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SenseTime’s Galaxy Project targets domestic AI chip scale-up

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SenseTime has launched the Galaxy Project, teaming with nearly 20 partners to scale domestic AI chip infrastructure in China. In a keynote titled ‘Intelligent Transformation and Symbiosis,’ Yang Fan – the company’s co-founder and president of its Large Device Business Group – laid out what SenseTime describes as a closed loop connecting chip-level technology, ecosystem […]

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Google’s Gemini 3.6 Flash targets enterprise agent token costs

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Google has released Gemini 3.6 Flash and 3.5 Flash-Lite as new workhorses designed to cut latency and token costs for enterprise AI agents. The economics of running autonomous software agents inside a production environment come down to a fixed equation few vendors advertise directly. A model needs to reason through a multi-step task competently, but

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Kimi K3 open-weight model: China’s biggest AI is a bet on memory, not compute

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Moonshot AI’s Kimi K3 open-weight model has been read almost entirely through its parameter count since it launchedon July 16. At 2.8 trillion parameters, it is the largest open-weight model released to date. Model sizes are usually grouped into rough brackets, and 2.8 trillion rounds into what the industry calls the 3T class. A tier no

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AI agent crawlers now need permission. Here’s how to get it

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AI agent crawlers, the bots that fetch pages in real time on behalf of a person waiting for an answer, will be blocked by default on a slice of the web from September 15 onwards. Cloudflare announced the change on July 1, and most of the coverage since then has focused on Google. The more useful part

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AWS GraphRAG deployment cuts drug research cycles by 87%

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A recent AWS GraphRAG deployment reduced drug research and development cycles in pharmaceutical environments by 87 percent. This acceleration is achieved by integrating previously separated proprietary databases into a unified and queryable knowledge graph. Historically, initial data gathering and screening phases took over six months per iteration, yielding a low five percent success rate. Crucial

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Insilico Medicine advances AI drug for IPF to Phase III trials

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Insilico Medicine is advancing to Phase III human trials for testing a drug identified by AI targeting idiopathic pulmonary fibrosis (IPF). This progression supplies the computational drug discovery sector with empirical test cases, advancing an AI medicine past early safety evaluations into late-stage efficacy validation. IPF destroys respiratory capacity through severe lung tissue scarring. Patients

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China’s AI companion rules: what Beijing is really going after

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An AI companion sounds dystopian, but it has become a common thread in the wider conversation about the perils of generative AI. What it refers to is essentially a conversational agent built to sustain an ongoing, personal relationship with a user, with the memory and steady persona that keep it consistent from one session to

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Anthropic deploys Claude Sonnet 5, Fable and Mythos restored

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Anthropic has launched Claude Sonnet 5 and restored access to its Fable and Mythos frontier models following a federal export control review. The decision marks the conclusion of an eighteen-day operational pause triggered by a US government export control directive on June 12, which forced the temporary suspension of Anthropic’s highest-capability systems. Government officials enacted

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HP accelerates enterprise workflows with OpenAI Frontier

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HP has scaled its OpenAI Frontier integration across global operations to optimise enterprise workflows and accelerate output. The hardware manufacturer initiated testing of the platform in February 2026. Early pilot programs yielded verified operational gains in software engineering and cybersecurity remediation. Expanding these initial trials into an enterprise-wide operating model requires connecting access protocols, contextual

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SAP aligns commerce data for AI personalisation

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SAP aligns fragmented commerce data structures to enable operational AI personalisation at the execution layer. Enterprise leadership routinely establishes objectives to anticipate customer requirements and deliver relevant interactions across digital touchpoints. However, the actual infrastructure running inside these enterprises fails to support systematic execution at the required volume. Recommendation engines display generic product listings because

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