After using AI to develop risc-v CPU cores, the same technique was used for developing openTPU. An open source AI inference engine. It's able to run most of the modern models like Qwen 3.5, Gemma 4, and many others. The TPU started able to produce only a few tokens per second and trough a recursive self improvement loop got to 80+ tok/sec on the smallers models.
It was widely noted (at least 15 years ago, maybe more) that every generation of CPU is somewhat dependent upon the computational capabilities of the previous generation being used in its design.
Really dumb question from a software guy. Why aren't the labs burning their frontier models into chips already? Seems like the performance gains and cost per request would be worth it. That said, I understand neither the economics nor the physical challenges to doing this.
From what I've read, not only are some labs doing it (other commenters already mentioned).
But it's complicated for other reasons, one being that the number of parameters for frontier models (especially with MoE models) are so high, and not always utilized (once again, thanks to MoE) that it would actually be incredibly cost prohibitive, if not impossible, to attempt to make giga-chips that would allow running it.
I definitely do believe that we will see more and more specialized chips over time, but putting the entire model on a chip is still a ways away.
I believe Taalas has a heavily handicapped llama 8-billion parameter model. And it still pulls >200W to run.
I can't imagine how anthropic or open ai would be able to burn a multi-trillion parameter model on a chip, we just aren't there yet.
Model SOTA moves faster than chips can be designed or produced. You'd need to commit to a particular model for years to get payoff while still burning buckets of money producing new SOTA models to keep up with the competition.
It's why everyone and their dog runs these things on GPUs. When a new model supercedes the previous one, so long as you've got the memory for it your chips aren't obsolete.
I'm looking forward to someone picking a model to be "good enough" (say, qwen 4.0 or something) and selling them as peripheral hardware
At this point, LLM's are "good enough" for all kinds of tasks. Instead of making them more capable, now the efforts are making them smaller and cheaper.
IMO this is the dream scenario! Cheaper and faster at the current level of capability gives us incredibly useful tools without the worst of the risks people fear. (Though there are certainly already great risks at the current level of capability as well.)
No, there still plenty of tasks that LLMs can’t do well or at all. Yes, we should absolutely make models cheaper but there’s a long way to go until they’re “good enough”. And I’m talking about the frontier closed models too, not just the open ones. So it’s not an “instead of” position we’re in. It’s both.
Yes this is why I don't invest in hardware. TPU is better than GPU when you have a good model already. The models are already good enough to sell as an accelerator card made as a TPU. I would buy an Opus 5.5 card.
They have trillions worth of obligations to their partners in terms of compute purchase agreements and equity. Burning models to chips isn't really something they can blow money on just for funsies, they need to be able to justify it. The above comments are hypotheses as to why they haven't yet.
> Model SOTA moves faster than chips can be designed or produced.
From what I remember working in that area the hardest part is getting masks for a design. Masks were developed in the span of half an year. Masks also reusable, they can be mixed and matched and this is why fabless companies work with fabs to produce specialized masks for them, it saves time for consumer to have masks for some macroblocks prebuilt.
Given some amount of work with the fab before main pipeline set (I think a year long process), one can then spew LM-on-a-chip in six months or less and much more than 2 per year, because there can be several LMs in pipeline.
FPGA speed is a common misconception. They often have run at quite modest frequencies, and come with limited memory capacity compared to a GPU at same price range.
Speed (clocks speed) , is dependent on your design layout, and for any resonably advanced layout, it takes lots of knowledge to push the clockspeed beyond 200Mhz on 'consumer'/prosumer models. (Compare to a few GHz for GPU/CPU).
FPGA speed shine where they can pipeline massively parallel calculations through pipelines with minimal lookups.
Wrong. This is one area where FPGAs have an insanely unfair advantage compared to CPUs and GPUs. Yes the SRAM is limited but you have so many individual blocks and all of them come with dual ports and getting the maximum frequency out of block RAM is much easier than getting the maximum frequency out of programmable logic.
If you wanted the highest possible memory bandwidth while being free to look up hundreds or thousands of independent memory addresses at the same time you're better off with an FPGA.
E.g. with an Efinix Titanium Ti180 you could hypothetically have 2560 simultaneous memory requests per cycle all pointing at a different address and process those requests at 1 Ghz.
Yes you are right, I didnt express what I meant very well.
looking up static values is quick and easy. When you need to lookup results from previous stages of pipeline rather than just feeding them forward, thats where I run into trouble. But I am a relatively fresh FPGA designer, so I am sure it can be done. And I probably need to level up my boards a bit too.
The bottleneck, for inference at least, is memory bandwidth. And that you can't make any faster by making it specific to your model.
So companies try to maximize the memory bandwidth they can get, balancing tradeoffs of power/area/programability of their chip. Right now they feel like the economy on power/area is not worth the decrease in programability/flexibility.
Presumably though the kernel has a pretty specific set of operations done against the weights in memory. Burning the weights into the memory with local memory cores capable of the kernel operations would be a lot more efficient than round tripping busses.
The primary constraint isn’t likely what’s possible to do, but that the kernel and weights are too variable right now and the patterns too poorly established to bake into hardware accelerators yet. Margin pressure is also not there yet.
I suspect as the marginal utility of the frontier improvement settles into diminishing returns (I suspect we are there already tbh) baking hardware models with ROM, working set, and kernel cores collocated will be the frontier space as the goal will become reducing capital spend to utility levels rather than research levels.
Once someone has a model that is sufficient for almost any practical use, making marginal inference cost effectively zero will be the competition frontier. I do shed a tear for all those lonely data centers as compute densities will almost certainly make most of them a terrible investment.
> Burning the weights into the memory with local memory cores capable of the kernel operations would be a lot more efficient than round tripping busses.
Sure, but now we're not talking about just burning the weights into the chip, but also designing a new architecture that has memory local to each core. A new architecture would then require a new programming model, which means new inference stack, which may mean new training stack.
I don’t think it would require a new training stack, and I’d imagine it makes more sense to distribute cores with memory. The cores can be simplified to the functions of the kernel since the inference kernel can be expressed as a reduced set of optimized functions in the pipeline rather than a general CUDA core. If the model is burned into ROM, the compute pipeline can be baked into the core.
Because the iteration speed on models is so fast that by the time they have an ASIC ready for one model version, they are already significantly ahead in capability. Think of how big the jump between Opus 4.8 and 5.5 has been. They were released four months apart.
