MarkTechPost (1 month ago)
Training and serving frontier models is now a networking problem as much as a compute problem. Collective operations like all-reduce and all-to-all synchronize thousands of accelerators during training, and the slowest transfer sets the pace for the entire job.
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European regulators are expecting AI-driven financial chaos sooner than later.
Chatbot prompts reveal the engineer’s alleged efforts to set up a covert listening device days before one was found in a meeting room used by high-level executives.
Saudi national Abdulrahman Alfozan was arrested Friday and terminated by OpenAI.
Cerebras CEO Andrew Feldman predicts a future where personal AI assistants become as commonplace as household appliances, running partly on local devices and partly in the cloud.
Graduates are increasingly using AI to misrepresent themselves in job applications, research suggests. But some say their peer group has no choice.
The 2026 model is faster and can hear you from afar, but the 2022 box gets Siri AI as well.
Meta released Muse Wonder and Muse Wonder Full, 1M-context agent models. Muse Wonder costs $4/$20 per million tokens on Meta Model API. Wonder Full, on Muse Code plans, leads DeepSWE and SWE-Marathon.
In this comprehensive coding tutorial, we explore Meta’s Rebalancer assignment solver using its C++ core and Python bindings.
Z.ai’s GLM-Legal is a legal AI model built on GLM-5.3-Flash. It scores 19.2% all-pass on Harvey’s Legal Agent Benchmark, nearly 3x its base, at $0.15 per test. Access is by application.
Alibaba’s Qwen team has introduced QwenGyre, an elastic reinforcement learning RL framework for extreme-long-horizon agent training. QwenGyre is an online reinforcement learning system from Alibaba that trains coding agents through unmodified, black-box harnesses.
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