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GPT‑5.3‑Codex‑Spark
371–380 of 415 posts
Re: GPT‑5.3‑Codex‑Spark
#372are there that many use cases for a model that you need code generated as fast as possible rather than better code at decent speeds?
I can see it being useful for stuff like renaming files, splitting hpp/cpp files, doing renaming etc.
Yeah but using a different model for that? Ideally a skilled model that works with the IDE functionality/API would “solve” this issue.
Re: GPT‑5.3‑Codex‑Spark
#373Earlier quoted context omitted.
In my AGENTS.md file i have a _rule_ that tells the model to use Apache ECharts, the data comes from the prompt and normally .csv/.json files. Prompt would be like: "After slide 3 add a new content slide that shows a bar chart with data from @data/somefile.csv" ... works great and these charts can be even interactive.
What about other ad hoc diagrams like systems architecture, roadmaps, mind maps, etc. These are the bane of any staff engineers life - lol. Because people above need to know a plan in art form. So seriously interested on how I can make it easier
I have it do svgs.
The layout isn't always great on first shot, but you iterate on that.
They can also natively generate e.g. github markdown mermaid diagrams (github markdown has a lot of extensions like that)
Re: GPT‑5.3‑Codex‑Spark
#374Earlier quoted context omitted.
Space is also confusing! Then it looks like two separate numbers. Underscore (_) is already used as a decimal separator in programming languages and Mathematics should just adopt it, IMO.
A competing format to simplify things?
Re: GPT‑5.3‑Codex‑Spark
#375Earlier quoted context omitted.
Nvidia seems cooked. Google is crushing them on inference. By TPUv9, they could be 4x more energy efficient and cheaper overall (even if Nvidia cuts their margins from 75% to 40%). Cerebras will be substantially better for agentic workflows in terms of speed. And if you don't care as much about speed and only cost and energy, Google will still crush Nvidia. And Nvidia won't be cheaper for training new models either.…
Man I hope someone drinks Nvidia's milk shake. They need to get humbled back to the point where they're desperate to sell gpus to consumers again. Only major road block is cuda...
The nice thing about modern LLMs is that it's a relatively large static use case. The compute is large and expensive enough you can afford to just write custom kernels, to a degree. It's not like CUDA where running on 1, 2, 8 GPUs and you need libraries that already do it all for you, and where researchers are building lots of different models.
There aren't all that many different small components between all of the different transformer based LLMs out there.
Re: GPT‑5.3‑Codex‑Spark
#376[dead]
Re: GPT‑5.3‑Codex‑Spark
#377Earlier quoted context omitted.
There have been discussions about this chip here in the past. Maybe not that particular one but previous versions of it. The whole server if I remember correctly eats some 20KWs of power.
A first-gen Oxide Computer rack puts out max 15 kW of power, and they manage to do that with air cooling. The liquid-cooled AI racks being used today for training and inference workloads almost certainly have far higher power output than that. (Bringing liquid cooling to the racks likely has to be one of the biggest challenges with this whole new HPC/AI datacenter infrastructure, so the fact that an aircooled rack ca…
> The liquid-cooled AI racks being used today for training and inference workloads almost certainly have far higher power output than that.
75kW is a sane "default baseline" and you can find plenty of deployments at 130kW.
There's talk of pushing to 240kW and beyond...
Re: GPT‑5.3‑Codex‑Spark
#378Earlier quoted context omitted.
Wooshka. I hope they've got good heat sinks... and I hope they've plugged into renewable energy feeds...
Nope! It's gas turbines
For now. And also largely because it's easier to get that up and running than the alternative.
Eventually, as we ramp up on domestic solar production, (and even if we get rid of solar tariffs for a short period of time maybe?), the numbers will make them switch to renewable energy.
Re: GPT‑5.3‑Codex‑Spark
#379Earlier quoted context omitted.
I can imagine how terribly bad their yield must be. One little mistake and the whole "chip" is a goner.
They have a blog post called "100x Defect Tolerance: How Cerebras Solved the Yield Problem": https://www.cerebras.ai/blog/100x-defect-tolerance-how-cereb...
One thing is really bothering me: they show these tiny cores, but in the wafer comparison image, they chose to show their full chip as a square bounded by the circle of the wafer. Even the prior GPU arch tiled into the arcs of the circle. What gives?
Re: GPT‑5.3‑Codex‑Spark
#380Earlier quoted context omitted.
Space is also confusing! Then it looks like two separate numbers. Underscore (_) is already used as a decimal separator in programming languages and Mathematics should just adopt it, IMO.
A competing format to simplify things?
A competing format that is understandable to probably everybody.
An ISO 8601 date is also comprehensible to anybody even if they never seen it before and have to figure it out themselves.