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Lily Mara explains how to avoid high-risk software rewrites through incremental FFI refactoring. She shares how engineering teams can replace Python bottlenecks with Rust via PyO3, demonstrating how to achieve dramatic function-level speedups, seamless integration testing, and meaningful infrastructure cost savings without microservice overhead.
Agentic AI architecture extends microservices and cloud-native architectures with AI agents that work with context, memory, and protocols such as MCP and A2A. The article covers bounded-context integration, task-dependent coordination strategies, new security risks, and observability needs. It expects gradual adoption alongside existing architectures, with platforms absorbing common components.
In this episode, Edoardo Vacchi discusses the evolving landscape of WebAssembly on the backend, running Java workloads on GPUs using TornadoVM, and optimizing distributed AI inference using llm-d and vLLM on Kubernetes. Edoardo explains how llm-d manages cache locality (KV and prefix caches), request routing, and batch inference across Kubernetes clusters to maximize expensive GPU utilization.
Vanessa Formicola shares how technical leaders can move beyond "wishful architecture" by practicing holistic socio-technical engineering. She discusses why engineering leaders must account for organizational maturity, reward structures, geopolitical climate, and team dynamics, framing the staff-plus role as designing the conditions for technology to thrive rather than just picking tools.
Colin Douch shares battle-tested strategies for building and scaling self-hosted observability stacks. He explains how to effectively correlate metrics, structured logs, and distributed traces using open-source tooling like Prometheus, VictoriaMetrics, and Jaeger, while navigating the engineering trade-offs of avoiding SaaS vendor lock-in.
AI Engineering Certification
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QCon San Francisco
What's working across AI, architecture, and leadership, from the teams doing it. Register. Early bird ends Nov 10.
QCon AI New York
Production AI across agents, context, evals, security, and infrastructure, from the senior engineers building it.Register. Early bird ends Nov 10.
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What early-adopter teams have proven in production, across 15 engineering tracks. Register. Early bird ends Nov 10.
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