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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

Maximizing the Utility of Large Language Models (LLMs) through Prompting

In this article, authors Numa Dhamani and Maggie Engler discuss how prompt engineering techniques can help use the large language models (LLMs) more effectively to achieve better results. Prompting techniques discussed include few-shot, chain-of-thought, self-consistency, and tree-of-thoughts prompting.

Numa Dhamani Maggie Engler
on Jan 19, 2024
AI, ML & Data Engineering

InfoQ AI, ML, and Data Engineering Trends Report - September 2023

In this annual report, the InfoQ editors discuss the current state of AI, ML, and data engineering and what emerging trends you as a software engineer, architect, or data scientist should watch. We curate our discussions into a technology adoption curve with supporting commentary to help you understand how things are evolving.

Roland Meertens Srini Penchikala Sherin Thomas Daniel Dominguez Anthony Alford
on Sep 06, 2023

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