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Showing 61–80 of 116 dossiers

Mixpanel AI can now investigate why a product metric changed and return the result as a working Board

Product teams can launch a root-cause investigation from an Insights report, an alert or Mixpanel Agent instead of manually trying breakdown after breakdown. The result is operationally useful, but it remains an automated statistical diagnosis rather than proof of causation.

GLM-5.3-Flash turns the anonymous Ox Alpha trial into an open-weight multimodal coding model

GLM-5.3-Flash combines open weights, multimodal coding/agent capability and an 18B-active sparse architecture with a large anonymous pre-launch trial. Z.ai has already issued a chat-template correction for early downloads, showing that day-one self-hosted deployments need artifact-level validation as well as model benchmarking.

Amazon Aurora Serverless gets faster burst scaling

Aurora Serverless can now add roughly 12 ACUs in the first second of a scale-up event on platform versions 3 and 4. The change is automatic and is most useful for bursty SaaS, API, batch and agent workloads, but it does not remove the separate resume delay when a database has scaled all the way to zero.

Railway has stopped new services adopting Config as Code ahead of the December 1 hard cutoff

The August 28 transition is now active, and Railway’s current documentation removes an earlier ambiguity about new services in existing projects. Config as Code is legacy-only from here; production users should migrate and validate `.railway/railway.ts` before the December hard cutoff.

AWS and NVIDIA plan 2 million more GPUs as their AI stack expands beyond accelerators

The scale of the AWS–NVIDIA expansion is the headline, but the builder consequence is broader: AWS is co-engineering more of the NVIDIA stack, from CPUs and interconnects to models, vector indexing and physical-AI infrastructure, rather than merely adding another GPU instance family.

OpenAI’s Codex client exposes a GenUI layer for refreshable interfaces inside conversations

RuntimeWire found a generic `genui` message path, a server-directed widget refresh endpoint and 467 versioned Learning Block manifests inside OpenAI’s Codex desktop client. The material development is not another visualization feature: it is evidence of a reusable interface layer beneath conversational answers, with important limits around what is actually public or enabled.

Supabase makes MCP access centrally governed through enterprise SSO

Supabase has implemented MCP Enterprise-Managed Authorization using identity-provider assertions, short-lived tokens and existing Supabase role boundaries. It gives organizations a central on/off switch for approved AI clients while keeping access scoped to the individual employee rather than sharing a powerful organization token.

Microsoft Project Zenith turns 64GB-class Windows PCs into a standardized local-AI developer tier

Project Zenith is not a new model or another Copilot feature. It standardizes a developer-focused Windows experience and hardware floor for local AI work, with preconfigured tooling and OS settings intended to reduce setup friction and dependence on metered cloud inference.

GitHub Copilot is moving enterprise seats to upfront billing and longer chat retention

From September and October, Copilot Business and Enterprise seat access becomes more tightly coupled to upfront payment. A separate September 28 policy migration enables a unified Copilot experience by default, retains github.com chat data for the life of the account and changes code review’s default effort from Lite to Balanced.

OpenAI’s Assistants API has shut down

The Assistants API shutdown date has passed. OpenAI’s deprecation documentation lists August 26, 2026 as the removal date and directs developers to Responses and Conversations for replacement workloads.

Google Ads starts showing how many conversions your first-party data recovered

The shift is broader than another Ads dashboard metric. Google is connecting first-party data pipelines, conversion-recovery estimates, open-source marketing-mix modeling and causal geo experiments into one measurement stack — useful, but still heavily dependent on Google’s own modeling and internal benchmark claims.