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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

From Microservices to Agents: the Next Evolution of Distributed Systems

The article examines the transition from microservices to agentic AI architecture in distributed systems. Microservices allow for functional decomposition, while agentic systems focus on decision decomposition. This shift introduces new complexities in observability, reliability, and system design, highlighting the need for updated architectural patterns and practices.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

Beyond the Prototype: Scaling Framework Agnostic AI Agent Infrastructure with Ray

The speakers discuss transitioning AI agents from local FastAPI prototypes to resilient production systems on Kubernetes using Ray. They share operational insights on RayCluster topology, stateful memory handling, auto-scaling on dynamic traffic, and robust control plane designs, while highlighting critical guardrails, multi-region setups, and LLM-as-a-judge evaluation pipelines.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

The Minimum Viable Manager: Building Trust, Feedback, and Alignment

In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Matt Gjertsen, founder of Built and author of Minimum Viable Manager, about the three foundational skills every first-time manager needs, how to build trust and give effective feedback, and why human leadership skills matter even more in the age of AI.

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

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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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Java

TornadoVM 7 Enables GPU-Native Workloads From Java Applications

TornadoVM, an open-source heterogeneous programming framework for Java has reached version 7.0. It automatically accelerates Java programs on multi-core CPUs and GPUs and provides a rich GPU programming ecosystem in Java that can be used with any mainstream JDK distribution e.g. Temurin, Red Hat OpenJDK, Coretto, Oracle, GraalVM or Zulu.

Ben Evans
on Oct 21, 2026
Java

GPULlama3.java Brings GPU-Accelerated LLM Inference to Pure Java

The University of Manchester's Beehive Lab has released GPULlama3.java, marking the first Java-native implementation of Llama3 with automatic GPU acceleration. This project leverages TornadoVM to enable GPU-accelerated large language model inference without requiring developers to write CUDA or native code, potentially transforming how Java developers approach AI apps in enterprise environments.

A N M Bazlur Rahman
on Jun 24, 2025

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