忙しいほど幸せ sounds beautiful. Help me break it down, letter by letter. Sing the beauty of the strokes, the words, and their meaning. (I don’t really mean sing. I mean, write in a way that’d really make me appreciate the beauty. But without going overboard. I mean, some wicked humor is always welcome! In fact, I’d love for you to think about who are some of the best authors who achieve this balance, and write in THEIR style.) ...
This week, I learned: Zillow Offers, the company’s “iBuying” arm, which was shut down in November 2021 after losing hundreds of millions of dollars. The core failure was not just an algorithmic error, but a fundamental misunderstanding of the limits of machine learning in high-stakes, low-frequency trading environments like real estate. Zillow relied on its “Zestimate” algorithm to predict future home prices and make instant cash offers, but the model failed to accurately account for real-time market volatility and “adverse selection”—savvy homeowners sold their properties to Zillow when the algorithm overvalued them, but kept them when the algorithm undervalued them. This left Zillow holding thousands of homes it had overpaid for and could not profitably resell, forcing a $304 million write-down and the layoff of 25% of its workforce. Zillow Q3 2021 Shareholder Letter (PDF) # There’re a good number of AI insurance products in the market. # Munich Re aiSure - for AI vendors and companies deploying AI; can cover business losses (like lost revenue / business interruption) and legal damages when AI performance errors (incl. hallucinations) cause harm. Munich Re aiSelf - for teams using self-built or bought ML models; helps cover the financial downside when models underperform or drift over time. Munich Re aiSure - General Liability - covers damages and financial losses from lawsuits (e.g., claims that AI decisions were biased/discriminatory). Armilla Insured (AI Liability Insurance) - affirmative AI liability cover (Lloyd’s coverholder; partners include Chaucer) that can cover legal defense costs, settlements, and third-party claims when an AI model underperforms. Armilla + Chaucer standalone AI liability (announcement) - focused on “mechanical underperformance” (incl. hallucinations and model drift) and the liability that follows. AXA XL GenAI Endorsement for CyberRiskConnect - add-on to cyber insurance for companies building their own GenAI; covers things like data poisoning, copyright/usage-rights mistakes, and AI-regulatory violations. Coalition Affirmative AI Endorsement - clarifies cyber coverage applies when AI causes a security failure, and extends funds-transfer-fraud triggers to deepfake-based instructions. Coalition Deepfake Response Endorsement - adds response support for deepfake incidents (technical analysis + legal + reputational help), not just “classic hacking.” Tokio Marine Kiln Technology Errors & Omissions - tech E&O with generative AI coverage available by endorsement (aimed at software/SaaS/tech services). Tokio Marine Kiln Cyber Ctrl suite - cyber/tech cover where AI-related add-ons can include AI regulatory proceedings, data contamination, and “LLM hijacking.” Hiscox Technology PI (UK) - AI clause - explicitly covers client claims arising from your use of AI (incl. genAI) as part of the services you deliver. A key lesson from Who Validates the Validators is that we learn our preferences as we evaluate. So make it cheap to evaluate (create outputs) AND cheap to revise criteria. Cookies taste wonderful when eaten hot. ⭐ Constraints as opportunities. On long flights, I read more since I’m less distracted by guilt (“Should I answer email or code instead of wasting time?”) or FOMO (“Let’s click that link”) since I have no choice. Setting aside “quiet time” doesn’t work as well, since I have more choice. This constraint (no Internet) became an opportunity (reading time). I knew this before-hand, but had to experience it to appreciate it, and acknowledge it consciously to realize it. That takes repeated (2+) trials and reflection. A workflow to convert constraints to opportunities could be: List constraints. (Like fish in water, we aren’t used to thinking of constraints as constraints. Also, this means more constraints => more latent opportunities!) List opportunities they offer. (Creative prompting helps; reflecting on the answers helps more.) Try any 2+ times. (Gives room to settle in.) Document learnings. (Explicit reflection is better than implicit awareness.) Notes from Thoughtworks Radar, Apr 2025 Architecture advice beats architecture review. Architecture Review Boards hinder workflow. An architectural advice process (anyone makes architectural decisions, taking advice from experts, logging in Architecture Decision Records) works better. VectorChord is a faster pgvector alternative. “Learning is not the product of teaching. Learning is the product of the activity of learners.” – John Holt Music labels never became streaming platforms themselves. The real money is in concerts. Streaming just makes you famous enough to book gigs. But movies/TV shows are far more expensive to produce than music. So streaming platforms invest in content (Netflix, Apple) and studios stream (HBO, Disney) Claude Notes from Better Ways to Build Self-Improving AI Agents Quotes from Life is more than an engineering problem, interview