In-depth tutorials and insights on AI, cloud computing, microservices, and modern software engineering. Built by engineers, for engineers.
In 2026, the gap between AI demos and real tools is defined by agency. This guide explains how to architect, orchestrate, and operate AI agents that can be trusted in production.
A deep dive into communication patterns for microservices. Learn when to use synchronous vs asynchronous communication and how to implement them effectively.
Explore the latest in generative AI, including large language models, image generation, and creative AI applications for modern software development.
3 articlesDeep dives into autonomous AI agents, multi-agent systems, and building intelligent automation that can reason, plan, and execute complex tasks.
4 articlesBest practices for workflow automation, CI/CD pipelines, infrastructure as code, and building robust automated systems at scale.
2 articlesArchitecture patterns, communication strategies, and practical guidance for building and scaling microservices-based applications.
1 articleComprehensive guides on designing scalable, reliable, and maintainable systems. From databases to distributed architectures.
1 articleCloud-native development, multi-cloud strategies, serverless architectures, and best practices for AWS, Azure, and GCP.
1 articleModern web development techniques, frameworks, performance optimization, and building exceptional user experiences.
1 articleInsights on business strategy, leadership, workplace culture, and miscellaneous topics for modern organizations and professionals.
2 articlesA statistics-flavored argument for bounded inequality: why extreme wealth outliers signal an unstable system, and why a narrower distribution can mean a healthier society.
Prompt engineering is no longer enough. Learn why flow engineering and agentic workflows now define how reliable, scalable AI systems are built.
Meta’s VL-JEPA research challenges the foundations of modern AI. Explore why leading researchers believe token-based language models may not be the future of intelligence.
A compact, hiring-manager-friendly ML pipeline that goes from raw CSV to reproducible experiments using DVC stages and MLflow tracking on DagsHub, with metrics and model artifacts logged every run.
