Companies running AI at scale have made a strategic shift: They have stopped treating modernization as an IT project. They have started to view it as an integral part of the AI strategy. Examples: →
AI
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Easy AI demos, hard production: weaknesses exposed
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Here is the uncomfortable reality I observe in companies: AI demos are easy. AI in production is not. Once AI moves past the pilot stage, it begins to expose every weakness in the foundation: → Fragmented systems
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AI pilots fail mainly because of the company, not the model
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Most AI pilots do not fail because the model is weak.
— Ronald van Loon (@Ronald_vanLoon) 4 juin 2026
They fail because the enterprise underneath it was never built for production AI.
→ Data volume
→ Latency
→ Deployment cycles
→ Legacy dependencies
→ Technical debt
This is the infrastructure problem nobody is… pic.twitter.com/pVbkLn2f1uMost AI pilots do not fail because the model is weak. They fail because the underlying company was never designed for production AI. → Data volume
→ Latency
→ Deployment cycles
→ Legacy dependencies
→ Technical debt It's -
Anthropic releases Claude Oceanus v1-p for Red Teams, hinting at Mythos models
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ANTHROPIC : A new "claude-oceanus-v1-p" has been made available to Red Teams. This appearance may signal an upcoming release of newer Mythos models, referenced earlier by Antropic. Soon?
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Machine learning identifies 14-protein signature predicting lung cancer risk and therapy response.
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A very impressive study for how we could prevent lung cancer more than 5 years before it is diagnosed. Using machine learning, discovery of a 14-plasma protein signature of risk that predicts responsiveness to an antibody therapy to interleukin, IL-1β
Validated across 8 cohorts -
Gautam Kamath thanks Peter for collaborative Byzantine robustness work
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Thanks Peter! Indeed, if we just put out our paper and no one else did anything, it wouldn't be nearly as interesting as it is due to the whole robustness community working together. As I recall, you famously also worked on this area (Byzantine robustness)
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NanoClaw AI traces shared with Hugging Face for analysis and model improvement.
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Shared my first trace from @NanoClaw_AI to @huggingface yesterday. Very cool! By default, all agents should store their traces on HF (in private) so that you can keep a history of them, analyze them,… & share them and post-train better models, harnesses and more. Excited
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Krea 2 Turbo: generate high-quality images in 2 seconds
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introducing Krea 2 Turbo.
— Krea (@krea_ai) 4 juin 2026
generate high-quality images in just 2s; compatible with style references, moodboards, and LoRAs.
try it for free at krea . ai pic.twitter.com/cG5wymDdmhintroducing Krea 2 Turbo. generate high-quality images in just 2s; compatible with style references, moodboards, and LoRAs. try it for free at krea . ai
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AI too powerful, finding zero-days, nerfed before release
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But it clearly was too powerful for public release – the thing is finding zero-days left right and center I entirely believe that Anthropic decided not to release it to general availability until they'd found a way to nerf it
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LLM finds forum copy, refuses info on mild claim question
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An LLM found a copy in a forum and then went into Whoops You Are Not Authorized To Access That Info mode when I asked a very mild question about a claim made.