What makes a ROM hard? I'm ignorant of the details of chip design, but I'd expect just a large array of essentially open or closed connections. Is that incorrect?
plus row and column decoders that get more and more complex with size. but consider the size of the weights. convert to mm^2. Gawk at the resulting value
Would you pay to crystalize one of today's models in silicon so you can use it in 2028, or would you wait for another 6 months to see how models improve before pulling the trigger on that kind of commitment?
If I controlled a budget like this, I think I would put some portion of it toward paying to crystallize one of today's models in silicon, yes. Not 100%, but I do think this makes sense to invest in at this point. I would not have said so a year ago.
Naive question: would flash or some kind of write once memory be dense enough to replace a mask ROM? I'm wondering whether it is feasible to manufacture "blank" chips at the semiconductor factory that have a fixed architecture, but are model-agnostic. These chips would get the then-current weights burned in on first use. It's probably quite wasteful, but it could keep the same chip design alive for a longer time.
DRAM seems to sit in the economic sweet spot of density and bandwidth. I'm not sure if any other storage technology can provide non-volatile or one time programable storage with either the same density and bandwidth. Although I must say, it seems insanely wasteful to use RAM to store model weights, there must a better solution.
The closest anyone has gotten is Cerebras with their Wafer Scale Engine. It uses SRAM embedded with the compute. A single chip is an entire wafer, but the headline spec, how much ram, only 44GB, which is tiny for the silicon area used.
I understand traditional IC production workflows are ludicrously expensive, and glacially slow, but surely at some point the economics are going to tip in favour of mask rom.
Say you setup your foundry/packaging/ai chip facility. You come up with a new set of model weights. Run your CI/CD pipeline to produce a new mask output. The only thing you've changed are the assignments of the bits, this is extremely low risk change. The new masks should be completely interchangeable with the current process.
You produce the new masks, swap them into your foundry process, and all of a sudden your new chips have the latest version of the model. This would probably manifest itself in the form of inference providers having yearly / bi-yearly "updates" to their models as new hardware is brought online.
Tiered subscription levels would gate keep access to the latest and greatest model, cheaper subscriptions will be limited to older versions of the model, and so on, until running the hardware is no longer economically viable (no demand/running costs exceeding what the market is willing to pay).
I think Taalus is a bit reluctant to publish precisely how they do it but vaguely:
> We basically have an architecture where we are embedding the models, and we are hard coding the models and the weights into our what we call the mask ROM recall fabric, which is paired with an SRAM recall fabric. Together, they are able to store both the model as well as do all the computations of KV cache. We have adapters and customizations – we support all of that. This design allows us to be super-dense in terms of compute and in terms of storage, and we can do compute on that storage incredibly fast, which is what drives density up and cost down
Why is everyone here is talking "improving silicon" == "baking in the model weights into hardware"
what happened to improve existing hardware in the ways not obvious to humans? Some novel transinstor placement, some new approach to make logic gates faster, matrix multiplications etc - the filed is huge
Models fully deprecate in a few months. Why would you burn an algorithm that fully depreciates in value faster than a bag of potato chips. The 'inefficient' general purpose hardware is constantly renewed with every released model. Even 6 year old Ampere GPUs are still usable.
We are still in the middle of the AI race. Commodity hardware is easy to use, can do everything and is fast enough.
Your optimized hardware chip might be obsolete before its back from the fab.
SOTA Frontiermodelhardwarechip is a benchmark point of a potential model slow down.
Google is doing it right now under project Frozen v2 which should be ready by 2028? which is either just a small experiment or flexible enough and thats why it takes so long for it to happen.
Yes, and startups do exactly that. Check out Etched https://www.etched.com/ where they made a Transformer specific GPU (basically a form of ASIC) where they bet that transformers would be the dominant GPU architecture for running AI / LLM workload.
Most responses here are along the lines of "model capabilites move too fast to build hardware for".
I think the fact that there are plenty of 1yr+ old models on openrouter serving hundreds of billions of tokens a month shows that there's plenty of use case for models that are "good enough. Cerebras' entire business is serving older models at high speed. I would happily use an opus 4.7 at 15k tokens per second. The intelligence per second of an ASIC still makes sense even with rapidly evolving models.
"Intelligence per second" is a striking phrase! I'll be turning it around my head at a moderate IPS until I hopefully make something of it.
But you're right in sense: moderate intelligence at superhuman rates (and presuming moderate energy usage) is very compelling compared to an intelligence that takes 1000 years to return "42"
I look at it this way. A year ago is a long time in AI terms, but not that long. Those models were already decent for the tasks we're using SOTA models for today.
So imagine taking a year-old SOTA model and running it at 100 tokens per second on an edge device. That's enough to feed a screen's worth of content through it and power decent multilingual message suggestions on IM.
Imagine running it at 1000 tps. That's enough to reparse that screen mid-keystroke, and give you semantic autocomplete in text. Or fully general "the phone has a good idea of what you're attempting to do" context at all times.
There's many, many new classes of features that will open up if decent enough models can be run on edge devices at 100+ "intelligence per second".
Or vice versa... 1000tps == 1tps via solar power + a capacitor (or whatever).
I'm poking (lightly) at the locallama game, have a fancy MBP5 with gobs of ram (so I can demo/trial locally) and have been semi-waffling between whether to chase a mini or studio for local "always on" type stuff.
My outcome was "CapEx v. OpEx", and dropping another $5k for an aluminum cube buys a lot of OpEx (eg: just trickle-drip HF/OpenAI credits to a raspberry pi or VPS orchestrator rather than trying to do the inference locally), ie: $5/mo inference for 1000 months.
HOWEVER, there's definitely a role for that 1-10 TPS type "ambient inference" that I wouldn't mind sustaining on any sort of always-on / local / private compute. My main email address is still on ...@yahoo.com and their spam filtering has gone to absolute shit.
Being able to have the always-on mini (local, trusted, no private data leaves my control) poke at the IMAP/email and thresh it into SPAM/HAM/Personal/Political ... random spot check, I'm getting ~5 emails per hour, and that's completely tractable for staged low-med-high processing. (Subject + rules.py? Subject + Body + LocalSlowTPS? Subject + Body + LocalDeepTPS? Subject + Body + RemoteLLM?)
Even if you did 10000tps of "jev" that's an incredible value... not quite "Literal AI Packet Router", but as you're dancing aroud saying... "Speed is a Weapon"
Look into "OODA Loop" => """The OODA loop is a four-step decision-making model—Observe, Orient, Decide, Act—created by U.S. Air Force Colonel John Boyd to help leaders make fast and accurate choices in chaotic situations. // The main goal is to cycle through the loop faster than an opponent or changing environment. By operating inside another person's loop, you create confusion and outpace their ability to respond. While initially designed for aerial combat, it is now widely used in business, sports, and crisis management."""
