Things I Learned - 14 Jun 2026

Sun, 14 Jun 2026 00:00:00 +0000

This week, I learned:

Overheard a journalist saying: “I can tell when humans are lying. There are no tell tale signs of AI lying. At least I don’t have any.”
rdt-cli is a Reddit CLI. It uses a clever trick: it auto-detects installed browsers and extracts cookies (supports Chrome, Firefox, Edge, Brave). So, if you’re logged into Reddit on any browser, uvx --from rdt-cli rdt whoami automatically shows who you are logged in as. (The public-clis repo also lists other useful CLIs like twitter-cli, )
Currently, a $20 Claude Pro gives you ~$400 and a $100 Claude Max gives you ~$2,000 of API usage. For ChatGPT, the numbers are ~$700 and $3,500. SemiAnalysis
When Fable 5 refuses to answer questions, here’s the message that appears: “Fable 5 has safety measures that flag messages on most cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we’re working to refine them. Send feedback or learn more.” I managed to trigger this once while researching an M&A acquisition target. Clicking on “Edit and retry with Fable 5” triggered Opus 5 again, twice.
DNA codons (A, T, C, G) encode proteins in triplets. There are 64 triplets that map to 20 amino acids. Some like Leucine, have 6 codons. Some like Methionine have only one. Why? When creating genes, there’s a wobble, sometimes, at the 3rd codon. THe mapping minimizes that impact: small errors map to similar proteins. The more common proteins have more codons. There’s a lot of fascinating information science going on here. Gemini
ChatGPT now shows a “Check in” button when it’s thinking. Clicking on that gives you a work-in-progress answer while it continues thinking. When done, it replaces the WIP answer with the final answer. A useful feature!

