OpenAI is building a smartphone where AI agents replace every app on your home screen. Qualcomm and MediaTek are co-developing the chip. Luxshare is assembling it. Mass production targeted for 2028. The pitch: no more app grid. No more switching between 40 apps to get one
SYSTEMS
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NVIDIA Uses cuOpt Agentic Workflows to Optimize Supply Chains
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Internally at NVIDIA, we use cuOpt based agentic workflows with agent skills to optimize our supply chains. Since it’s open source, you can too.
— NVIDIA AI (@NVIDIAAI) 4 mai 2026
With optimizations ready in minutes instead of weeks, the workflow uses multi-agent LangChain Deep agent orchestration and… pic.twitter.com/V5BJnMOzJbInternally at NVIDIA, we use cuOpt based agentic workflows with agent skills to optimize our supply chains. Since it’s open source, you can too. With optimizations ready in minutes instead of weeks, the workflow uses multi-agent LangChain Deep agent orchestration and
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AI Full Stack: Five Layers Defining the Next Industrial Era
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AI isn't just software. It's a five-layer cake: energy, chips, infrastructure, models, and applications. The countries and companies that build the full stack will define the next industrial era. Read more here: https://t.co/Lc9Cfjg2nv https://t.co/v3KMwPFBgv
— NVIDIA (@nvidia) 4 mai 2026AI isn't just software. It's a five-layer cake: energy, chips, infrastructure, models, and applications. The countries and companies that build the full stack will define the next industrial era. Read more here: https://
futurumgroup.com/research-repor
ts/five-layers-of-the-ai-cake?ncid=so-link-329109-vt04
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Web2BigTable: Bi-Level Multi-Agent LLM System for Web-Scale Information Extraction
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Web2BigTable A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction paper: https://
huggingface.co/papers/2604.27
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NVIDIA Megatron Core Adds Muon and Advanced Optimizers for LLM Training
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Training Kimi K2 and Qwen3 30B-scale models efficiently requires more than standard data-parallel tricks. NVIDIA Megatron Core now provides end-to-end support for emerging higher-order optimizers like Muon, alongside research optimizers such as MOP and REKLS, to push training
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Speculative Decoding Accelerates RL Post-Training Rollouts
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"Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decoding" Speculative decoding for RL rollouts! This paper speeds up post-training without changing the target policy’s sampling distribution. So a draft model proposes multiple tokens, and the policy
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ASI’s potential for permanent advantage via chip innovation
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I agree those bottlenecks imply it won’t go foom overnight but an RSI-ing ASI may well invent better chip production process, better ways to use current chips, etc. in a way that adds up to permanent advantage.
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Local LLMs Web Stack Setup With SearXNG Firecrawl and Camofox
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PRO TIP Using local LLMs? Give them a web stack My setup: – SearXNG: candidate source discovery – Firecrawl: known-URL scraping and crawling – Camofox: browser fallback when JS/interaction gets annoying Search → Extract → Interact Tell your favorite agent to set this up,
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Futurum and NVIDIA Frame AI as Five-Layer Stack with $690B Spend
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Futurum Group just published a report with NVIDIA that frames AI as a five-layer stack: energy, chips, infrastructure, models, applications, and the data is worth sitting with. The five largest US hyperscalers are on track to spend up to $690B on infrastructure this year alone,
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Config Migration Fix for Gateway requireMention Setting 2026.5.3
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This looks like a config migration issue in 2026.5.3. Until 2026.5.4 is out later today, put requireMention under groups, not at the top level, then restart the gateway. (we add automatic migration in the next version) Telegram:
channels.telegram.groups["*"].requireMention =