Also "faster" indirectly means "more intelligent" with reasoning models.
For example, Opus 4.6 Max was somewhere between Opus 4.7 Medium and High in some benchmarks, but it was slower. If there was a way to run it 10x faster, the economics would be different.
This question would be better answered if people were careful about distinguishing between "models" and "transformer architecture".
If you bake a given transformer architecture into silicon and then, a year later, changes in transformer architecture give a large inference performance boost, you may have to throw away all that now nearly-useless silicon that gets outperformed by humble GPUs.
Cuz once they're in chips they can be put into robots, and once they're in robots it won't be so easy to reach the off switch, and once we can't easily reach the off switch, we're doomed.
I think one limiting factor here is that Big AI cannot focus on building solid, stable products that do a job well. It's okay if it happens incidentally, but doing it on purpose would be self-defeating.
Their stock price, be it public or estimated, is heavily pricing the notion that they are first and foremost Growth companies. Therefore their focus must remain on ever better and greater things. If they lose focus and get distracted by lesser endeavors, their valuations crumble, their ability to raise capital vanishes, and their runways collapse before they ever have a chance to reach their end goal, whatever that may be.
That means the boring job of productizing AI models into reliable systems that won't vanish in six months is left for a smaller company willing to pick up the crumbs. Unless they get acquired by Big AI before getting it done.
Its coming, AMD snapped up the company behind https://chatjimmy.ai/ Taalas that did exactly that with an older model. As others have been saying the point is to know when to do it. At what point is there a model that cannot drastically improve that you can then do this lets say for the mid and low tier models leaving frontier to run on gpus.
A hybrid approach might work better for robotics. A model-in-silicon for body control and sensing, then a different model on processors for reasoning, speech and decision making.
It will happen once models get good enough for specific applications like robotics; there the hardware is fixed and power requirements are strict, so it makes sense to have dedicated silicon. But for use cases that doesn't require onboard, it's always better to get access to the latest and greatest, until we get to the point where improvements are marginal between model releases (we're not there yet)
As a SW person (working with silicon folks though), I was always interested in the same idea. So I just vibecoded a little compiler from LLM (config.json, weights) to RTL (happy to share if you want). Then synthesized it via yosys.
I picked TinyStories-1M, synthesized via yosys against SKY130 PDK. It's GPT-Neo with 8 layers. At int8 it's ~30mm^2 per layer (including kv caches, multipliers and all the attention stuff). So it's 250 mm^2 for the full toy model.
I'm kinda surprised, I spent no time, so if you want to etch a toy model, you can do that (you can probably reduce the area significantly). If you want a mask for that is probably going to cost 100k though.
Yep, 99.9% of people are completely oblivious to what LLMs can do. Just wait until the next gen of CPUs/GPUs designed by LLMs start coming out (fyi chip development tools have advanced centuries in the last few months) and you'll start seeing exponential gains in hardware.
Which tools have made that leap? Faster design iteration makes sense, but what points to exponential hardware gains rather than shorter development cycles?
What tools? Ummmm Synopsis type tools? I like FPGA’s and ASIC chips but you know, the tooling is ecosystem is a big black box of wtf. Verilator is great though. Any tools I should be looking up and looking out for? Thanks!
I haven't really dug into the results yet, but my guess is
that a SOTA model has been able to produce an accelerator that runs a model since around December.
The obvious next step is to get enough memory throughput to run that SOTA model itself so that it develop its own hardware.
But perhaps the more interesting question is this: Can an AI be given a big FPGA and design a model architecture that takes advantage of the fabric being reconfigurable.
I suspect an AI could design a purpose-built FPGA-like replacement that would be, for its purpose, significantly more effective than the current general-purpose FPGAs.
FPGA is not efficient in any way except producing very few pieces of a particular logic very quickly.
But I think the higher level logic could check out. If we can abstract the majority of the compute into dedicated ASIC then we might use something similar to FPGA glue to connect and reconfigure them to adapt to new model updates. A bit similar to LoRA layers that you find tune to adapt the model to your particular needs.
I assume that it starts making sense once you have big multi year contacts to run a particular model with only minor updates.
There doesn't exist a single FPGA that can fit an entire AI ASIC. You would need dozens stitched together, then comes the issue of clock speeds, FPGAs typically run far below reference. There also memory issues with FPGAs.
Companies typically combined multiple platforms together such as HAPs, Zebu, Palladium, fleets of FPGAs, and Virtual Platforms in order to design and verify ASICS. So, AI would need access to tens of millions of dollars of HW and Software in order to build and verify a chip design.
Yes a seed round is how much A0 tape out costs. The entire seed round. Let me know how you plan to pay for A1, A2, B0, etc and then the full production run (and then do it again in 6 months when the arch changes).
My bet is that promptgrammers will be so common they'll become devalued full-stack engineers, and the few experienced programmers that still know software engineering from the ground up will become expensive gurus sought for critical tasks.
Or just tell them to integrate them. Which I guess takes some knowledge/experience to even ask for. Then again, LLM know it too; in fact, currently, if you let them set up a project, they overengineer the heck out of it by integrating more "best practices" at once that's reasonable.
It's less a matter of being more expert than AI, and more a matter of being socially and legally accepted for certain roles. It seems likely that AI will be rejected for certain tasks on a matter of principle.
I doubt I'd want a human doing anything in the future, like what, surgery? I'd want a robot. Really anything. It's like no one needs nails made by blacksmiths, no one needs some maybe tired or distracted person performing surgery on them.
It remains to be seen. Most of the effort of a normal decent software engineer, up until now, is not in "writing code" but in "writing code that is correct, understandable and maintainable going forward". We are now in a phase of rapidly losing our ability to understand programs. On this trajectory, programs will soon become as impenetrable as the LLMs themselves. We'll start finding out whether that matters or not soon.
Ultimately it will probably come down to liability, though. If someone receives the wrong dose of a drug due to a software error, whose fault is it? If you want it to be my fault, then I'll want to understand the software. Loads of people would take on the liability without fully understanding it, though. The future doesn't look good, but we'll just have to wait and see.
Can anyone comment on the performance of this hardware? How does it compare to state of the art, human-designed hardware? Is this actually an improvement? (To get to recursive self-improvement, you first have to improve at all.)
This is the smallest unit of a typical AI ASIC, for example Google's TPU would have several dozen more compute units inside of it per chip.
