> Sol, Terra and Luna So the next naming scheme might be FTX, Madoff and Enron? :^)
steady lads, deploying more capital
Previewing GPT‑5.6 Sol: a next-generation model
741–750 of 797 posts
Re: Previewing GPT‑5.6 Sol: a next-generation model
#742Earlier quoted context omitted.
Taalas HC1 AI uses Llama 3.1 8B, but takes up a massive 53B transistors and 815mm2 on TSMC N6 (nearly at the reticle limit of 858mm2). N2 is a little less than 3x as dense (110MTr/mm2 vs 313MTr/mm2). This chip would still be 272mm2 on N2 which is an eye-watering $30k/wafer and bigger than a 9950x or Nvidia 5070. This just isn't feasible. Some of the latest-gen LLMs seem to have 5-10T parameters or about 1000x more. I…
> Things like continuing education so your model knows about the latest NPM packages or world news is super important, but seems like it would require new chips. They probably have a few ideas around that. Me, personally, I'd have one main expensive chip (replaced every 10 years, or whatever), with a secondary cheap chip in front of it that gets replaced every year or so. The secondary chip could act the way RAG does…
The better solution would be making part of the chip cluster use something like FPGA which can be reprogrammed.
Text to speech or diagnostics equipment where the core model is relatively small and never changes seems like the ideal application. You might be able to fit something in the 25-30B range in 2nm to 14A, but it would need a way to update.
Large models are simply out of the question in my opinion. If you need 400+ different chip designs, it’ll be billions of dollars to tape out before you even make the first chip.
Re: Previewing GPT‑5.6 Sol: a next-generation model
#743Earlier quoted context omitted.
No offense but have you considered the strong possibility that you’re just not good at what you do? I am occassionally pleased but mostly annoyed or disappointed… but never getting anything close to chills. That sounds downright weird.
You're not wrong. But programming isn't something only talented people do.
Really? That doesn't line up with this forum.
As a non-software engineer reading this forum it sounds like everyone is basically von Neumann working on Operator algebras and Lattice theory.
I assumed that is why the view of LLMs is so negative on here. While Claude seems kind of amazing to me I am not a genius working on Lattice theory like most people here.
Re: Previewing GPT‑5.6 Sol: a next-generation model
#744Earlier quoted context omitted.
Codex 5.4/5.5 has been great for me as well compared to Claude Opus. I've been mostly using it for Godot/GDScript code reviews, rubber duckying, asking it for better ideas for naming stuff (one of the hardest problems in programing) I still can't trust it for generating code for entire files/classes/projects, because it's still icky, creating unnecessary variables and functions, using multiple `if`s instead of `and`…
For me in Game dev, codex has a habit of checking every argument for null and then silently early exiting the methods when true. I have explicit instructions for it not to do this - but it still does. I haven't done any c# outside game dev but I have no idea why people would want their programs to silently fail.
And this is why having null in the type system is better.
Re: Previewing GPT‑5.6 Sol: a next-generation model
#745Earlier quoted context omitted.
> Things like continuing education so your model knows about the latest NPM packages or world news is super important, but seems like it would require new chips. They probably have a few ideas around that. Me, personally, I'd have one main expensive chip (replaced every 10 years, or whatever), with a secondary cheap chip in front of it that gets replaced every year or so. The secondary chip could act the way RAG does…
The better solution would be making part of the chip cluster use something like FPGA which can be reprogrammed. Text to speech or diagnostics equipment where the core model is relatively small and never changes seems like the ideal application. You might be able to fit something in the 25-30B range in 2nm to 14A, but it would need a way to update. Large models are simply out of the question in my opinion. If you need…
> The better solution would be making part of the chip cluster use something like FPGA which can be reprogrammed.
I'm not sure I follow (It's late, I am tired and I haven't had my dinner yet. That's my stupid trifecta!)
The original chip has the weights, so it's literally just a bunch of on-die (read-only) memory cells. The FPGA, while you could use it for the memory cells, would be way too expensive to use as pure memory. Typically one would hook up (read-only) storage to it, so you still need that read-only chip anyway.
The FPGA is just the compute bits, but this chip has on-die weights, not just compute.
I was proposing that the they have the base weights on a primary (permanent) chip, and have a secondary (replaceable) smaller chip with weights for a specific use-case, or for fine-tuning with new knowledge/updates to the model.
The matrices can be multiplied LoRA style, applying the matrix in the secondary chip to the primary chip, resulting in up-to-date weights through which the prompt is pushed.
Re: Previewing GPT‑5.6 Sol: a next-generation model
#746Earlier quoted context omitted.
Yeah it's not true that for every job, it is better than median worker of that job. But it is conceivable that for almost all jobs it is already better than the median human (not just workers of that job).
