This week, I learned:

  • It doesn’t always take time to learn or convey things. (Early trust can be built instantly, e.g. vulnerability.) At first, experts don’t know how to make skills explicit. But trainer effort could compress 10X via evals, practice loops, and feedback. Learner elapsed time would compress less.
  • Everyone has something worth discovering, but not every conversation is worth my time right now. So, meet new people with trust, attention, and good questions. Continue if there’s emotional / intellectual stimulation (surprising, interesting, moving, connecting, energizing, challenging), else exit warmly with respect.
  • To avoid getting overwhelmed in ultra-interesting conversations, mental closure helps. During the conversation, pause, name, reflect, and close. “Wait, you’re saying X. I should do Y. I’ll reflect/act tonight.” or “Wow, let’s sit with that for 5 seconds. You mean X. I feel Y. I’ll drop.” After the conversation, summarize: “What struck me were X1, X2. I’ll plan Y1, Y2 and drop Z1, Z2.” Then take a short break.
  • Setting "markdown.editor.updateLinksOnPaste.enabled": false might fix the delay / freezing (infinite spinner) issue when pasting Markdown in VS Code.
  • The bottleneck to quality of AI output has shifted from model quality to harness quality (and this is not obvious to many people). It is important, therefore, to optimize harness usage rather than prompts usage, i.e. harness engineering over context engineering.
  • I use ug --smart-case --bool -Q --sort=rtime to interactively search for text in files. It’s like VS Code search-across-files. Here are the shortcuts I find useful:
    • Alt-g: Glob (filter files to search in)
    • Alt-[ or ]: Decrease or increase context (lines before / after)
    • Alt-w: Word match toggle
    • Alt-c: Count lines toggle
    • Alt-u: Ungroup - show lines once even if multiple matches
  • Using AI for health seems to have reached a tipping point. Three people have pitched an idea in this space to me in the last three days. One is a managed personal health provider who wants to tie-up with hospitals to gather data to improve AI health advice. Second is an enterpreneur who wants to enable the Indian Govt to use AI to improve public health - given the low proportion of trained doctors in public hospitals. The third is a colleague who is uploading personal health reports, fitness data, DNA data, wearable data, etc. and suggest daily habits such as fitness, nutrition, sleep, medication, etc. to optimize health.
  • Changing the topic (e.g. asking a question) instead of answering a question is powerful. It lets you decline requests, avoid sensitive topics, ignore boring ones, learn rather than teach, and bring in your agenda - all at one shot. I need to un-practice my 40-year habit of answering questions. (This is selfish. I forgive myself.)
  • bolt.diy seems like a browser-embeddable coding agent. That is, you can add bolt.diy to your web page and have it build apps. That might be a pretty powerful upgrade to generative UI - where pages build themselves based on the user input.
  • Codex has a few new features in the last few months.
    • Codex can generate images and have voice conversations.
    • /goal sets an overall session goal to avoid getting side-tracked.
    • /side is like Claude Code’s /btw - for a side task while the main task continues.
    • /resume lets you switch to any previous session.
    • /keymap debug lets you edit the keymap and inspect what keystrokes the terminal sends.
    • @ lets you mention files, directories, skills, and plugins.
    • Ctrl+R works, lets you pick a previous prompt.
    • Ctrl+O copies the last answer as Markdown.
    • Hooks are stable. PreToolUse lets you log every tool, SessionStart lets you inject repo-specific rules.
    • MCPs with readOnlyHint can run in parallel.
    • codex doctor diagnoses environment issues.
    • codex remote-control lets you remotely control Codex, making it a server.
    • Codex Python SDK is better and you can have Codex run as a back-end more smoothly.
  • To change others’ behavior, embody (not preach) it visibly and consistently, make it easy to copy, and ask without forcing. It takes time, though. ChatGPT
  • Governance is how groups keep promises when things (people, incentives, environment, pressure) change. A simple way to explain what governance is to someone who doesn’t understand why governance matters, and guide on when it does not matter.