In essence this is the simplest unit of an entire AI chip. The more complicated units of AI ASICS are actually the periphery, especially around PCIe and Ethernet and the sub-systems that link many AI ASICs together to move huge amounts of data around ultimately to each TPU.
Its a SYSTEM on Chip, evaluating 1 function on performance is superficial. This could have the best performance in the world, and it doesn't matter if the bottleneck is upstream
I honestly feel the human should be allowed to pick some (not fire) weapons, like a long steel bo stick, a metal club/bat, or something.
Because from what I saw in the clip, it wasn't the robot kicks or punches at all, all the robot needed to do is stay upright and not run out of battery, because of course human meat limbs aren't going to punch and kick down a metal machine.
Seems too complex when you can just create a basic metal casing that can kill people. Why would they care if its anatomically accurate or not, it doesn't need us to relate to the characters like the movies do
> AI is now capable of developing its own inference hardware
No, it isn't.
A human prompted an LLM to build a software simulation environment for hardware design, enabling an LLM, when prompted by a human, to optimize hardware designs against constraints in the simulation.
Are we confident that no existing LLM is capable of similarly effective prompts to those this author used? (I agree it's a stretch, but would not reject it out of hand.)
Even if not yet, will the existence of this repo soon change that, because LLMs will soon ingest it?
An LLM can prompt an LLM when first prompted by a human.
I think OP is trying to convey the idea that LLMs do not take initiative to do anything, and these are not 'beings' capable of doing things. These are tools being used by humans.
By this line of thinking, you would also have to conclude that humans can't do anything by themselves because they can't do anything unless conceived by their parents.
An agent is not "just an LLM in for loop" (for what? Loop over what? Sorry, but such statements are hopelessly simplistic and devoid of careful thought.) -- agents take actions.
One possible design of an agent would be to prompt an LLM to suggest a goal that would make the world a better place, then run an LLM in a loop proposing actions to implement that goal, executing those actions, and then rinse and repeat with another goal. See Ursula K. Leguin's "Lathe of Heaven".
> Built by whom? Acting as an 'agent' on behalf of whom?
Because the hard part is getting the simulation to be very accurate such that if you design something that works in your simulator, it will actually work in real life. And I would be extremely surprised if any LLM can actually do that correctly.
Getting an LLM to design something in its own simulator that is not accurate w.r.t reality is not useful nor terribly impressive.
Given an budget and the right amount of access, I see no reason why an LLM/agent couldn't right now order parts from a "supplier", instruct the human meat machine to insert the device onto a connected test bed, and iterate.
This is essentially what Claude Fable can do currently with any connected device and tools it had access to.
The repo claims they tested with an FPGA. To be fair it is not the same as an ASIC, and we have no idea what errata abound, but it actually did make a thing that spat out tokens.
It cant "invent new things". It can only reuse what it already knows about.
Thats why the math breakthrough a few weeks ago was so hotly debated. Because OpenAI is desperate to demonstrate that AI isnt just a fancy regurgitation machine, but it can actually develop novel thought. Because that would be the stock price jumps to end all stock jumps.
But then it turned out it was really just listening in on a math professors supposed-to-be-private conversations with another instance of openai, and it used his novel work as the trigger to prove the breakthrough first.
The reason people conclude AI 'thinks' is because tt can reference obscure or poorly documented things quickly (which is its primary advantage along with processing natural language prompts into tasks), which is why a lot of people with emotions confuse that action with inventing things.
You’re assuming the “listening in” aspect, which as far as I know is not proven (even in the less loaded “the model was trained on sessions including the ones in question” form of the claim.
Plausible? Absolutely. Did OpenAI behave badly in other ways regarding this issue? Yes. Does it help to assume unproven facts and then accuse people of reaching emotional decisions? Nope.
I just don’t know enough to agree with you, but I would argue, probably poorly, that an AI or collection of agents could “invent things” simply by virtue of trying essentially everything in a reasonable bound and stumbling into a solution.
Call that brute force perhaps, but I would consider it technically inventing something on the merit that it would at least be an abstraction above naively throwing everything against a wall to only throwing things that would most likely be sticky.
Nature brute forces a ton of problems. Hell, humans also do it by having 1 to 8 billion copies of ourselves around too. Even then we are the most guilty species of "copying someone else's work" right behind viruses. We copy things nature has figured out by brute force, then use slightly more targeted methods of forcing to see if it creates new things.
2024 called, it wants its talking points back. I don't think claims like these are defensible after all the progress we have witnessed in the last year alone.
That's quite literally how an LLM works though? That's like scoffing at people calling computers binary generators. Or am I not getting with the programme enough and it emits pixie dust and rainbows?
An "AI" is a box which lies dormant until a human, with motivation and agency, enters a prompt into it.
A person or company can use it in a way where it might invent something. but at the end of the day, its a tool, and its actually not doing anything on its own.
Its not solving math problems, a mathetmatician is using it to solve math problems. Just cus OpenAI is acting like its AI is solving stuff, its really not. They're just paying people to use AI to hammer problems.
If my boss tells me to fix a bug in our database, and I fix it, is it really my boss who fixed it, since he has the agency and I'm just a tool he uses?
I'd say that people's motivation and agency, like their taste, creativity, and opinions, are shaped by external inputs, so saying that their agency is "owned" by them is a stretch.
The alternative would be the proposition that a slave, lacking agency, has no ability to solve problems. I find that idea unpalatable.
> that an AI or collection of agents could “invent things” simply by virtue of trying essentially everything in a reasonable bound and stumbling into a solution
on a surface level a human solving an (unsolved) math problem can look like this, and of course the tree of all possible symbols you can send to a proving assistant is much wider than what the human samples, and the same goes true for an LLM in a proving loop. It isn't "truly random", it can't possibly be (and solve the problem). Both humans and LLMs solving unsolved math problems are aggressively pruning mathematical syntax and logical strategy trees.
i think the proper understanding of LLM based AI is "contexting"; we describe the world we want them to fill, we build/direct the outside context for them to understand, then "they" take off from there.
If we do a bad job building context, they do a horrible job contexting. People who have trouble working with AI have the same problem people have in general: if they can't figure out the context of the direction, then they make random decisions of doing anything. On the flip side, if you can build the proper context around a sufficiently powerful LLM, they can derive the context via the contexting they're good at.
This is why building documents, tests, and code all in some intent pattern via prompting allows them to do a significant amount of work a normal person would have a great effort t
I've heard that before but it just doesn't make sense in context of what I've seen llms do. If I ask an llm to write a poem about magnetic resonance and vampire rabbits it can do that it created a new thing. I can ask it to build a website for managing rabbit breeding that's also a new thing.
