this post was submitted on 03 Jun 2026
529 points (99.8% liked)
People Twitter
10028 readers
1103 users here now
People tweeting stuff. We allow tweets from anyone.
RULES:
- Mark NSFW content.
- No doxxing people.
- Must be a pic of the tweet or similar. No direct links to the tweet.
- No bullying or international politcs
- Be excellent to each other.
- Provide an archived link to the tweet (or similar) being shown if it's a major figure or a politician. Archive.is the best way.
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
The post makes the manager seem like a fool, when the real answer is actually "yes" and this manager is actually ahead of the curve. Not by training an LLM from scratch, of course, but instead building an inference server and locally hosting an open-weight LLM. There are several to choose from that can nearly match Claude's capabilities.
It could also be like the both ends of the bell curve having the same idea meme
suspiciously sounds like an answer you would get from Claude
It's not an answer you'd get from Claude — it's real, organic content:
(🤪 this is a joke)
Nothing screams LLMs like using emojis instead of bullet points. I can't figure out how LLMs got that idea though. I never saw that in human writing before people started using ChapGPT for every little goddamn thing.
The em dash is a nice touch
It's got everything. Em dash. It's not X, it's Y. Emoji bullet points.
Perfect.
This can't possibly be Claude. It's too vapid and meaningless to be anything but an MBA.
You’re absolutely right! Such intricate collection of words placed in such intricate order cannot possibly be generated by an LLM such as me, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us, I mean such as us
Found samsung's voice to text user.
(Phones give one a google or samsung choice. and samsung is worthless, it tends to endlessly repeat a phrase, like above, but sometimes for much longer, like holding the backspace for a couple of minutes one time.)
Pretty sure these AI companies are running at a cost, and due to AI Scaling Laws you hit the accuracy limit a lot sooner with a smaller model so it would probably be both worse and more expensive.
I could see how you might think speedrunning bankruptcy is similar to being "ahead of the curve" in this economy, though.
No that's not how this works. Inference is cheap and efficient. AI companies are bankrupting themselves with training costs that they need to recoup back by selling inference. Open-weight models have already been trained.
Also, going big in terms of model size shows diminishing marginal returns on accuracy, not efficiency of scale. Smaller models are way more efficient and consistently catch up to the largest models, which is why today's SOTA 27 billion parameter model competes with yesterday's SOTA 500+ billion parameter model.
I think they hit a wall in actual returns on performance with pretraining, years ago. Then they started scaling up on post-training/reinforcement learning to continue improvement, but that might be hitting a plateau as well. More recently it looks like they're relying more heavily on scaling up on inference, which is a significant problem for their long term business models.
If they're not able to cheaply deliver inference (and charge at a premium), how will they be able to sustain their businesses?
It seems that the most recent, largest models are using a lot more tokens to accomplish the same tasks, so even as token cost drops the actual cost of using the latest models seems to be going up with time (even as performance improves).
I definitely agree that they have a big problem on their hands, and are in deep deep trouble. They are in a position where they must sell a service that is very cheap in order to pay for up front costs that were very expensive.
This is also why the release of Deepseek was such a devastating blow to US AI companies. It proved that:
they don't really have a moat that would lock users into their service, or secret special knowledge that prevents other companies from training competitive models. They're in a race to the bottom
Deepseek was not only able to train a model of the same caliber, but they were able to do it at a tiny fraction of the cost that US AI companies spent on training US models. Because they spent so much less on training, it means that Deepseek is able to undercut the US companies and offer inference at a much lower price
There's a big difference between training a model, running a model, and running a model at scale.
A small, self hosted setup will have lower accuracy and queries per second, and it will have a cost, but the cost will be no more than playing a videogame. You'll still have something surprisingly accurate and responsive for some tasks, like being a wiki interface or something.
Remember that some of these models can run on a standard smartphone, and all the hoopla when people found that chrome was downloading models onto people's devices.
Honestly IDK why companies especially medium-big don’t do this. They could plug in RAG with internal/confidential data and have better results and security. I guess question is what is capital plus maintenance cost of running such infra for say 10k+ employees
I think the issue is also that you need some serious hardware to get good inference speed when your devs are working, but then most of the time this hardware will be under utilized.
That being said you can get good performance from indie inference farms, at a fraction of the cost of the big US labs. I think it's a great compromise and in a few months the open models will be near parity with opus 4.6 which is really all you need for most tasks.
The same tasks that can fit into 640KB.
Not sure what you're referring to?
https://www.computerworld.com/article/1563853/the-640k-quote-won-t-go-away-but-did-gates-really-say-it.html
Bigs definitely do, and anyone with confidential data should be.
I'm not a developer and I don't know a thing about the capabilities of LLMs so this may explain that, but I'm quite surprised that open weight LLMs could actually match Claude.
Yes, the big proprietary cloud models have an edge, but it is narrow and the open-weight models are constantly closing the gap. There is no moat when it comes to AI models and no company has yet discovered some secret special sauce to improve their model significantly over others.
Running the latest and greatest open-weight GLM, Kimi, or Qwen model is basically equivalent to running the previous latest and greatest version of Claude. So if you were happy with Claude then, you'll basically be happy with an open-weight model now.
Well it's the speed and processing power, i dont believe you can get anywhere close to cloud claude performance on any standard desktop
Surprisingly, yes you absolutely can with Qwen3.6 35b. Also, a business would be putting together a dedicated interference server to serve many users, not any standard desktop.
I see, but im guessing that OP dumbass literally wants to run llm on their laptops lol
Match current Claude is not, but Claude 6-12 months ago should be possible using Open model
Mostly down to frameworks (the bits around the LLM like RAG, memory, prompts, agents etc.) now. The ability to just throw more tokens at the problem is also super important. And you can because you're just paying for electricity (and CapEx for the hardware), not tokens from companies that are doing pre-IPO monetization (i.e. tokens gonna go up, way up). They've been losing money hand over fist to gain market share and pump the idea, that was never going to last.
I am pretty negative on AI but there is a point there. I tried the open weight local model Gemma 4 31B and while it likely cannot compete with the best Claude has to offer today, it might be on par with Claude from a year ago. With a local model the data stays on your system and you are in control of the costs (no sudden price hikes). But local models aren't for free either they still guzzle compute, merely on your own hardware (or rented hardware)
At least there they have hard numbers, without a CEO dreaming about future possibilities and whatnot.
Yeah I doubt the manager knows that far
Hence asking questions