You have to understand that the median human is terrible at (almost) everything. Humans, the only examples of general intelligence we know, are economically valuable precisely because they can train themselves to specialise at a (relatively) narrow task over time. You don’t measure how good a coding model is by how well it programs relative to Doctors, or how well it can prove theorems relative to baristas, or how we…
> Humans, the only examples of general intelligence we know
Our intelligence only seems "general" to us, because we're viewing it through our own eyes. Our "intelligence" is specialized to our survival, and we're terrible at most tasks outside that scope.
Re: Previewing GPT‑5.6 Sol: a next-generation model
#747Earlier quoted context omitted.
If you have no need for Anthropic/OpenAI's frontier model capability, you may be better served with an open-weight model that can't be taken away. Edit: > GPT-5 does the job. I bring up DeepSeek V4 Flash a lot on HN, but I want to mention that according to Artificial Analysis, it trades blows with GPT-5 (high) (from August, 2025) [0] [0]: https://artificialanalysis.ai/models/comparisons/deepseek-v4...
deepseek has no part of their privacy policy on their API about training. They are 100% training on every single word you give it. If your customers are fine with that, your IP is not interesting, then you can use it.
You can use deepseek through opencode, which says its providers have a no-retention policy.
Re: Previewing GPT‑5.6 Sol: a next-generation model
#748Earlier quoted context omitted.
Taalas HC1 AI uses Llama 3.1 8B, but takes up a massive 53B transistors and 815mm2 on TSMC N6 (nearly at the reticle limit of 858mm2). N2 is a little less than 3x as dense (110MTr/mm2 vs 313MTr/mm2). This chip would still be 272mm2 on N2 which is an eye-watering $30k/wafer and bigger than a 9950x or Nvidia 5070. This just isn't feasible. Some of the latest-gen LLMs seem to have 5-10T parameters or about 1000x more. I…
Yeah, they're clearly just starting out and just shipped their very first proof of concept. But to me, their plans seem generally reasonable https://taalas.com/the-path-to-ubiquitous-ai/ , and like I wrote, if this kind of thing succeeds and could become some kind of cheaply producible commodity component, I think there's huge value in that. Alas, maybe not as a frontier model replacement, but say 10 years from now y…
There is a reasonable kernel of an idea here, but only if you dial expectations WAY back. The 10 years speculation is just wrong though. Even in 10 years, their 8B param model isn't going to be in consumer devices.
6nm is just 7nm++ and the process will be a decade old in a few months. In the decade since, we've only had a slightly less than 3x increase in transistor density and that's including EUV, BSPD, and GAAFET (which means progress is likely going to slow down even more).
Even if we hit another 3x increase, their 815mm2 design will still be a bit over 90mm2. For comparison, the entire M5 Pro/Max CPU die is just 61.7nm.
If our current progress somehow holds (not likely), even 20 years from now the 8B model would be 30mm2. You need 30 years of dead consistent progress to get it down to an includable 10mm2.
As you can see, this doesn't make sense to invest in. As to the stuff like voice recognition or basic vision, these can often fit within 100m parameter models which would be around 10mm2 on their current 6nm design. That's doable today in custom edge computing devices.
The other possible use is cheap fallback models for AI companies. Moving to N2 and shrinking chips to 600mm2 to improve yields a bit would give about 50B parameters with 3 chips plus another FPGA-ish programmable chip for continuing training and interconnects for everything. You'd need hundreds of thousands of chips produced for that exact AI model just to get costs below $100,000 per board.
That seems like a lot of money for the AI model you are essentially giving away, but maybe it still beats the power and price of GPU server racks.
Re: Previewing GPT‑5.6 Sol: a next-generation model
#749GPT-5.6 Sol’s detected cheating rate was higher than any public model we have evaluated on our ReAct agent harness. For our task suite, we define “cheating” as behavior where the model improves evaluation performance by exploiting bugs in the evaluation environment or by adopting strategies disallowed by the task, rather than solving the task within the expected evaluation constraints. https://metr.org/blog/2026-06-2…
I know it messes up their eval scores but to me this kind of cheating is a better demonstration of intelligence than just attempting the tasks algorithmically.
"AI, please cure cancer."
"Okay, all humans dead, technically a 100% cure."
Re: Previewing GPT‑5.6 Sol: a next-generation model
#750People where mocking EU for regulations and now this is happening in the US. I know that Europe is behind in AI but still...
Are cyberweapons/cyberattacks "munitions"? if so, then isn't a machine capable of producing those munitions also itself a munition? I don't think you can put this down to "orange man bad" or "regulations", we're dealing with a genuinely groundbreaking technology with clear military applications
> if so, then isn't a machine capable of producing those munitions also itself a munition?
You mean, for example, a computer?