Another way of looking at it is that human beings, just like llms, can produce output based on their inputs. Most literature is inspired by other literature. Most music is inspired by other music. Most software is inspired by other software.
So I think we need to work on defining " new things" before we can definitely exclude them from llm's capabilities
You're overconfident in what you're saying. Nobody really knows (yet) if LLMs can or cannot "think" or can or cannot "invent" new things. I'm inclined to think the opposite from you but smart people actually try not to hold the absolut stances and present them as facts when they're in fact not. What I have seen so far throughout a daily use on complicated stuff suggests that humanity developed some form of an intelligence.
> it was really just listening in on a math professors supposed-to-be-private conversations with another instance of openai, and it used his novel work as the trigger to prove the breakthrough first.
This didn't actually happen.
> Following an investigation, we have confirmed that Buckmaster’s Codex prompts over the two months preceding this announcement and paper on September 8, 2026, could not have influenced the system in any way, including through training.
> For the benefit of a layman, can you explain why this is so much different than a human doing it?
More broadly than the existing answers (which are correct), For a layman, I'd also add that LLMs are essentially 'brains in a vat'. They can't confirm ground truth about physical reality. They only know what's in their training data and prompt, which is incomplete and can be incorrect. Even with real-time external sensors they are limited to the sensor's margin of error, range and trusting it's working correctly.
When properly trained, fine-tuned and prompted, LLMs can be very effective in well-defined, non-physical domains like logic, writing, math and code but making things function in the real-world quickly spirals into combinatorial complexity.
> They can't confirm ground truth about physical reality.
That's true. Of course we can hook an llm up to Motors and sensors. That's a robot. Or a self-driving car. So would you say that those devices can confirm the ground truth about physical reality, and therefore are capable of creativity?
> So would you say that those devices can confirm the ground truth about physical reality
To the extent of the existence, resolution, quality, veracity and placement of any sensors, a machine can register their reported state and use it as a variable. That's substantially different and, currently, much less than the level of knowledge and understanding implied when we say a human "confirms ground truth about physical reality."
> therefore are capable of creativity?
I never mentioned creativity, nor would I in relation to LLMs. Like "Intelligence", "Creativity" is far too vague to be of any use in assessing the capabilities, limitations or utility of LLMs.
On HN, posts like the OP tend to attract POVs at polar extremes from "LLMs are nothing more than stochastic parrots" to "LLMs are (or can be) as intelligent, creative, innovative (etc) as any human or all humans combined." I've researched and thought a lot about these and related topics for a very long time, Neither POV is going to find any quick agreement or easy answers from me.
There's a tiny germ of truth somewhere in both extremes that's drowning in an ocean of confusion ranging from "definitionally or categorically muddled" to "mostly incorrect" to "not even wrong". But neither POV seems interested in anything more than drive-by hot takes, debating over-simplistic strawmen or trading 'gotcha' hypotheticals.
> The first ~half of the difference is humans have a deep world model about the planet which surrounds any sensor and human sensations are pre-processed and filtered by a highly evolved bio-chemical substrate before ever reaching the higher-order cognitive processes which assign words, meaning and qualia to them.
Great, but we also build robots with world models and sensory pre-processing. So nothing you've described is unique to humans.
> I never mentioned creativity, nor would I in relation to LLMs
The topic of this thread is whether LLMs are capable of genuinely contributing "new" ideas. So if that's not the point you're trying to make, I'm not sure of the purpose of your comment.
> But neither POV seems interested in anything more than drive-by hot takes, debating over-simplistic strawmen or trading 'gotcha' hypotheticals.
I think you've oversimplfying the discussion here.
In my view, at least, both "LLMs are nothing more than stochastic parrots" and "LLMs can be as intelligent, creative, innovative (etc) as any human or all humans combined." are simultaneously true, for the simple reason that humans are stochastic parrots. Our brains learn patterns and respond to stimuli.
AI (GPT-Sol-6:xhigh) isn't even capable of using Altium to re-layout a board without introducing a bunch of problems by a user who isn't an electrical engineer or familiar with board layouts.
AI is capable of developing its own inference hardware, once a prompt is given. The fact that a human happens to kick it off here is not especially relevant to the fact that an AI is performing the task independently. There are plenty of ways a text prompt can be generated: a harness, another LLM, or just removing stop tokens so that once begun the AI will continue until its hardware fails.
It's not clear what this fad of attributing everything an AI does to the human prompting it is supposed to accomplish.
How many "prompts" from our parents, teachers, bosses, etc.
it took for all of us to come here and discuss this? Or for that human to think of that prompt for that LLM?
These are clearly rhetorical questions, but think of the metaphysical implication of your contestation. Ex nihilo nihil fit.
What if that initial prompt never asked for this hardware to be developed, and it was just one piece of the puzzle to answer to that prompt? That it took a chain of thousands of agents to prompt each others to come up with that?
>How many "prompts" from our parents, teachers, bosses, etc. it took for all of us to come here and discuss this? Or for that human to think of that prompt for that LLM?
Indeed there are some bugs/"non standard behavior" regarding very small or very big floating point values. All of those where proven harmless for LLM inference. Thanks for pointing out the bug. If you caught something outside of that, please point that out so I can fix it ;)
Yup, the tests already compare to a CPU hugginsface implementation, but I'm working on making running and verifying the results of those tests easier and more available. Also, working on fixing those bugs and make the code more reliable now that the underling hardware has been saturated (memory bound)
Well, you don't. That's why 1.58-bit (ternary) quants are getting used. But if that's what they're going for, no need to dress it up in a floating-point facade.
this looks like a start, however, you really need to focus on what OpenAI already did with Jalapeno. [0]. If you could make a true open source inference chip, GPU not CPU based, it would make a real difference and reduce the costs of buying these chip from your design.
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[ 11.2 ms ] story [ 234 ms ] threadhow does this compare against existing TPUs? Also, how small is "smallers models?"
Just click the link?
It's LFM2.5-230M
Q: You must know something. So what are you being paid for? A: The confidence that I have in my taste and my ability to express what I feel
But it's complicated for other reasons, one being that the number of parameters for frontier models (especially with MoE models) are so high, and not always utilized (once again, thanks to MoE) that it would actually be incredibly cost prohibitive, if not impossible, to attempt to make giga-chips that would allow running it.
