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Presentation: Lessons Learned From Shipping AI-Powered Healthcare Products
Clara Matos discusses the journey of shipping AI-powered healthcare products at Sword Health. She explains how to implement input/output guardrails for regulated industries and shares a framework for robust evaluations using human and LLM-based ratings. From prompt engineering to RAG and user feedback loops, she shares a data-driven roadmap for building reliable AI care agents at scale. By Clara Matos
Article: Where Architects Sit in the Era of AI
As AI evolves from tool to collaborator, architects must shift from manual design to meta-design. This article introduces the "Three Loops" framework (In, On, Out) to help navigate this transition. It explores how to balance oversight with delegation, mitigate risks like skill atrophy, and design the governance structures that keep AI-augmented systems safe and aligned with human intent. By Dave Holliday, João Carlos Gonçalves, Manoj Kumar Yadav
Google Cloud Launches Managed MCP Support
Google Cloud's introduction of fully managed Model Context Protocol (MCP) servers revolutionizes its API infrastructure, streamlining access for developers. This enterprise-ready solution enhances AI integration across services such as Google Maps and BigQuery while promoting wide-scale adoption. New tools ensure governance and security, and are currently in public preview. By Steef-Jan Wiggers
Article: Architecture in a Flow of AI-Augmented Change
While AI adoption is surging, most organizations fail to scale past pilots. The solution lies in organizational structure, not just technology. This article details how architects can enable "fast flow" by defining clear domains and guardrails. Learn how to shift from controlling outcomes to curating context, allowing AI to drive continuous, valuable business change. By Jonathan McPhail, Juan Medina, Jake DeCrane, Isuru Wijesundara
QCon AI New York 2025: Moving Mountains: Migrating Legacy Code in Weeks Instead of Years
David Stein, Principal AI Engineer at ServiceTitan, presented “Moving Mountains: Migrating Legacy Code in Weeks instead of Years” at QCon AI New York 2025. Stein demonstrated how migrations don’t have to be synonymous to “moving mountains” and introduced the concepts of the Principle of Acceleration and the Assembly Line Pattern. By Michael Redlich
OpenAI at QCon AI NYC: Fine Tuning the Enterprise
At QCon AI NYC 2025, Will Hang from OpenAI unveiled Agent RFT—a cutting-edge reinforcement fine-tuning approach for tool-using agents. By optimizing prompts and tasks before model adjustments, Hang showcased effective strategies to enhance decision-making and efficiency, emphasizing a balanced grading system. The session revealed a future where smarter agents reduce latency and improve outcomes. By Andrew Hoblitzell
Visual Studio 2026 Released with AI-Native IDE and Performance Boost
Microsoft has officially launched Visual Studio 2026 (version 18), marking what they call the first ‘AI‑native’ release of its flagship integrated development environment. The general availability rollout follows extensive validation via the Insiders channel and reflects a blend of performance optimisations, deep GitHub Copilot integration, and tooling updates across core languages and workloads. By Edin Kapić
Article: NextGen Search - Where AI Meets OpenSearch Through MCP
In this article, authors Srikanth Daggumalli and Arun Lakshmanan discuss next-generation context-aware conversational search using OpenSearch and AI agents powered by Large Language Models (LLMs) and Model Context Protocol (MCP). By Srikanth Daggumalli, Arun Lakshmanan
TornadoVM 2.0 Brings Automatic GPU Acceleration and LLM support to Java
The TornadoVM project recently reached version 2.0, a major milestone for the open-source project that aims to provide a heterogeneous hardware runtime for Java. The project automatically accelerates Java programs on multi-core CPUs, GPUs, and FPGAs. This release is likely to be of particular interest to teams developing LLM solutions on the JVM. By Ben Evans
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Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
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Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.
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Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
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A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.