alt Hacker News> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
I'm a big fan of Data Oriented Design. Once you conceptualize how data is stored and transformed in your program, it just has to be reflected in data structures that make it possible.
Modern design approaches tend to focus on choosing a right abstraction like columnar/row layout, caching etc. They mostly fail to optimally work with the data. Optimal in this case meaning getting most of all underlying hardware capabilities, for example reading large and preferably continuous blocks of data from magnetic storage, parallel data processing, keeping intermediate results in CPU caches, utilizing all physical SSD queues.
The fundamental tension between nouns and verbs and the attempts to unify them like events have made programming a long art form to study.
It's all use-case and priority-specific, and I think the more varied your experience and more tools you have in the tool belt, the better off you can be to bring the right solution to bear. Of course, then you think you have the right solution in mind (lets say using partitions in postgres for something) but you find the ORM your service is using doesn't support it, then what is "best" becomes not only problem-specific but also tool-specific. Finally, even if you have the best solution and your existing ecosystem supports it but the rest of the engineering staff you have is unfamiliar with it, it may again no longer be "best".
this ladder of problem-fit, ecosystem-fit, staffing-fit is something I have grappled with in my career.
LLMs are only so-so at any of the above (even when including the agent as "staff".)