theanonymousone

https://xcancel.com/Alibaba_Qwen/status/2078754377473601787?...

Yes. It's the one. Honestly I'm wondering too how it chooses articles to post.

I created a channel for myself some time ago, and the only way that I could think of was to put a 12h delay and then count score+comments.

It's on the front page now.

I know two HNTG channel (one "normal", one with summaries. I don't think they "official" in any sense of the word.

I wouldn't call it borderline. If we take the chart seriously (if), it means for every budget, the best performance is achieved by a 5.6 variant.

It's not about the table, unfortunately:

Users on legacy annual Copilot plans will not receive access to new models and features.

I don't know what MT5, strat, OHLC, or YF are. But I have been loving streamlit since its start in 2019 for exactly being this much intuitive and quick to start with and prototype.

Congrats. Go ahead and have fun.

They say it here: https://docs.github.com/en/copilot/reference/copilot-billing...

But then again they released MAI despite this, so I don't know.

A very sharp slap in the face of those of us who kept our annual plans and didn't ask for s refund: It seems it will not be available to annual subscriptions.

What is meant by a "loop" here? Just repeating the same prompt until you get the desired result? Are subsequent repetitions too close to each other?

Th is really not good advertisement for Claude-Oriented Programming

Dupe? https://news.ycombinator.com/item?id=48590056

Yes. Whenever I tried, I found no reason to use DeepSeek v4 Pro versus MiMo v2.5 Pro, price-wise or performance-wise. Benchmarks also confirm this.

I hope Over-16s be next.

But the huggingface link mentions BF16, F16, and I32?

In OpenRouter, there is an "int4" tag for Moonshot provider of Kimi K2. 7 Code. Isn't that too low, particularly coming from the very developer of the model? Os that a mistake? How is it in their direct API offer?

"You're absolutely right"?

For me it is a tool I avail to an LLM so that it can provide correct answers to a certain category of questions, instead of hallucinating nonsense.

P.S. I was casually searching for "sandboxed Python" for an experiment I'm working on, and reached this article that was published "today". Very nice coincidence! Thanks.

I fully agree with the title (with reservation for "dialects"), and I believe the same can be said for JSON and Markdown, among possibly others.

Thanks a lot. How about Q8 vs FP16/BF16? Have you checked them too?

Have you seen the 8bit quantisation matter a lot? The "consensus" in r/LocalLlama is that up to 4 bits the loss is tolerable.

My question as well. Isn't Tencent a very well-known company? Maybe the mystery is in the model itself?

This is a big deal when/if it's working, to me at least. Where can I contribute?

Isn't this link a duplicate? Or I have déjà vu?

Hi. Is there a significance to that date or that commit? The commit doesn't look very special, and Git was apparently being used from early April already: https://en.wikipedia.org/wiki/Git

Or I may be entirely wrong.

Is it worth getting worse results for that reason?

accuracy ranging from 80.8% for Very Polite prompts to 84.8% for Very Rude prompts

I can live with that, for now at least.

I have always said please and thank you to LLMs, not to increase accuracy or because I'm stupid. I believe it is more about me than about the LLM, and this is anyway a habit I don't want to lose.

Yes, of course you can destroy it. But how far can you "improve", beyond decent "common sense" behaviour.

Isn't caching a server-side thing? How does the agent affect it, significantly at least?

Is it not safe to assume Window source code is not present in the LLM training data?