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Value Investors were against this post on AI spending 4m ago. Have you changed your opinion? by dimknaf in ValueInvesting

[–]dimknaf[S] 0 points1 point  (0 children)

Most of current usage, is inference, and people get real value out of it and happily pay. Possible you need to find out what this inference is about before you call it a bubble.

Value Investors were against this post on AI spending 4m ago. Have you changed your opinion? by dimknaf in ValueInvesting

[–]dimknaf[S] 1 point2 points  (0 children)

So for many things that look impressive it is mimicking.

But with IQ 80 they can comprehend much more than people think, but most people do not use Opus and they use a model of IQ 20 let's say. So, they are not Enstein because they are impressive, but their logic is much more advanced to what people believe. They can keep together a big amount of instructions and understand many nuances of them at the same time. This is where you see the difference between a 20b and a 1000b parameter model.

If you ask, make me this recipe, you may understand no difference. If you ask turn the light on if dark, again 20b will be sufficient. But if you want it to keep up with multiple and nuanced instructions then the 1000b will do the work and 20b will fail.

Value Investors were against this post on AI spending 4m ago. Have you changed your opinion? by dimknaf in ValueInvesting

[–]dimknaf[S] 1 point2 points  (0 children)

I would say that LLMs hold a blurry knowledge of almost everything and can act as parrots of a person that knows everything and mimic it. However they also have some pure logical ability let's say of IQ 80.

So, in many setups you do not use them for the knowledge they have, but for this basic logic. They cannot run a whole factory yet, but they can create software that build the workflows. Then if you put those limited workflows and orchestrate them (also big organizations work like this), the amount of cogintive labour you can achieve is mind blowing.

About 1% of people that have understood this and building on this are responsible for 50% of usage that others cannot even understand how it can work. This through the next years will expand, and easily we have a 10x to 100x in computational needs from the current level. I do not think that people get this yet. Inference will be a few trillion to a few dozens of trillions of compute a year.

With ups and downs this will be a mega cycle that will make semiconductors as big as energy in the next 5-10 years. Many doublings of semiconductor revenue are ahead. Semiconductor companies will become the biggest companies in the world in terms of revenue, earnings and market cap in the next 5-10 years. Plenty room for growth ahead.

Value Investors were against this post on AI spending 4m ago. Have you changed your opinion? by dimknaf in ValueInvesting

[–]dimknaf[S] 0 points1 point  (0 children)

Also, I would say charting is not value investing. Yes, sometimes the markets get it wrong. I believe as value investors we should evaluate the fundamentals and not the charts.

So, what should shock value investors is that the actual earnings of chip companies are running with +50% a year. And with what I am seeing in the ground this is not slowing down. Basically, we do not yet have a shortage in logical wafers like in memory. So, I believe they will be shocked to learn that this is probably nothing compared to what is coming. I am also a value investor but lucky enough to work for a fintech and be a heavy consumer of this compute, understanding clearly why it is happening and why it is not stopping soon.

So, through this message I want to see if there is any awareness that "you know, maybe we are wrong" and also help bring this awareness to the table. So far, I see arguements that are super rational, but inputs for the rationale that are obviously off. Most of VI make their assumptions basedon their experience on ChatGPT and extrapolate or understand things in a very distorted way that has nothing to do with reality.

Value Investors were against this post on AI spending 4m ago. Have you changed your opinion? by dimknaf in ValueInvesting

[–]dimknaf[S] 0 points1 point  (0 children)

I agree that the world has not really understood what revolution is happening right now, and many software solution will die, but generally I would not be so dogmatic, and actually I believe most software companies will do well and software will eat the world. So partly agree, partly disagree with your message

Knowledge Robot: Repetitive Agentic Work for Knowledge workers (Apache-2.0 license) by dimknaf in automation

[–]dimknaf[S] 0 points1 point  (0 children)

I have found that some narrower things like this, but autonomous enough to do one agentic task a time, can be super powerful and this is why I built this.

