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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

Move Fast, Don’t Break Trust: Shipping Constantly with Humans and Beyond

Suhail Patel discusses how Monzo structures its software engineering organization and technical stack to ship safely hundreds of times a day. He shares insights into opinionated microservice abstractions, tenancy-based staging, static analysis checks, continuous profiling, demonstrating how a strong internal platform enables engineers and LLMs to iterate rapidly without sacrificing reliability.

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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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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AI, ML & Data Engineering

Claude Code Subagents Enable Modular AI Workflows with Isolated Context

Anthropic has recently made Claude Code Subagents generally available, enabling developers to create independent, task-specific AI agents with their own context, tools, and prompts.

Hien Luu
on Aug 19, 2025
AI, ML & Data Engineering

Anthropic Proposes Transparency Framework to Safeguard Frontier AI Development

Anthropic has proposed a new transparency framework designed to address the growing need for accountability in the development of frontier AI models. This proposal focuses on the largest AI companies that are developing powerful AI models, distinguished by factors such as computing power, cost, evaluation performance, and annual R&D expenditures.

Daniel Dominguez
on Jul 29, 2025
AI, ML & Data Engineering

Claude Code Gains Support for Remote MCP Servers over Streamable HTTP

Anthropic has recently introduced support for connecting to remote MCP servers in Claude Code, allowing developers to integrate external tools and resources without manual local server setup.

Sergio De Simone
on Jun 22, 2025

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