That's the thing. You can't change it. They do not accept PRs from what I've seen. Whilst it's open source it's only in name. There may be the odd PR accepted from outside the team. But very very rarely.
It's honestly embarrassing to call this open source.
Pi on the other hand, whilst they have their own unique issues with PRs, if you came to them with a genuine problem, or fix, they seem to accept it.
Now I point back to those issues you pointed out, all have PRs... Unmerged. Same with the infinite depth subagent calling.
I really wish they would figure out a better way to ship it for Mac. Last time I tried it a few months ago, it spewed a bunch of stuff into the system's pip archive and broke other software.
Unsloth has better quants, but when I tried their studio a couple months back it seemed like a buggy mess. Ollama seemed very basic but low-frustration.
> That is a very astute and concise way to explain everything about how the frontier labs are behaving and how they're trying to push more people to pay token rates for the best models.
Are they really the best models? Like take anthropic. Without mythos, it's the what? Third best?
Sure openAI just leapfrogged them but .. seriously to get there it's a giant model that costs insane per token.
Nobody needs that, it's like NVIDIA or Intel claiming they have the best gaming performance, but to achieve that they are using more power per frame than anything else.
Yes. I mean, most people agree they are. I've used all of the serious contenders (well not Grok 4.5, because Musk, and not Meta Spark because Zuck, but everything else I've used on at least a couple of projects to get a feel for them). My experience roughly matches the vibes. But, Fable is remarkable when it doesn't refuse to do the work (which it does, quite a lot, since my areas of interest are security and training specialist models).
Anyway, the vibes strongly indicate Fable is the best model, but not by an amount that is noticeable to most people. You could pick any of the top 10 models on this chart and do most of the tasks most people are doing with models:
> Are they really the best models? Like take anthropic. Without mythos, it's the what? Third best?
Everybody is just judging all of this by vibes anyway. Every week, a new model comes out and there's 500 comments simping for it within the first hour of its release. Both OpenAI and Anthropic have been practically indistinguishable to me.
My biggest gripe with Java has always been the tooling ecosystem, almost every java engineer I know solves this with the attitude of "just use intellij".
No. I won't, I refuse to use that, and I refuse to give a company money just to use a language as efficiently as other languages.
I am very happy to see that there's progress being made here. I wish you the best of luck!
Since I started running my own inference server, I've had zero degradation that I didn't do myself. Basically the only time I see it get worse is if I drop one of the quants.
Which is what I suspect the providers are doing to fit more inference on the same amount of hardware over time.
I think the majority here have stated the same... That CLAUDE.md or AGENTS.md effectively do this. Either that or the readme.
The only tip I can give is that your skill that builds or wraps up work. You should have it update those files if anything has changed.
Claude/Agents files shouldn't be bloated, but should imho act as a basic amount of context on the project so your agent and skills can pick up and go, with even the most basic initial prompt.
> The only tip I can give is that your skill that builds or wraps up work. You should have it update those files if anything has changed.
Depending on the scope of work you’re doing, it might be better to have this removed from the context of the work that was done.
I keep a “Last Updated Hash” in my md and every so often will have the LLM pull a diff from that hash to the current head, then determine what doesn’t match.
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