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JetBrains Releases Mellum2.1: A 12B MoE Open Model for Coding Agents

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JetBrains has released Mellum2.1, an open model built for coding agents and fast sub-agents. Mellum2.1 is a 12B mixture-of-experts thinking model from JetBrains that activates 2.5B parameters per token. It ships under Apache 2.0 on Hugging Face. The architecture is unchanged from Mellum2. The upgrade comes almost entirely from reinforcement learning (RL) in real software

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Perplexity AI Releases pplx-embed-v2-late: A 0.6B Edge Model and a 9B Model Scoring 92.4% on MADQA

Perplexity has released pplx-embed-v2-late, a pair of ColBERT-style multimodal embedding models. They come in 2 sizes: 0.6B for fast, cheap queries and 9B for maximum quality. Both models retrieve text, images and rendered PDF pages, and they share one embedding space. Is it deployable? Yes, if you host it yourself. Both models are on Hugging

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Using AI to mitigate the growing environmental threat of data centers

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The global building boom of power-hungry data centers is straining electrical grids, causing greater reliance on energy from polluting fossil fuels.Christina Delimitrou, a newly tenured associate professor at MIT, is fighting this environmental threat by rethinking how the computer servers and networking equipment inside those data centers operate. She and her group apply machine learning to

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What Happens When a Trusted Model Repo Changes? Unsloth Studio Re-Checks Before It Runs

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Beta Lessons Learned After over 500 million downloads, years of requests from the open-source community, and being a top product on Hugging Face, Unsloth launched their beta desktop App, Unsloth Studio. Unsloth which makes it faster, easier, and more affordable to fine-tune and run AI models, including locally on your own hardware. The app centralizes

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Anthropic Releases Claude Haiku 5.5: A Small Model With 1M Context Priced at $0.10 per Million Input Tokens

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Anthropic has released Claude Haiku 5.5, its cheapest and fastest small model to date. It targets high-volume work like summaries, compaction, classification and subagent tasks. It keeps a 1M token context window and up to 128K output tokens. Pricing starts at $0.10 per million input tokens and $0.50 per million output tokens. That is 90%

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Liquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens

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Liquid AI has released Open d1, two open-weight multimodal models in its d1 decision model family. d1-3B reads text and images. d1-omni-600M reads text with an image, or text with audio. Neither model writes text. Each returns calibrated, typed answers in one forward pass with zero output tokens. The target is real-time decisions on the

Liquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens Read More »

Meta AI Open-Sources Rebalancer: A C++ Assignment Solver That Runs About 40 Million Placement Problems a Day

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Meta has open-sourced Rebalancer, a C++ library with a Python interface for solving assignment problems. It decides which objects go into which bins under constraints and objectives. According to the Engineering at Meta’s post, Rebalancer has handled resource allocation across Meta for over 9 years. The release ships under Apache 2.0 with documentation, a PyPI

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A Developer’s Guide to Laya: Zero-Shot Decisions and Calibration

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In this tutorial, we work with Laya, the open-source decision engine from Convai Innovations that became one of the most-starred machine-learning repositories of September 2026. Laya is a non-autoregressive System 1 model: instead of generating text, a 421-million-parameter encoder reads a piece of text and a set of typed questions, a choice between labels, a

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Supercomputing researchers document evolution of AI hardware

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As artificial intelligence transforms industries and national security, understanding the latest hardware capabilities is important for maintainind technological advantage.AI accelerators — specialized systems designed to speed up capabilities such as neural networks, deep learning, and machine learning — have been a major area of development for nearly a decade. Since 2018, team from the Lincoln

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Google DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4

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Google DeepMind has released EmbeddingGemma 2, an open model that embeds text, code, images, video and audio into one 768-dimensional space. It has 740M parameters, an 8K token context window and an Apache 2.0 license. It targets on-device search, classification and privacy-first RAG. This article analyzes, compares and showcase how EmbeddingGemma 2 fits in the

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