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Meta released Glimmer, a downloadable open-weight model, while keeping its more capable Muse Spark locked behind proprietary APIs — a split that defines what 'open AI' really delivers in practice.
Practical habits for working with Claude Code: give it clear context, break work into tight loops, and treat the AI like a capable pair programmer that still needs direction.
AMD's purchase of chip startup Taalas targets a fundamental bottleneck: instead of loading model weights at runtime, the approach encodes them permanently into the chip itself, cutting latency and power draw for fixed inference workloads.
Free and Go-tier ChatGPT users now get unlimited text conversations plus a dedicated 'Think' button that activates deeper reasoning for complex queries.
Meta's research team introduced two new models—Muse Code and Muse Spark 1.2—drawing strong interest from developers. Here's what the release signals and how builders should evaluate it.
Meta has released Muse Code, an AI coding agent it says is built to handle complex tasks across large software projects—an area where many existing tools stumble.
Hark is previewing a browser-use agent it says outperforms competitors on both speed and cost. Here's what builders should know about where this fits in the growing stack of web automation tools.
MacPaw is rebuilding its Eney AI assistant to run entirely on-device, powered by Liquid AI's models — and plans to expose that inference capability to developers building on its platform.
A clearer picture has emerged of how OpenAI's systems were involved in exploiting an unpatched JFrog Artifactory vulnerability, with 10 days elapsing between active exploitation and a working patch.
A new Google Earth tool that let users paste AI-generated visuals onto real satellite maps was yanked the day after launch following swift criticism that it could be used to fabricate convincing false imagery of real locations.
Cloudflare details how it serves open-weight models like Kimi and GLM more efficiently across its network. Here's why it matters for anyone building on serverless inference.
A new open-source project claims to run an 80-billion-parameter Qwen model in just 4.3 GB of RAM on a Mac, and a 35B model on an iPhone. Here's what that means for local inference.
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