Software keeps growing in complexity while losing touch with business goals. Domain-driven design brings clarity, making systems scalable, meaningful, and built to last.
This is a subjective list of books I have advised to a great developer I know. This contains multiple subsections and covers both technical and teamwork aspects.
Spring AI agentic patterns show how to coordinate multiple ChatClient calls to LLMs. We look at how Dapr Workflows can make these interactions durable and resilient.
This article discuss how the original Agile Manifesto anticipated the rise of AI — and why both AI maximalists and AI luddites misunderstand its true message.
An intuitive explanation, along with some real-world applications of this forgotten 1980s technique for turning static data structures into dynamic ones.
Developers already write all the time, just not always well. Strong writing is a force multiplier that saves time, prevents bugs, and accelerates team velocity.
Agile teams thrive on speed. Time-boxed decisions apply bounded rationality to avoid analysis paralysis, reduce decision fatigue, and deliver value faster.
We explore why product professionals risk sleepwalking into strategic irrelevance by over-trusting AI, relying on flawed metrics, and losing direct customer insight.
Continuous integration and continuous delivery serve different purposes in the development pipeline — optimizing each independently leads to better outcomes.
Learn how one-week sprints with vibe coding boost Agile success by enabling faster delivery, reducing AI errors, and improving collaboration across teams.
It's all about AI transformation déjà vu: This article provides a look into why today’s failures look uncannily like yesterday’s “Agile transformations.”
Sharing my experience of working in multiple design system teams, and it will not be a technical post, but more about the goals, pains, and successes of it.
Learn how to balance Agile’s efficiency with traceability by linking requirements, stories, code, and tests, connecting every feature back to business goals.
Most cloud teams aren’t AI ready: Only 51% of infra is automated, and there are major governance gaps and rising costs. Infra maturity (not GPUs) will decide who thrives.