not by itself. Smart students may click it to save time. Combine it with performance and behavior data before deciding intervention.
  • Question: Do forward deployed engineers just produce “insights on steroids,” or should they deploy AI into workflows?
    Answer: They should move through stages: identify use cases, solve like an analyst, drive action, then embed it into production. Insight is only the first useful step.
  • Question: How do we move from after-the-fact AI analytics to AI that prevents workflow errors?
    Answer: Put the check where the data enters. Let the agent inspect current controls, propose guardrails or code, and turn recurring insights into monitored workflow.
  • Question: Will enterprise AI deployments mostly live inside existing platforms rather than custom infrastructure?
    Answer: Yes. Most deployments will happen where the data and workflow already live. Master the platform harnesses instead of building everything from scratch.
  • Question: What do I do when I don’t even know the problem in a broad domain like rights?
    Answer: That is the problem. Give the context to an agent and ask it to find, rank, validate, and build the easy use cases.
  • Question: If clients can also use AI agents, what value do I add as a media expert?
    Answer: If they could do it, they would have. Your value is harnessing agents with private data, schemas, validation code, skills, and test cases they don’t yet have.
  • Question: Should we position our R&D product-plus-service offering as AI?
    Answer: Maybe don’t. Use AI to serve more clients better and faster, but sell the outcome. The client neither cares nor needs the AI story.
  • Question: What should I not do in GTM while selling AI plus services?
    Answer: Don’t fight with your co-founder. Put someone in the US. Don’t be dogmatic: it is okay to do what the business needs.
  • Question: Is there a case for building small language models for industry-specific process knowledge?
    Answer: Use case, yes. SLM as default solution, no. Put a modern agent on the problem and let it choose tools; SLMs are usually expensive, depreciating, and behind frontier agents.
  • Question: Is adding AI sentiment and renewal probability into CRM enough?
    Answer: It is only half a step. Don’t give reps another signal; use bulk data to create watchlists, proactive calls, and specific actions.
  • Question: Is a quick AI-built sponsorship visualization valuable for a new executive?
    Answer: Useful once, but not enough. Executives need decisions: who pays most, what expires, what action to take, and how much money is at stake.
  • Question: As a GenAI engineer, do I need deep ML knowledge?
    Answer: Not as the main bet. If it is teachable and testable, AI will do it. Learn to define the problem, test the output, and use the model’s expertise.