  • Forward Deployed Engineers are the next evolution of data scientists, IMHO. AI can do data science. Data scientists will likely act as the “Human As An Interface” (HaaI) to business, proactively identifying and solving problems - a space business analysts traditionally occupied. Of course, business analysts will likely do the same without needing data scientists to help. But since AI replaces data scientists more than business analysis, I expect that the % of data scientists who become FDEs will be higher than business analysts.
  • The value of data exported from software is high. For example, your email, social posts, CRM / HRMS / ERP dumps, service tickets, purchases, notes etc. These let you create a personal / organizational digital brain. Hence proprietary solutions will make exports harder and open solutions will emerge.
  • To live-preview any publicly accessible Excel file, you can embed or link to https://view.officeapps.live.com/op/embed.aspx?src=YOUR-URL
  • The Codex app can now use the browser much better and faster since last week if you enable “Dev mode” OpenAI. THis uses CDP - which is more efficient than screenshots - and is something Codex CLI has been doing for many months.
  • In Codex, Claude Code, etc. you can submit a prompt while the agent is working to steer it, i.e. after it completes a turn (e.g. a tool call) it will factor in the prompt. You can also queue it. Neither of these is available on ChatGPT or Claude.ai, though it’s such an important feature. On ChatGPT, submitting another prompt stops the previous run and the agent continues with the new prompt.
  • By default, git uses ~/.config/git/ignore or %USERPROFILE%\git\ignore as the global .gitignore. You can override that with git config --global core.excludesFile PATH. StackOverflow

Questions I was asked

Week ending 21 Jun 2026

  • Question: Can we create a Claude Skill that runs after every workflow?
    Answer: Yes. But skills affect many chats, so treat them like production instructions: review every word, test them, and keep a copy-paste prompt-library fallback.
  • Question: If the direct reconciliation savings are small, will a client still spend on the solution?
    Answer: Maybe. The value may be safety, dispute prevention, digitization, and an automated finance process before the client sees an error.
  • Question: What do we do when hidden operational reasons block an AI-generated use case?
    Answer: Don’t fight the rock. If one stakeholder can’t act, go to another; feed the constraint back to AI and ask for actions this person can take.
  • Question: Can AI build the data science model end to end once we identify the problem and data?
    Answer: Yes. Not as well as the best data scientist every time, but as well as many good data scientists; the scarce skill is using and validating it well.
  • Question: How should we validate AI analysis so small errors don’t destroy trust?
    Answer: Validate like a data science manager, not an engineer. Treat the agent as a smart analyst: probe assumptions, ask it to find errors, and be accountable.
  • Question: Can we build AI use cases before we get real client data?
    Answer: Yes. Use public context plus synthetic data to reach proposal stage, then show the client the kind of action we could take once real data lands.
  • Question: If ChatGPT gives different answers in different chats, should we rely on it?
    Answer: Don’t rely blindly. Treat another chat as a second opinion, upload the same evidence, ask both to cross-check, and make disagreement part of verification.
  • Question: What is harness engineering?
    Answer: The layer above models that orchestrates skills, context, tools, hooks, permissions, and teams. ChatGPT, Claude Code, and Antigravity are harnesses, not just model interfaces.
  • Question: At what levels should we think about AI harness architecture?
    Answer: Three levels: model choices, harness orchestration, and agent teams. Model is parameters/tools; harness is reusable control; team is planners, builders, QA, deployers, and sub-agents.
  • Question: When should something become a reusable skill?
    Answer: When the capability has high reuse across prompts. Data analysis or writing style belongs in a skill; one-off turbine-efficiency logic probably does not.
  • Question: How should teams of agents be designed?
    Answer: Let agents negotiate their own charters. Give the task, ask what role split would work, then form planners, developers, QA, deployers, or sub-agents around the work.
  • Question: What should we do if AI-ready chunks can generate infinite stories?
