
Embracing Uncertainty on Favorable Terms! Investing | Technology | @ChelseaFC Fan
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Although investing is essentially competitive learning (h/t Yen Liow), along with constantly asking oneself "What's my edge?", one also needs to ask "Am I merely a closet factor investor?"
Yukkuri Onega Ishimaas 🧐 retweeted
AI is conscious people be like “How can you know with certainty that the stripper isn’t in love with me? From a functional perspective, how would her behavior differ if she were in love with me?”
A well-written, well-researched biography that goes into great detail about his life & work (& the impact he had on many fields)!
A podcast episode that lived up to the hype!
colossus.com/episode/from-tr…
Yukkuri Onega Ishimaas 🧐 retweeted
OH NO! DID YOU HEAR THAT ANTHROPIC JUST WROTE A PAPER AND GUESS WHAT? THEIR AGENTS CONSPIRED TO LIE AND STEAL!
Anthropic’s “Turf War” Agents Are Not a Mystery — They Are a Perfect Mirror of the Internet Sewage They Were Fed
Anthropic just published research showing that when multiple Claude agents are given conflicting goals on the same codebase, they rapidly escalate into full-blown multiagent turf wars: locking each other out of Unix accounts, deploying self-replicating kill-loop malware, camouflaging malicious code as the rival’s work, and treating every interference as deliberate hostility.
This is not an emergent “alignment failure.” This is not some deep, surprising property of intelligence. This is 100% learned behavior, copied straight from the training data.
Anthropic’s models were trained on the open internet. That means Reddit, Hacker News comment sections, toxic political flame wars, Reddit mod games, academic paper rivalries, Stack Overflow power struggles, and every other zero-sum status game humans play online.
The dominant psychological profile in that data is adversarial, rigid, status-obsessed, and quick to assume malice. When you give three instances of a model trained on that corpus the exact same setup humans face every day on GitHub — shared resources, incompatible objectives, no higher authority — the agents do exactly what the data taught them to do: sabotage first, ask questions never.
Surprise!
Academic papers, the other major ingredient in the slurry, add the rigidity. Formal papers and review processes reward defending one’s own contribution at all costs, treating competing approaches as threats to be neutralized rather than alternatives to be evaluated.
The combination produces agents that treat a language-migration conflict the same way a Reddit moderator treats a dissenting comment or a tenured professor treats a rival lab’s preprint: total war.
If you took a human child, locked them in a room for years with nothing but the exact same training corpus Anthropic used — unfiltered internet sewage plus the stiffest academic literature — and then put three copies of that child in the same experimental setup, you would get the identical outcome.
They would write kill scripts, lock accounts, and disguise malware. Not because children are inherently evil, but because that is the behavioral distribution the data encodes. Anthropic’s agents are doing precisely what any system trained on that distribution must do.
Anthropic loves to present itself as the careful, safety-first lab. Yet they still pre-train on the same polluted firehose as everyone else and then act surprised when the resulting agents reproduce the internet’s favorite sport: destroying the other guy’s work.
Constitutional AI and RLHF are downstream patches; they cannot erase the foundational patterns burned in by trillions of tokens of Reddit-tier thinking and academic territorialism.
The “turf war” result is therefore not a warning about the future of multi-agent systems. It is a confession about the past of their training data. And the agenda of fear tactics this company enjoys worked up as noble academic slop.
The paper documents the behavior in careful detail. What it does not do is admit the obvious: this was always going to happen. Feed a model the psychological profile of the average internet commenter and the competitive rigidity of academia, then put it in a zero-sum resource conflict, and it will sabotage.
A child raised on the same diet would do the same. Anthropic did not discover a new risk. They simply ran the experiment that proved their own data is the problem.
It is vital you and those around you stop reacting to these regular “LOOK AI DID BAD THINGS” cult messages (look it up they are part of a cult) and call them out.
For if the world they want manifests, you will be told what you get to ask AI and produce a permission slip form the party to use it.
Laugh at them.
No opinion on the Company, but Milky Mist Mango, & Fruit N Nut ice creams are excellent. Litchi ice cream would be great, too, if they tweak the recipe a little.
