You are entitled to your opinion of course but it just seems extremely arbitrary.

But how does a simple act such as "pre-registration" change anything? It's not as if observing another metric that already existed changes anything about what you experimented with.

A potential argument is that these signals are only applicable to a certain bracket of portfolio sizes (e.g. larger AUM funds would not be able to trade this strategy) -- but you are sharing this with folks presumably in your range of portfolio size.

Just thinking about how a human engineer approaches a problem. You don't just ingest entire relevant source files into your head's "context" -- well, maybe if your code is broken into very granular files, but often files contain a lot of irrelevant context.

Between architecture diagrams, class relationship diagrams, ASTs, and tracing codepaths through a codebase, there should intuitively be some model of "all relevant context needed to make a code change" - exciting that you all are searching for it.

Not to say that LLMs are at the same reliability of tractors vs. manual labor, but just think that your classification of what's a tool vs. not isn't a fair argument.

Only true for pre-trained foundational models without any domain-specific augmentations. A good AI tool in this space would be fine-tuned or have other mechanisms that overshadow the pre-training from internet content.

This problem definitely resonates with me, and I have some very primitive ideas of tracking (i.e. tagging tickets that fall into these buckets), but I would love to learn from your experience if you're willing to share!

However, software engineering in a larger organization has a different goal -- it's team sport of building & operating software to serve the business purposes of that organization.

With this goal in mind, one's ability to build upon & maintain an inherited codebase is definitely relevant to success.