Questions I was asked

Week ending 14 Jun 2026

Question: How should we verify that an AI/download tool captured all files?
Answer: Use another chat as an independent checker and ask “what is missing?” Don’t ask “does it match?” because that invites lazy confirmation.
Question: What prompt helps convert data into a shareable dashboard?
Answer: Ask for a single-page HTML file. It gives you something portable, inspectable, and easy to email or publish.
Question: How should audience feedback be used in AI-generated data stories?
Answer: Dump the feedback back into the model. Since generation and verification are cheap, the scarce skill shifts to collecting, interpreting, and iterating on feedback.
Question: How do we stop AI data-story sessions from becoming repetitive loops?
Answer: Start a new chat, switch model or provider, or meta-prompt: give the failed conversation to AI and ask it how to make the next prompt more novel.
Question: If AI keeps refining answers, do humans stop learning?
Answer: The new skill is steering smarter intelligences. Editors, judges, auditors, teachers, and coaches already guide work they cannot fully reproduce.
Question: When should humans verify AI analysis?
Answer: Treat AI like a fresh journalist. Check heavily at first, stratify by risk, build confidence, delegate some verification, and periodically test for regression.
Question: Is a paid AI subscription worth it for occasional users?
Answer: Buy Plus for one month and use it hard. If it is paisa vasool, continue; if not, cancel and retry in six months because the frontier moves fast.
Question: How do Chinese LLMs compare with frontier models?
Answer: Use them when cost matters at scale. They are good and cheap, but frontier models still lead; default to frontier unless economics force optimization.
Question: What can AI add to geospatial storytelling?
Answer: It can scan satellite grids for change and surface story leads. But indices create false positives, so visual inspection and narrative judgment still matter.
Question: After AI identifies bottlenecks and recommended actions, what is missing before sending it to leadership?
Answer: Add monetizable business benefit, estimated impact, and evidence for that impact. Process benefit is your problem; business benefit is what buys attention.
Question: Which coding agent should we recommend when a fresh client has no preference?
Answer: Default to the client’s cloud provider: Gemini for Google, GitHub Copilot for Microsoft, OpenAI or Anthropic if they already prefer them.
Question: What should students do when ATS filters reject qualified resumes?
Answer: Hack the system and publish the hack. ATS is another machine-mediated system; learn how it fails and use that knowledge.
Question: How should agents organize unstructured folders for repeated future questions?
Answer: Tell the agent many questions are coming and ask it to design the organization: summaries, entities, intents, tags, and evidence. Let the semantic layer emerge from usage.
Question: When does a knowledge graph make sense for enterprise documents?
Answer: When relationships matter: clauses, asset classes, customer segments, obligations, exceptions. The graph captures institutional checklists that humans otherwise carry in their heads.
Question: How should we calm leaders who think data must be fully cleaned before agents can use it?
Answer: Tell them it may not be as hard as they think. Let agents try inside their system; the downside is small and the learning is immediate.
Question: How should an AI/data-viz dialogue be structured so it stays participatory?
Answer: Plan the mechanics, takeaways, and sequence, but order everything by droppability. Use audience volunteers and leave room for improvisation.
Question: How should we choose charts for an AI-vs-human visualization exercise?
Answer: Use embeddings or UMAP to pick a diverse set, then create paired AI-generated alternatives. Don’t pick naïvely; use the corpus to sample the space.
Question: Is the real question whether people can detect AI charts?
Answer: No. The first question is what makes a chart good. The AI reveal is secondary: does knowing the source change how people judge quality?
Question: What is the right unit for comparing AI and human visualization work?
Answer: Data visualization, not chart mechanics. The hard parts are topic selection, insight choice, framing, and presentation, not whether D3 was written by hand.
Question: What is the practical skill people need as AI makes more charts?
Answer: Curation. People need explicit, communicable judgment about what is useful, truthful, beautiful, and worth publishing, whether the maker is human or AI.
Question: How should educators deal with students copy-pasting into ChatGPT?
Answer: Don’t teach what ChatGPT can already do. Teach what it cannot do, then evaluate both foundational understanding and AI-enabled execution.
Question: How should we test whether AI can help with patient-specific implants and CAD?
Answer: Don’t start with the full clinical workflow. Give AI a basic tool task: create a mesh, create a fitting patch, get feedback, then iterate.
Question: How should we use AI for hard research problems like moving-boundary FEM?
Answer: Don’t ask it for the final answer. Ask it to ideate, mock the physics, test multiple approaches, show evidence, and make the researcher smarter.
Question: How should we evaluate students in an AI-enabled course?
Answer: Simulate industry: give 10x workload, allow AI and collaboration, grade outcomes, and remove questions once the batch collectively learns the pattern.
Question: How should an AI services startup think about pricing when software is depreciating?
Answer: Discount the commodity extraction and charge for verification and value-add. Find the money leaks from the first batch, then price against savings or outcome.
Question: Are demos now the right way to pitch?
Answer: Yes, if the demo solves their specific domain problem with their data or public data. Generic code demos impress the middle; evidence-backed recommendations impress sophisticated buyers.
Question: What does productionization look like when the coding agent is the production software?
Answer: Productionization becomes delivering real paid output through a process, not deploying an app. SME plus coding agent plus review loop can itself be the production system.
Question: Doesn’t TDD work better for production software?
Answer: Yes, if you’re delivering software. But if you’re delivering the output software would produce, test the output and system benchmarks, not just the code.
Question: For an IDP platform, what should delivery look like if not software handover?
Answer: Sell the extracted XML, JSON, or results from day one with human-on-the-loop review. Zero CAPEX, zero lead time, lower TCO.
Question: How should we judge hallucinating models?
Answer: Compare them to people, not perfect machines. Subject matter experts disagree and err too; if a pocket PhD hallucinates, the question is what it enables with review.
Question: If humans ask us to ask ChatGPT for them, is that valuable?
Answer: Yes. You are not just entering the prompt; you are the evaluator and filter. “Human as interface” may be monetizable when trust and judgment are scarce.
Question: How should we use non-frontier or local models?
Answer: Use a task checklist of what models currently cannot do. Benchmark model-task fit, not generic intelligence; local models may be good enough for narrow extraction or graph work.
Question: How do we convert a personal Co-work audit checklist into firm-wide agents?
Answer: Export the best representative conversations with inputs, outputs, and prompts. From those, create reusable agents for financial-statement review, audit-report checks, and other audit workflows.
Question: How should we respond when a client worries our custom AI solution is not SaaS and will be hard to maintain?
Answer: Don’t defend point-by-point. Say they’re right, revise the positioning, and offer Solution-as-a-Service: the outcome and maintenance headache are ours.
Question: What is the expected FDE output format?
Answer: An email to a real person: “Please do this because of this reason, and here is the evidence.” One use case is fine; many solved use cases are better.
Question: What if the client gave only partial data and our recommendation misses context?
Answer: State the boundary: “Based on the data I have…” Add what context may be missing. If they provide more data, rerun; otherwise move to the next useful use case.
Question: Can we use generic or synthetic data for a problem from the spreadsheet?
Answer: Yes, but anchor it to a real human who would benefit from a real action. Synthetic data is acceptable only when the recommendation is still useful and honest.
Question: What do you actually teach in Tools in Data Science now?
Answer: Not data science, really. I teach how to use AI to do data science and pass tasks by hook or by crook.
Question: How can AI use rich student reflection, game, and story material?
Answer: Use it for concept-space mapping: clusters, outliers, negative space, and unusual student thinking. It can reveal how students think, not just grade them.
Question: How should Engineering Design explore AI?
Answer: Start with verifiable environments. Connect AI to CAD, simulation, FEM, SPICE, MuJoCo, or Blender so it creates outputs, gets tool feedback, and iterates.
Question: Is ChatGPT better at math or literal work than Claude?
Answer: For literal instruction-following, yes. ChatGPT treats “all” like “do not miss anything”; Claude often treats it more casually.
Question: What can I do with 25 years of curated fraud, health, and technology articles now that AI can search?
Answer: Start with what AI can do, then use your archive and judgment to add the missing 10–20%. Use AI to surface, triage, and direct.
Question: What happens after AI can generate everything quickly?
Answer: The bottleneck shifts. First verification becomes the constraint, then deciding what to do with the flood of outputs, then the work AI still cannot do piles up.
Question: How should a large organization manage AI infra and token cost?
Answer: Meter visibly from day one. Give small default budgets, publish usage, raise limits by project/P&L, and make owners own direct costs.
Question: If clients ask us to re-estimate because Claude reduces effort, how do we respond?
Answer: Reposition from effort to accountable outcome. Clients still pay for ownership, assurance, and “catch us if it goes wrong,” not just the report or code.
Question: If an agent is like an employee, how do we onboard it?
Answer: Ask AI to read existing training material and create its induction guide. Give it an email ID, manager, examples, rules, and feedback like a new hire.
Question: How should we think about prompt-only 3D/product design workflows?
Answer: Use an agent-tool loop. Claude Code connected to Blender through MCP can create 3D output with no bespoke software, just prompting, verification, and iteration.
Question: Who can become a forward deployed engineer inside a client environment?
Answer: Anyone with client data access, AI access, initiative, and curiosity. The job is not to pitch projects; it is to solve problems and send actionable outputs.
Question: How should we make HR, Finance, and Travel look AI-native internally?
Answer: Run demand-generating sessions on what current AI tools can already do. Pull transformation from real functional pain instead of pushing generic AI from the center.
Question: What is the real training gap in enterprise AI platforms?
Answer: Often it is initiative, not education. People wait for step-by-step internal-platform training instead of finding docs, people, and workarounds themselves.
Question: How should we hire or filter FDEs?
Answer: Test attitude first. Did they solve real problems outside curriculum, learn on their own, and push through bad documentation? Then test communication, engineering judgment, and explanation.
Question: Do freshers work as FDEs?
Answer: Sometimes, but they often vibe-code blindly. Experience matters for judging architecture, explaining trade-offs, communicating status, and navigating ambiguity.
Question: Should data strategy wait for a cleaned data lake before agents?
Answer: No. Agents can clean, script, structure, and improve data on the fly. Data strategy should start from what agents need and do, not a parallel lake-cleanup program.
Question: How do we make AI assumptions memorable for leaders?
Answer: Show their own assumption crumbling live. If they think a report takes a week, have AI make a draft in eight minutes and ask what it would have cost.
Question: Where should we look for horizontal AI disruption ideas?
Answer: Visual AI. Anything that produces engineering drawings, 3D models, architecture, circuits, or design artifacts is ripe because code-like outputs are verifiable.
Question: How do we quickly create an AI workshop brochure for CXOs?
Answer: Use my talks page and LLM blog posts as source material. Ask Claude or ChatGPT to tailor the poster or PDF to the audience, theme, and call-to-action.
Question: What does “LLM Psychologist” mean?
Answer: It means studying how models behave under different prompts. Same model, different inputs; same input, different models; understand how to talk to them effectively.

