Microsoft

Microsoft is adding three capabilities to Copilot. Home merges Chat and Cowork into one starting point with Word, Excel and PowerPoint built in. Code lets non-developers describe an app, tracker or dashboard in natural language, and Copilot picks an approach and builds it, running in a sandboxed environment powered by the same underlying technology as GitHub Copilot and hostable inside the tenant. Autopilot, previously called Scout, is a persistent, proactive personal agent with its own identity, memory, computer and workspace, reachable by @mention in Teams, Outlook and elsewhere. Home and Code start rolling out through the Frontier program in the coming weeks, and Autopilot expands to private preview at the end of the month.

Microsoft is adding three capabilities to Copilot. Home merges Chat and Cowork into one starting point with Word, Excel and PowerPoint built in. Code lets non-developers describe an app, tracker or dashboard in natural language, and Copilot picks an approach and builds it, running in a sandboxed environment powered by the same underlying technology as GitHub Copilot and hostable inside the tenant. Autopilot, previously called Scout, is a persistent, proactive personal agent with its own identity, memory, computer and workspace, reachable by @mention in Teams, Outlook and elsewhere. Home and Code start rolling out through the Frontier program in the coming weeks, and Autopilot expands to private preview at the end of the month.

Vercel and Microsoft AI (MAI) have partnered to bring MAI models to AI Gateway, making the platform one of a limited number of places that offer access to MAI models. Three audio models are supported: MAI-Voice-2.1 generates expressive speech in 23 languages and keeps a consistent speaker across longer passages, MAI-Voice-2.1-Flash targets lower-latency interactions, and MAI-Transcribe-2 Streaming returns partial transcripts as audio arrives. AI Gateway bills these models at their listed rates, with no platform fee or markup on inference.

Vercel and Microsoft AI (MAI) have partnered to bring MAI models to AI Gateway, making the platform one of a limited number of places that offer access to MAI models. Three audio models are supported: MAI-Voice-2.1 generates expressive speech in 23 languages and keeps a consistent speaker across longer passages, MAI-Voice-2.1-Flash targets lower-latency interactions, and MAI-Transcribe-2 Streaming returns partial transcripts as audio arrives. AI Gateway bills these models at their listed rates, with no platform fee or markup on inference.

Microsoft released VibeVoice-ASR-Streaming, which turns the "who said what" transcription that previously required processing a whole audio segment into continuous output as speech arrives. The earlier VibeVoice-ASR handled 60-minute long-form audio in a single pass, producing structured transcriptions with speaker, timestamps and content within a 64K token length. The new model emits text once per chunk and supports customized hotwords and 10 languages.

Microsoft released VibeVoice-ASR-Streaming, which turns the "who said what" transcription that previously required processing a whole audio segment into continuous output as speech arrives. The earlier VibeVoice-ASR handled 60-minute long-form audio in a single pass, producing structured transcriptions with speaker, timestamps and content within a 64K token length. The new model emits text once per chunk and supports customized hotwords and 10 languages.

Microsoft has made the Copilot harness built by GitHub engineers, already adopted company-wide, available through Microsoft Foundry so developers can build agents that reason, use tools and finish long-running tasks as a managed service. Previously, keeping up with frontier models meant building and maintaining your own harness or model router; now one harness reaches 20+ frontier models plus bring-your-own-key, with the platform routing work to the right model for the task. The harness runtime has been ported to Rust, which Microsoft says cuts session overhead and time-to-first token, and it already runs across product surfaces at the scale of millions of users.

Microsoft has made the Copilot harness built by GitHub engineers, already adopted company-wide, available through Microsoft Foundry so developers can build agents that reason, use tools and finish long-running tasks as a managed service. Previously, keeping up with frontier models meant building and maintaining your own harness or model router; now one harness reaches 20+ frontier models plus bring-your-own-key, with the platform routing work to the right model for the task. The harness runtime has been ported to Rust, which Microsoft says cuts session overhead and time-to-first token, and it already runs across product surfaces at the scale of millions of users.

Agents often claim a task is done while leaving the wrong database record. ThinkingBox turns deterministic checks of terminal database state into rewards and trains Qwen3.8-27B on tasks outside its benchmark: after 50 GRPO steps, average success rose from 51.70% to 60.78% and tasks passing all twenty attempts rose from 38 to 107, while tasks passing at least once fell from 453 to 446. The training pool was selected offline by policy-relative difficulty, and none of the 507 benchmark tasks were used for training.

Agents often claim a task is done while leaving the wrong database record. ThinkingBox turns deterministic checks of terminal database state into rewards and trains Qwen3.8-27B on tasks outside its benchmark: after 50 GRPO steps, average success rose from 51.70% to 60.78% and tasks passing all twenty attempts rose from 38 to 107, while tasks passing at least once fell from 453 to 446. The training pool was selected offline by policy-relative difficulty, and none of the 507 benchmark tasks were used for training.

A machine-learning postprocessing layer that learns to correct errors in existing forecasting models doubles the accuracy of a gold-standard model over the two-to-six-week subseasonal range and, for the first time, consistently beats climatological averages.

A machine-learning postprocessing layer that learns to correct errors in existing forecasting models doubles the accuracy of a gold-standard model over the two-to-six-week subseasonal range and, for the first time, consistently beats climatological averages.