Models & Labs
Adobe expands Firefly and releases MCP for Creative Cloud
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Why it matters
- →MCP integration allows external LLMs to programmatically control Adobe Creative Cloud apps, expanding automation possibilities.
- →Proactive agents in Firefly shift the paradigm from reactive tool usage to context-aware assistance.
- →The move positions Adobe as an interoperable component in broader AI workflows rather than a standalone suite.
Adobe announced the release of a local Model Context Protocol (MCP) at its MAX event, enabling external AI assistants such as ChatGPT, Claude, and Gemini to interact with Creative Cloud applications. The company also introduced proactive AI agents in Firefly and new features including relighting in Photoshop and object removal in Premiere Pro. Additionally, Adobe is expanding Firefly’s capabilities with a prototyping tool for Figma users and image models from its Topaz Labs acquisition. This strategy aims to integrate Adobe's creative tools into wider AI-driven workflows while exposing more endpoints based on user feedback.
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TopicDisney And Character.AIRising
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llama.cpp b12340 adds CUDA 13 and ROCm 10.0
This release quietly closes the hardware gap for local inference by adding default support for CUDA 13 and ROCm 10.0 across Linux and Windows. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get performance, while Apple Silicon builds now ship KleidiAI disabled by default, shifting the focus back to standard ARM optimizations. The inclusion of Snapdragon support for Linux and Android marks a significant step toward on-device AI beyond the x86/ARM desktop ecosystem. It is less about new features and more about ensuring llama.cpp remains the universal runtime for every available accelerator.
llama.cpp Releases
Models & Labscoding
llama.cpp b12342 adds few-row MMQ for speculative decoding
Speculative decoding just got a significant speed boost on NVIDIA GPUs. This release introduces a specialized kernel for 'few-row' matrix multiplications, targeting the small batch sizes typical of verification passes in speculative decoding workflows. By optimizing these specific operations for quantized formats like Q8_0 and IQ4 variants, llama.cpp reduces latency where it matters most during token generation. The change is CUDA-only for now but leverages hardware capabilities from Ampere onwards, making local inference faster without requiring new model architectures or complex configuration changes.
