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Meta AI Introduces MetaRoCE: A Clean-Sheet RDMA Transport Built for AI-Scale Ethernet

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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. Even small amounts of network friction directly strand significant compute capacity. This week, Meta introduced MetaRoCE. […]

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Fastino Releases GLiNER2.5: A Boundary-Prediction Architecture That Removes Span Enumeration From Information Extraction

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Information extraction teams face a recurring choice. Small encoder models are cheap but rigid, and large language models are flexible but expensive per document. Fastino released GLiNER2.5 to narrow that gap. The release replaces span enumeration with boundary prediction: the model scores where an entity starts and ends instead of scoring every candidate span against

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Generalist AI Releases GEN-1.5: A Robot Foundation Model That Learns New Tasks From One 3–12 Second Demo

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Generalist AI has released GEN-1.5, a robot foundation model that learns a new physical task from a single demonstration. Drop 3–12 seconds of sensorimotor data into its 30-second context window, and the robot performs the task. No gradient updates, no fine-tuning, no task-specific programming. Across 10 diverse manipulation tasks, this one-shot in-context prompting averaged 59%

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Google Research Introduces ME-POIs: A Mobility-Informed Framework that Adds “How a Place Is Used” to Text-Based POI Embeddings

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A team from Google Research and USC has released Mobility-Embedded POIs (ME-POIs), a framework that folds aggregate human movement into text-based place embeddings. The premise is that language models describe what a place is, but not how it is used. Two coffee shops can share a category, an address block, and a text vector, while

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Harvey Introduces Harvey Tenet: A Kimi K3 Base Post-Trained with Fireworks for Long-Horizon Legal Agent Work

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Harvey has released Harvey Tenet, its first post-trained model, as a research preview as of today. Tenet is a Kimi K3 base post-trained with Fireworks through asynchronous reinforcement learning on long-horizon legal work. The training corpus combined synthetic data, publicly available legal data, and human expert data. Harvey states no customer data was used. Against

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Meet FreeToken: An Edge-Native MoE Serving Engine that Runs 753B GLM-5.2 on a Single Workstation GPU

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Frontier open-weight models are shipping faster than the hardware assumptions around them. Kimi-K3, GLM-5.2 and DeepSeek-V4-Flash are closing the capability gap with proprietary systems, but releasing parameters only determines who can obtain a model — not who can afford to run it. Serving them still assumes datacenter-class GPU clusters, and as agentic workloads push inference

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Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each

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Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment, changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the question. If the harness decides quality, then how you run the loop becomes

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Meet S1-mini: Superwhisper’s 462 MB Open-Weights Text Normalizer That Turns Raw ASR Transcripts Into Clean Written Text

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Superwhisper has released the S1 family of models: S1-Voice, S1-Language, and S1-mini. S1-Voice is a cloud speech-to-text model, and S1-Language is a cloud instruction-following model for cleanup and formatting. The one that is quite interesting outside the app is S1-mini, released with open weights on Hugging Face. S1-mini is a 0.6B text normalizer, not a

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Meet UPDF: A Lightweight Adobe Alternative Built for the Agentic Era

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PDFs sit at the end of almost every workflow. Contracts, invoices, research papers, scanned intake forms, manuals, and compliance filings all arrive as PDFs. The format is very good at freezing a layout. It is much worse at letting you change one number inside that layout. That gap has become more visible as language models

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Liquid AI Releases LFM2.5-DSpark Draft Models That Deliver Up to 3.18x Faster Decoding Without Changing Model Outputs

Liquid AI has released DSpark draft model checkpoints for three models in its LFM2.5 family: LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and LFM2.5-8B-A1B. Each drafter adds a speculative decoding path to an existing target model. A roughly 300M-parameter draft proposes a block of nine candidate tokens, and the target model verifies the whole block in a single forward pass.

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