I take a LOT of value out of it for myself.

I have a gaming PC with a 7900XT with 20gb of VRAM and I happily use GPT-OSS-20b, KQ8 VQ8 with max context (130k), at 130 tok/s at the start and 100 tok/s at the end of context. I provide it to my local network using LM studio and connect by URL from my company's laptop either using Codex (with an LM studio profile) or using Jan (for adhoc Q/A). This works absolutely great in the sense that I can freely send all kinds of crazy stuff directly to the assistant and not have a care in the world about sensitive data, let it run free in private repositories and glance at anything it needs and not have a damn in the world regarding what I feed it. OSS-20b is consistent enough to be able to work with codex itself and can create one-shot ad-hoc scripts for several things if instructed to do so, as well as entirely manage my git usage with more complex stuff that would be part of a release that would otherwise bore me to death (like figuring out which commits are which features, creating branches for them, merging them all, guaranteeing it's all nice and clean).

With Codex, I know what ticks the latent space in the right way so I provide the right context in my code (functions, methods, names, locations, files) and ask the right questions (descriptively narrate the problem, what I can do, what is the expected result) and the model toils away at boring / menial stuff while I do something else in the 6\~8 minutes per task. (At some point, one might argue that it would have been better to do some of those things myself if I do describe them with such detail, but I'd argue that I'm much better at breaking down higher level concepts into lower levels than I am with managing pure syntax, so the assistant is really damn helpful. And I really hate doing some of the more menial stuff).

It also works reasonably well to translate my ideas into appropriate spark code when I forget the names of some of the spark utility helpers, and Codex also allows the model to do the aforementioned git boring stuff by itself and assisting me with some more esoteric git stuff. Finally, I also use it as a "final pass" for code reviewing merge requests (mine and from my team's) as it sometimes catches small inconsistencies that I glanced over (thanks to the thinking stuff and the large context and the speed of inference). The model is REALLY GOOD at short bursts of capabilities, in the sense that you wouldn't feed it an entire SQL script for Pyspark adaptation and hope it works one-shot, but rather, I can parse the original SQL script using an AST and feed it small parts (CTEs, large self-contained expressions, etc) and be 99% sure it will do it okay.

Finally, in my most recent experiment, I downloaded all of the Databrick's documentation and converted it to pure raw text, and set up a folder where I can just ask Codex running on that folder to "deep research" in the documentation to ask whatever I want of it. OSS-20B does that very well and fast and has not failed me so far. I suspect I can improve its capabilities by doing something similar by providing my local agent access to updated documentation that it can read and grep freely, and improve its context management as it looks for its own "few shot" examples in the documentation.