Exclusive | The Fundraising Tactic AI Startups Are Using to Juice Valuations - WSJ
Exclusive | The Fundraising Tactic AI Startups Are Using to Juice Valuations WSJ
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5 Interesting Startup Deals You May Have Missed: Blood-Drawing Robots, Inboxes For AI Agents, Franchised Defense Manufacturing, And More - Crunchbase News
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Synthetic Population Testing for Recommendation Systems
Offline evaluation is necessary for recommender systems. It is also not a full test of recommender quality. The missing layer is not only better aggregate metrics, but better ways to test how a model behaves for different kinds of users before launch. TL;DR In the last post, I argued that offline evaluation is useful but incomplete for recommendation systems. After that, I built a small public artifact to make the gap concrete. In the canonical MovieLens comparison, the popularity baseline wins Recall@10 and NDCG@10 , but the candidate model does much better for Explorer and Niche-interest users and creates a very different behavioral profile. I do not think this means “offline evaluation is wrong.” I think it means a better pre-launch evaluation stack should include some form of synthetic
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ggml-webgpu: move from parameter buffer pool to single buffer with offsets ( #21278 ) Work towards removing bitcast Move rest of existing types over Add timeout back to wait and remove synchronous set_tensor/memset_tensor move to unpackf16 for wider compatibility cleanup Remove deadlock condition in free_bufs Start work on removing parameter buffer pools Simplify and optimize further simplify profile futures Fix stride Try using a single command buffer per batch formatting 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 (CU



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