I think the reasonable thing that needs to happen here is frontier labs need to look at ways to incorporate better up to date gilded chunks of gold data into th

I think the reasonable thing that needs to happen here is frontier labs need to look at ways to incorporate better up to date gilded chunks of gold data into their post training. I'd love to be able to throw a chunk of scenarios and expected outputs for using various tooling and have this applied to gpt-next / opus-next. I recently did a test with having Claude do this for generating a skill for jujutsu vcs that was based on eval-ing and ablating many many instructions over hundreds of scenarios. This is good, but skill based post training costs tokens for every user rather than at the model level. Maybe there should be a submit your post training corpus here thing somewhere. E.g. this really comes down to advice changing in short time frames that aren't represented in data that satisfies knowledge cutoff which can be as long as 14 months in some things or more for older models. It's not just the problem of knowledge, but the grading of seen output. The models have been trained to produce the older style code because it compiles and solves the problems. Here, the reframe that likely makes sense noting is that "Deno and runtimes like Cloudflare Workers implement the Web API surface natively", that's the strongest single thing that would help steer an agent to correctly write code for the code in question (assuming the Web API surfaces that are key are in distribution). Add something like - "Where there may be reasonable obvious updates that can be used in 2026, use them" ...