71–80 of 620 posts
Subsidies don't last forever.
Earlier quoted context omitted.
Any recommendations on good open ones? What are you using primarily?
qwen3.5/3.6 (30B) works well,locally, with opencode
I hit my 5 hour limit within 2 hours yesterday, initially I was trying the batched mode for a refactor but cancelled after seeing it take 30% of the limit within 5 minutes. Had to cancel and try a serial approach, consumed less (took ~50 minutes, xhigh effort, ~60% of the remaining allocation IIRC), but still very clearly consumed much faster than with 4.6.
It feels like every exchange takes ~5% of the 5 hour limit now, when it used to be maybe ~1-2%. For reference I'm on the Max 5x plan.
For now I can tolerate it since I still have plenty of headroom in my limits (used ~5% of my weekly, I don't use claude heavily every day so this is OK), but I hope they either offer more clarity on this or improve the situation. The effort setting is still a bit too opaque to really help.
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>we don't want a hard dependency on another multi-billion dollar company just to write software One of two main reasons why I'm wary of LLMs. The other is fear of skill atrophy. These two problems compound. Skill atrophy is less bad if the replacement for the previous skill does not depend on a potentially less-than-friendly party.
You can argu that you will have skill atrophy by not using LLMs. We have gone multi cloud disaster recovery on our infrastructure. Something I would not have done yet, had we not had LLMs. I am learning at an incredible rate with LLMs.
What an interesting paradox-like situation.
Earlier quoted context omitted.
>we don't want a hard dependency on another multi-billion dollar company just to write software One of two main reasons why I'm wary of LLMs. The other is fear of skill atrophy. These two problems compound. Skill atrophy is less bad if the replacement for the previous skill does not depend on a potentially less-than-friendly party.
You can argu that you will have skill atrophy by not using LLMs. We have gone multi cloud disaster recovery on our infrastructure. Something I would not have done yet, had we not had LLMs. I am learning at an incredible rate with LLMs.
And not even just understanding, but verifying that they’ve implemented the optimal solution.
We dropped Claude. It's pretty clear this is a race to the bottom, and we don't want a hard dependency on another multi-billion dollar company just to write software We'll be keeping an eye on open models (of which we already make good use of). I think that's the way forward. Actually it would be great if everybody would put more focus on open models, perhaps we can come up with something like the "linux/postgres/git…
Any recommendations on good open ones? What are you using primarily?
Earlier quoted context omitted.
>we don't want a hard dependency on another multi-billion dollar company just to write software One of two main reasons why I'm wary of LLMs. The other is fear of skill atrophy. These two problems compound. Skill atrophy is less bad if the replacement for the previous skill does not depend on a potentially less-than-friendly party.
You can argu that you will have skill atrophy by not using LLMs. We have gone multi cloud disaster recovery on our infrastructure. Something I would not have done yet, had we not had LLMs. I am learning at an incredible rate with LLMs.
But I’m so much more detached of the code, I don’t feel that ‘deep neural connection’ from actual spending days in locked in a refactor or debugging a really complex issue.
I don’t know how a feel about it.
To me this seems more that it's trained to be concise by default which I guess can be countered with preference instructions if required.
What's interesting to me is that they're using a new tokeniser. Does it mean they trained a new model from scratch? Used an existing model and further trained it with a swapped out tokeniser?
The looped model research / speculation is also quite interesting - if done right there's significant speed up / resource savings.