141–150 of 240 posts
Earlier quoted context omitted.
> put out something really polished Like Apple Intelligence? Which was quite crap
Specifically what was crap about it? It seems to do what was advertised.
Earlier quoted context omitted.
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.
For how many times does this article mentions "agentic" and "agents"... Am I correct assume the hardware has nothing to do with "agents"? I assume it's just about a new generation of more efficient transformers / deep-learning layers.
It is about agents in that the design is for long context, many requests where the initial “chunk” is cached but spread across many requests.
They don’t call this out specifically but in the technical details like about the sram, how it’s all interconnected nodes in a pod it’s “designed” for it.
As others have been capturing news cycle eyes, seems to me Google has been going from strength to strength quietly in the background capturing consumer market share and without much (any?) infrastructure problems considering they're so vertically integrated in AI since day one? At one point they even seemed like a lost cause, but they're like a tide.. just growing all around.
At this point, when you are doing big AI you basically have to buy it from NVidia or rent it from Google. And Google can design their chips and engine and systems in a whole-datacenter context, centralizing some aspects that are impossible for chip vendors to centralize, so I suspect that when things get really big, Google's systems will always be more cost-efficient. (disclosure: I am long GOOG, for this and a few o…
In fact I am opposite of this hypothesis for two reasons. Google has artificially limited production. And because TSMC favours whoever could pay for the most capacity(as incremental capacity is very cheap for them). So Nvidia gets first slot for new process.
Also the second reason is that GCP's operating margin is very high compared to say Hetzner or lambdalabs and you can get GPUs much cheaper there compared to GCP. So students/small researchers are stuck on GPU.
Earlier quoted context omitted.
> 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…
I wonder what I am missing, because I can use gemini-cli with English descriptions of features or entire projects and it just cranks out the code. Built a bunch of stuff with it. Can't think of anything it's currently lacking.
Earlier quoted context omitted.
I'd go long Google too if using Gemini CLI felt anything close to the experience I get with Codex or Claude. They might have great hardware but it's worthless if their flagship coding agent gets stuck in loops trying to find the end of turn token.
I use Claude Code all day and use Gemini CLI for personal projects and I don't see the huge gap that other people seem to talk about a lot. Truthfully there are parts of Gemini CLI I like better than Claude Code.
The real problem is that scientists doing this sort of early work more often than not want to burn hardware under their desks. Renting infrastructure in Google cloud isn't the only way...
Earlier quoted context omitted.
Isn't Amazon doing the same thing, making their own TPU's?
Yeah trainium and inferentia. They’re just not nearly as well supported on the software level. Google has already made sure this new generation will be supported by vllm, sglang, etc. Amazons chips barely support those and only multiple versions back. Super under invested in (at least on the open source side)
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…
> 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…
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 wide web.
Except now, so much of the WWW is filled with AI slop that it breaks the system.