I definitely do believe that we will see more and more specialized chips over time, but putting the entire model on a chip is still a ways away.
I believe Taalas has a heavily handicapped llama 8-billion parameter model. And it still pulls >200W to run.
I can't imagine how anthropic or open ai would be able to burn a multi-trillion parameter model on a chip, we just aren't there yet.
It's why everyone and their dog runs these things on GPUs. When a new model supercedes the previous one, so long as you've got the memory for it your chips aren't obsolete.
I'm looking forward to someone picking a model to be "good enough" (say, qwen 4.0 or something) and selling them as peripheral hardware
Lots of people would have happily taken GPT-4o as good enough for a lot of use cases a year ago and not lived to regret it.
Here's my analysis of how to etch relatively big LM into silicon: https://news.ycombinator.com/item?id=47109252
Given some amount of work with the fab before main pipeline set (I think a year long process), one can then spew LM-on-a-chip in six months or less and much more than 2 per year, because there can be several LMs in pipeline.
Speed (clocks speed) , is dependent on your design layout, and for any resonably advanced layout, it takes lots of knowledge to push the clockspeed beyond 200Mhz on 'consumer'/prosumer models. (Compare to a few GHz for GPU/CPU).
FPGA speed shine where they can pipeline massively parallel calculations through pipelines with minimal lookups.
Yes SRAM is limited but that's more of a ram-process limitation than a limitation of FPGA
Wrong. This is one area where FPGAs have an insanely unfair advantage compared to CPUs and GPUs. Yes the SRAM is limited but you have so many individual blocks and all of them come with dual ports and getting the maximum frequency out of block RAM is much easier than getting the maximum frequency out of programmable logic.
If you wanted the highest possible memory bandwidth while being free to look up hundreds or thousands of independent memory addresses at the same time you're better off with an FPGA.
E.g. with an Efinix Titanium Ti180 you could hypothetically have 2560 simultaneous memory requests per cycle all pointing at a different address and process those requests at 1 Ghz.
looking up static values is quick and easy. When you need to lookup results from previous stages of pipeline rather than just feeding them forward, thats where I run into trouble. But I am a relatively fresh FPGA designer, so I am sure it can be done. And I probably need to level up my boards a bit too.
So companies try to maximize the memory bandwidth they can get, balancing tradeoffs of power/area/programability of their chip. Right now they feel like the economy on power/area is not worth the decrease in programability/flexibility.
The primary constraint isn’t likely what’s possible to do, but that the kernel and weights are too variable right now and the patterns too poorly established to bake into hardware accelerators yet. Margin pressure is also not there yet.
I suspect as the marginal utility of the frontier improvement settles into diminishing returns (I suspect we are there already tbh) baking hardware models with ROM, working set, and kernel cores collocated will be the frontier space as the goal will become reducing capital spend to utility levels rather than research levels.
Once someone has a model that is sufficient for almost any practical use, making marginal inference cost effectively zero will be the competition frontier. I do shed a tear for all those lonely data centers as compute densities will almost certainly make most of them a terrible investment.
But such is the cycle
Sure, but now we're not talking about just burning the weights into the chip, but also designing a new architecture that has memory local to each core. A new architecture would then require a new programming model, which means new inference stack, which may mean new training stack.
Think of something novel.
Much of a model are weights, and high-density ROMs are very very very hard.
The closest anyone has gotten is Cerebras with their Wafer Scale Engine. It uses SRAM embedded with the compute. A single chip is an entire wafer, but the headline spec, how much ram, only 44GB, which is tiny for the silicon area used.
I understand traditional IC production workflows are ludicrously expensive, and glacially slow, but surely at some point the economics are going to tip in favour of mask rom.
Say you setup your foundry/packaging/ai chip facility. You come up with a new set of model weights. Run your CI/CD pipeline to produce a new mask output. The only thing you've changed are the assignments of the bits, this is extremely low risk change. The new masks should be completely interchangeable with the current process.
You produce the new masks, swap them into your foundry process, and all of a sudden your new chips have the latest version of the model. This would probably manifest itself in the form of inference providers having yearly / bi-yearly "updates" to their models as new hardware is brought online.
Tiered subscription levels would gate keep access to the latest and greatest model, cheaper subscriptions will be limited to older versions of the model, and so on, until running the hardware is no longer economically viable (no demand/running costs exceeding what the market is willing to pay).
> We basically have an architecture where we are embedding the models, and we are hard coding the models and the weights into our what we call the mask ROM recall fabric, which is paired with an SRAM recall fabric. Together, they are able to store both the model as well as do all the computations of KV cache. We have adapters and customizations – we support all of that. This design allows us to be super-dense in terms of compute and in terms of storage, and we can do compute on that storage incredibly fast, which is what drives density up and cost down
Source: https://www.nextplatform.com/compute/2026/02/19/taalas-etche...
what happened to improve existing hardware in the ways not obvious to humans? Some novel transinstor placement, some new approach to make logic gates faster, matrix multiplications etc - the filed is huge
> Why aren't the labs burning their frontier models into chips already?
Your optimized hardware chip might be obsolete before its back from the fab.
SOTA Frontiermodelhardwarechip is a benchmark point of a potential model slow down.
Google is doing it right now under project Frozen v2 which should be ready by 2028? which is either just a small experiment or flexible enough and thats why it takes so long for it to happen.
I think the fact that there are plenty of 1yr+ old models on openrouter serving hundreds of billions of tokens a month shows that there's plenty of use case for models that are "good enough. Cerebras' entire business is serving older models at high speed. I would happily use an opus 4.7 at 15k tokens per second. The intelligence per second of an ASIC still makes sense even with rapidly evolving models.
But you're right in sense: moderate intelligence at superhuman rates (and presuming moderate energy usage) is very compelling compared to an intelligence that takes 1000 years to return "42"
So imagine taking a year-old SOTA model and running it at 100 tokens per second on an edge device. That's enough to feed a screen's worth of content through it and power decent multilingual message suggestions on IM.
Imagine running it at 1000 tps. That's enough to reparse that screen mid-keystroke, and give you semantic autocomplete in text. Or fully general "the phone has a good idea of what you're attempting to do" context at all times.
There's many, many new classes of features that will open up if decent enough models can be run on edge devices at 100+ "intelligence per second".
I'm poking (lightly) at the locallama game, have a fancy MBP5 with gobs of ram (so I can demo/trial locally) and have been semi-waffling between whether to chase a mini or studio for local "always on" type stuff.
