LLMs should be for general/high level work. For example, an LLM with the right harness could fetch and use that CNN, or train it's own CNN or other model to use for this task.
The harness in my experience makes a huge different too. Out of curiosity I tried having Gemini chat (with Argon) try to solve a level of this new puzzle game I'm linking (Order of the Sinking Star). It just got in an infinite loop and crashed. Then I try Antigravity with Flash 3.8 and it solved it easily. I saw the harness looked at other sokoban puzzles, coded some stuff, tried to replicate the level, etc. All things that a pure Gemini Chat doesn't really do.
LLMs are great for the high level harness, but if you want to count letters for example, it will still rely on "tools" like a piece of code to do it more reliably. Similarly in the above example, you wouldn't have the LLM directly try to analyze the images, but instead fetch and use the best tool for the job. But that can only be as good as the harness/tools it has access to.
I'm a bit confused, all the announcements say that this is availabe to Ultra and Pro subscribers, but in antigravity, it's grayed out and says it's only available to Ultra.
Right, the better argument would've been to say that we should wait until the drug undergoes some early trials, rather than just "some expert looking at it".
I guess if one of them hits singularity, it could in theory just wipe out all the rest, seeing how they keep escaping and hacking into other systems :)
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I guess if one of them hits singularity, it could in theory just wipe out all the rest
The story that some AI company might reach singularity and then "everything will be different" is another science-fiction story that executives of AI companies love to tell to justify the staggering amount of necessary investments and cash burn. :-)
If we theoretically hit the point where it could do all of its research itself, better than a human, things _would_ be different. I guess it's a question of whether we think we'll get there.
> If we theoretically hit the point where it could do all of its research itself, better than a human, things _would_ be different.
If we theoretically found a way to shield or reverse gravity, things in aviation or space travel would be different. Or if we theoretically found a way to make cold fusion work, things would be very different. :-)
It is in my opinion not a good idea to invest in companies for which the feasibility of the business models depends on the capability of making science-fiction stories work.
I'm skeptical of anyone that has absolute confidence in either direction, to be honest. It's clearly an unknown.
Our current capabilities were science fiction a very short time ago, and we are still improving in multiple areas simultaneously (hardware, algorithms, scaling, data efficiency, inference...). We don't really know what the limit is yet.
Reversing gravity seems to counteract the current knowledge of the physical laws, but human-level intelligence doesn't (it has already been achieved once), and there's enough reason to believe that human-level intelligence itself is not a fundamental limit (energy usage constraints in evotution, brain-size limit fitting through the birth canal, etc).
I find it quite unique how many people buy into this. Its the worlds most blatant conflict of interest, I dont even know why Sam and Dario bother doing interviews
> and google just started letting all their engineers use claude
That's misleading.
1. Having different models available is useful for A/B testing and helping improve Gemini itself.
2. They have an enterprise offering for Antigravity (their agentic coding platform), and they need to test that it works well with non-Gemini models too.
That's an eternity when it comes to coding models.
In my personal experience, we've had almost a step change every ~3 months this year, at least for bigger one-shot tasks. For example looking at Gemini Flash 3.0 vs 3.5 vs 3.8, it went 5% -> 30% -> 75% on DeepSWE, all since the start of the year.
In another one, Opus 4.6 level already solved 90% of my work-day tasks, so while better models have been instrumental into handling a higher % that does not mean that defaulting on cheaper models can't be good.
I run DS 4.1 flash daily, and then cross check with gpt-6-astra and I've nuked 90% of my AI monthly bill while having higher limits and better performance/intelligence than I did just at the beginning of this summer.
Because they’ve essentially exhausted pre training scaling and are looking to post training to expand capabilities, which is really just optimization via reinforcement learning against specific tasks aka bench maxing.
The harness in my experience makes a huge different too. Out of curiosity I tried having Gemini chat (with Argon) try to solve a level of this new puzzle game I'm linking (Order of the Sinking Star). It just got in an infinite loop and crashed. Then I try Antigravity with Flash 3.8 and it solved it easily. I saw the harness looked at other sokoban puzzles, coded some stuff, tried to replicate the level, etc. All things that a pure Gemini Chat doesn't really do.
LLMs are great for the high level harness, but if you want to count letters for example, it will still rely on "tools" like a piece of code to do it more reliably. Similarly in the above example, you wouldn't have the LLM directly try to analyze the images, but instead fetch and use the best tool for the job. But that can only be as good as the harness/tools it has access to.
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