devotd.
Browse the Asia/Seoul (KST) issue calendar. Retrospective entries show their assigned coverage day; actual publication times remain separate.
The new version expands model support and changes defaults for repeated-input caching and request handling. Conflicting setup guidance for new models calls for separate checks of deployment configurations and existing service compatibility.
Temporary chats now encrypt messages in participants' browsers without storing them on the server. Administrators must configure feature activation and user permissions separately and check communication settings.
Tencent Hunyuan has released a dedicated model that selects and updates information for long-term memory systems. It replaces the writing model in existing memory management setups. Evaluation results need to be considered alongside the fixed samples used and the scope of validation.
Support for a new model's text, image, and video inputs has been added. The cache manager's Python implementation is gone, so existing settings and event collection paths need checking before an upgrade.
The terminal coding tool adds a settings search panel and support for follow-up instructions during responses. Saving settings differs from applying them, and interrupting work does not guarantee automatic rollback.
Error handling now better distinguishes failures when features require unavailable WebAssembly or executable memory. New built-in WebSocket masking and unmasking functions let library developers move these operations out of add-ons.
Visual Studio Code 1.142 gradually changes the default runtime for new agent chats and adds a preview of a workspace that users and agents edit together. Check policy-driven chat migration and the sandbox's actual limits.
Transformers adds support for Meta's multimodal model and native loading for InclusionAI's model. Input handling and loading are unified. Deployments still need separate checks for large-model memory needs and expert-parallel inference slowdowns.
GitHub Copilot plans to introduce automatic device–cloud routing by late October 2026. Developers must assess execution location and command permissions separately. Device measurements and multi-model research evaluations use different conditions.
NVIDIA released weights for training and evaluation, plus memory-saving deployment weights, for a model handling long documents, images, and video. Built-in prediction can accelerate generation, but actual speed and quantization's quality impact must be assessed by deployment conditions and task.