Google’s new TurboQuant algorithm speeds up AI memory 8x, cutting costs by 50% or more

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the “Key-Value (KV) cache bottleneck.” Every word a model processes must be stored as a high-dimensional vector in high-speed memory. For long-form tasks, this “digital cheat sheet” swells rapidly, devouring the…

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The AI skills gap is here, says AI company, and power users are pulling ahead

Anthropic’s latest research suggests that while AI is rapidly changing the way work gets done, it hasn’t meaningfully eliminated jobs. At least, not yet. But beneath what Anthropic’s head of economics, Peter McCrory, says is a “still healthy” labor market, early signs are pointing to uneven impacts, especially for younger workers just entering the workforce. …

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How xMemory cuts token costs and context bloat in AI agents

Standard RAG pipelines break when enterprises try to use them for long-term, multi-session LLM agent deployments. This is a critical limitation as demand for persistent AI assistants grows. xMemory, a new technique developed by researchers at King’s College London and The Alan Turing Institute, solves this by organizing conversations into a searchable hierarchy of semantic…

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