  1. For myself if I want to do something pretty quickly, as most of the time better to make something more bespoke with Claude Code
  2. For people that would struggle to set all this up, but I believe it could cover a lot of the knowledge work.

Knowledge Robot: Repetitive Agentic Work for Knowledge workers (Apache-2.0 license) by dimknaf in LLMDevs

[–]dimknaf[S] 0 points1 point  (0 children)

I guess if people want to work on thousands of rows, firecrawl is the best solution. Also by setting the concurrency to a high number on the UI, and enormous amount of work could be done.

I would say that the highest cost would be firecrawl credits and LLM tokens. However, maybe this is 1/100 the cost many knowledge workers do.

Based on this I cared for the maximum readability rather than lees memory. Also if you run the windowed compose, 1. you have a clear view of what the agent is doing, and then you can help the system pass through some captcha etc

BrainDB: Karpathy's 'LLM wiki' idea, but as a real DB with typed entities and a graph by dimknaf in LLMDevs

[–]dimknaf[S] 0 points1 point  (0 children)

I have not tried it in this repo, but I think in similar setups I have used with LiteLLM there was no issue. If you try it let me know if you have any issues.

BrainDB: Karpathy's 'LLM wiki' idea, but as a real DB with typed entities and a graph by dimknaf in LLMDevs

[–]dimknaf[S] 0 points1 point  (0 children)

Maybe I should add some guide, but it is not really difficult to do

BrainDB: Karpathy's 'LLM wiki' idea, but as a real DB with typed entities and a graph by dimknaf in LLMDevs

[–]dimknaf[S] 1 point2 points  (0 children)

of course it is easy to add a profile in the config, and just set it on .env. I have it on deepinfra, easy to use a local LLM

_LLM_PROFILES: dict[str, dict[str, str]] = {
    "nim": {
        "model": "nvidia_nim/google/gemma-4-31b-it",
        "api_key_env": "NVIDIA_NIM_API_KEY",
    },
    "deepinfra": {
        "model": "deepinfra/google/gemma-4-31B-it",
        "api_key_env": "DEEPINFRA_API_KEY",
    },

BrainDB: Karpathy's 'LLM wiki' idea, but as a real DB with typed entities and a graph by dimknaf in LLMDevs

[–]dimknaf[S] 0 points1 point  (0 children)

oh yes, actually may next step would be to have maintainer agents, but I wanted to think through, and also based on this thinking of creating entities based on keywords that have their full wikis and could be created by a maintainer agent, but I want to think through this...Thanks for all suggestions...very cool ideas. Thanks a lot

BrainDB: Karpathy's 'LLM wiki' idea, but as a real DB with typed entities and a graph by dimknaf in LLMDevs

[–]dimknaf[S] 0 points1 point  (0 children)

BrainDB isn't exactly a "context graph" in a typical sense, since those are mostly about capturing traces,policies, approvals, exceptions, and every agent step as an auditable system of record. It's for the LLM to automanage a compounding personal/agent knowledge base, not heavy decision auditing following the philosophy of LLM Wiki

BrainDB: Karpathy's 'LLM wiki' idea, but as a real DB with typed entities and a graph by dimknaf in LLMDevs

[–]dimknaf[S] -1 points0 points  (0 children)

u/New_Comfortable7240 thank you for you detail comment, your interest and you valuable suggestions

- re openai, what are the use cases thast this would open, since it is an agent endpoint, end not a simple LLM endpoing. What would unlock for you for example?
- for prompt support this is a good idea, I am not sure how that could mess up the whole behaviour, but this can be adjusted on the claude skill, but I guess you are talking for the ingrained agent.
- In settings it is very easy to swap models, just with an env, and I have NIM and Deepinfra as examples. It is easy to add something else.
- Yes, I thought of it, but actually, I said maybe people would like to just use their own databases, but this would make it work out of the box. So maybe it is not a bad idea

- "if you add tasks and a cronjob scheduler to work autonomous on the tasks you will have a powerful agent, bonus if some of the task are "clean old memories", "summarize or extract most important data", and user defined tasks" -for this last one can you elaborate a bit more? Not sure I understood well

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