Things I Learned - 17 May 2026

Sun, 17 May 2026 00:00:00 +0000

This week, I learned:

I had GPT-5.5 and Opus 4.7 analyze a few of my conversations and learnt that I need to ask myself: “What must they take away? What must you take away?” in my conversations. That lets me speak with intention rather than instict. (Instinct has its place. I happen to over-use it.)
Turns out there are several well-established taxonomies. It makes sense to align with these. Linked data is powerful and AI makes linkage easy.
- General Knowledge: Wikidata, DBpedia, YAGO.
- People: VIAF, ISNI, ORCID, LC Name Authority, GND.
- Places: GeoNames, Getty TGN, ISO 3166.
- Organizations: LEI, ROR, Wikidata.
- Books/Media: Open Library, WorldCat, MusicBrainz, IMDB.
- Chemicals/Biology: PubChem, ChEBI, GBIF, ITIS.
- Legal/Units/Math/Events: EuroVoc, QUDT, OEIS, PeriodO, etc.
BitWarden supports a bw CLI that seems handy for quick CLI access to passwords. It’s a step towards me moving away from saving passwords unencrypted on my local file system.
Singapore has banned prediction markets like Polymarket and Kalshi. Pity. I was hoping to use AI coding agents to play them. Yahoo
flipbook.page is a fascinating generative UI exploration. It’s a visual browser, i.e. it generates an image based on text, you click anywhere, it generates an image interpreting based on where you clicked, and so on. A very different style of exploration!
Vercel’s deepsec uses Codex / Claude to search for vulnerabilities, but “scans can cost thousands or even tens-of-thousands of dollars for large codebases”.
When I charge my Lenovo Thinkpad (P1 Gen 7) with the 170W charger that came with the laptop, it delivers ~60W of power to the battery, charging the laptop in about an hour. A 65W laptop delivers half the power and takes twice as long.

