News For Dev-ops

Amazon Simple Email Service (Amazon SES) now supports tenant-level suppression lists, allowing email senders to isolate bounces and complaints per tenant. Previously, all tenants in an account shared a single suppression list, meaning one tenant's email issues caused emails for other tenants to be suppressed. With this feature, each tenant maintains a separate suppression list, ensuring that bounces and complaints affect only the tenant that generated them. This capability benefits any sender managing distinct email streams from a single SES account. Key use cases include SaaS providers sending on behalf of multiple customers, enterprises separating transactional and marketing mail across business units, agencies managing campaigns for different brands, or any application where a complaint from one sending program shouldn't suppress delivery for another.  You can configure suppression behavior using two settings: suppression scope (TENANT or ACCOUNT) and suppressed reasons ...

Amazon Connect Customer Assistant is now integrated within the UI builder, enabling contact center managers to create and modify views using natural language. Managers describe what they need, such as "Create a feedback form with rating and comment fields," and the assistant generates the corresponding UI components for review before publishing. This reduces the time and expertise needed to build Views for Step-by-Step Guides and Workspace pages by up to 70%. Managers can use conversational prompts to create views, configure layouts with conditional UIs, set component properties, and apply styling without manual work. The assistant recommends components, explains options, and troubleshoots issues to accelerate builds.  Post Updated on May 28, 2026 at 02:51PM

Modern AI systems have transformed software engineering, but their impact in silicon development has been limited. The primary constraint is that silicon engineering depends on assembling and reasoning over context that is distributed across many systems. Specifications, logs, regressions, waveforms, and prior debug history all contribute to understanding a problem, yet they are rarely accessible within a single workflow. Early applications of AI in this space focused on code generation and isolated tasks. These approaches proved useful for scripting and tooling, but had little effect on core design and verification work. The underlying issue is that most engineering effort is spent reconstructing context before meaningful reasoning can begin. When that context remains fragmented, AI systems operate on partial information and cannot participate in the full problem-solving process. This reflects a fundamental difference in how progress is made. Software workflows can rely on rapid i...