In the beginning of August 2026, I started a hobby project: building a MongoDB-like database. I have 20 years of industry experience and a master's degree in computer science, so I followed a disciplined, spec-driven development model using Claude, Kiro, Qwen Coder, and Cursor.
The first version was built in about two weeks of part time work. Then I started exploring. I learned relational algebra, researched almost every kind of database, reworked the internals, built a small relational algebra layer, a query planner, and an executor, covering everything from the backend storage to the query language. I learned more in those two months than in the previous 20 years.
Did I care what code the agents wrote? No. I read zero lines of generated code. What I cared about was correctness, verified through tests, and the high-level product features. For the first time in my career, I acted as a senior product manager, steering the project along the right roadmap. Without AI, I wouldn't have been able to do that.
When you have superpowers in your hands, you don't need to worry about the laundry. For the first time in my career, I can produce code in C, C++, Java, .NET, or any other language. Sometimes it takes me longer than a senior developer in that language, but does that really matter? Absolutely not.
Writing documentation and code by hand in 2026 is like driving a horse and buggy. It doesn't matter how skilled you are with the reins; you'll never compete with a car.
My hobby db project isnt opened source yet.
I'm sorry, I just don't buy the hyperbole. Something with your previous approach was deeply wrong if you condensed 20 years of learning into 2 months.
Databases, programming languages, compilers, operating system internals, networks these things are pretty complicated in itself. With your day jobs you can't master any of this without spending years in your part time.
But now at least you can start something of your interest and learn on the way. Learning a new concepts, implements it, test it and reproduce cycle is so fast that it is only possible because of AI. You need to find the process which works for you. AI already knows every programming language, its best practices, entire computer science knowledge is already mastered by current state of AI. There isn't any single database internals which Ai doesnt know. If you dont believe just start implementing JavaScript or C compiler yourself without even having knowledge of compilers and in a few weeks you will where you stand. You will not only having a functional compiler but you will know the internals of compiler constructions, all the concepts.
tegeek · · focus · HN ↗
The first version was built in about two weeks of part time work. Then I started exploring. I learned relational algebra, researched almost every kind of database, reworked the internals, built a small relational algebra layer, a query planner, and an executor, covering everything from the backend storage to the query language. I learned more in those two months than in the previous 20 years.
Did I care what code the agents wrote? No. I read zero lines of generated code. What I cared about was correctness, verified through tests, and the high-level product features. For the first time in my career, I acted as a senior product manager, steering the project along the right roadmap. Without AI, I wouldn't have been able to do that.
When you have superpowers in your hands, you don't need to worry about the laundry. For the first time in my career, I can produce code in C, C++, Java, .NET, or any other language. Sometimes it takes me longer than a senior developer in that language, but does that really matter? Absolutely not. Writing documentation and code by hand in 2026 is like driving a horse and buggy. It doesn't matter how skilled you are with the reins; you'll never compete with a car. My hobby db project isnt opened source yet.
xpct · · focus · HN ↗
tegeek · · focus · HN ↗