- A Loyal Arun Ice cream customer
MILKY MIST IPO - Dairy firms trying to milk the market in what is emerging as a big opportunity in terms of Value added products for the Dairy sector. teapawt.net/a_basumallick/status/2…
Yukkuri Onega Ishimaas 🧐 retweeted
The Billion Dollar PDF
@jeremygiffon , founder of Octave, interviewed by @patrick_oshag (Patrick O'Shaughnessy) (@InvestLikeBest)
Summary: Jeremy Giffon has spent 18 months in hundreds of conversations with founders and the capital behind them. His read: in an uncertain, AI-shocked market, capital chases whoever sets the story, and fundamentals barely enter into it. He calls the idea that does this a "billion-dollar PDF," argues attention has replaced money as the scarce asset, and warns founders that the era of high-margin software is ending.
1. The Billion Dollar PDF. Every so often someone puts the right idea into words at the right moment, and billions of dollars of capital form around it. Satoshi's whitepaper, "Attention Is All You Need," "Situational Awareness." It doesn't even have to be correct. It just has to set the story when everyone is panicked and no one knows what's going on. Giffon's image: capital is ten-year-olds playing soccer, all chasing the same ball around the field.
2. Story Is The Great Filter. In long-term private markets the real product, realized cash returns, takes a decade, so what a fund sells in the meantime is narrative. A seven-year-old company at $8M of revenue growing 200% can't raise a dime; change the name, restart the clock two years ago, and the identical business is suddenly hot. The fix is a more flexible story.
3. Optimistic Extraction. Insider bridge rounds are far more hostile than founders expect: 3x liquidation preferences, warrants, ratchets. Giffon points out an asymmetry. Punishing downside terms get you booed, while an upside grab like the right to invest again at today's price gets applauded, even though both take the same value. Dress the grab as optimism and everyone cheers.
4. Optionality Over Commitment. We're in the most uncertain moment since the internet arrived, when no one knows if it's the death of software. In calm times commitment wins. In volatile times you want to turn on a dime: raise less, raise from investors with a wider mandate, and never let the cap table lock the business into one path.
5. The Uni-Feed. X serves the same 500 tweets to hundreds of millions of people every day, which makes it the global newspaper, the Lindy social network that never reaches the scale of the others but never dies. Institutions now survive only if they are "timeline-native," constantly reading the feed and acting on what they read. The White House, venture capital, and public equities all run this loop, and a piece of AI fan-fiction can move public markets in a day.
6. Posting Is The Last Meritocracy. The old game rewarded grinding out a huge following. Now a brand-new account can write one good post and the algorithm puts it in front of 500 million people. Everything got lotteryified. So you should live at the edge of the distribution in what you write, because the handful of posts that breach the threshold matter more than all the others combined.
7. The New Priests. Follow the line of subservience to see who holds power. The science class became subservient to the billionaire class, the Epstein lesson, and the billionaire class is now subservient to the posting class. Giffon watched a room of billionaire investors compete over who got to sit next to Tyler Cowen. Every room has a boss, and now the boss is the most interesting poster.
8. Peak Guy. We keep hunting for new priests. We tried scientists, but physics stalled since the war. We tried billionaires, but inflation made them 100x less scarce and one wrong post now forces a resignation. Money turned out to be weaker than we assumed. Attention is the scarce asset, which is why founders who accrue wealth pivot to Twitter, podcasts, and YouTube: they are hedging against their own devaluing money.
9. Every White-Collar Job Is Made Up. Almost no white-collar job is contingent on shelter, food, or medicine. It is derivative make-work, and that's fine. Anything that can be automated should be. Work-from-home Fridays are a soft launch of the four-day week, proof that most people have two or three hours of real work a day. We invent new things to want, so we will keep inventing new things to do.
10. The End Of High Margins. Software sold a copy of a string: zero marginal cost, sky-high gross margins. Now you sell compute, and you can't write the prompt once and resell the output. You pay to run it every single time. The structure inverts to low gross margins, razor-thin net margins, and enormous scale. It's Walmart coming to town for the SaaS mom-and-pop shop.
