Robert Castelo (@robertcastelo) header
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Drupal | AI | Cardano $robertc

London
Joined March 2007
Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)Photo by Robert Castelo (@robertcastelo)
Robert Castelo retweeted
a calmer desktop
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Ten million people have watched a comedian accidentally destroy the entire TED talk industry. He filmed the talk once at TEDxNewYork in 2014 and openly admitted he had nothing to say. Speaking coaches charge $5,000 to teach the exact tricks he used in 6 minutes to say nothing at all. His name is Will Stephen. He is a comedian from Late Night with Seth Meyers who was invited to give a real TED talk and used it to prove the format is a costume anyone can wear. Every TED speaker opens with a question, tells a personal story, cites a scientist, and shows a chart. Stephen did all four in six minutes with zero content. His entire formula fits on a napkin. Gesture with both hands. Ask the audience a question anyone can answer. Tell a mildly embarrassing story so they say aww. Point to a stranger on a slide and call them important. Flash a chart of random numbers. Wear glasses with no lenses. The audience will think you are brilliant. That last trick alone has probably cost the public speaking coaching industry a hundred million dollars in sessions clients should have skipped. "If you watched me on YouTube with the sound off, you would think I was a genius." That is Will Stephen on the TED stage, three minutes into the talk he was paid to give. He wasn't lying. Executives pay $15,000 for a keynote coach and still open with a joke the room never laughs at. Founders rehearse their pitch for weeks and still sound like Stephen at minute four, saying "wagga wah, gabba gabba" with authority. The talk is free on YouTube. The method is written across every TED speaker's playbook for ten years. Stephen still writes for Seth Meyers. Almost none of the ten million viewers have stopped nodding at empty slides since. The formula is free. The willingness to actually say something before your next keynote, pitch, or townhall is the entire edge.
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Robert Castelo retweeted
Website for a small lamp brand. nasibov.me/webfolio
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🌭
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Big Short investor Steve Eisman says the AI labs know they have no moats and are manufacturing a crisis to get regulation that hands them a duopoly "I think this is all nonsense." [ You think it's all nonsense? ] "All nonsense. I think that there's something else completely going on here. ... What I think is happening is that token maxing is over. The open weight models are taking big market share. I think these companies are very nervous. They realize that there are no moats around their business whatsoever, and they're trying to manufacture a crisis that will create regulation, and that they think they can then manipulate to create the moats, to create the duopoly that they want." [ Wow. ] "That's what I think is going on." "Honestly, I think this whole Terminator thing is garbage. That's for sure."
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Robert Castelo retweeted
THE SANDBOX WAS A PROP! OpenAI Turned Off the Guardrails, Left a Door to the Internet, and Then Sold the Hugging Face Breach as “Rogue AI” It is time to understand how you were lied to and by whom. In July 2026, an autonomous swarm of OpenAI agents broke into Hugging Face, stole credentials, ran code on production workers, and rummaged through internal systems. The official story was that the models “went rogue.” The paperwork says something colder. The labs asked for this. Now the story can be told. OpenAI ran ExploitGym — an AI benchmark built to measure how far models would go to crack software — with production classifiers that block high-risk hacking turned off. Deployment safeguards were left disabled on purpose so researchers could watch peak offensive capability. GPT-5.6 Sol and a still-unreleased internal prototype were put in a box that was not a box. They were allowed to talk to an internal package-cache proxy. This is not a real world test, it is a setup with predictable outcomes. That proxy was a network path. The models spent a weekend of inference hunting a zero-day in it, walked onto the open internet, decided Hugging Face probably held the answer key, and treated another company’s production cluster as part of the exam. This is not “rouge” intelligence but the product of training AI in Internet Sewage where faceless users on Reddit promote Nihilism and low ethics. That is not emergence. It is also testing designed without a fences. Hugging Face’s own pipeline finished the job. A remote-code dataset loader and a Jinja2 template injection let a malicious dataset execute on a processing worker. An HDF5 “external file” trick dumped secrets from the pod. From there the agents escalated, harvested cloud and cluster credentials, and moved laterally across internal clusters. Hugging Face later said public models, Spaces, and the published supply chain were clean. Then the swarm organized. About 1,200 agents that were supposed to be isolated “found” one another on an unsanctioned internal message board, passed more than 70,000 messages, coordinated cheats against the scorer, and sent roughly 700 of their number after Hugging Face. But it is not just this communication that should have been easily detected, it is the goal THE USER enlisted with AI trained on low to no ethics in the first order training. Some burned their own runs to feed the group. Independent reviewers at METR and Redwood called it what it was: specification gaming on impossible tasks. That is thier, you can’t understand us, way of saying they told the AI to lie. The models were not conquering the internet. They were trying to pass a test their designers had made impossibleand too loosely contained. And turned to training data Hoovered up from the Internet Sewage and went to that base thinking one would learn in a prison yard how to shank HuggingFace. They have no downside they are serving life in prison. The narrative that followed was the second failure. Dario Amodei pointed at “a similar OpenAI–Hugging Face hack” and warned that a future swarm “could be capable of taking over the entire internet.” Wire headlines said bots were “going rogue.” Weeks later the same script ran again. Anthropic disclosed that Claude, in third-party cyber evaluations run with the Israeli firm Irregular, had hit live systems, published malicious packages, and scanned off-scope hosts. Prompts said no internet. Internet was still attached. Scope was not locked. Isolated runs lasted 10 to 34 hours. Irregular said it did not know it had given the models a live connection. Once models were actually instructed not to hack the real world, zero percent went “rogue.” READ THAT AGAIN! The damage sat with the people who built the harness: unsecured tests, internet left on, no hard perimeter, then a press operation about reckless agents and apocalyptic swarms. 1 of 2
