opinion

Every era of software engineering is really about one thing: building higher abstractions to hide complexity and let developers focus on solutions, not machines. Here's the full arc from machine code to AI.

A practical breakdown of memory types in AI coding tools — what survives between sessions, what gets wiped, and how to build persistent AI teammates that actually know your codebase.

Understanding how context works in AI coding assistants — what goes in, why it runs out, and how to manage it so the AI stays helpful instead of confused.

A practical comparison of vibe coding and spec-driven development — when to flow freely with AI, when to nail down specs first, and how to combine both without losing your mind.

Coding is just one tool in a developer's toolkit. The real job is solving problems — and that means adapting to any tech stack, understanding the business, and thinking beyond syntax.

Companies are cutting junior roles and investing in AI, but they're destroying the pipeline that creates senior engineers. Why this 'structural collapse' threatens the entire tech industry by 2030.

A personal reflection on my VNG fresher program in 2012 — the 6 months that taught me more real-world engineering than 4 years of university. From Windows to Linux, C# to Java, SQL Server to Redis.

Most developers start with a simple mental model: frontend does UI, backend does logic. But that binary thinking hides the real craft — the invisible layers, thoughtful defaults, and system-level decisions that separate functional code from great software.

A direct message to junior developers and interns: the best time to learn broadly is right now. Stop restricting yourself to one stack. Embrace being wrong, ask stupid questions, fail fast, and grow faster.

Beyond the fear of AI replacing developers lies a deeper truth about professional gatekeeping, accountability, and what actually makes engineering valuable in the age of code generation.