It’s reasonable to expect that the next iteration will be better. It would be surprising if GPT-5.6 wasnt an improvement over GPT-5.5. But the more interesting part is token efficiency. As models move into more complex, longer-running, agentic workflows, every wasted token
AI
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Dynamic workflow execution with subagents (Claude Code)
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ANTHROPIC JUST DROPPED A MASSIVE UPDATE FOR CLAUDE CODE: DYNAMIC WORKFLOWS. Instead of a single pass, Claude can now write an orchestration script on the fly and spin up tens to hundreds of parallel subagents for complex tasks ↓ First, they divide the work, run
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How Generative AI persuasion bombs users and how to fight back
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How #GenerativeAI ‘persuasion bombs’ users — and how to fight back
by Dylan Walsh @MITSloan Learn more: https://
bit.ly/4cDhCNN #ArtificialIntelligence #ML #MachineLearning #Tech -
LeCun: Intelligence equals trained world model plus optimal control
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Essentially. Or rather: Trained world model + optimal control = intelligence
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Old model vs new: from syntax to plain language
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The old model was simple:
you had an idea,
then translated it into syntax,
then fought the tooling until it worked. The new model looks very different: → describe the app in plain language
→ generate the interface, logic, and structure
→ test it
→ refine it through feedback -
AI-coded ForgeTrain framework trains MiniCPM-5 1B 10% faster than NVIDIA Megatron
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And here’s the part that feels like foreshadowing: MiniCPM-5 1B was pre-trained with ForgeTrain, an AI-coded training framework claimed to be 10% faster than NVIDIA Megatron. AI isn’t just running apps now. It’s starting to build the factory. Try it. Break it. Contribute:
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Low VRAM AI deployment numbers enable real workflows everywhere
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The deployment numbers are the real flex: FP16: ~2GB VRAM
INT8: ~1GB
INT4/Q4: ~0.5GB That puts real AI workflows on normal machines, edge boxes, tablets, browsers, and local dev stacks. Less “enterprise AI theater.” More shipping. -
Desktop pet demo proves capable on-device AI without cloud API
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The best proof is the desktop pet demo.
— God of Prompt (@godofprompt) 30 mai 2026
Not because it’s adorable.
Because it shows a capable AI app running locally without begging some cloud API every 4 seconds.
This is what on-device AI should look like:
fast, cheap, responsive, and actually usable. pic.twitter.com/xY3pGgWNRJThe best proof is the desktop pet demo. Not because it’s adorable. Because it shows a capable AI app running locally without begging some cloud API every 4 seconds. This is what on-device AI should look like: fast, cheap, responsive, and actually usable.
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MiniCPM-5 1B: Tiny Open-Source Model Runs Locally, No GPU Needed
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The AI crowd keeps worshipping giant models.
— God of Prompt (@godofprompt) 30 mai 2026
Meanwhile, MiniCPM-5 1B is trying to make them look bloated.
A 1B open-source model that runs locally, on CPUs, edge devices, and even browsers.
Tiny model. Real deployment. No GPU cult required. pic.twitter.com/XWH8uso0SEThe AI crowd keeps worshipping giant models. Meanwhile, MiniCPM-5 1B is trying to make them look bloated. A 1B open-source model that runs locally, on CPUs, edge devices, and even browsers. Tiny model. Real deployment. No GPU cult required.
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Microprocessors, infrastructure, data, AI: the true dominance order
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This is the law of true technological dominance: μ-processors > Infra > Data > AI But most people are not ready for this debate, as the focus remains solely on AI software applications. The long-term reality check will be harsh for some.