InfoQ Homepage Development Content on InfoQ
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/articles/practical-robustness-going-beyond-memory-safety-rust/en/smallimage/beyond-memory-safety-what-makes-rust-different-lessons-from-autonomous-robotics-thumb-1773646654979.jpg)
Beyond Memory Safety: What Makes Rust Different – Lessons from Autonomous Robotics
This article explores that question through the lens of a real-world Rust project: a system responsible for controlling fleets of autonomous mobile robots. While Rust's memory safety is a strong foundation, its true power lies in the type system and ownership rules. The session will go beyond memory safety and explore ways to encode behavior and protocols directly into types.
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/articles/evaluating-ai-agents-lessons-learned/en/smallimage/evaluating-ai-agents-lessons-learned-thumbnail-1773307288872.jpg)
Evaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned
This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to combine benchmarks, automated evaluation pipelines, and human review to measure reliability, task success, and multi-step agent behavior. The article also discusses the challenges of evaluating systems that plan, use tools, and operate across multiple interaction turns.
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/articles/read-copy-update/en/smallimage/read-copy-update-thumbnail-1772618412119.jpg)
Read-Copy-Update (RCU): the Secret to Lock-Free Performance
Innovative software engineer with expertise in optimizing concurrency through advanced techniques like Read-Copy-Update (RCU). Proven track record of boosting read performance by over 110% in read-heavy workloads. Skilled in leveraging RCU principles across production systems, enhancing architecture efficiency, and streamlining data handling to maximize scalability and minimize overhead.
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/articles/kotlin-scalable-swiftui-patterns/en/smallimage/thumbnail-kotlin-scalable-swiftui-patterns-1771922819056.jpg)
Borrowing from Kotlin/Android to Architect Scalable iOS Apps in SwiftUI
Building iOS apps can feel like stitching together guidance from blog posts and Apple samples, which are rarely representative of how production architectures grow and survive. In contrast, the Kotlin/Android ecosystem has converged on well-documented, real-world patterns. This article explores how those approaches can be translated into Swift/SwiftUI to create maintainable, scalable iOS apps.
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/articles/building-ai-agent-gateway-mcp/en/smallimage/building-ai-agent-gateway-mcp-thumbnail-1771417896950.jpg)
Building a Least-Privilege AI Agent Gateway for Infrastructure Automation with MCP, OPA, and Ephemeral Runners
This article presents a least-privilege AI Agent Gateway that places clear controls between AI agents and infrastructure. Agents do not access infrastructure APIs directly. Instead, every request is validated, authorized using policy as code with Open Policy Agent (OPA), and executed in short-lived, isolated environments, with built-in observability using OpenTelemetry.
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/articles/architecting-agentic-mlops-a2a-mcp/en/smallimage/architecting-agentic-mlops-a2a-mcp-1770303550343.jpg)
Architecting Agentic MLOps: a Layered Protocol Strategy with A2A and MCP
In this article, the authors outline protocols for building extensible multi-agent MLOps systems. The core architecture deliberately decouples orchestration from execution, allowing teams to incrementally add capabilities via discovery and evolve operations from static pipelines toward intelligent, adaptive coordination.
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/articles/jakartaee-12-milestone-2/en/smallimage/jakartaee-12-milestone-2-thumbnail-1770367607817.jpg)
Jakarta EE 12 Milestone 2: Advent of the Data Age along with Consistency and Configuration
Jakarta EE 12 Milestone 2 marks the beginning of the next generation of enterprise Java. It introduces Jakarta Query, a unified query language across Persistence, Data, and NoSQL, while aligning the platform with Java 21. This milestone focuses on integration, modernization, and improving developer productivity for cloud-native enterprise applications.
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/articles/building-llms-resource-constrained-environments/en/smallimage/building-llms-resource-constrained-environments-thumbnail-1770217548603.jpg)
Building LLMs in Resource-Constrained Environments: a Hands-On Perspective
In this article, the author argues that infrastructure and compute limitations can drive innovation. It demonstrates how smaller, efficient models, synthetic data generation, and disciplined engineering enable the creation of impactful LLM-based AI systems despite severe resource constraints.
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/articles/skeleton-architecture/en/smallimage/skeleton-architecture-thumbnail-1769508521191.jpg)
Working with Code Assistants: the Skeleton Architecture
Prevent AI-generated tech debt with Skeleton Architecture. This approach separates human-governed infrastructure (Skeleton) from AI-generated logic (Tissue) using Vertical Slices and Dependency Inversion. By enforcing security and flow control in rigid base classes, you constrain the AI to safe boundaries, enabling high velocity without compromising system integrity.
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/articles/why-ml-projects-fail-production/en/smallimage/why-ml-projects-fail-production-thumbnail-1769072877092.jpg)
Why Most Machine Learning Projects Fail to Reach Production
In this article, the author diagnoses common failures in ML initiatives, including weak problem framing and the persistent prototype-to-production gap. The piece provides practical, experience-based guidance on setting clear business goals, treating data as a product, and aligning cross-functional teams for reliable, production-ready ML delivery.
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/articles/agent-reinforcement-learning-apache-spark/en/smallimage/thumbnail-1769431334097.jpg)
Autonomous Big Data Optimization: Multi-Agent Reinforcement Learning to Achieve Self-Tuning Apache Spark
This article introduces a reinforcement learning (RL) approach grounded in Apache Spark that enables distributed computing systems to learn optimal configurations autonomously, much like an apprentice engineer who learns by doing. The author also implements a lightweight agent as a driver-side component that uses RL to choose configuration settings before a job runs.
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/articles/engineering-speed-scale/en/smallimage/thumbnail-article-1767006608903.jpg)
Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs
Sub‑100-ms APIs emerge from disciplined architecture using latency budgets, minimized hops, async fan‑out, layered caching, circuit breakers, and strong observability. But long‑term speed depends on culture, with teams owning p99, monitoring drift, managing thread pools, and treating performance as a shared, continuous responsibility.