InfoQ Homepage Articles
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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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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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The Friction Fix: Change What Matters
Friction is the invisible current that sinks every transformation. Friction isn’t one thing – it’s systemic. Relationships produce friction: between the people, teams and technology. The fix isn’t Kubernetes, the Cloud or AI. The fix is changing our patterns of thinking, communicating, and organizing.
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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.
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Preventing Data Exfiltration: a Practical Implementation of VPC Service Controls at Enterprise Scale in Google Cloud Platform
Implementing VPC Service Controls is more about people and process than technology. Organizations must conduct extensive upfront discovery, use phased rollouts to avoid breaking production systems, and design VPC Service Controls that enable rather than block work. Success requires automation, clear exception processes, tracking both security and business metrics, and continuous improvement.
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Platform-as-a-Product: Declarative Infrastructure for Developer Velocity
Declarative infrastructure config hides complexity, enabling developers to focus on application code. Unified YAML per service allows early cost validation, while independent CI with centralized CD balances team autonomy and deployment consistency. This standardized approach scales across organizations, making infrastructure invisible and operations automatic.
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Spec Driven Development: When Architecture Becomes Executable
Spec-Driven Development inverts traditional architecture by making specifications executable and authoritative. It transforms declared intent into validated code through AI generation and provides architectural determinism. It eliminates drift through continuous enforcement, but demands new engineering discipline in schema design and contract-first reasoning.
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Agentic Terminal - How Your Terminal Comes Alive with CLI Agents
In this article author Sachin Joglekar discusses the transformation of CLI terminals becoming agentic where developers can state goals while the AI agents plan, call tools, iterate, ask for approval where needed, and execute the requests. He also explains the planning styles for three different CLI tools: Gemini, Claude, and Auto-GPT.
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The Architect’s Dilemma: Choose a Proven Path or Pave Your Own Way?
Software platforms and frameworks act like paved roads: they accelerate MVP/MVA delivery but impose decisions teams may not accept. If the paved roads don't reach your destination, then you may have to take an exit ramp and build your own solution. Experiments are necessary to determine which path meets your specific needs.
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Stop Guessing, Start Improving: Using DORA Metrics and Process Behavior Charts
Delivery performance rarely changes in a straight line. Small degradations caused by tooling, environment instability, or team changes can accumulate quietly, while real improvements take time to emerge. This article shows how combining DORA metrics with Process Behavior Charts helps teams zoom out, detect meaningful shifts early, and validate improvement hypotheses.