Adobe Rolls Out AI Video Generation & Editing Features for Premiere Pro - dailyexcelsior.com
<a href="https://news.google.com/rss/articles/CBMiowFBVV95cUxOdi1pWEhDZ2lBUUk1RFRQQUR1LXo3QlZKMTFhNVhjV2ttNzlObHE5RWpuQUx4X2ZJRFc1RmlEaXliUEJSZWJTM1RoYXpBNFNIYm9MY3htdXllZ1BQeVEtUkM4eWs0MmR1M1psS1dmNDd3Vi1tOXd1U0ZjVTR1ZHVkd3BoSHkyYkhmOFdOSnZvdzgwVzBDUGRQNXBOTGFQbnVpMzVZ?oc=5" target="_blank">Adobe Rolls Out AI Video Generation & Editing Features for Premiere Pro</a> <font color="#6f6f6f">dailyexcelsior.com</font>
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TurboQuant on Apple Silicon: real benchmarks on Mac Mini M4 16GB and M3 Max 48GB
I’ve been testing TurboQuant this week on two machines and wanted to share the actual numbers. Why this matters: TurboQuant compresses the KV cache, not the model weights. On long contexts, KV cache can take several GB of memory, so reducing it can make a big difference even when throughput stays similar. In the setup I tested, K stays at q8_0 and V goes to turbo3 (~3-bit). That asymmetric tradeoff makes sense because errors in the keys affect attention routing more directly, while values often tolerate heavier compression better. Benchmark 1: Mac Mini M4 16GB — Qwen3-14B Q4_K_M at 8K context → Without TurboQuant: KV cache 1280 MiB, K (f16): 640 MiB, V (f16): 640 MiB — 9.95 t/s → With TurboQuant: KV cache 465 MiB, K (q8_0): 340 MiB, V (turbo3): 125 MiB — 9.25 t/s Almost 3x compression, wit
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hexagon: slight optimization for argosrt output init ( #21463 ) macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
Stop Prompting; Use the Design-Log Method to Build Predictable Tools
The article by Yoav Abrahami introduces the Design-Log Methodology, a structured approach to using AI in software development that combats the "context wall" — where AI models lose track of project history and make inconsistent decisions as codebases grow. The core idea is to maintain a version-controlled ./design-log/ folder in a Git repository, filled with markdown documents that capture design decisions, discussions, and implementation plans at the time they were made. This log acts as a shared brain between the developer and the AI, enabling the AI to act as a collaborative architect rather than just a code generator. By enforcing rules like read before you write, design before implementation, and immutable history, the methodology ensures consistency, reduces errors, and makes AI-assi
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