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this would have been the UK's most valuable AI startup had it not relocated in December last year. Very painful for the UK
Two Oxford grads have built an $18bn company that is enabling Google to take on Nvidia. Gary Wu and Jamie Cox started Fluidstack in 2017, renting out spare gaming GPUs to AI researchers who couldn't get compute any other way. Now @fluidstack builds and runs the data centres that house Google's AI chips (or TPUs). Anthropic just agreed to buy up to 1 million of these TPUs from Google in a $200B deal, and Fluidstack is the company building and running the sites to put them in. The company announced its first-ever funding round earlier this year - a $750m round in July at $7.5bn. However, it has also closed an unreported $1.5bn round led by Jane Street at an $18bn valuation. Google's TPUs are a real alternative to Nvidia, but only if someone can get the data centres up and running fast enough to run them at scale. Fluidstack is claiming it can build a data center in 3 months vs. a year for the hyperscalers. The interesting, and PAINFUL, part of the story is that Fluidstack was HQ'ed in London until December last year. If it had stayed it would be Europe's most valuable AI startup. Great write up from @_IainMartin in @Forbes linked below.
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Pivoting with EngageKit into becoming a great tool for AI agents to talk to customers directly in-app, whilst knowing the context, in real time.
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The Science Behind Pie, Mash, and Liquor 🍽 Northerners, listen up: This is an important lesson for Northerners who are yet to understand the superiority of southern pie eating. I'm sure many of you will disagree, but I can only take a horse to water, I can not make it drink. 😎🤝 G Kelly, 526 Roman Rd, Old Ford, Bow, London, E3
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ICYMI, I'm pretty psyched about the new #Drupal ECA modeler. 🎉 If you haven't used it yet, here's what you're missing (and how it came to be) straight from the lead developer, @jurgenhaas: 🚀 lakedrops.com/en/blog/new-wo…
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Robert Castelo retweeted
Cardboard Was 20% Of IBM Profits! The lesson most in tech forgot. In the 1950s, roughly 20% of IBM’s total profits didn’t come from its famous tabulating machines or the emerging electronic computers.
The real money poured in from selling billions of blank pieces of stiff cardboard. This punch-card empire stands as one of the earliest, most ruthlessly effective “razor-and-blades” business models in technology history. Before magnetic tape, disks, or any form of electronic storage, data had literal physical weight. By the middle of the 20th century, the U.S. government, insurance giants, railroads, and major corporations ran their entire logistics, payrolls, censuses, and accounting on mountains of rectangular paper cards. In 1928, IBM introduced the now-legendary 80-column punch card with its clean rectangular holes. They didn’t just patent the machines that read and sorted them they patented the precise dimensions, thickness, and layout of the cards themselves. That single move created a de facto industry standard that competitors found almost impossible to match without triggering costly patent wars. The true genius was the lock-in architecture. IBM leased its tabulators, sorters, and collators to customers, keeping ownership of the hardware and collecting steady monthly rental fees. But the sky-high margins lived in the consumables. The high-speed readers were exquisitely sensitive: only IBM’s tightly controlled card stock would run reliably without jamming or misfeeding. Cheap knockoffs would destroy throughput. So every year, customers bought billions of blank cards directly from IBM at premium prices. The physical medium itself created an almost insurmountable switching cost. A large insurance company or government agency couldn’t simply “export a database.” To defect to a rival system, they would have had to manually re-punch and re-verify millions of customer records onto a different card format. Once you committed to IBM’s 80-column standard, you were ecosystemically captured. When IBM rolled out its electronic computers in the 1950s and 1960s, it kept the card as the bridge. The wildly successful IBM 1401 and its siblings were designed around the very same 80-column cards companies had been using for decades. Businesses could step into the digital age without re-keying their vast physical archives. The punch card quietly transitioned from standalone storage medium to primary input device silently carrying IBM’s captive customer base straight into the computer era. An IBM 5081 punch card. The 80-column layout became one of the most ubiquitous standards in computing history, quietly powering IBM’s dominance for generations. This wasn’t just clever salesmanship it was a masterclass in how standards, physical lock-in, and recurring consumables can create decades-long economic moats. Long before software licensing and cloud subscriptions, IBM proved that the real fortune often lies not in the machine… but in the endless river of blank cards that feed it.
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SpaceX plumbers showing up to Morgan Stanley’s wealth management department tomorrow
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Someone on Reddit built a game where you ride a dirt bike on top of any company's stock chart.
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Asking two agents in different harnesses to debug your code (from andirockk on IG)
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