This week, I learned:

  • BitWarden seems to be sneakily jacking up prices and going towards a PE sale. Might be time to shift out or self host. Sigh, I just migrated into it… Source
  • Andrej Karpathy has joined Anthropic. Likely to use Claude to build better Claudes - automating AI research. Also, it probably isn’t a good time to build an AI education platform. Claude
  • The open-source Chinese models about 6 months behind frontier models. Qwen 3.7-Max is on par with Claude 4.5 Opus (Nov 2025) and Gemini 3 Flash (Dec 2025).
  • Google basically became Gemini. Entirely! I’m not sure there’s a difference any more. Which means it will scrape websites and not send traffic through - just killing the search economy. But it’s far more useful. Claude
  • I wanted a list of sites I log into with my Google Account. Google’s Linked apps page does that. Unfortunately, I can’t find a way to use Google Takeout to export that data. So I wrote a scraper which can be single-shot prompted these days.
  • As long as you remember to exhale, your chances of recovery from being ejected into space is pretty good for the first 15-60 seconds. Gemini
  • I don’t understand half the comments I read on LinkedIn. Earlier, I was able to separate good from bad. Now, I’m not sure if what I read is actually insight or idiocy. Is the AI use making their comments too smart or making my brain too dumb?
  • “Pax Memoriae”: peace of memory. Putting past conflicts to rest. The best part of it was, I learnt the phrase by typing “Pax” into VS Code and wasn’t sure what to write next. Before I could search for it, GitHub Copilot completed it. I searched for what it meant, and it was so apt!
  • Children’s vision is worse than adults, but filter less and absorb ore irrelevant information than adults. This is useful for learning and surprise detection, but costly for focus, speed, and relevance. ChatGPT
  • The word phobia comes from the Greek god of fear, Phobos, which is the name of one of Mars’ moon. Deimos, the other moon, is the Greek god of dread/terror. They’re the children of Ares (Mars), the god of war. Nice planet.
  • On WhatsApp, I can type @Meta AI and then /imagine to have it draw an image. The quality is OK - not great, not terrible.
  • Surprising but GPT Realtime Whisper ( new model) isn’t as good as the older open-source Whisper models. Also, Gemini 3 Flash Preview is as good at transcription as Gemini 3.1 Pro Preview for up to medium-length text. LLM Audio Transcription benchmark
  • Google Maps typically shows me a cycling time of 30 minutes when it take me 40 minutes and a walking time of 40 minutes when it take me 30 minutes. Either I walk much faster and cycle much lower than the typical person or Google Maps is not well calibrated to Singapore and India.

