InfoQ Homepage Transcripts Content on InfoQ
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The Socio-Technical Staff+ Engineer: Architecture, Culture and Organisational Change
Vanessa Formicola explains how staff-plus engineers must act as socio-technical landscape architects, aligning technical design with organizational culture, reward systems, and human dynamics.
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Zero Trust Agent Systems that Pass Audits and Still Ship
Advait Patel explains how to secure AI agents in production by moving security boundaries to the tool layer, enforcing workload identity, and capturing structured audit traces for compliance.
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Panel: Who Builds the Frontend Now? (And What Breaks When They Do)
The panelists discuss AI's impact on frontend architecture, vibe coding risks, agent interfaces, code quality, and open-source sustainability.
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Beyond the Harness: Canvas for AI Agents
Nimisha Asthagiri explains why shifting from rigid harness engineering to a bounded canvas model empowers autonomous AI agents while managing architecture drift and cognitive debt.
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The Ladder is Missing Rungs: Engineering Progression When AI Ate the Middle
Alasdair Allan shares data on the "missing rungs" of software engineering, explaining how AI coding tools stunt junior developer skill growth and disrupt future technical leadership pipelines.
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Wrangling Telemetry at Scale: a Guide to Self-Hosted Observability
Colin Douch explains the trade-offs of self-hosted observability, detailing how to connect metrics, logs, and traces while managing complexity and scaling costs across microservice architectures.
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Serving LLMs at Scale: the Hidden KV Cache Advantage
Khawaja Shams explains how distributed systems engineering solves modern LLM inference bottlenecks, covering KV caching, memory cliffs, prompt prefilling, and disaggregated execution.
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Move Fast, Don’t Break Trust: Shipping Constantly with Humans and beyond
Suhail Patel explains how Monzo built a paved platform stack, opinionated tooling, and fast CI checks to help engineers and AI agents ship microservices safely and continuously at scale.
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Actors for Agents: Patterns for Production AI
Manju Rajashekhar discusses why the actor model provides the ideal runtime architecture for moving AI agents from simple demos to production-grade, long-running, fault-tolerant systems.
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AI is an Amplifier: Scale High Performance, Not Dysfunction
Nathen Harvey discusses key findings from the 2025 DORA research, explaining how AI adoption, internal platforms, and organizational guardrails drive real software engineering performance.
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Beyond the Prototype: Scaling Framework Agnostic AI Agent Infrastructure with Ray
The speakers explain how to run and autoscale LLM agents in production using Ray, detailing system architecture, control planes, multi-region deployments, and CI/CD pipelines with LLM evals.
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Exploding GPUs and the AI Infrastructure Challenge
Andrew Martin explains why AI security relies on Kubernetes security, highlighting new threat vectors like prompt injection, unconstrained agents, and the unique risks facing GPU infrastructure.
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