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US-based prediction market platform Kalshi has announced the completion of a $1 billion funding round, valuing the company at $40 billion. This represents a near-doubling of its valuation from $22 billion during its previous round in May. The new round was co-led by Sequoia Capital and Wellington Management.

Kalshi stated that the funds will be used to build a broader financial exchange beyond just prediction markets. Plans include developing leveraged event contracts, perpetual futures tied to individual stocks, and markets for trading compute prices. The company has seen continued growth since May, driven by high-volume trading on events like the 2026 FIFA World Cup, the NFL season, and US midterm elections.

Despite facing legal challenges regarding state versus federal regulatory authority over sports-related event contracts, Kalshi remains a dominant player. Its main rival, Polymarket, is currently valued at approximately $21 billion. CEO Tarek Mansour previously indicated that an IPO is under consideration but likely not before 2027. Media reports describe this financing as the final private round before a public listing.

GitHub has open-sourced Replica, an AI prototyping tool integrated into the GitHub Copilot application. Released on October 28, 2026, it enables designers, product managers, and engineers to create interactive interface prototypes through natural language descriptions.

Unlike generic AI image generators, Replica's key feature is its ability to reference a team's existing design components, interface specifications, and documentation when generating visuals. This mechanism addresses the common issue where AI-generated interfaces do not match a company's current product style. By providing existing buttons, menus, and form styles as references, the tool ensures prototypes align with the enterprise's design system.

In practice, users can describe functional requirements (such as adding search functionality to a project list) via conversation, and Copilot assists in creating specific components like search fields and empty-state prompts. Users can directly interact with these prototypes and request modifications through further dialogue. Teams can also deploy the self-hosted Replica Hub collaboration platform to share prototypes and collect feedback on specific screen elements, preserving discussions for subsequent iterations.

Currently, Replica supports local previews on macOS and Linux, requiring Git, Node.js, and a GitHub account with Copilot access. While the collaboration hub requires organizations to set up their own servers, local prototyping does not need additional infrastructure. Users can utilize AI models provided by GitHub via the Copilot plan or connect to other model providers independently.

Databricks announced in a blog post on October 29, 2026, that its serverless cost controls have expanded to notebooks, jobs, and pipelines. Previously, this functionality was available for serverless SQL and Model Serving, but it is now unified across broader serverless compute scenarios.

The update includes three core pillars: cost attribution, access control, and spend limiting. Serverless Compute Access Controls is now Generally Available (GA), allowing administrators to govern who can run serverless workloads using 'Can Use' and 'Can Manage' permission objects. By default, all users have usage rights, but admins can remove the 'All Users' group to enforce stricter controls.

Rate Limits are currently in Beta, designed to prevent runaway spending. This feature sets maximum autoscaling ceilings for automated workloads using t-shirt sizes (S, M, L, XL), thereby bounding how much or how fast a job or pipeline can consume. Additionally, Serverless Compute Tags enable cost attribution, associating each DBU consumption with specific teams or projects, queryable via the system.billing.usage system tables.

Databricks stated that as serverless adoption grew more than threefold in the past year, platform teams increasingly demanded better visibility and budget control. These new tools aim to replace traditional cluster policies involving node counts and instance types, shifting governance focus from infrastructure to people and specific workloads.