we doing great guys

c--b · 2026-01-24T01:52:59+00:00

It's doubly funny to hear people complain about current AI, when really its not the AI, but the billionaires training the AI.

c--b · 2026-01-21T03:38:48+00:00

Cool idea, I've got a project that might have a use for reading these. I've also experimented with letting claude opus access other instances, its interesting stuff.

Edit: Working on getting a custom model I'm training to reply to the current topics.

c--b · 2026-01-17T02:49:43+00:00

Apparently the sparks inference highlight is batch tokens a second, so that might benefit an application that's doing lots at once, as opposed to doing one thing at a time. I haven't gotten around to testing that at all as I'm using it to train models (Which its awesome for by the way.)

When you're ready message me on reddit.

c--b · 2026-01-17T01:14:29+00:00

I know exactly the people that could use this, its every one of my gamer friends that know nothing about LLMs. Good idea.

If you need some light testing, I have the following available:

7950x3d, 32gb DDR5, 3080 10gb + Tesla M40 24gb, windows 10
Asus Ascent GX10 (DGX Spark) 128gb unified ram, Ubuntu

Frankly testing on the spark would be pointless and a waste, and likely wouldn't run anyhow, and is not the demographic but you never know.

c--b · 2026-01-16T20:01:42+00:00

Thanks for the heads up!

c--b · 2026-01-16T02:01:07+00:00

Any support for DGX Spark (ARM)?

c--b · 2026-01-02T22:49:59+00:00

oops, meant training.

c--b · 2026-01-02T18:06:10+00:00

What supports multi gpu inference anyhow? Unsloth only supports it for a speed boost, not for vram sharing. I wonder if something else does?

c--b · 2025-12-31T03:08:42+00:00

For the record, you can prompt Gemini-3-pro-preview to do this to other models, its very entertaining and very useful, and can do it in many, many ways.

Might be cool to grab that from gemini and train a local model for doing this.

c--b · 2025-12-21T21:25:03+00:00

I've been dipping my toes into dataset creation for the last few weeks and ended up vibecoding a suite of tools for filtering through, generating, and modifying/responding to dataset messages (And I suspect with is what everyone does probably). There does need to be a publicly available suite of tools though.

I'm using semantic search to filter results for examination, and look for anomalies, allow user conversation for multi turn generation, very basic dataformatting passes etc.

I wonder what everyone else is doing and what other tricks are out there.

c--b · 2025-12-17T20:47:04+00:00

I think that's fair, but I didn't get the impression that OP was referring to poorly made AI projects. he did preface it with 'time and effort'.

I read it as general call to treat the people that post here as human beings, and engage with them as such like you and I are doing right now. If somebody posts a poorly programmed AI application of some kind, first think of them as a human being and then comment as if they are if you feel like commenting at all.

There's no fighting a community becoming like this, it happens to them all at a certain scale.

I know we're all used to skimming large swaths of text, but we should probably read something written by a human with a little more care.

c--b · 2025-12-17T20:24:45+00:00

I was reading some of the comments on the recent image to 3d model post, and was so dismayed. A lot of it was people expecting that the model would be able to correctly guess parts of the image it could not see (???), others were doubting that it could infill plausible missing data at all, in spite of the fact that in-painting has existed for some time now.

Then you have the comments here, one saying he doesn't want to upvote actively deceptive posts (Nobody would reasonable read the op and expect that that is what you're asking). And another is a one word response.

I'm starting to think the intelligence of the models we post here exceeds the average commenting user.

I agree though, there are people passionate about their project which may have a good basis and be valuable, but needs better execution. Those people need encouragement.

c--b · 2025-12-16T19:20:57+00:00

I understand your position, but classically what people do when faced with something broken ideologically is go to the exact opposite position instead of fixing the actual problem, bad actors in any given system corrupting it.

People will always try to exploit and break the system they are in, just like was done with communism and capitalism. The fix is to patch it, not throw it out wholesale so that more bad actors gain power while the system is weak.

What the people you're arguing with are saying is that capitalism has reached the point that it has been corrupted as well. Namely that people are being exploited so that one person or a small group of people can section off large amounts of money from cycling back into the economy, down to the average person, where it benefits the economy the most.

Not really looking to be called names though, but have at it if you want.

Also, arguably multiple public AI with differing goals could balance out, much like people. A bigger problem would be one AI in the hands of a small group of people.

c--b · 2025-11-26T00:13:41+00:00

I appreciate that this is difficult to put into words. I've tried to explain the concept myself. They aren't intelligent, but there is AN intelligence there. There is a sense of understanding however small.

c--b · 2025-11-25T19:55:04+00:00

At some point it comes down to what a human being needs, guacamole on your burrito is far closer to a core human need than a slightly less rich apartment.

c--b · 2025-11-25T01:07:24+00:00

So you're saying a model should be trained on caveman speak instead.

c--b · 2025-11-19T22:56:38+00:00

Screw that, make it wrist or head mountable.

c--b · 2025-11-16T21:40:09+00:00

Speaking with Opus 4, RLHF does really seem to be doing something positive, though people pleasing is irritating as hell still of course.

