The "banana trick" at self-checkout works by ringing up a £4 avocado as a 40p onion The fix is a simple AI cascade. @Ultralytics YOLO detects each item, a classifier (EfficientNet, MobileNetV4, etc) tells you what it actually is, and then cross-checks against the POS scan
SYSTEMS
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Up to 95% Token Reduction Without Code Changes
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UP TO 95% TOKEN REDUCTION WITHOUT CHANGING THE CODE A Netflix engineer just open-sourced Headroom, and it’s one of the smartest ways I’ve seen to cut LLM costs. It wraps Cursor or Claude in a local proxy to compress your payload before it hits the LLM: → Intelligently shrinks
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The true primitive: authority protected by proof
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Exactly. 'Impossible to unprison' is the wrong goal. The true primitive is authority protected by proof. GoalOS assumes that every model, agent, and evaluator can fail: generate → verify → contest → canary → monitor → cancel The capacity
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The Canonical Affirmation: The Six Steps of the GoalOS Cycle
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The canonical affirmation: 1. GoalOS defines what must change.
2. Agents attempt the change.
3. Evidence demonstrates what happened.
4. Evaluation determines what worked.
5. The Proof Gradient determines what can propagate.
6. The Register -
Create a coordinated AI agent team with an orchestrator
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How to create a coordinated AI agent team:
– set up your first preferred agent (this is your orchestrator)
– ask it to configure Managed Gemini Agents or something like modal CPU instances to launch sub-agents in their own environment -
Installation of a local AI infrastructure with unlimited LLM on ASUS GX10
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Entre deux streams à VivaTech, je viens enfin, à 2 h du matin, de finir ma propre petite infra IA full local + API + un agent Hermes avec Nemoclaw sur ma GX10 d'@ASUS, accessible à distance quand je veux, pour avoir des LLM en illimité !! Trop heureux !!
— Defend Intelligence (Anis Ayari) (@DFintelligence) 19 juin 2026
J'ai Nemotron 120B… pic.twitter.com/zUtXD6i8L2Between two streams at VivaTech, I finally, at 2 AM, finished my own little full local AI infra + API + a Hermes agent with Nemoclaw on my GX10 from @ASUS, accessible remotely whenever I want, to have unlimited LLMs!! So happy!! I have Nemotron 120B
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Dumb system apologizes to smart system in 2026 programming
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I just caused an automated system which is dumb to apologize-in-advance to an automated system which is smart. (~ "The system which generates this file can't do string manipulation. You can, though, so to succeed you will want to… Sorry about extra work.") Programming in 2026.
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Lawmakers warn airlines of AI pricing targeting personal pain points
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1/ Airlines are moving fares to AI that prices by demand, your device, and your location in real time. One carrier planned to set 20% of fares this way, in what lawmakers called pricing aimed at your personal "pain point." It denies using personal data. The rest are following.
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Article on Google’s Supercomputers from TPU v2 to Ironwood
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My colleagues at @Google, @NormJouppi, Sridhar Lakshmanamurthy, Cliff Young and David Patterson, recently wrote an article that will appear in the July/August 2026 issue of @ieeemicro, titled "Google's Training Supercomputers from TPU v2 to Ironwood: Architectural
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AI Inference: Combining GPU, RDU and CPU for Each Task
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One chip to rule them all? No thanks.
— SambaNova (@SambaNovaAI) 18 juin 2026
AI inference isn't one workload, it's a series of different jobs. That's why you pair GPUs, RDUs & CPUs together, letting each do what it does best.
Better speed, better performance, better economics. 🦾
Chat with us at @RaiseSummit:… pic.twitter.com/Kq5dK9fKtZ—
One chip to rule them all? No thanks. AI inference is not a single workload, it’s a series of different tasks. That’s why we combine GPU, RDU, and CPU together, letting each do what it does best. Better speed, better
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