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One pattern I have observed and guilty of myself is that simple hybrid search patterns work poorly on single-word intent queries. It is better to do pure lexical search for certain queries and then fallback to semantic or hybrid.

I've settled into a habit of asking codex to write down summary of key decisions in a ledger as I end a unit of work. It keeps iterating natural but maintains a system of record on decisions in the same repo. The "ledger" is the new code.

Perhaps model size and reasoning length trade off to some extent, similar to CPU vs. RAM. A smaller model with a longer reasoning trace has more intermediate structure to latch onto and build on.

Suppose I write a distributed algorithm in Rust. To verify it, I might describe the algorithm again in TLA+, model-check that specification, and prove that it satisfies the properties I care about.

Now I have two artifacts:

TLA+ specification --> proved

Rust implementation --> runtime

But the proof establishes something like:

TLA_Spec => Safety

What I actually need is:

Rust_Program => Safety

I believe this is called model-code gap and there are ways to address it but I haven't found an easy-to-follow approach.

Good observation. This is an employer hiring demand index, not a measure of overall technology usage and my coverage is strongest among larger employers and organizations publishing through tracked ATS/job posting sources, so that could explain it.

Yup, and my data only captures a part of professional job market which itself is small part of labor market. I'd use this as additional data point to other sources to correlate etc. However, the experiment is mostly to test my own thesis that unless there is a major event like covid, these trends are really hard to spot.

The annoying part is a large percentage of misbehaving bots (not obeying robots.txt for example) are via end user proxies across the world. However most of these aren't doing full-browser loop, so if you are behind cloudflare, you can do non-interactive challenge and that can help quite a bit.