with author Ted Chiang. Magic is intent-centric. “Magic means that … the universe responds to your intentions in a way that the laws of physics as we understand them don’t.” LLM reasoning is a weak analogy. “My liver was running this old program, but all I needed to do was update the software and now my liver is functioning much better, even though the hardware is the same.” No one says that. It’s not a useful way of thinking about the liver, and it is not a useful way of thinking about the brain either. Art won’t die. Art is all about context. It’s not an activity like tightening bolts, where I don’t really care whether someone used a conventional wrench or a pneumatic wrench, as long as the bolts are tight. Alignment may not happen. When corporations behave badly, should we consider that an alignment problem? But why do large corporations behave so much worse than most of the people who work for them? And could that be fixed by solving a math problem? I don’t think so. LLM relationships are different from human. … people have their own preferences, while things do not; you do them harm because you are ignoring their preferences. (Companies) might create the illusion that AI systems have preferences. .. it’s theoretically possible for us to build digital entities that have subjective experience. Notes from Developing our position on AI by Recurse Center: Learning happens at the edge of competence. AI has a moving jagged edge, so constantly re-try your impossibility list. Learning happens on what you care about. Use AI to expand your agency (by complementing or deepening), not replace it. Learning generously means being open to different perspectives, without judgement or dogma. Try new perspectives. ⭐ ‘We tested one of the most common prompting techniques: giving the AI a persona to make it more accurate We found that telling the AI “you are a great physicist” doesn’t make it significantly more accurate at answering physics questions, nor does “you are a lawyer” make it worse. This doesn’t mean that personas can’t be useful - for example, they change how the AI answers questions, the format of output, and maybe other factors as well.’ Prompting Science REport 4: Playing Pretend: Expert Personas Don’t Improve Factual Accuracy If YouTube embeds fail with an “Error 153 View player configuration error”, it’s because the server probably has a Referer-Policy: same-origin and needs to switch to Referer-Policy: strict-origin-when-cross-origin. Simon Willison Adding a [dependency-groups] section to pyproject.toml with dev = ["pytest"] ensures that pytest is automatically installed by uv because dev is a default group. Simon Willison CloudFlare Python Workers has full Pyodide support. That means most Python apps will now run on CloudFlare Workers, with low latency worldwide. This is a big deal. Smart contracts are programs that run on blockchains like Ethereum, e.g. to convert currencies, lend/borrow, buy NFTs, etc. These may contain bugs. Anthropic built a benchmark of real smart contracts with known bugs, had agents exploit them, and simulated $550 mn in theft. They also nade $3.5K exploiting real bugs - at a cost of $3.5K. So AI agents are currently at break-even for crypto-theft. Anthropic # Notes from Cory Doctorow’s summary of The Reverse Centaur’s Guide to Criticizing AI: # When tech monopolies saturate their markets, their P/E collapses, reducing share value. This incentivizes bubbles. Automation blindness negates human-in-the-loop. When AI makes rare mistakes, humans don’t catch them. TSA misses guns, not water bottles. AI doesn’t need to do your job. The AI salesman just needs to convince your boss it can, especially senior jobs. Reference letters from professors used to signal value since they were hard to write, so professors would do it only for good students. Copyright expansion and regulation will likely benefit corporates, not labor. US Copyright Office making AI content non-copyrightable means corporates NEED labor. Else every AI work goes to public domain. There is no strong evidence yet that Neuro-Linguistic Programming (NLP) works broadly (ChatGPT). Some NLP techniques help sometimes, but no more than other established techniques (goal-setting, visualization, etc.) ChatGPT ⭐ Just repeating a statement makes it seem truer because the brain finds it familiar, hence easier to process. This seems well-established research. The Truth about Truth PGlite is a WASM-based Postgres implementation. It’s ~3MB. You can embed it in the browser, NodeJS, Deno, etc. It has plugin support, including pgvector. Pejoration is when words acquire negative connotations. Euphimism escalation is another term for it. Third World → developing countries → emerging markets → Global South. Old → elderly → senior citizen → older adult. Lunatic → insane → mentally ill → mentally challenged. Janitor → custodian → sanitation engineer → facilities maintenance specialist. The opposite is amelioration. Minister moved from servant → servant of church → government official. marshal: horse-servant → horse-officer → senior military officer. Knight: servant → armed retainer → mounted warrior → knighthood honor. # # OmniDocBench 1.5 is a benchmark for parsing realistic PDFs. Gemini 3 Pro does well on the list among the commercial LLMs. PaddleOCR-VL (0.9B) tops the benchmarks, overall.