My outcome was "CapEx v. OpEx", and dropping another $5k for an aluminum cube buys a lot of OpEx (eg: just trickle-drip HF/OpenAI credits to a raspberry pi or VPS orchestrator rather than trying to do the inference locally), ie: $5/mo inference for 1000 months.
HOWEVER, there's definitely a role for that 1-10 TPS type "ambient inference" that I wouldn't mind sustaining on any sort of always-on / local / private compute. My main email address is still on ...@yahoo.com and their spam filtering has gone to absolute shit.
Being able to have the always-on mini (local, trusted, no private data leaves my control) poke at the IMAP/email and thresh it into SPAM/HAM/Personal/Political ... random spot check, I'm getting ~5 emails per hour, and that's completely tractable for staged low-med-high processing. (Subject + rules.py? Subject + Body + LocalSlowTPS? Subject + Body + LocalDeepTPS? Subject + Body + RemoteLLM?)
Even if you did 10000tps of "jev" that's an incredible value... not quite "Literal AI Packet Router", but as you're dancing aroud saying... "Speed is a Weapon"
Look into "OODA Loop" => """The OODA loop is a four-step decision-making model—Observe, Orient, Decide, Act—created by U.S. Air Force Colonel John Boyd to help leaders make fast and accurate choices in chaotic situations. // The main goal is to cycle through the loop faster than an opponent or changing environment. By operating inside another person's loop, you create confusion and outpace their ability to respond. While initially designed for aerial combat, it is now widely used in business, sports, and crisis management."""
For example, Opus 4.6 Max was somewhere between Opus 4.7 Medium and High in some benchmarks, but it was slower. If there was a way to run it 10x faster, the economics would be different.
If you bake a given transformer architecture into silicon and then, a year later, changes in transformer architecture give a large inference performance boost, you may have to throw away all that now nearly-useless silicon that gets outperformed by humble GPUs.
Their stock price, be it public or estimated, is heavily pricing the notion that they are first and foremost Growth companies. Therefore their focus must remain on ever better and greater things. If they lose focus and get distracted by lesser endeavors, their valuations crumble, their ability to raise capital vanishes, and their runways collapse before they ever have a chance to reach their end goal, whatever that may be.
That means the boring job of productizing AI models into reliable systems that won't vanish in six months is left for a smaller company willing to pick up the crumbs. Unless they get acquired by Big AI before getting it done.
I picked TinyStories-1M, synthesized via yosys against SKY130 PDK. It's GPT-Neo with 8 layers. At int8 it's ~30mm^2 per layer (including kv caches, multipliers and all the attention stuff). So it's 250 mm^2 for the full toy model.
I'm kinda surprised, I spent no time, so if you want to etch a toy model, you can do that (you can probably reduce the area significantly). If you want a mask for that is probably going to cost 100k though.
Also: Here is our recursive self-improvement hard at work...
The obvious next step is to get enough memory throughput to run that SOTA model itself so that it develop its own hardware.
But perhaps the more interesting question is this: Can an AI be given a big FPGA and design a model architecture that takes advantage of the fabric being reconfigurable.
But I think the higher level logic could check out. If we can abstract the majority of the compute into dedicated ASIC then we might use something similar to FPGA glue to connect and reconfigure them to adapt to new model updates. A bit similar to LoRA layers that you find tune to adapt the model to your particular needs.
I assume that it starts making sense once you have big multi year contacts to run a particular model with only minor updates.
Companies typically combined multiple platforms together such as HAPs, Zebu, Palladium, fleets of FPGAs, and Virtual Platforms in order to design and verify ASICS. So, AI would need access to tens of millions of dollars of HW and Software in order to build and verify a chip design.
So, a seed round?
It is still a very good read, but the machines are much better written than the people.
I don't see a reason why expert humans will remain more expert than AIs.
But really, not everything needs to be maximally efficient and perfect. Human to human interaction is important for its own sake.
Ultimately it will probably come down to liability, though. If someone receives the wrong dose of a drug due to a software error, whose fault is it? If you want it to be my fault, then I'll want to understand the software. Loads of people would take on the liability without fully understanding it, though. The future doesn't look good, but we'll just have to wait and see.
In essence this is the simplest unit of an entire AI chip. The more complicated units of AI ASICS are actually the periphery, especially around PCIe and Ethernet and the sub-systems that link many AI ASICs together to move huge amounts of data around ultimately to each TPU.
So its missing ALOT
You should only upgrade when the bottleneck becomes memory, right now the bottleneck is still on compute until you are at 100% max perf
https://youtube.com/shorts/TC2jGXr0fig
I honestly feel the human should be allowed to pick some (not fire) weapons, like a long steel bo stick, a metal club/bat, or something.
Because from what I saw in the clip, it wasn't the robot kicks or punches at all, all the robot needed to do is stay upright and not run out of battery, because of course human meat limbs aren't going to punch and kick down a metal machine.
No, it isn't.
A human prompted an LLM to build a software simulation environment for hardware design, enabling an LLM, when prompted by a human, to optimize hardware designs against constraints in the simulation.
Are we confident that no existing LLM is capable of similarly effective prompts to those this author used? (I agree it's a stretch, but would not reject it out of hand.)
Even if not yet, will the existence of this repo soon change that, because LLMs will soon ingest it?
I think OP is trying to convey the idea that LLMs do not take initiative to do anything, and these are not 'beings' capable of doing things. These are tools being used by humans.
One possible design of an agent would be to prompt an LLM to suggest a goal that would make the world a better place, then run an LLM in a loop proposing actions to implement that goal, executing those actions, and then rinse and repeat with another goal. See Ursula K. Leguin's "Lathe of Heaven".
> Built by whom? Acting as an 'agent' on behalf of whom?
Someone who didn't read the book.
It literally is a loop with some markdown and harnesses. There's no magic.
Like sure it didn’t have the inclination to make the sim and hardware designs, but it did make them though yes?
Getting an LLM to design something in its own simulator that is not accurate w.r.t reality is not useful nor terribly impressive.
Thats why the math breakthrough a few weeks ago was so hotly debated. Because OpenAI is desperate to demonstrate that AI isnt just a fancy regurgitation machine, but it can actually develop novel thought. Because that would be the stock price jumps to end all stock jumps.
But then it turned out it was really just listening in on a math professors supposed-to-be-private conversations with another instance of openai, and it used his novel work as the trigger to prove the breakthrough first.
The reason people conclude AI 'thinks' is because tt can reference obscure or poorly documented things quickly (which is its primary advantage along with processing natural language prompts into tasks), which is why a lot of people with emotions confuse that action with inventing things.
