Beyond prompts or .cursorrules, I explored whether code structure itself can serve as context an AI can understand. Most models’ context windows are small and hinder free-form exploratory coding; if structure can act as additional context, it could be a big win.
Test:
Two codebases implement the same behavior but differ in structure.
Code A: goal-driven, structure-agnostic.
Code B: lightly refactored with some structural improvements.
Both were 100% AI-written. I fed the same model the two bases separately and issued the same modification request.
Results:
On Code A, the AI grafted features onto a structureless base; subsequent edits easily broke existing behavior.
On Code B, the AI respected the existing composition/structure/pattern; follow-up edits were far less likely to cause regressions.
Conclusion:
The AI does leverage existing code structure; on a good structure, it tends to produce sturdier, higher-quality code.
Personal note:
This is an interesting direction for AI-assisted development. The write-up is short but setup-heavy; to really grasp the test, hands-on is needed. A tighter focus on the test, results, and implications would make it stronger.
I’ve heard that in North Korea it is difficult for ordinary people to learn or own a computer. It is assumed that a small number of elite operatives are selected and trained to carry out such tasks, and it is somewhat surprising that they possess the latest technology and conduct hacking.
From a Bill Gates documentary, I saw research with partner companies aimed at improving nuclear power generation mechanisms to reduce waste and increase efficiency. Bill Gates’ endeavors always seem positive and fascinating.
Whoa, this is a bit scary. As mentioned earlier, it should basically be used in a way where other energy sources are tapped first, and only the shortfall is covered.
I expected there would be constraints, but the chosen range is quite intriguing. The PostgreSQL spec says the 4-byte date type spans 4713 BC to 5,874,897 AD. It gives much more headroom for future dates—did they assume preserving data before 4713 BC is unlikely?
Beyond prompts or .cursorrules, I explored whether code structure itself can serve as context an AI can understand. Most models’ context windows are small and hinder free-form exploratory coding; if structure can act as additional context, it could be a big win.
Test:
Two codebases implement the same behavior but differ in structure.
Code A: goal-driven, structure-agnostic.
Code B: lightly refactored with some structural improvements.
Both were 100% AI-written. I fed the same model the two bases separately and issued the same modification request.
Results:
On Code A, the AI grafted features onto a structureless base; subsequent edits easily broke existing behavior.
On Code B, the AI respected the existing composition/structure/pattern; follow-up edits were far less likely to cause regressions.
Conclusion:
The AI does leverage existing code structure; on a good structure, it tends to produce sturdier, higher-quality code.
Personal note:
This is an interesting direction for AI-assisted development. The write-up is short but setup-heavy; to really grasp the test, hands-on is needed. A tighter focus on the test, results, and implications would make it stronger.