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Why not work as before (on 3D XPoint) with Micron and try to pursue PIM (processing in memory) on DRAM? It doesn't go around the DRAM manufacturing constraint, but it helps them sell new PIM-DRAM coupled to Xeons. Right now, they have "pie in the sky" GPUs and nVidia has their boot on Intel's throat, while Xeons and let alone normal cpus are going down the drain.

Intel needs to innovate under constraints and, if history is to be reinterpreted, this won't end well for them. They need less management and to double-down on engineering and their lithography pipeline.

Bold move. Although, I don't think "bold" is what they need where they are going. What's up with these guys? You have something and you wanna turn it into nothing. Just take the site down it will be far less painful.

Aren't shareholders interested in how RDDT is doing?

The relevant question: is herdr going to be sunset by herdr-app backed by herdr-core?

To me this is a valid signal.

Super nice.

Such a stupid comment.

Despite it being an old article things for amazon haven't changed:

you want edge inference, from decision to small models.

That's it. It will be 2c, please.

I guess they used Genie 4 to make a video about people using Genie 4 in different contexts. This may have some implications.

I agree.

They aren't selling tokens only: they are selling tokens in return for money and, most importantly, harvesting of semantic trajectories.

I think, sooner or later this data coupled to model capability will be exploited successfully and will cause an interesting point in human civilization as we know it.

At present, you are right, this is not the case -- the data is not exploited successfully and the models are lacking.

The difficulty comes from the novelty on the information level: we are building systems which fold and compund information. We are beginning to learn how to manipulate them.

There needn't be any time frame to any of this.

I fry my brain everyday trying to keep pace, but honestly, stop it guys. Give the guy a break.

This is the most magical time in my adult life. The 70s, 80s, and 90s, look like a painting compared to the current pace of things. I am literally tired at the end of each day. Dog tired.

Great job OP. I mean. This is nuts.

Ultimately, more people serving.

- gpu market is starving, demand is higher than supply = leads to high gpu price

- number of openrouter shovel sellers rises = lower token prices

Well written.

I think there might be (dare I say) a middle ground to get the productivity of the llm, esp as we evolve them, while still maintain a global and even fine-grain comprehension of a code base.

It is not a simple change, however, but a fundamental one.

Overall I think we are still living in the past and try to apply ourselves to the future. But if the ai craze is to be taken clear-headedly for what it is, it is a complete break from the von Neumann computer and all its resulting artifacts. So why should we use the same tools?

So planning is tied to the spec. This much is clear.

What the author I think is hinting at is not planning alone but "the development and evolution of any program and the state of this program throughout the planning, elaboration, and eventual runtime".

I chuckle at the thought that throwing an md file or a prompt at this problem is sufficient.

So, I posit that if we want any agentic code to evolve meaningfully in the short future and over the long run, we have to have a system which holds and presents this information, the state of a program, in a coherent manner to a human operator. No other way. No other way. And this I say to both nay and yay sayers.

You can argue also that this is part of an even bigger thing. But it is not part of the current discussion on planning and speccing in agentic systems.

OpenAI and anthropic can throw all the billions they don't have at this and adjacent issues, but if this is not solved then they don't have anything.

I also have made similar agents. cool stuff happens when you compose them and a composition is merely different prompts. this strips down the agent to the minimal.

It is not clear what is the best position for the agent or in other words how colesly one wants to integrate with the terminal, shell, or multiplexer.

But over all it does show that maybe the future of agents is not huge blobs of semi-reingeneered multiplexers or see above, but a lean agent with a session stream for context.

great job, i will follow your progress.

Hear, hear!

I also find it hilarious. I wonder what will happen if they don't solve this.

I not only think it is not trivial, I know it is not solved.

let's say that they ask a single question for each session they get. they are immediately doubling the compute they need in processing and then post-processing the same session twice.

nothing trivial about it. not saying they cannot feed "their own LLM" saying it isn't trivial especially at scale.

if you do not trust me try it without the "at scale" part.

People are focused on the drama but the problem showing is the data. This is the elephant in the room and I am surprised that openai can be that stupid with it.

How can openai do this, what is being claimed, at the scale of their entire userbase? If they do this only for particular sessions then how do they sieve through sessions for the good stuff?

How are sessions stored, how are they processed, how much storage and how much compute is used in these pipelines, how economical is it, how fast are the requirements on the storage on the compute growing as userbase grows and generated data grows.

All these questions are far more pertinent than the navier-stokes, but i can only imagine all at openai doubling down on this "very important" mathematical milestone.

what is behind "process" it and "further selection" and "refinement" and how big are these "datasets"? These companies ship the encrypted session to you not because they want to.

I agree that they have pipelines for what you are describing but how effective they are at scale and at focusing is the question.

Hah, what is the infrastructure which takes user sessions (chats with API keys, directions, navier-stokes math/progress) and regurgitates this into pre-training, RL, fine-tuning data? Or better, in-context data?

People talk about the "compute" but what about the "storage"? Is storage exponentially greater, or soon to be, than the compute? Is the storage going to slow down growing to some constant rate, i.e. all people on earth using chatgpt, or no, on the contrary, it will keep growing?

If there were any shady business, I do not condone it, but technologically we are not there yet for said shady business to happen.

All this AGI/ alien nonesense trumpeting from these guys (OpenAI, Anthropic, nVidia) makes me think we are drifting even further from stated goal and money is running dry.

The interesting question is who is left standing after the party is over.

I think it is simpler, but bannin open weight is a collateral worth scoring nonetheless.

AI fails to deliver on the multi trilion usd promises:

Gov/venture says: oh, wow, what will we do with all these datacenters, we need money back!

OpenAI/ Anthropic: let's monitor the citizenry. They may be plotting nefarious schemes using AI models.

Gov: great idea. Whew.

Investors: whew!

Tax payers: paying to be in prison.

So not so much as defense against foreign actors, but failure of the self-tooted AGI goal + gov being gov.

I think this is the wrong approach because everything is external to the model. You end up creating an ad-hoc externalized model scaffolded out of coarser systems, RAGs, files, and so on.

This leads to, if useful at all, to this process of ad-hoc recall inference which has to happen in time. By itself this is not a problem.

The problem is that the model has to be constantly injected in context with the newest version of the "memory state" at each turn or relevant turn.

The newest coherent memory state is also a problem. More or less 50 years of not failures, but not success either. This may be even deeper problem than the externalization problem.

I think we are just in the very beginning and we are slapping database stuff to the transformer hoping it will work, but these deep neural-net architectures categorically show that they are not databases.

There will be a synergistic middle ground, but its shape is still not clear.

In your closing thoughts, you don't mention what is this staying firmly in control? how do you stay firmly in control while reaping the benefit of the model while being more productive?

because if you start a curriculum over what the model wrote then, one can argue, you are better off writing it yourself in the first place.

What is the purpose of the "zellij" in the top left corner and why isn't there an option to remove it?

are people getting the `<!-- -->` sentinel'd reasoning summaries?

I find it funny that some comments are arguing why "the innocent users have to fall victims, becoming/ being collateral damage" to this american governmental whim and thus being deprived of access to these models.

But hold on, collateral to what? Is this "our own personal jesus" access that we cannot live without or what?

People don't and cannot learn how to code or what? We don't know how to think?

I am calling their bluff. Fill your own gddam datacenters with "meaning".

Panta rei.

Yes, but it isn't.