codex is great for any kind of work done with a computer:
COMPUTING
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iPhone equals MacBook thanks to server side agents
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iPhone ~= MacBook, thanks to server side agents
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Huawei’s EDCO: Dynamic Curriculum Orchestration for Real-Time LLM Adaptation
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What if fine-tuning a large language model could adapt its training in real-time, not just follow a static plan? Researchers from Huawei introduce EDCO, a dynamic curriculum orchestration framework. Instead of pre-ordering training samples by difficulty, EDCO prioritizes data
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Edge and physical AI poised to upend enterprise networks
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Edge and physical #AI poised to upend enterprise networks
by Beth Pariseau @TechTargetNews Learn more: https://
bit.ly/4wKpvJa #EdgeComputing #Robotics #Automation #ArtificialIntelligence -
Successful optimization over trillions of variables: unprecedented feat
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Even if it's not AGI, successful optimization over trillions of variables is an unprecedented feat for humanity.
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Higgsfield Supercomputer: AI Agent for Complete Creative Workflows
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🚨Higgsfield Supercomputer is a cloud-native AI agent that turns one chat into a full creative workflow. It helps with planning, scripting, visuals, voice, editing, and publishing in one place.
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 26 mai 2026
▪️40+ built-in tools for content creation.
▪️3 layers of memory to keep context… pic.twitter.com/78Zcsy6K1jHiggsfield Supercomputer is a cloud-native AI agent that turns one chat into a full creative workflow. It helps with planning, scripting, visuals, voice, editing, and publishing in one place. 40+ built-in tools for content creation.
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NVIDIA Vera CPU for Agentic AI: Performance Benchmarks
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We built the NVIDIA Vera CPU for agentic AI, and the latest benchmarks from @Phoronix confirm it delivers. 1.5x overall performance vs. leading x86 processors
2x faster Linux kernel compilation
4x greater STREAM TRIAD memory bandwidth Vera achieves the performance that AI -
New Method Significantly Reduces Compute Demands for One-Shot LLM Training
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One-shot LLM training demands reliable scaling laws to predict model behavior, but current scaling techniques are compute-intensive. New research introduces a method that reduces training demands significantly, lowering the time and cost of scaling:
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Heterogeneous Hardware Strategy for Enterprise AI Inference
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Enterprises don’t need a single chip to handle all inference workloads.
— SambaNova (@SambaNovaAI) 26 mai 2026
The better approach is heterogeneous: GPUs for compute-heavy prefill, RDUs for fast decode, and CPUs for orchestration and integrations.
Right work, right hardware layer. That’s how you avoid tradeoffs. 🦾 pic.twitter.com/B1tqmROeQ2Enterprises don’t need a single chip to handle all inference workloads. The better approach is heterogeneous: GPUs for compute-heavy prefill, RDUs for fast decode, and CPUs for orchestration and integrations. Right work, right hardware layer. That’s how you avoid tradeoffs.
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Reservoir Computing Enhances Soft Robotics Control
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Unlocking soft #Robotics control with #AI's cousin: Reservoir computing
by Alex Parrish @TechXplore_com Learn more: https://
bit.ly/4f3sTIO #Robots #Engineering #TechForGood #Innovation #Technology