191–200 of 240 posts
If ai ends up having a winner I struggle to see how it doesn’t end with Google winning because they own the entire stack, or Apple because they will have deployed the most potentially AI capable edge sites.
FTA: > One pod of TPU 8t is 121 ExaFlops; or 121,000 PetaFlops. Meanwhile, the compute capacity of the top 10 supercomputers in the entire world is 11,487 Petaflops.[1] I know, I know, not the same flops, yada yada, but still. Just 1 pod alone is quite a beast. Edit: [1] https://top500.org/lists/top500/2025/11/
[1] https://blogs.nvidia.com/blog/google-cloud-agentic-physical-...
> A single TPU 8t superpod now scales to 9,600 chips and two petabytes of shared high bandwidth memory, with double the interchip bandwidth of the previous generation. This architecture delivers 121 ExaFlops of compute and allows the most complex models to leverage a single, massive pool of memory. This seems impressive. I don't know much about the space, so maybe it's not actually that great, but from my POV it look…
it is. itll still not create AGI without some breakthrough in instruction vs data separation of concerns
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What benefit is there to sticking on older models? If the API is the same, what are the switching costs?
Consistency, new models don't behave the same on every task as their predecessors. So you end up building pipelines that rely on specific behavior, but now you find that the new model performs worse with regards to a specific task you were performing, or just behaves differently and needs prompt adjustments. They also can fundamentally change the default model settings during new releases, for example Gemini 2.5 mode…
If this is a deal breaker, then self-hosting is the only solution. Due to the hardware premium, all models hosted by 3rd-parties will be deprecated to make room for newer, better, and more efficient models.
Are they refreshing the coral project with these? The coral project for edge ai apps seems like it needs a refresh.
They announced this Synaptics coral board last month, but you can't buy it anywhere AFAIK. I'm guessing it's going to be a lot more expensive than the original hw.
https://www.synaptics.com/products/embedded-processors/sl261...
I am curious what workloads Citadel Securities is running on these TPUs? Are you telling me they need the latest TPUs for market insights?
Not Citadel, but Jane Street is training LLMs for trading: https://www.janestreet.com/join-jane-street/machine-learning... > We build on the latest papers in LLMs, computer vision, RL, training libraries, cuda kernels, or whatever else we need to train good models. > We invent our own set of architectures and optimizations that work for trading.
Are they refreshing the coral project with these? The coral project for edge ai apps seems like it needs a refresh.
I already felt that gemini 3 proved what is possible if you train a model for efficiency. If I had to guess the pro and flash variants are 5x to 10x smaller than opus and gpt-5 class models. They produce drastically lower amount of tokens to solve a problem, but they haven't seem to have put enough effort into refinining their reasoning and execution as they produce broken toolcalls and generally struggle with 'agent…
I really wonder what I’m missing with Gemini. It’s a second rate model for me at best. I find it okay (not great) at collecting information and completely useless at agentic tasks. It’s like it’s always drunk. When the Claude credits expire in Antigravity, I’m done for the day. > They produce drastically lower amount of tokens to solve a problem I LOLed at this because I of the constant death loops that don’t even so…
i feel like the geminicli app is missing some tools for making sure the session history is actually valid
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You can park a lot there. No offence but I love how AGI doesn't mean anything. It used to be that AI was a goal post. Now it is AGI. We could use characters from sci-fi culture to describe milestones. In order to achieve robocop level, we must solve the instruction vs data problem.
Thus it always was. I’m old enough to remember when “if AI could beat a grandmaster at chess” was considered the finish line.
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> They produce drastically lower amount of tokens to solve a problem, but they haven't seem to have put enough effort into refinining their reasoning and execution as they produce broken toolcalls and generally struggle with 'agentic' tasks, but for raw problem solving without tools or search they match opus and gpt while presumably being a fraction of the size. Agreed, Gemini-cli is terrible compared to CC and even…
Google doesn't need to give a shit, because so much of the internet is infested with with google ad trackers and adwords, and everybody uses Chrome, that they will continue to make billions even without AI. Facebook did the same with their pixel so they could soak up data. Gemini will be dead in 2 years and there'll be something else, but the ad and search company will remain given that they basically own the world w…