Better information is not always beneficial

Sat, 24 Sep 2005 12:00:00 +0000

Better information is not always beneficial.

For $795, LegalMetric LLC will tell you which judges rule most swiftly and which tend to favor patent holders. For lawyer and client, this knowledge can be very valuable. But does it increase the chances that the judge will come to a just decision?

It is the sort of information that Nobel laureate Kenneth Arrow labeled “socially useless but privately valuable.” It doesn’t help the economy produce more goods or services. It creates nothing of beauty or pleasure. It simply helps someone get a bigger slice of the pie. Sure, if the product helps win cases, then both sides will buy it – just as both sides in high-stakes product-liability cases invest in jury-selection experts and software – and neither will have an unfair advantage. But does that make the society better off?

Comments

Arun 24 Sep 2005 4:51 pm: Interesting article. An internet bubble qualifies too, i presume? :-)

Limits of computing power

Wed, 04 Jul 2001 12:00:00 +0000

A bizzarre calculation on the limits of computing power using fundamentals of quantum mechanics.

Worlds information growth

Wed, 01 Nov 2000 12:00:00 +0000

A study shows that the world is producing about 250MB of info per person per year. It means that, on average, each of us is writing 50 times the size of the complete works of Shakespeare every year! If you’re wondering how that’s possible, just wander around this web page :-)

information-theory on S Anand

Things I Learned - 14 Jun 2026

Questions I was asked

Classic texts in computer science

Comments

Better information is not always beneficial

Comments

Limits of computing power

Worlds information growth