Is there an understanding of what OpenAI intends to do with that memory?
Surely they need GPU capacity and would need memory for those GPUs but OpenAI doesn't build GPUs or any hardware, right? So did they pay to keep the supply locked up, or do they have the ability to put that ram into use?
Could you say more about the differences between Aider and OpenCode?
I briefly dabbled with Aider some months back but never got any real work done with it. Without installing each one of these new tools I'm having trouble grokking what is changing about them that moves the LLM-assisted software dev experience forward.
If Python has a "very weak ORM situation", what is it about the TS ORM scene that makes it stronger by comparison? Is there one library in particular that stands out?
I have a fire tablet that I’ve tried that with, but for various reasons I prefer to have Linux on all the things. As a long time Android phone user Android still gives me an irrational ick, non-standard android even more so.
Ideally all my home devices would controlled and managed by the same underlying OS and tooling
I have to stop being such a prude, it just frustrates me that after so many years I can’t buy a cheap Linux tablet
I just want affordable, linux-powered displays in a slim tablet form factor.
Performance and battery life are not a priority for my use case
Unfortunately I feel anything sold as a tablet comes with the assumption that it needs to compete with an iPad and be used for content consumption and gaming.
Ive seen raspberry pi kits sold that do just about everything else but this.
Thanks, yeah I understand their poor performance and energy efficiency for use in a tablet for content consumption or gaming.
And I am guessing that a part of the reason for a lack of any such RaspPi tablets is that marketing such a tablet would come with the need to negatively differentiate it from any similarly priced android tablet.
However I can think of many use cases, mainly for folks in the maker space, that are not content consumption or gaming or long battery life. I am thinking of dashboards or smart home control panels.
Right now I have a few raspi4s mounted on the back of an official touchscreen encased in an adjustable plastic stand. Been working great for years, but the size is clunky and processing power is more than what I need it for, which is just displaying a web page with some information and buttons.
Would love a thin display to mount on a wall near a door or have others lying flat on a table next to a beside or couch. Basically always plugged in but with an included battery for the odd moment when I need to carry it somewhere.
So many other uses i could think of.
Ive looked at Amazon Fire tablets, but the locked-down android and really android of any kind is just not something I am interested in.
Ive seen raspberry pis used for just about everything else but not this
The link[1] at the top of their article to HuggingFace goes to some models named Qwen3-Omni-30B-A3B that were last updated in September. None of them have "Flash" in the name.
The benchmark table shows this Flash model beating their Qwen3-235B-A22B. I dont see how that is possible if it is a 30B-A3B model.
I don't see a mention of a parameter count anywhere in the article. Do you? This may not be an open weights model.
I see that their HuggingFace link goes to some Qwen3-Omni-30B-A3B models that show a last updated date of September
The benchmark table in their article shows Qwen3-Omni-Flash-2025-12-01 (and the previous Flash) as beating Qwen3-235B-A22B. How is that possible if this is only a 30B-A3B model? Also confusing how that comparison column starts out with one model but changes them as you descend down the table.
I don't see any FLASH variant listed on their Hugginface. Am i just missing it or are these specifying a model only used for their API service and there are no open weights to download?
Does anyone have insights on how compatible that hardware might be? Or how it might compare to something like a Pinetab?
For some years I have been looking for a low cost tablet with good Linux support for use in home automation or information displays. Surprised there is still nothing like this in the Raspberry Pi ecosystem.
I haven’t used Jupyter in a few years. Wondering what is the current standard practice of starting a new Jupyter project.
Do users typically have one system-wide Jupyter install that gets reused for each data analysis project that then have their own dependencies in a virtual environment that Jupyter activates?
Or is Jupyter installed inside each project’s virtual environment?
Surely they need GPU capacity and would need memory for those GPUs but OpenAI doesn't build GPUs or any hardware, right? So did they pay to keep the supply locked up, or do they have the ability to put that ram into use?