Section
All published posts.
The pace of software development has undergone unprecedented compression. What was once simply programming is now trad coding: line-by-line work shaped by human problem-solving time.
In Go, context.Context is the standard mechanism for propagating cancellation, deadlines, and values across Goroutines and function calls. But passing context forward has a subtle trap.
You have probably experienced this: one moment, GPT or Claude solves a complex coding problem in seconds; the next, the same AI forgets basic context or invents nonexistent information.
If you follow the GitHub Copilot ecosystem, you have probably heard of *.agent.md files. They are great for simple things, basically a boosted prompt that runs inside Copilot
The paradox of modern speed We live in the paradox of technical abundance: in the age of Artificial Intelligence, we generate code in minutes, but organizations still struggle to convert that volume into real value.
There is an efficiency gulf between biological architecture and silicon. While a 12-year-old child already masters human language, models like GPT-3 require far more data.
Engineering leaders and developers are constantly searching for more productivity. The pressure to deliver faster is relentless, but the path to that speed is rarely clear.
If you are starting to venture into AI Engineering and want to go beyond the basics, you need to deeply understand what **Retrieval-Augmented Generation (RAG)** is.
You know when you are trying to solve a complex problem with AI and it feels like a single model cannot handle the job? Multi-agent systems help, but only in the right cases.
Exactly one year ago, a commit with the message "Golanged" marked the beginning of a new phase in my career as a developer. It...