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.
Okay, you're going to need to go into a lot more detail about exactly how you learned more in 2 months than you did in 20 years having read zero generated code.
Code examples are literally bread and butter when it comes to learning.
How can you learn without looking at code? That's like saying that you can learn to be an architect without looking at drawings...
I didnt know what is relational algebra is? why query plannering execution done in certain ways. What are backend storage system available in open source or commercial products. What are the traitts of each backend storage and how does that effects the replication and failover scenarios. I know all of this, I implemented a small subset of sql's relational algebra in my project and its correct and concise. I expored different backend storage systems, first tried one, then move to next and then 3rd in line, all that in span of less than a month without ever looking what code the coding agents generates.
>I didnt know what is relational algebra is? why query plannering execution done in certain ways. What are backend storage system available in open source or commercial products. What are the traitts of each backend storage and how does that effects the replication and failover scenarios. I know all of this, I implemented a small subset of sql's relational algebra in my project and its correct and concise. I expored different backend storage systems, first tried one, then move to next and then 3rd in line, all that in span of less than a month without ever looking what code the coding agents generates.
To be as frank as possible--you "understand" these topics in the same way someone who watches pop-science videos about astronomy "understands" astronomy. That is to say, you have a surface level understanding at best.
This is the sort of behavior people have been talking about over the last few years. People confuse what the AI "understands" with what they understand, and it makes them say delusional stuff like "I learned more in the last 2 months than in 20 years."
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.
adverbly · · focus · HN ↗
Code examples are literally bread and butter when it comes to learning.
How can you learn without looking at code? That's like saying that you can learn to be an architect without looking at drawings...
tegeek · · focus · HN ↗
jplusequalt · · focus · HN ↗
To be as frank as possible--you "understand" these topics in the same way someone who watches pop-science videos about astronomy "understands" astronomy. That is to say, you have a surface level understanding at best.
This is the sort of behavior people have been talking about over the last few years. People confuse what the AI "understands" with what they understand, and it makes them say delusional stuff like "I learned more in the last 2 months than in 20 years."