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Showing 81–88 of 88 dossiers
The htmx 4.0 release changes several long-standing browser and application assumptions while keeping the hypermedia model intact. Builders get streaming HTML and modern fetch internals, but need to test inherited attributes, event names and history behavior before migrating.
The staged release is complete: GLM-5.3’s public weights and serving artifacts are now available. That makes Z.ai’s coding and cyber-capability claims independently testable while turning the earlier safety delay into a concrete self-hosting and audit decision.
The price changes are not uniform: H100/H200 rise about 14%, B200 30%, B300 25% and GB300 about 11%. Builders using dedicated inference or training should re-run workload economics before assuming newer accelerators remain the cheapest route per completed task.
Sentence Transformers 6 now has both unified multi-vector inference and a documented end-to-end training workflow. A new project-authored benchmark shows fast domain adaptation on a single GPU, but the result is workload-specific and index costs remain high.
Google’s September Search changes now form a broader migration story: legacy campaign-level Broad Match and standalone Automatically Created Assets settings will be converted into AI Max, while language targeting stops affecting Search delivery and related API mutations begin failing.
Google has moved the Smart Campaign API creation cutoff to September 23. New create operations will fail, while existing campaigns can still be updated and served; Google points developers toward Performance Max, Search or Demand Gen for new automation.
Cloud Run sandboxes now cover all resource types. The August 5 expansion matters for builders whose agents or automation run in batch jobs or continuously pulling workers rather than HTTP services, while the feature remains pre-GA and shares CPU and memory with the host container.
LFM2.5-DSpark adds roughly 300M-parameter draft models for LFM2.5 1.2B, 2.6B and 8B-A1B. Liquid reports large throughput gains on H100 and M4 Max, but the gains vary sharply by model and workload and current llama.cpp integration still has practical edge cases.