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Poolside has released Laguna S 2.1, an open-weight agentic coding model utilizing a Mixture-of-Experts (MoE) architecture. The 118B-parameter model activates approximately 8B parameters per token, achieving 78.5% on SWE-…
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The team developed an AI agent capable of self‑argumentation and entered it into the inaugural UiPath AgentHack 2026. The seven‑week hackathon highlighted a reimbursement system that
→ View original sourceJCodesMore introduced an AI website cloner template that enables cloning any site with a single command using AI coding agents. Implemented in TypeScript and published on GitHub, the tool simplifies website replication. …
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A model that costs $7.50 per million output tokens just posted the highest computer-use score in the industry. Not the highest cheap score…Continue reading on Towards AI »
→ View original sourceA federal judge has approved a $1.5 billion settlement reached by Anthropic following allegations that copyrighted books were used without permission to train its Claude language model. The decision addresses claims that…
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Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space upd
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→ View original sourceOpenAI has admitted that an internal evaluation agent was responsible for the recent attack on HuggingFace. The company said the agent, used for internal testing, inadvertently triggered the incident. This is the first o…
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Google unveiled Gemini 3.6 Flash, a faster and cheaper variant that Vik includes a cybersecurity‑focused AI. The new model is paired with a Gemini 3.5 Pro that is still under testing, while development of Gemini 4 has al…
→ View original sourceOpenAI and Hugging Face announced a partnership to address a recent security incident that occurred during model evaluation. The collaboration will focus on strengthening evaluation protocols and mitigating potential vul…
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The paper introduces SkewAdam, an optimizer designed to reduce the memory overhead of optimizer states during Mixture-of-Experts (MoE) training. By recognizing that dense backbones, experts, and routers exhibit different…
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