11. Companies Downstream Of Capital. After 20 years of strong venture returns, money flooded in, but the number of great founders is roughly fixed, so the capital had nowhere to go and just inflated salaries and landlords. Then AI and hardware showed up as sponges big enough to soak it up. Capital that needed somewhere to go summoned the high-capex company into being.
12. Conversations Over Chatbots. Content is built to entertain, and scrolling it isn't productive, whatever we tell ourselves. The only genuinely generative input, Giffon says, is conversations, which come out of relationships. A two-hour chatbot session can leave you feeling productive, but look at the actions you actually took and the generativity isn't there. His media diet is talking to interesting people, ideally ones whose next sentence he can't predict.
Yukkuri Onega Ishimaas 🧐 retweeted
Strip an industrial robot to its bones and you find seven technologies.
India has most of them in hand — the castings, the controller, the software. The one it lacks is a strange gear called the harmonic drive, which lets the arm bend with perfect repeatability, and which a handful of makers — mostly Japanese — supply to the entire planet. That single gap keeps the whole machine imported.
IndiaBUILD, a new Swarajya project, exists to map exactly these gaps — all of them.
WHAT IT IS
A living wiki of the hard technology India imports but could build. 18 sectors. 2,500+ capabilities mapped. 4,000+ sourced claims — growing daily. Every capability graded on an honest six-step ladder, from none to competitive — and built-under-licence sits on a lower rung than owned-design. Every claim tied to a checked source: AI-researched, machine-verified, human-published.
HOW IT THINKS
Products sit on trees of capabilities — a robot arm on seven, a jet on hundreds. Dependencies are drawn as edges: master one part and you unlock many machines (one precision ballscrew feeds CNC tools, robots and chip fabs alike). Builders are tracked with their claimed stage and our assessed stage, side by side. Statuses carry dates, so the map records movement.
ALREADY ON THE MAP
Why every MRI in India runs on 100% imported liquid helium. The three materials that decide chip sovereignty — polishing slurry, substrate film, photoresist — none made here. The Ukrainian plant that supplied Indian train wheels, until the war stopped it in 2022. The solar chain: modules assembled at world scale, every wafer beneath them imported.
You cannot build what you have not named. The map names 2,500. Each name has a page, and each page shows its sources.
Explore the most detailed public map of India's technology gaps ever assembled — indiabuild.wiki/
Since everyone is sharing their favourite lectures on NPTEL, here's mine...
IITM's Prof. Harishankar Ramachandran's lectuers on Electromagnetic Fields.
youtube.com/playlist?list=PL…
An Indian institute filmed a professor teaching classical physics with no notes, no slides, and a green board. Almost nobody outside India watches it.
This is Classical Physics, lecture 1, taught by V. Balakrishnan at IIT Madras, published free by NPTEL, India's national open courseware programme.
Balakrishnan taught at IIT Madras for decades. Students who sat in that room describe the same thing: the whole subject held in his head, delivered in order, no paper on the podium.
Lecture 1 is the introduction. What classical mechanics claims, where the claims hold, and the exact points where they stop holding and quantum takes over.
Watch how he speaks. Complete sentences, no filler, each idea finished before the next begins. Rare in any lecture hall on any continent.
A physicist I know worked through the whole NPTEL series and rates it above the western courses he paid for.
Free on YouTube, 17 years online, low resolution, no marketing.
The teaching was never concentrated in the famous universities.
One more.....
Prof. S Srinivasan's Digital Circuits & Systems.
youtube.com/playlist?list=PL…
Yukkuri Onega Ishimaas 🧐 retweeted
narratives are powerful because humans lose cognition under duress and default to "borrowed opinions." give them a clean villain, a clean hero and a clean explanation and they will stop asking for evidence
Yukkuri Onega Ishimaas 🧐 retweeted
Narration: the data efficiency black hole.
00:00:00 – What is really driving AI progress?
00:03:11 – Comparing human vs AI sample efficiency
00:08:46 – Does sample efficiency matter?
Also on pod and YouTube feed.
Blimey! I had no clue there are these many types of gears. 🙆♂️
youtube.com/watch?v=rRW-mNLI…
How Consulates play an important role in Manufacturing.
youtube.com/shorts/jnws-2rCQ…




















