I write about coding and being a full-time software engineer.

A seasoned engineer's lessons from a year rebuilding a search system from the ground up, shifting from engineering-first to product-first thinking.

Your LLM-as-a-Judge system might be lying to you. This post uncovers 5 critical biases like positional, verbosity, and moderation bias that silently corrupt your AI evaluations, leading to poor product decisions. Learn practical, actionable fixes to build a resilient evaluation framework you can actually trust.

AI coding assistants are accelerating development—but also magnifying quality risks. Here’s how to write Python tests that survive refactors, scale with your codebase, and tame the chaos of augmented coding.

Most AI coding implementations fail because teams choose the wrong approach for their context. Here's how to pick the strategy that will actually give you a competitive advantage.

Exploring the paradigm shift from AI-assisted coding to agentic coding - where AI agents can handle complex, multi-step development tasks with significant autonomy.

Stop firefighting software issues and start understanding the underlying system dynamics through Systems Thinking. This post introduces models like the "technical debt bathtub" and key leverage points to improve flow, stability, and team performance. Build better software, faster and more reliably, by focusing on systemic health.

High-performing teams often seem to operate seamlessly, navigating challenges such as tight deadlines or resource limitations while consistently delivering exceptional results. Efficient teams in project-based industries are 2.5 times more likely to meet quality standards and operate with 19% improvement in productivity.

Separating decision gathering from decision making enhances agility by allowing teams to collect diverse input without being slowed by the need for consensus. Empowering individuals to make informed decisions based on collective insights fosters innovation and swift progress in tech environments.

Explores the concept of coupling in software development, its various forms, practical implications, and strategies to balance coupling and decoupling for better system architecture and maintainability.

A guide to processing real-world events in applications, with practical steps, strategies, and real-world examples. Elevate your system's resilience and adaptability.

Unlock the secrets to mastering cloud computing fundamentals with this comprehensive guide. From understanding key AWS components to hands-on training, the content is designed to refine knowledge and offer critical insights for exam preparation.

Strategies for optimizing virtual meetings in a remote work environment focus on balancing various types of meetings and personalizing schedules to individual work rhythms.

Learn accessing, searching and mutating array elements and gain essential knowledge to master array handling in JavaScript.

Get ahead in your career and business by using ChatGPT's advanced prompt techniques, including generating tables, controlling creativity with temperature settings, simulating an expert, creating unique business ideas, and explaining complex concepts in simple language.

Become a data-engineer in only one year and learn the basics of Python, SQL, Git, Linux, NoSQL, Big Data with Spark and Hadoop, AWS Cloud, DBT, Airflow and Kafka.

Self-reflection is the most important habit for success. Five habits for self-assessment to help developers continuously grow and succeed.

In many applications, you need random numbers...

Java is one of the most popular programming languages...

You might've seen the term k8s in different sources, and wondered what it means. Well, it means Kubernetes.

Containers are an essential tool for software development today. Running applications in any environment becomes easy when you leverage containers.

Containers make software engineering easier and more efficient, and Docker containers are popular and easy to use.

Containers enable you to package your application in a portable way that can run in many environments. The most popular container platform is Docker.

Containers are everywhere in today’s tech world. The most popular technology for container management is Docker. It makes using containers easy and helps you easily get applications up and running.

Word embeddings are used to create neural networks in a more flexible way. They can be built using neural networks that have a certain task, such as prediction of a target word for a given context word.

There are lots of data-storage options available today. You have to choose between managed or unmanaged, relational or NoSQL, write- or read-optimized, proprietary or open-source — and it doesn't end there.

There are different ways of running production services at a high scale. One popular solution for running containers in production is Kubernetes. But interacting with Kubernetes directly comes with some caveats.

Nowadays, two of the essential tools in a developer's toolbox are Docker and Kubernetes. Both let developers to package applications into containers to run them in different environments.

Docker has been widely adopted and is used to run and scale applications in production. Additionally, it can be used to start applications quickly by executing a single Docker command.

Docker has been widely adopted and is a great vehicle to deploy an application to the cloud (or some other Docker-ready infrastructure). It is also useful for local development. You can start complex applications quickly, develop in isolation, and still have a very good performance.

For small projects the bad performance is not a critical issue. For huge application rsync is our hero. Good old tools, and still reliable and important.