Questions I was asked

Week ending 17 May 2026

Question: How should outcome pricing replace software-hour pricing?
Answer: Price what the client really values: the compliance reports, reconciled data rooms, research outputs, etc. instead of the software. Software is cheap. Price in the integration, verification, and accountability.
Question: If AI can generate software and reports, where is our value?
Answer: Find out. Use AI for everything, log where it fails, and turn those failures into our assets: prompts, tests, evals, tools, SKILL.md.
Question: How should people reskill for AI?
Answer: Use AI for everything. Build skills around where AI fails.
Question: Should we build an agentic learning-trace tool for students?
Answer: Decouple tools. Capturing learning with a lightweight authenticated terminal recorder. Don’t build an agent platform - reuse what’s available.
Question: Is it just a one-line prompt or do we need trained agents?
Answer: Start minimally. When it fails, meta-prompt to understand how to improve. Agents keep improving, so re-evaluate to simplify periodically.
Question: Why does AI make even experts feel they “know nothing”?
Answer: The frontier is moving faster than we can learn. Experts now have much more framing and verification than production work - a change that requires effort.
Question: What is different about AI-native delivery versus a 7-14 day POC?
Answer: Day zero. Instead of waiting for a POC, put people with agents inside the workflow immediately, deliver the needed output, then evolve prompts, connectors, code, and automation behind the scenes.
Question: How should outcome-based pricing work?
Answer: Price useful outputs and decisions, not development hours. Start with variable OpEx, add minimums later, and let the improving workflow/context become the asset.
Question: Does Anthropic use client data for training when you use Claude commercially?
Answer: No - enterprise and API usage is explicitly excluded from training data.
Question: If everyone opts out of training data, how does Claude get better?
Answer: Anthropic uses synthetic data which is quite effective. (Also: separately consented/purchased datasets, red-teaming.)
Question: What if the client wants only a tool and no human service layer?
Answer: Say yes, but reframe. Software depreciates. The workflow, context, evals are worth more. Build the tool if they want IP, but deliver outcomes from day one.
Question: Private-company data is messy, unstructured, and often local-language; how do we verify outputs we cannot easily read?
Answer: Use checker agents and let native-speakers humans review just the exceptions.
Question: How long does prompt refinement take in real projects?
Answer: Five minutes for rough directional changes; one or two days for a reusable workflow; months for true productionization handling edge cases, with evals, tools, governance, and client acceptance.
Question: Can Claude or Codex automate Bloomberg, CapIQ, PitchBook, or Mergermarket workflows?
Answer: Technically, often yes; contractually, be careful. Scrape manually when automation is not allowed and price higher.
Question: What is the real takeaway from paying $20 for ChatGPT Plus?
Answer: For a tiny monthly cost, each analyst gets a high-capability research assistant that can read files, browse, reason, draft, rewrite, and analyze data. The real benefit is when the team learns to delegate.
Question: Clients have asked us not to use GenAI; what should we do?
Answer: Don’t use it where the client prohibits it. Use public-data demos, anonymized examples, and internal productivity experiments.
Question: Should we use Perplexity for research output?
Answer: Prefer ChatGPT and Claude, which have better tools - notably code execution - that is often required.
Question: Can agents read non-editable PDFs?
Answer: Yes. They have vision models that can read scanned documents, images, and PDFs.
Question: Can we force the AI to use only official regulatory, ministry, NRA, or government sources instead of blogs and news sites?
Answer: Yes. Tell it explicitly, give it the process manual, require citations, and reject non-official sources. Treat it like briefing a researcher: “Use only equivalent NRA and government sites; redo the research.”
Question: Can AI create an Omdia-style telecom regulation report for another country from an existing South Africa report?
Answer: Yes. Upload the sample report, ask for an identical report for Vietnam / India / Germany, and let ChatGPT or Claude research, synthesize, and draft. It can shrink the human time of a few days to 10-30 minutes.
Question: How do you infuse your personal AI practice into your engineering team?
Answer: Encouraging coding agents in documentation, testing; standardizing practicess across repositories and teams; training on verification: LLM-as-judge, TDD, synthetic data stress-tests; and using coding agents themselves as the solution.
Question: Is “chat with big data” supposed to make hour-long queries run in seconds?
Answer: No. AI speeds up query generation, not execution speed. But it can optimize and enable pre-aggregation or caching.
Question: For financial analysis and report writing, which AI tool is better - Gemini, ChatGPT, Claude, or Copilot?
Answer: This month: Claude beats ChatGPT beats Gemini. Next month, it may change. Use paid frontier models. Compare outputs regularly.
Question: Can I upload an existing financial model spreadsheet and ask AI to roll it forward for the latest quarter?
Answer: Yes. Upload the spreadsheet and ask AI to update it. Then verify formulas and assumptions.
Question: Will Claude help with financial models too, or only research?
Answer: Models too. Delegate the whole task: research, extraction, calculations, formatting, even sanity checks.
Question: Copilot seems better at analyst-style writing than Claude or Gemini - is that right?
Answer: Only if you compare it with weak or free versions. Against paid frontier models, Copilot style is comparable or worse, and it can’t execute code.
Question: Should each executive-facing claim have traceable sources?
Answer: Yes. Source links are not enough. Include quotes behind the claims for fast verification.
Question: How deterministic is a financial agent for executive use?
Answer: Code is deterministic. Output and interpretations still need validation. I’d keep the UI light and focus on verification.
Question: Our client says any AI use requires permission. What do we do?
Answer: Start where it’s easiest: new work - with no incumbent or competition, public data, secondary research. Prove value. THEN ask for permission.
Question: If we give AI all the input, can it create a 50-60% ready sell-side or buy-side research report?
Answer: Yes. Put the best SMEs in the room. Finish real reports DURING the workshop. Show, rather than tell.
Question: Are big one-shot prompts worse than step-by-step prompts?
Answer: Yes, for weaker models. Aim high first; if it fails, break it down; after model updates, retry longer tasks so you don’t stay stuck in an old workflow.
Question: Should we collect everyone’s prompts and ask AI what works best?
Answer: Yes. Put your Cortex prompts in an append-only Snowflake table, capture what worked and failed, and turn it into a reference and onboarding asset.
Question: Can prompts help new joiners understand complex databases better than KT documents?
Answer: Yes. Store business context as retrievable text. Let prompts teach by doing. AI-native KT beats documentation for changing systems.
Question: For the casino and hotel marketing team, what should we pitch beyond a dashboard?
Answer: Pitch an always-on AI-enabled advisory team that delivers insights and actions. No upfront software; just rapid research, recommendations and outcomes.
Question: Do clients need clear KPIs before we start an outcome-style AI engagement?
Answer: No. Just pick an area. Even if they don’t know the KPI, the agent can infer role-relevant KPIs and propose something useful.
Question: Does the agent need to understand cross-sell instead of just searching for the word?
Answer: Correct. That is the difference between search and agentic reasoning: infer the plan first, THEN execute the search.
Question: Are you giving Claude access to your files, and how?
Answer: Yes, through MCP and a detailed prompt. Give access, make it plan, and tell it to reframe bad questions like an expert.
Question: Why don’t we train a custom model with all our knowledge already inside it?
Answer: Don’t. Custom training is costly and slow. Save your knowledge in SKILL.md, databases, folders, custom code… that’s cheaper and faster.
Question: Can AI learn corrections and store them?
Answer: Yes, but only if we deliberately convert corrections into assets: skills, checklists, habits, tests or knowledge snippets.
Question: Is the agent building software behind the scenes?
Answer: Yes, when needed. Software is plumbing; the product is the answer or action the user wanted.
Question: How do we ensure board members have the same baseline knowledge but can still ask follow-ups?
Answer: Generate a common board pack for everyone, then let individuals drill down privately. Standardize the baseline, but don’t limit curiosity.
Question: After AI generates an HTML or PowerPoint answer, can users continue the conversation?
Answer: Yes. The report is not the end; it’s just a by-product.

Google markets

Thu, 22 Sep 2005 12:00:00 +0000

Google markets. It’s not a new Google service. Just Google “putting the wisdom of crowds to work” by using online markets.

Yahoo Buzz Game

Mon, 18 Apr 2005 12:00:00 +0000

Yahoo’s Buzz Game tracks various technology markets, and lets people bet on which one will win. So, for instance, C leads language, Livejournal leads blogs, Flickr leads tags, and Bittorrent leads P2P.

Exchanges using Wisdom of Crowds

Fri, 13 Aug 2004 12:00:00 +0000

Alternate exchanges: Hollywood Stock Exchange, Innovation futures, Blogshares. These exchanges are supposed to be remarkably accurate in predictions.

Ideosphere

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