Questions I was asked

Week ending 24 May 2026

  • Question: How do you give Claude and ChatGPT the same context?
    Answer: Manually first. Type in one, paste into the other, attach the same files. Crude, but it works.
  • Question: How do you structure prompts well?
    Answer: Don’t over-engineer. Write roughly, reuse, tweak when it fails, and store what you repeat.
  • Question: How do you use AI to improve prompts?
    Answer: Run post-mortems over conversations: what prompt would have improved this? Then simplify because post-mortems overfit and models change, making micro-changes less useful than broad intent.
  • Question: What is MCP?
    Answer: Model Context Protocol exposes tools and programs so agents can use them. Powerful, but not beginner material for a 2-minute explanation.
  • Question: What are skills or SKILL.md?
    Answer: On-demand permanent prompts. Use them for expert workflows where the base model typically fails out-of-box.
  • Question: Once stored, do skills become context?
    Answer: Yes, on demand. The model sees skill names and descriptions, then loads the right skill only when relevant.
  • Question: How do you protect against hallucinations?
    Answer: Make outputs falsifiable. Ask for evidence, quotes, links, checklists, tests, samples and independent challenge; use one model to red-team another.
  • Question: Why do models get worse in long chats?
    Answer: “Context rot” is known behavior. Start a new chat and carry forward a summary, memory or copied context.
  • Question: Why can’t ChatGPT retrieve old answers from memory/search?
    Answer: You’re probably not doing anything wrong. Chat search is weak; don’t treat chat history as a serious knowledge system. Rename chats with keywords and search.
  • Question: Are wrappers above LLMs a superior way to use AI?
    Answer: Yes. They’re called harnesses. ChatGPT and Codex are harnesses on top of the GPT models. Raw models are intelligence; harnesses give them tools, files, memory and workflows.
  • Question: Which engineering domains look most ripe for AI?
    Answer: Manufacturing, electronics and PCB design, 3D printing, CAD, and civil. Anything codified in software with an API or MCP-like control surface becomes automatable.
  • Question: Why is engineering so ripe for AI?
    Answer: The codification of engineering. Once the work already happens inside controllable software, an agent can observe, operate, and iterate.
  • Question: Is a PhD topic safe when AI progress makes work obsolete so fast?
    Answer: The bar has moved. What was a PhD three years ago may now be a freshman-with-AI project; choose bigger, faster, more foundational problems.
  • Question: If AI can code and simulate, what are engineers and designers supposed to do?
    Answer: Teach delegation. Give students problems where AI is unavoidable; they learn what to hand off and where the human jagged edge remains.
  • Question: Is synthetic data versus real data the right framing?
    Answer: No. Treat it as a continuum: start with real data, jitter it using realistic behavioral rules, and use synthetic edge cases to stress-test models.
  • Question: What metric tells us whether an AI answer is good?
    Answer: Three checks: are the inputs enough to answer, does (LLM) rubric think the answer is good, and ultimately, do the end users like it and find it useful?
  • Question: What AI tools should we start using?
    Answer: Just a few ChatGPT Plus ($20) monthly subscriptions are a good start. Don’t buy specialized tools or annual licenses until people prove usage and value.
  • Question: There is a lot of pressure on us to do AI; how should we explore what is possible personally and professionally?
    Answer: Workshop mode, not discussion mode. Get hands-on, do real tasks, and feel what is possible and what is not.
  • Question: If we double-check across five agents, is it one LLM with five methods or different models?
    Answer: Both work. Same model with different prompts is useful; different models add diversity and usually improve consensus.
  • Question: If you fire five models, aren’t you paying five times per query?
    Answer: Technically yes, but cost is collapsing. Use cheaper models for cross-checking; for most business questions, verification is now cheaper than manual rework.
  • Question: How do you keep consistency when model versions change?
    Answer: LLM Ops. Run automated eval suites before any model/version switch; don’t silently promote a model just because it is new.
  • Question: What about factual consistency when an LLM confidently gives a wrong answer?
    Answer: Ask it for links with verbatim citations. You can programmatically check if the link and citation exist, and use another LLM to catch bad reasoning.
  • Question: Should we build the knowledge graph first or start with agents?
    Answer: Parallel. Agents deliver value immediately; knowledge graph and data engineering mature alongside with use.
  • Question: How does a CTO manage a thousand agents?
    Answer: Not as a thousand apps. Build one agent operating system or enterprise agent platform; each agent is a versioned configuration with registry, evals, observability, rollback and ownership. Azure Agent Registry might be an example.
  • Question: Should the deliverable be software or the output of the software?
    Answer: Prefer the output of the software. Software is WIP pipeline we keep improving; the client buys an outcome, not a depreciating asset that “their intern can build with Claude Code.”
  • Question: When the model misunderstands a prompt, how do we improve next time?
    Answer: End with a post-mortem prompt: what did you misunderstand, what context was missing, and how should I prompt next time? Store that learning.
  • Question: How do we decide whether AI-suggested use cases are valuable?
    Answer: Don’t sell use cases. Convert them into concrete actions backed by evidence, then email the business asking whether those actions matter.
  • Question: Should we train custom models for film or design generation?
    Answer: No. Frontier models improve too fast. Start with prompt engineering, reference images, image search, workflow automation, and human review. Train only when there is proprietary data, repeated demand, and a clear advantage - which is almost never.
  • Question: Is hardware or physical engineering more AI-proof?
    Answer: Better protected. AI accelerates design, simulation, planning, and documentation; physical experimentation, procurement, instrumentation, and validation still need humans.
  • Question: How should I personally learn AI?
    Answer: Use AI for everything. Record where it fails. That failure log becomes is where human expertise is needed - and is what we should teach.
  • Question: How can engineering research leverage agentic AI?
    Answer: Use AI to find what to research, do the literature search, do the actual research, write-up the research, verify the research, find publications, revise based on review comments, … Practically every part of the chain.
  • Question: Does AI kill creativity?
    Answer: It redefines creativity. What we thought was creativity may die and these are the lower levels, but new higher levels forms emerge.