They all are pretty similar. And if it was copying anything, I would say more inspiration from claude desktop then codex. The Zcode is more like codex.
> People familiar with OpenCode internals (if you are on the OpenCode dev team I assume this doesn’t include you) might have objected to my python3 example above.
Cache Misses are pretty bad, i have a locally running deepseek v4 flash, i have tuned it now to have 1100-1300 prefill. Its not great but properly useable. Imagine having a session with already 100k and half of it has to be prefilled it would be waiting minutes with worse numbers. And if you are paying by the token, for hosted models, you are wasting money.
Diagnosed with ADHD, ultimately does not change anything for me even through i had the same idea as you. Reason is that i can now start even more stuff in parallel. And some part of them get finished more before i can just prompt more when in focus, but instead of finishing i add more features.
same, but you need more then 100k of hw to run something like kimi k2.6 for a bigger team. on the other hand there is a ds4 flash that you can run on a macbook with 128gb ram. an that one is perfectly usable for a lot of tasks.
The problem is not website, the problem is discovery and discovery is on Instagram, TikTok, and social networks. You don't have any incentive to build a website for a regular audience. What you might do is build an audience on a social network and then try to move them to a website.
But at that point you're big enough to build it properly.
depends, a super small one finetuned to do function calling instead sending it to big model and waiting, instead, you ask for a revenue in last month, i do a small llm function call -> show results. some bigger ones, analysis, summary, classification.
what is great with smaller ones, and im looking at 2b, 4b is you can get a huge throughput with just vllm and a couple of consumer gpus.
what i usually do is basically distillation of a big one onto smaller one.
i dont know what are you talking about, i replaced an older gpt4o with a finetuned qwen. there is a huge amount of "AI, that can be done with those models, or partly by those models." Huge amount of people would not notice the difference. And if you prepare the context correctly, even bigger slice of people would not notice.
nice, will run it later agains qwen3.6 27b, the speed was one of the reasons why in was running qwen and not gemma. the difference was big, there is some magic that happpens when you have more then 100tps.
was part of the beta, its properly good model, in some sense i forgot that im not on opus or gpt. opus is still better. gpt is the one struggling for me. it has some niche in backend work but you can get the same with opus with skills, its lacking in almost all others.
i have glm and kimi. kimi was in most of the cases better and my replacement for claude when i run out of tokens. Now im finding myself using glm more then kimi. Its funny that glm vs kimi, is like codex vs claude. Where glm and codex are better for backend and kimi and claude more for frontend.
as kimi did a huge amount of claude distilation it seems to be somewhat based in data
Yeah it seems they did not align it to much, at least for now. Yesterday it helped me bypass the bot detection on a local marketplace. that i wanted to scrap some listing for my personal alerting system. Al the others failed but glm5.1 found a set of parameters and tweaks how to make my browser in container not be detected.
When it works and its not slow it can impress. Like yesterday it solved something that kimi k2.5 could not. and kimi was best open source model for me. But it still slow sometimes. I have z.ai and kimi subscription when i run out of tokens for claude (max) and codex(plus).
i have a feeling its nearing opus 4.5 level if they could fix it getting crazy after like 100k tokens.
https://drive.google.com/file/d/1JFfgfMO0nO7HR0WHwEqEEjXIIQj...