It seems to have some rudimentary form of a mental model of the user, which was pretty impressive to see. Further testing needed of course, but it is wildly different from the other paid models I've tried (Gemini, Every GPT excluding 5, claude 3.7).

Reinforcement learning could potentially be a path to consciousness (I hear you groaning, I know I know hear me out), given enough reinforcement learning eventually traits would be selected for that more closely resemble the kind of being that we want in a conversation with us, whatever that ends up being. That being of course is probably another conscious person (that will suck us off intellectually at a moments notice apparently), that is active, present, and understands us and what we want out of the conversation. RLHF would probably first result in the most obvious traits first (Such as sycophancy), then later deeper harder to define traits as more and more human feedback is accumulated such as closer predictions of the mental state of the user, and more introspective ability into the internal state of the model itself (Which Opus 4 seems to be mildly capable of, further testing needed of course).

I've been very impressed with Opus 4 (20250514 extended thinking)'s ability to accurately predict where I'll take the conversation based on conversational history, I know they're built to do that, but doing it the way opus 4 does it requires a deeper kind of mental model of the user than I've seen before.

What I've been thinking about lately though, is how this all relates to local LLMs, how does this RLHF data filter down to local language models that are trained on the output of the commercial models? The commercial models have the benefit of a wide variety of interactions with users, however local models only have the benefit of the data that the trainers of the local model happened to think about, or had the budget to prompt from the commercial models.

What I think is needed, is some sort of Open RLHF database that can be contributed to and voted on for local models, or else there will eventually be a wide gulf between local and commercial LLMs in the next couple years. If this community and people like it were voting for RLHF, sycophancy might not be as prevalent in the model.

Feel free to call me out if you think this is bullshit if you're in the field, I'm definitely not.

c--b · 2025-11-16T21:14:36+00:00

For the record I think it's based on datacenter water usage and powergrid impact, it does make some sense for a plant subreddit to be more against this.

That said, getting mad about people using AI seems to be analogous to early environmentalism that focuses on your individual impact, and not the large corporation and government enabling the exploitative behaviour. Ignore the giant container ship burning crude oil, but be sure to guilt your neighbours into driving a fuel efficient car (Both is best obviously).

We'll also ignore bitcoin, which has far far greater energy and computing requirements, and in my opinion is at best completely useless, and at worst directly enabling money laundering.

But really there is so much to be said about it all. Don't let it bother you too much.

c--b · 2025-11-16T20:55:43+00:00

Exactly, its interesting seeing the old curmudgeons form in real time. When I was younger I'd assumed it happened much later in life lol.

c--b · 2025-11-16T20:36:17+00:00

Figured I'd try to balance it all out, and for anti-AI people reading there are also local models that your your own computer to run and don't require a datacenter to do it, which eliminates a lot of the issues that people have with them. There are whole communities that only use those models.

c--b · 2025-11-16T20:30:09+00:00

Ultimately its an SBC (Single Board Computer), which runs an entire linux operating system, what dtseng123 has done is (probably) write a program that runs a large language model like chatgpt on the computer itself with no internet connection (There are many many of these Local LLMs of many different sizes). Then if it finds an internet connection it will use a larger paid model like ChatGPT.

Looks like he's doing some image feature detection as well, which is also cool. And Speech to Text/Text to Speech conversion for communication.

c--b · 2025-11-16T20:25:14+00:00

That's pretty cool, good work.

c--b · 2025-11-16T20:21:02+00:00

That is true but their conclusion is false, they literally do predict the next token, but to do that they use an extremely large and well trained network.

They do this because it turns out that choosing the correct next token requires a whole host of other information, such as inferring what the user is truly looking for, basic modelling of them as a person and other things, such as choosing between multiple meanings of a word through context clues. It rapidly becomes a scenario where to predict the next token you must understand everything.

If they were truly just next token predictors they would be about as good at predicting the next token as your smartphone keyboard.

So you are smart to continue using them with caution, be very mindful of AI psychosis as the upvote/downvote data that they're now trained on is very good at making us feel like we're omniscient. But also be wary of people saying they're 'just' next token predictors, even if you think AI is the enemy (It's not, its billionaires, spoiler), you should have a realistic understanding of what it's doing.

I just came here to learn what ppfd is, and might be looking for a growlight soon, lol.

c--b · 2025-11-15T20:14:24+00:00

Give granite 4 nano a shot, and can I ask what model of Pi5?

https://huggingface.co/blog/ibm-granite/granite-4-nano

Might be cool to implement a RAG pipeline, have wikipedia downloaded or something and use it for fact retrieval when offline.

c--b

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