181–190 of 443 posts
I kind of lost interest in local models. Then Anthropic started saying I’m not allowed to use my Claude Code subscription with my preferred tools and it reminded me why we need to support open tools and models. I’ve cancelled my CC subscription, I’m not paying to support anticompetitive behaviour.
Earlier quoted context omitted.
Is this still the case? Is Anthropic still not allowing access to OpenCode?
Officially, it's against TOS. I'm told you can still make it work by adding this to ~/.config/opencode/opencode.json but it risks a ban and you definitely shouldn't do it. { "plugin": [ "opencode-anthropic-auth@latest" ] }
how can anyone keep up with all these releases... what's next? Sonnet 5?
Compared to RISC core designs or IC optimization, the pace of AI innovation is slow and easy to follow.
I kind of lost interest in local models. Then Anthropic started saying I’m not allowed to use my Claude Code subscription with my preferred tools and it reminded me why we need to support open tools and models. I’ve cancelled my CC subscription, I’m not paying to support anticompetitive behaviour.
Claude Opus 4.5 by far is the most capable development model. I've been using it mainly via Claude Code, and with Cursor. I agree anticompetitive behavior is bad, but the productivity gains to be had by using Anthropic models and tools are undeniable. Eventually the open tools and models will catch up, so I'm all for using them locally as well, especially if sensitive data or IP is involved.
I can't comment on Opus in CC because I've never bit the bullet and paid the subscription, but I have worked my way up to the $200/month Cursor subscription and the 5.2 codex models blow Opus out of the water in my experience (obviously very subjective).
I arrived at making plans with Opus and then implementing with the OpenAI model. The speed of Opus is much better for planning.
I'm willing to believe that CC/Opus is truly the overall best; I'm only commenting because you mentioned Cursor, where I'm fairly confident it's not. I'm basing my judgement on "how frequently does it do what I want the first time".
Earlier quoted context omitted.
you do realize claude opus/gpt5 are probably like 1000B-2000B models? So trying to have a model that's < 60B offer the same level of performance will be a miracle...
I don't buy this. I've long wondered if the larger models, while exhibiting more useful knowledge, are not more wasteful as we greedily explore the frontier of "bigger is getting us better results, make it bigger". Qwen3-Coder-Next seems to be a point for that thought: we need to spend some time exploring what smaller models are capable of. Perhaps I'm grossly wrong -- I guess time will tell.
There is also the counter-intuitive phenomenon where training a model on a wider variety of content than apparently necessary for the task makes it better somehow. For example, models trained only on English content exhibit measurably worse performance at writing sensible English than those trained on a handful of languages, even when controlling for the size of the training set. It doesn't make sense to me, but it probably does to credentialed AI researchers who know what's going on under the hood.
Earlier quoted context omitted.
They did ship that feature, it's called "&" / teleport from web. They also have an iOS app.
That's non-local. I am not interested in coding assistants that work on cloud based work-spaces. That's what motivated me to developed this feature for myself.
Earlier quoted context omitted.
I wonder if the future in ~5 years is almost all local models? High-end computers and GPUs can already do it for decent models, but not sota models. 5 years is enough time to ramp up memory production, consumers to level-up their hardware, and models to optimize down to lower-end hardware while still being really good.
Plus a long queue of yet-undiscovered architectural improvements
Just the other day I was reading a paper about ANNs whose connections aren't strictly feedforward but, rather, circular connections proliferate. It increases expressiveness at the (huge) cost of eliminating the current gradient descent algorithms. As compute gets cheaper and cheaper, these things will become feasible (greater expressiveness, after all, equates to greater intelligence).
Earlier quoted context omitted.
[flagged]
> just (expensive) magic trick Related: as an actual magician, although no longer performing professionally, I was telling another magician friend the other day that IMHO, LLMs are the single greatest magic trick ever invented judging by pure deceptive power. Two reasons: 1. Great magic tricks exploit flaws in human perception and reasoning by seeming to be something they aren't. The best leverage more than one. By t…
For those interested, made some Dynamic Unsloth GGUFs for local deployment at https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF and made a guide on using Claude Code / Codex locally: https://unsloth.ai/docs/models/qwen3-coder-next
Asking from a place of pure ignorance here, because I don't see the answer on HF or in your docs: Why would I (or anyone) want to run this instead of Qwen3's own GGUFs?