Holding both those positions would be hypocritical all right, but are you sure it's the same people commenting/voting in both cases? I don't think there's a strong consensus on Hacker News. Even something like the time of day an article is posted might get different engagement depending on who is active in which time zones.

As for DeepSeek or any other Chinese lab, I’m not aware of any practices that would make me consider them a bad actor. Can you say the same about OpenAI, Meta or Anthropic?

Regular ppl in the west now hold mildly positive views of the ccp and how 'advanced' china is than usa.

Then there are europeans who now are looking for china to give them the technology handout now that relationship with usa has soured.

Perpetually kneecapped by one of the worst management cultures I've ever seen

One can do no right regardless, the other can do no wrong.

At least in HN.

Unfortunately there are a few topics that short circuit some terminally only people. One of them being anything related to meta. Few others recently emerging is Flock or Musk. It's really exhausting since you can't have a discussion relating to anything that may be adjacent to said topics. It's like a black hole.

Go vibecode something to auto upvote all downvoted posts, call it "Antiechochamber.HN" or something, and if enough people used it this website might improve a bit.

Perhaps none of the AI companies are shining examples of high ethics, but basically all of them have ethical high ground over Meta.

At least Anthropic isn’t sending private videos from pervert glasses to contract workers in Africa. It’s a low bar but it’s a bar nonetheless.

</div> is four Gemma4 tokens, but one Qwen3.6 token.

Each turn is about 45-60 seconds to generate all of the various responses. The GM and director have reasoning on, and the NPCs/Location/Narrator do not.

It's a fairly good "engine" for that. I'm not sure how a denser Qwen would do here regarding speed.

Try a system prompt requiring it to think in Mandarin, while still delivering the response in the user’s language.

- Middle of the day, servers busy? Swap to Sonnet while pretending it's still Opus. Many people won't notice, and nobody can prove anything if they suspect.

- Middle of the night, server load is light? Put it into extra thinky mode so it burns more tokens to ramp up the bills. Flip the switch where it gets really pedantic about writing lots of extra test cases and verifying against documentation.

- Demand increases, but don't feel like running more hardware? Switch to low bit quants, but have a monitor model swap back to quality if it can tell you're running a benchmark.

Assuming model capability plateaus (I think it will), token providers will be in a race to the bottom to maximize profits at the expense of quality that's very difficult to measure.

Do you have a good source for this?

And super noteworthy is that a 27B model (Qwen 3.6 27B) from this year is a huge improvement over a 120B model (gpt-oss:120b) from last year. The goal posts are moving, but at some point "good enough" is good enough for the kind programming I like to do.

On the other hand, at the GTC was a talk about coding in different lanugage (like spanish) and explaining that the quality between spanish and english is relevant different.

But i have not found a good article about the impact of learning data with practical experiments or even if the order of the learning data matters.

At least I think i remember that Meta mentioned having better and less data can be better than more data with lower quality.

As long as these models can explain to you facts about any other topics, its still overfitted for the task though.

128gb hardly runs deepseek v4 flash which is almost free via api pricing.

Its not just that you send a sentence to an API endpoint, you always send EVERYTHING to that agent as a context.

You want to analyse your spending history? You now send everything to someone.

Either no one cares but understands this implication on how easy it is to really capture you or no one really things about it.

But i'm a lot more diligent on what I send. I disabled the gemini activity feature for example because google started telling me that my stuff could be reviwed by humans.

When it's free, you are the product.

In the short term, the big challenge is being able to afford hardware that can run a ~30B model. Last month I got to experiment with LLMs on a NVIDIA RTX 6000 Ada Generation as a visiting researcher during my summer break. I see the power of local LLMs for agentic coding; they’re no Claude, but they are quite useful. I wish I had gotten into local LLMs before hardware has gotten prohibitively expensive and in some cases unavailable; Apple discontinued certain Mac Minis and Mac Studios with high amounts of RAM due to the RAM shortage.

Hopefully high RAM prices don’t become a new normal, though the next year or two doesn’t look good.

The models you can run on a high-spec laptop today are approximately where frontier models were 12-18mo ago (albeit at a lower tok/s rate). If you scan back through hn comments from that era, you’ll find plenty of people saying “this is powerful enough to massively increase my productivity”.

Privacy. Security. Not bulk uploading your trade secrets and intellectual property to Sam and Dario’s servers.

I've been able to accomplish incredible feats (for myself) since GPT-4, so model intelligence is secondary.

My local NAS is private and I’m not going to send it off to APIs for captioning or metadata; but even Qwen3VL 8B does an excellent job at this, despite being quite old.

They are also really excellent for fine tuning. Unsloth and Tinker (from Mira’s TML) are great places to start.

If your use case is narrower than “coding agent for everything”, you can probably match frontier performances on that narrow domain with ~30b and exceed it with ~100b+.

It’s not completely open source, but they actually release their pretraining and post-training datasets with some redactions for (cough) pirated content.

They also have very good code and playbooks for actually doing a fine-tune, CPT, etc.

Even if you’re not tuning a Nemotron model, its mixes are very excellent for your replay data slice; or general experiments. Way better curation and quality than Dolma, etc; or other large huggingface data mixes I tested.

There's a large market, very large, who want the best regardless of what it costs. Probably a large enough market to keep that domain of research afloat (as opposed to shifting research manpower to cost cutting).

The reasoning is just that the marginal cost of AI is very secondary to fixed costs of the businesses themselves; it's not an excuse to sacrifice performance.