While meditating, I realized 75% of “LULL” is the letter “L”. (This sort of thing happens a lot when I meditate.) MUMMY (60% M) and DADDY (60% D) have lower percentage, but are longer, so maybe get a bonus? I asked Claude Code what would top such a list. It picked a dictionary, generated the 333 words with 4+ letters and >50% concentration. What did I like best? “ASSESSES”. 5/8 letters are “S”. That’s nearly two-thirds. ...
There are three English words ending in “-gry”. Two are “angry” and “hungry”. What is the third one? See the answer. Comments Dhar 29 Dec 2006 2:50 pm: I thought “augry” was a proper English word?? S Anand 29 Dec 2006 7:07 pm: Well, Google can’t find augry. Cameron 1 Apr 2007 11:20 pm: it is person 27 Dec 2006 12:00 pm: None it’s a riddle and you didn’t phrase it right. But this is how it goes: There are three words in The English language that end in “gry”. ONE is angry and the other is hungry. EveryONE knows what the third ONE means and what it stands for.. EveryONE uses them everyday, and if you listened very carefully, I’ve given you the third word. Look carefully at the first sentence and at the part ‘The english Language’ There are three words in that phrase, the english language the third is the word language. So language is your answer. The rest is a big trick to get you away from the obvious first sentence which holds the answer.
I need to get used to the British way of responding with “No” when they really agree with you. For example, in response to “The weather’s not looking good,” I would say “Yeah.” The British say “No.” (No, it isn’t.) It’s a bit jarring – feels like they’re disagreeing. For instance, “London isn’t expensive.” “No.” (what? you’re saying it’s expensive?) “I don’t have a ticket.” “No.” (what? you’re telling me I have a ticket?) ...
The 591 spellings of Britney Spears on Google searches. What’s also interesting is to read the sites that have linked to the mis-spellings.
Word Spy’s top 100 words has Google on top right now, probably because Google asked them not to verbify them.
Infosys, Wipro and TCS are “expected to see a higher than expected rise” in their revenues. Hmm… I didn’t know that was logically possible.
Esperanto is quite an easy language to learn. This article from kuro5hin teaches you the basics in 10 minutes. If only it were a widely spoken language… Still, I’m going to learn it.
The etymology of hello.
The top 20 dictionary searches on Cambridge’s dictionary puts serendipity on top. It means “the lucky tendency to find interesting or valuable things just by chance.”
Upsizing. Funny.
Fun with words: anagrams, palindromes, oxymora, etc.
Grammer: the first casualty of war. Funny.
The American declaration of independence in American.
What does your name mean? More info about asian names.
The Rosetta Project: keeping a thousand languages alive.
Pun of the day
Why do people say “Bless you” when you sneeze? The practise may date back to the plague in London, though there are several theories.
This is why you should not use computers for translation. While you are at it, check out www.engrish.com.
Cliches. Tells you what cliches like ass backwards mean.
Word detective traces the origins of strange words. For instance, Yahoo means an ignorant brute. Hamburgers have nothing to do with ham – they’re so called because they come from Hamburg, Germany. Beefeaters are the guards at the The Tower of London (Yeomen guards). Word detective doesn’t know why they’re called that, but I can confirm first-hand that the Yeomen guards don’t know why themselves.