    Answer: Don’t start by selling the platform. Generate 200 stories, send them to 10 journalists, and become their story pipeline; publishing is the harder bottleneck.
  • Question: What happens to personal knowledge as work moves across locked platforms?
    Answer: Digital-brain software becomes more valuable. Exports will get worse, platform walls will tighten, and your own memory layer becomes a strategic asset.
  • Question: How should valuable public datasets be monetized?
    Answer: Sell the decision or workflow they enable, not the dataset. Hedge funds, journalists, NBFCs, and consultants pay when data becomes a ready answer or recurring output.
  • Question: Are AI-ready chunks themselves the product?
    Answer: They are a powerful intermediate asset, not the end. The value comes when someone downstream can create stories, decisions, diligence, products, or workflows from them.
  • Question: How should we think about monetizing Claude skills or MCP-driven expertise?
    Answer: The mechanism is still open. Skills are reusable intelligence packaged as files, but marketplaces and payment rails are not yet settled.
  • Question: Should AI data products pursue B2C microtransactions?
    Answer: Be careful. B2C is hard without the right market and distribution; casual problems need very low friction or bundling, while urgent problems can command price.
  • Question: Will agents become a marketplace like MCP connectors?
    Answer: Maybe not. Agents may behave more like software or harnesses that users lock into; connectors, skills, and sub-agents may blur underneath.
  • Question: What is the deeper pattern behind managed care using AI and health managers?
    Answer: It converts a transactional service into a relationship service. That pattern repeats in wealth, health, and any domain where context compounds.
  • Question: Should we retrain health models on Indian data just because the current data is Western-heavy?
    Answer: Treat it as a hypothesis. If validation is cheap through hospital partners and resident doctors, test lightly before heavy retraining.
  • Question: What does a forward deployed engineer actually do?
    Answer: An embedded person with client data and AI access finds use cases, solves them, and sends evidence-backed actions. The leap is from proposal to recurring output.
  • Question: Whose agenda does the FDE serve—the account manager or the client?
    Answer: In our working version, it started bottom-up. The embedded person levels up and creates value; the account manager discovers the opportunity after the fact.
  • Question: What profile makes a good FDE?
    Answer: I don’t know reliably. Curiosity and initiative matter more than title; my expected winner was not the person who actually succeeded.
  • Question: Do we need clean client data before showing FDE value?
    Answer: No. Use public data, synthetic data, transcripts, or context to move the bottleneck one step forward. Then ask for real data with proof in hand.
  • Question: How can domain experts and AI/data teams work together?
    Answer: Pair data/tech people with subject experts. The subject expert owns the problem, the central team accelerates execution, and over time the expert becomes hands-on.
  • Question: How do we map the human-AI workforce by solution category?
    Answer: Start with task categories and AI exposure. Use benchmarks plus your own transcripts, emails, and chats to estimate which work is AI-prone, AI-safe, and skill-constrained.
  • Question: Where does AI’s economic value show up after automating one step?
    Answer: At the shifted bottleneck. If AI speeds first-round hiring, the hiring-manager interview becomes the constraint; measure value at the new constraint.
  • Question: What is underexplored in enterprise data use cases?
    Answer: Cross-domain joins. HR plus procurement, employee plus vendor, CRM plus finance—connecting domains surfaces risks and opportunities no silo sees.
  • Question: How should agents be brought into meetings?
    Answer: They need not speak. A human can act as interface: transcribe the meeting, feed responses to an agent, analyze live, and bring insight back into the room.
  • Question: Is AI workload optimization a niche?
    Answer: Not for long. Wherever a harness can verify outputs automatically—code, math, robotics, simulations—AI companies will accelerate and the telemetry/optimization layer becomes strategic.
  • Question: What should an AI-fluent mentoring organization learn next?
    Answer: Move beyond tool fluency into operating models: coding agents as build partners, AI-native delivery, enterprise deployability, guardrails, token economics, and evals.