Every AI response depends on more than compute. It also depends on: Memory access speed Interconnect efficiency Chip-to-chip communication overhead Data movement costs End-to-end system latency When any of these slow down, your AI gets slower and more expensive. The model
AI
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Huawei’s Tau Scaling Law changes AI optimization layer focus
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Most enterprises are optimizing the wrong layer of AI.
— Ronald van Loon (@Ronald_vanLoon) 27 mai 2026
Cost per token doesn't start with the model.
It starts underneath it.
Huawei's Tau Scaling Law (Her's Law) changes the conversation entirely.
Here's the breakdown…#HuaweiPartner @huawei pic.twitter.com/P5fnE5CtGuMost enterprises are optimizing the wrong layer of AI. Cost per token doesn't start with the model. It starts underneath it. Huawei's Tau Scaling Law (Her's Law) changes the conversation entirely. Here's the breakdown… #HuaweiPartner @huawei
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Agentic AI and Premium Inference Speed Explained
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Agentic AI changes what speed actually means.
— SambaNova (@SambaNovaAI) 27 mai 2026
Behind every response, multiple agents are reasoning and exchanging tokens in real time. Faster inference means faster outcomes.
🎧 @SumtiJairath explains why premium inference matters @dcdnews: https://t.co/9Wwc0vkssJ pic.twitter.com/4eKEfKJjyRAgentic AI changes what speed actually means. Behind every response, multiple agents are reasoning and exchanging tokens in real time. Faster inference means faster outcomes. @SumtiJairath explains why premium inference matters @dcdnews
: https://
podcasts.apple.com/us/podcast/epi
sode-102-the-training-to-inference-passageway/id1607349232?i=1000764784324
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Krea 2 API release with platform and agent support
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today, we're releasing the API for Krea 2.
— Krea (@krea_ai) 27 mai 2026
now available in platforms like @fal or @ComfyUI, through agents like Hermes from @NousResearch, and with full support for Claude, Codex, or OpenClaw.
learn how you can set it up 👇 pic.twitter.com/R4jdyyTxQktoday, we're releasing the API for Krea 2. now available in platforms like @fal or @ComfyUI
, through agents like Hermes from @NousResearch
, and with full support for Claude, Codex, or OpenClaw. learn how you can set it up -
Akshay Pachaar’s 47-minute walkthrough on building autonomous agents
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Okay.. @akshay_pachaar’s Hermes article was already top-tier, but his new 47-minute walkthrough is INSANE 🤯
— Charly Wargnier (@DataChaz) 27 mai 2026
Skip Netflix, grab a coffee, and watch this masterclass on how to build self-improving, 24/7 autonomous agents locally on your machine 👀↓pic.twitter.com/AClp9LOocn https://t.co/YVecaPzsb1Okay.. @akshay_pachaar
’s Hermes article was already top-tier, but his new 47-minute walkthrough is INSANE Skip Netflix, grab a coffee, and watch this masterclass on how to build self-improving, 24/7 autonomous agents locally on your machine ↓ -
ChatGPT Image Recreation Prompt Engineering Experiment
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ChatGPT pidió 74 veces que “recreara perfectamente esta imagen, sin cambios” pic.twitter.com/Vf44smKm8y
— SONIA (@S0N_IA_) 27 mai 2026ChatGPT asked 74 times to “perfectly recreate this image, without changes”
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Novel Block-Based Backprop Reduces AI Training Memory
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For over a decade, we’ve accepted that end-to-end backprop is the only way to train deep networks. But holding the entire network in memory all at once is why AI training is hitting a resource wall.
— hardmaru (@hardmaru) 27 mai 2026
We found a new way to break the network into blocks and train them… https://t.co/vxyMR6goTDFor over a decade, we’ve accepted that end-to-end backprop is the only way to train deep networks. But holding the entire network in memory all at once is why AI training is hitting a resource wall. We found a new way to break the network into blocks and train them
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DiffusionBlocks: Block-wise Neural Network Training via Diffusion
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Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretationhttps://t.co/45Xvzl2qQS
— Sakana AI (@SakanaAILabs) 27 mai 2026
What if we didn’t have to hold an entire neural network in memory to train it?
Standard neural net training optimizes all parameters jointly. As a result, the… pic.twitter.com/jJtFg61nJhIntroducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation http://
pub.sakana.ai/diffusionblocks What if we didn’t have to hold an entire neural network in memory to train it? Standard neural net training optimizes all parameters jointly. As a result, the -
Agentic AI Pipeline for Hardware Model Testing
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We created an agentic AI model pipeline utilizing a fleet of our new TT-QuietBoxes and Tenstorrent Galaxies to download random models from @huggingface to port to our hardware, compile, and test for accuracy.
— Tenstorrent (@tenstorrent) 27 mai 2026
After thousands and thousands of models, the model pass rate has been… pic.twitter.com/6BlKUNRL5dWe created an agentic AI model pipeline utilizing a fleet of our new TT-QuietBoxes and Tenstorrent Galaxies to download random models from @huggingface to port to our hardware, compile, and test for accuracy. After thousands and thousands of models, the model pass rate has been
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Agentic AI Production Adoption Slower Than Tech Hype Suggests
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There are not a lot of signs of agentic AI ramping into production at normal companies so far. There is a lot of boosterism from tech companies about agentic AI. Without trillions spent on agents by enterprises, the whole thing could come crashing down… Kudos to Tavis McCourt