u/Top-Sense-5006

Is there any difference between players like Bailiba, Anderson and Garner? They're not like Bruno Guimarães or Declan Rice who have proven themselves over multiple seasons. Anderson, Garner and Bailiba are all players who only shone for one season. The same goes for Ward. They immediately showed their true colors in the second season. If that's the case, why look down on a pure No.6 like Bailiba and chase all-round midfielders like Garner instead?

I wanna go to major research institutions and have over 1200 hours of research but no pubs at now. I'm stuck with finding a good topic and get started. My PI don't let me get into his research, I have no ideas what can I do next. Should I start from finding one direction in which I am really intersted in? How can I find a not bad way to start my research? :(

i'm burnt out evaluating api aggregators for our dev teams.

we hooked one up recently, and someday our models started giving random, inconsistent responses. Even worse, when an upstream provider goes down and the failover doesn't trigger smoothly, everything just stops. we checked the latency monitors, trying to figure out why the logic on our production setup was acting so unpredictable compared to the week before.

When we looked for a better solution, we noticed that some aggregators brag about having hundreds of different models, half of which are obscure things nobody is using for serious work. tbh, we just need the few we rely on to be fast and dependable. chasing variety seems to come at the expense of quality control and uptime.

we eventually went with zenmux cuz it runs pretty stable overall. They don't have a massive count of models, but the main ones we rely on like chatgpt 5.5 and opus 4.7 perform well. Even if one provider drops sometimes, it auto switches to another one so it doesn't interrupt our active projects.

Anyway, i honestly don't get why some aggregators prioritize the number of integrated models over optimizing for stability. how do you filter through these aggregators to find something reliable?

Seeing people here build token-usage visualizers and compare provider cost got me thinking that the real Kilo question might not be “which model wins the benchmark race?”

It might be: which model gives you the best cost per useful action once you’re actually deep in a coding loop.

A model can look great in one-off prompts and still be annoying in practice if it burns too many tokens, expands too much, retries too often, or loses the structure of the task once the session gets long.

That’s why Ling-2.6-1T stands out to me as a category to watch. The interesting part is not just that it’s large. It’s that the pitch seems much more about execution: lower token overhead, stricter instruction following, better long-context discipline, and getting useful work done without as much wasted motion.

If Kilo exposed more models with that profile, would that actually matter to you? What do you optimize for most inside Kilo right now: raw coding quality, token cost, long-loop stability, tool reliability, or how often the model needs to be corrected mid-task?