InfoQ Homepage Development Content on InfoQ
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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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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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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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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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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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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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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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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.
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/articles/durable-objects-handle-inflight-requests/en/smallimage/durable-objects-handle-inflight-requests-thumbnail-1769161248259.jpg)
One Cache to Rule Them All: Handling Responses and In-Flight Requests with Durable Objects
Traditional caching fails to stop "thundering herds" where multiple clients trigger the same work during a miss. This article proposes using Cloudflare Durable Objects to treat in-flight work and finished results as two states of one cache entry. By routing to a single owner, systems eliminate redundant tasks. This pattern replaces complex locks with simple promises, simplifying the system design.
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/articles/ai-developers-rewriting-software-process/en/smallimage/ai-developers-rewriting-software-process-thumbnail-1768398859653.jpg)
Virtual Panel - AI in the Trenches: How Developers Are Rewriting the Software Process
This virtual panel brings together engineers, architects, and technical leaders to explore how AI is changing the landscape of software development. Practitioners share their insights on successes and failures when AI is incorporated into daily workflows, emphasizing the significance of context, validation, and cultural adaptation in making AI a sustainable element of modern engineering practices.
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Article Series: AI-Assisted Development: Real World Patterns, Pitfalls, and Production Readiness
In this series, we examine what happens after the proof of concept and how AI becomes part of the software delivery pipeline. As AI transitions from proof of concept to production, teams are discovering that the challenge extends beyond model performance to include architecture, process, and accountability. This transition is redefining what constitutes good software engineering.