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Development

Featured in Development

Accelerating Performance by Incrementally Integrating Rust into Existing Codebase

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.

All in development

Architecture & Design

Featured in Architecture & Design

The Evolution of Agentic Harnesses: From Chains to Graph to Code

Agentic LLM systems have moved from linear prompt chains to graph-based workflows and, more recently, code-executing harnesses. Chains are simple but fragile, graphs add branching, retries, and validation, and code harnesses verify outputs through execution. The article advises validating model outputs and choosing the least complex design that meets reliability, speed, and cost needs.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

Actors for Agents: Patterns for Production AI

Manju Rajashekhar explains how the actor model solves the complex runtime challenges of production AI agents. By treating agents as distributed systems, he discusses state ownership, message passing, write-through state, and supervision boundaries to manage failures, continuations, and mutable execution graphs, moving agentic workflows beyond fragile demos to resilient architecture.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

AI is an Amplifier: Scale High Performance, Not Dysfunction

Nathen Harvey shares insights from DORA research on AI adoption in software delivery. He explains why AI acts as an amplifier, how the DORA AI Capabilities Model drives outcomes, and why engineering leaders must evolve beyond bottlenecks like code reviews to achieve sustainable productivity, reduce delivery instability, and empower developers without burning out their teams.

All in culture-methods

DevOps

Featured in DevOps

Exploding GPUs and the AI Infrastructure Challenge

Andrew Martin discusses why AI security is fundamentally tied to Kubernetes security. He shares insights into the evolving AI threat landscape, covering non-deterministic agent behavior, GPU supply chain vulnerabilities, shared compute risks, and practical defense strategies - from prompt firewalling to agentic identity - to help secure cloud-native AI workloads in production environments.

All in devops

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AI Security & Privacy Engineering Certification

Secure and govern production AI systems, from sensitive data to guardrails, evals, and audits.
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Work through your production AI decisions with senior engineers from other companies. Apply their perspectives to challenges from your own work.
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AI Engineering Certification

Building RAG pipelines or AI agents? Work through retrieval, context, evals, and reliability decisions with experienced practitioners.
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High-Performing Teams Certification

Compare team design, delivery flow, AI roles, and metrics with experienced practitioners.
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Architect Certification

Distributed systems, decentralized decisions, platform engineering, and AI architecture.
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QCon San Francisco

What's working across AI, architecture, and leadership, from the teams doing it.
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QCon AI New York

Production AI across agents, context, evals, security, and infrastructure, from the senior engineers building it.
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QCon London

What early-adopter teams have proven in production, across 15 engineering tracks.
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QCon AI Boston

Learn from the decisions and trade-offs behind AI systems already running in production.
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Cloud

OVHcloud Raises Prices as AI Memory Demand Reprices Non-AI Infrastructure

OVHcloud will raise prices from September, with 2026-edition gaming servers up 87 percent and other recent servers 40 to 59 percent. Founder Octave Klaba says memory cost six times more in June than a year earlier, as RAM suppliers shifted capacity toward high-bandwidth memory for AI. AWS, buying years ahead, has repriced one reserved GPU product.

Steef-Jan Wiggers
on Aug 23, 2026
DevOps

Uber’s Zero Growth Stack: Scaling Services, While Optimising Infrastructure and AI Cost

Uber's "Zero Growth Stack" focuses on scalable infrastructure that separates capacity growth from business demand, reducing hardware needs while enhancing service scaling. Central to this is garbage collection optimisation. Additionally, generative AI is integrated into development, elevating developer productivity while introducing cost management measures to maintain economic efficiency.

Olimpiu Pop
on Jul 28, 2026

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