Plausible? Absolutely. Did OpenAI behave badly in other ways regarding this issue? Yes. Does it help to assume unproven facts and then accuse people of reaching emotional decisions? Nope.
Call that brute force perhaps, but I would consider it technically inventing something on the merit that it would at least be an abstraction above naively throwing everything against a wall to only throwing things that would most likely be sticky.
2024 called, it wants its talking points back. I don't think claims like these are defensible after all the progress we have witnessed in the last year alone.
That has nothing to do with the question of whether the mechanism that generates those signals is complex enough to say that it's "creative."
An "AI" is a box which lies dormant until a human, with motivation and agency, enters a prompt into it.
A person or company can use it in a way where it might invent something. but at the end of the day, its a tool, and its actually not doing anything on its own.
Its not solving math problems, a mathetmatician is using it to solve math problems. Just cus OpenAI is acting like its AI is solving stuff, its really not. They're just paying people to use AI to hammer problems.
I'd say that people's motivation and agency, like their taste, creativity, and opinions, are shaped by external inputs, so saying that their agency is "owned" by them is a stretch.
The alternative would be the proposition that a slave, lacking agency, has no ability to solve problems. I find that idea unpalatable.
on a surface level a human solving an (unsolved) math problem can look like this, and of course the tree of all possible symbols you can send to a proving assistant is much wider than what the human samples, and the same goes true for an LLM in a proving loop. It isn't "truly random", it can't possibly be (and solve the problem). Both humans and LLMs solving unsolved math problems are aggressively pruning mathematical syntax and logical strategy trees.
If we do a bad job building context, they do a horrible job contexting. People who have trouble working with AI have the same problem people have in general: if they can't figure out the context of the direction, then they make random decisions of doing anything. On the flip side, if you can build the proper context around a sufficiently powerful LLM, they can derive the context via the contexting they're good at.
This is why building documents, tests, and code all in some intent pattern via prompting allows them to do a significant amount of work a normal person would have a great effort t
I've heard that before but it just doesn't make sense in context of what I've seen llms do. If I ask an llm to write a poem about magnetic resonance and vampire rabbits it can do that it created a new thing. I can ask it to build a website for managing rabbit breeding that's also a new thing.
Another way of looking at it is that human beings, just like llms, can produce output based on their inputs. Most literature is inspired by other literature. Most music is inspired by other music. Most software is inspired by other software.
So I think we need to work on defining " new things" before we can definitely exclude them from llm's capabilities
No, it didn't turn out to be that. Someone made a claim, which is silly for many reasons. There's no serious support for this happening.
This is outdated. With RLVF, LLMs can create their own training data instead of relying on what it's been fed.
This didn't actually happen.
> Following an investigation, we have confirmed that Buckmaster’s Codex prompts over the two months preceding this announcement and paper on September 8, 2026, could not have influenced the system in any way, including through training.
https://openai.com/index/navier-stokes-solution/
More broadly than the existing answers (which are correct), For a layman, I'd also add that LLMs are essentially 'brains in a vat'. They can't confirm ground truth about physical reality. They only know what's in their training data and prompt, which is incomplete and can be incorrect. Even with real-time external sensors they are limited to the sensor's margin of error, range and trusting it's working correctly.
When properly trained, fine-tuned and prompted, LLMs can be very effective in well-defined, non-physical domains like logic, writing, math and code but making things function in the real-world quickly spirals into combinatorial complexity.
That's true. Of course we can hook an llm up to Motors and sensors. That's a robot. Or a self-driving car. So would you say that those devices can confirm the ground truth about physical reality, and therefore are capable of creativity?
To the extent of the existence, resolution, quality, veracity and placement of any sensors, a machine can register their reported state and use it as a variable. That's substantially different and, currently, much less than the level of knowledge and understanding implied when we say a human "confirms ground truth about physical reality."
> therefore are capable of creativity?
I never mentioned creativity, nor would I in relation to LLMs. Like "Intelligence", "Creativity" is far too vague to be of any use in assessing the capabilities, limitations or utility of LLMs.
On HN, posts like the OP tend to attract POVs at polar extremes from "LLMs are nothing more than stochastic parrots" to "LLMs are (or can be) as intelligent, creative, innovative (etc) as any human or all humans combined." I've researched and thought a lot about these and related topics for a very long time, Neither POV is going to find any quick agreement or easy answers from me.
There's a tiny germ of truth somewhere in both extremes that's drowning in an ocean of confusion ranging from "definitionally or categorically muddled" to "mostly incorrect" to "not even wrong". But neither POV seems interested in anything more than drive-by hot takes, debating over-simplistic strawmen or trading 'gotcha' hypotheticals.
Great, but we also build robots with world models and sensory pre-processing. So nothing you've described is unique to humans.
> I never mentioned creativity, nor would I in relation to LLMs
The topic of this thread is whether LLMs are capable of genuinely contributing "new" ideas. So if that's not the point you're trying to make, I'm not sure of the purpose of your comment.
> But neither POV seems interested in anything more than drive-by hot takes, debating over-simplistic strawmen or trading 'gotcha' hypotheticals.
I think you've oversimplfying the discussion here.
In my view, at least, both "LLMs are nothing more than stochastic parrots" and "LLMs can be as intelligent, creative, innovative (etc) as any human or all humans combined." are simultaneously true, for the simple reason that humans are stochastic parrots. Our brains learn patterns and respond to stimuli.
Getting LLMs to prompt other LLMs in a loop is not hard, it doesn't produce great results most of the time, but that is changing.
https://en.wikipedia.org/wiki/Unmoved_mover
It's not clear what this fad of attributing everything an AI does to the human prompting it is supposed to accomplish.
These are clearly rhetorical questions, but think of the metaphysical implication of your contestation. Ex nihilo nihil fit.
What if that initial prompt never asked for this hardware to be developed, and it was just one piece of the puzzle to answer to that prompt? That it took a chain of thousands of agents to prompt each others to come up with that?
oh. they do. I built that. It's pretty fancy.
But there's still human direction behind most projects in the cybersphere.
>These are clearly rhetorical questions
No, they are not.
Reading Doesn't Fill a Database, It Trains Your Internal LLM <https://tidbits.com/2026/02/28/reading-doesnt-fill-a-databas...>
Could you share link(s) to those proof(s)?
[0] https://openai.com/index/jalapeno-first-results/
I'm definitely a fan of anything Open Hardware, FPGA's, TPU's, Verilog and anything that can increase education / learning in the AI space...
This GitHub repo scores points in all of those areas!