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Igor Riabchuk 🇺🇦

@igorok

AI Implementation Consultant & Builder Founder of https://teapaw.org/t.co/NponWDEYXn, https://teapaw.org/t.co/PYxgouO9xS, https://teapaw.org/t.co/8t52JzgqhP

 Amsterdam, The Netherlands
thatukrainianguy.rocks/
Joined April 2008
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Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)Photo by Igor Riabchuk 🇺🇦 (@igorok)
Uber published how it secures its AI agents, and one rule is particularly interesting. An agent can hold valid credentials for a user with full legitimate access and still get denied, because the action drifts outside what that agent was built to do.
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So. Passing auth is no longer the whole check. Uber is open about what intent-gating costs: false blocks traded against false allows, plus latency, accuracy, and getting developers to adopt it. A company that size is still experimenting in production.
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Carly Dober, a psychologist writing in the Guardian, wants us to fight AI brain rot by switching off and embracing boredom. Good luck with that. Boredom was gone long before chatbots arrived, eaten by the infinite scroll that fills every idle minute in a checkout line.
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So. Her own words say each new product is built to make life easier and keep us mindlessly entertained. That's a social feed, word for word. Chatbots handle the first half at best, and blaming them for lost boredom lets the feeds that ate our idle hours walk off clean.
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Revolut moved 2,000 engineers off Claude onto self-hosted GLM-5.3, served on 56 Nvidia B200s, and says AI coding inference costs fell about 90% in under a year. That hardware is north of $3 million. Nobody published the old API bill.
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So the 90% sits on a baseline we never see, and you can still back into it, because $3 million in GPUs only pencils out if Claude spend across 2,000 engineers was heading into the millions every year. That gap is the rent on frontier models at Revolut's size.
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Dr Lena Glaser had 719 people aged 18 to 65 turn their showers as cold as they could stand and keep it up for 12 weeks. She puts the saving close to recycling. People stuck with it when they saw a personal benefit and didn't feel they were giving something up.
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My cold shower happens at the end of a warm one, so the boiler has already done its work by the time I flinch. I guess that buys me the health story and none of the emissions one. Which makes me the loophole)
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Protein-maxxing began as a gym and biohacking habit. It has now reached the family kitchen, where parents are buying protein supplements for children because the supermarket aisle and their social feeds keep insisting everything needs more of it.
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So. A growing child and an adult chasing muscle mass have different needs, and the evidence that kids gain anything from extra powder is thin. Kids eating normal meals are probably covered already.
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Bloomberg asked advisers where they'd put $1M this quarter. Most skipped hyperscalers and chips for energy, logistics and live events, with safaris filed under treat-yourself money. AI and the IRL craving now share a portfolio.
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An adviser called it "the other side of the digital coin," the side I'm building heerish and peerish on. Scarcity, basically. As software eats the day, hours in a room with other people get scarcer, and allocators now price those hours like energy or freight capacity.
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The AI bot at my Dutch bank didn't know how the bank's own systems worked, and every answer left me more confused than the last, until I finally got through to a human. 51% of consumers say AI made things slower or never solved the problem at all.
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Yale calls it timing. Spending ran on the capex clock and productivity on a slower one, so only ~6% of companies get 5%+ of EBIT from AI, unchanged from a year ago. Goldman's Hatzius sees the buildout's growth impulse peaking in late 2026 or early 2027.
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Nine out of ten AI pilots reportedly never reach production. Model quality stopped being the constraint a while ago. They stall in plumbing humans laid before AI existed: data nobody cleaned, systems never built to connect, governance written for human sign-off.
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TechGig also flags constant model reassessments as a drag. Makes sense. Swapping models is the one change a team can ship in a quarter, so every stalled pilot gets a new model, while the data and integration work behind the 'Autonomy Gap' waits for budget.
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Google is building a robot medic for NASA with Johnson Space Center and JPL. It runs on CMO-DA, a decision-support system with its models onboard, because a crew officer treating an injury can't wait out deep-space communication lag for a doctor on Earth.
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So. The robot also hauls cargo and does maintenance, but the medical half is the part that travels back to Earth. A ship far from home and a farmhouse with one bar of signal share the same constraint, and Google is solving the harder case first.
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Eko's CORE 300 just got FDA 510(k) clearance, and the detail I keep coming back to is that you can drain the battery to zero and the chestpiece still works as a plain acoustic stethoscope. Twist it for digital: up to 35x amplification with active noise cancellation.
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That's the shape I'd want for most clinical AI. A narrow model trained on one kind of sound, built into a tool clinicians already hold, with the acoustic stethoscope still underneath for when the electronics quit. Cleared for adults and kids.
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