
Apple's self-driving car program may not have succeeded, but it catalyzed the development of powerful AI chips. The need for advanced on-device AI processing led to the creation of the Neural Engine, first seen in the iPhone X. This innovation has become central to Apple's hardware, supporting features like FaceID and augmented reality. As Apple accelerates the development of its M7 Ultra chip, expected to support up to 1.5TB of RAM, the company continues to leverage its AI hardware prowess, despite lagging in AI software.
Read originalEarlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
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© The Verge AICapcom is quietly pivoting from its strict no-AI-assets stance to integrating generative tools directly into the RE Engine workflow. This isn't about replacing artists; it's about solving the crushing time costs of AAA production by letting developers co-create with the engine. The shift signals a pragmatic industry realization: if AI can accelerate iteration, studios will adopt it regardless of previous ethical red lines. We are moving from 'AI in games' to 'AI for making games.'
© The Verge AIDavid Robinson’s departure from OpenAI marks a significant shift in the internal narrative around AI safety. As the former author of safety reports for major model releases, his public critique carries weight beyond typical employee grievances. He argues that the industry's 'move-fast' culture is fundamentally incompatible with managing existential risks, advocating for nuclear-level safeguards instead. This aligns with a growing trend of insiders leaving firms like Anthropic and Google DeepMind to voice similar concerns. The real story here isn't just one resignation, but the erosion of trust in self-regulation from within the labs themselves.
© The Verge AIMeta is handing the keys to its Muse AI agent by open-sourcing the software needed to run it on custom hardware. Developers can now hook Muse into ESP32 boards or Raspberry Pi setups, effectively turning workbench scraps into personalized AI terminals. This moves Muse beyond a cloud-only interface into tangible, local devices like E Ink displays or HDMI sticks. It signals a shift toward decentralized, user-owned AI interactions rather than relying solely on proprietary apps.
This release quietly cements llama.cpp as the universal inference runtime by finally bringing first-class ROCm 10.0 support to both Linux and Windows. AMD GPU users no longer need workarounds, effectively closing a long-standing parity gap with NVIDIA's CUDA ecosystem. Equally notable is the new Snapdragon binary for Linux, which unlocks local AI on ARM-based laptops using Adreno GPUs and Hexagon NPUs. While KleidiAI on macOS has been disabled in this build, the expansion to AMD and Qualcomm hardware makes this a critical update for anyone running inference outside of the NVIDIA walled garden.
This release quietly cements llama.cpp as the universal inference runtime by finally bringing first-class ROCm 10.0 support to both Linux and Windows. AMD GPU users no longer need workarounds, effectively closing a long-standing parity gap with NVIDIA's CUDA ecosystem. Equally notable is the new Snapdragon binary for Linux, which unlocks local AI on ARM-based laptops using Adreno GPUs and Hexagon NPUs. While KleidiAI on Apple Silicon has been disabled in this build, the expansion to AMD and Qualcomm hardware makes llama.cpp significantly more accessible for diverse consumer hardware.
© The AI Daily BriefMeta's stock price surged following positive market reaction to its new Muse model capabilities.