Can someone ELI5 what the difference between AI Studio, Antigravity, and Colab is?
Ai studio is a web chat.
Antigravity is an IDE you install.
Colab is a place to run notebooks in the cloud.
Gemini 3
951–960 of 1001 posts
Re: Gemini 3
#952Earlier quoted context omitted.
Unlike the general public though, these models have advanced dementia when it comes to learning from corrections, even within a single session. They keep regressing and I haven't found a way to stop that yet. What boggles the mind: we have gone for so long to try to strive for correctness and suddenly being right 70% of the time and wrong the remaining 30% is fine. The parallel with self driving is pretty strong here…
I think the real killer feature would be that they stop making basic mistakes, and that they gain some introspection. It's not a problem if they're wrong 30% of the time if they're able to gauge their own confidence like a human would. Then you can know to disregard the answer, or check it more thoroughly.
> It's not a problem if they're wrong 30% of the time if they're able to gauge their own confidence like a human would.
This is a case where I would not use human performance as the standard to beat. Training people to be both intellectually honest and statistically calibrated is really hard.
Re: Gemini 3
#953Earlier quoted context omitted.
I gave it a spin with instructions that worked great with gpt-5-codex (5.1 regressed a lot so I do not even compare to it). Code quality was fine for my very limited tests but I was disappointed with instruction following. I tried few tricks but I wasn't able to convince it to first present plan before starting implementation. I have instructions describing that it should first do exploration (where it tried to disco…
just say "don't code yet" at the end. I never use plan mode because plan mode is just a prompt anyways.
Plan mode is more secure.
Re: Gemini 3
#954Can someone ELI5 what the difference between AI Studio, Antigravity, and Colab is?
Ai studio is a web chat. Antigravity is an IDE you install. Colab is a place to run notebooks in the cloud.
Colab has significant Gemini functionality built in. How isn't it a combination of the first two?
Thanks for sorting all this out! Still exploring the first two, so I really don't know.
Re: Gemini 3
#955Earlier quoted context omitted.
I also used Gemini 3 Pro Preview. It finished it 271s = 4m31s. Sadly, the answer was wrong. It also returned 8 "sources", like stackexchange.com, youtube.com, mpmath.org, ncert.nic.in, and kangaroo.org.pk, even though I specifically told it not to use websearch. Still a useful tool though. It definitely gets the majority of the insights. Prompt: https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%...
Terrence Tao claims [0] contributions by the public are counter -productive since the energy required to check a contribution outweighs its benefit: > (for) most research projects, it would not help to have input from the general public. In fact, it would just be time-consuming, because error checking Since frontier LLMs make clumsy mistakes, they may fall into this category of 'error-prone' mathematician whose net c…
It depends a lot about the ratios here. There's a fast flip between "interesting but useless" and "useful" when the tradeoff flips.
How fast can you check the contribution? How small of a part is it? An unsolicited contribution is different from one you immediately directed. Do you need to reply? How fast are followups? Multi-day back and forths are a pain, a fast directed chat is different. You don't have to worry about being rude to an LLM.
Then it comes down to how smart a frontier model is vs the people who write to mathematicians. The latter groups will be filled with both smart helpful people and cranks.
Re: Gemini 3
#956This is wild. I gave it some legacy XML describing a formula-driven calculator app, and it produced a working web app in under a minute: https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%... I spent years building a compiler that takes our custom XML format and generates an app for Android or Java Swing. Gemini pulled off the same feat in under a minute, with no explanation of the format. The XML is fairly…
This is exactly the kind of task that LLMs are good at. They are good at transforming one format to another. They are good at boilerplate. They are bad at deciding requirements by themselves. They are bad at original research, for example developing a new algorithm.
Figuring out detailed requirements requires a lot of contact with reality. Specific details about not only the technical surface area but also the organizational and financial constraints. An AI model with the appropriate context would probably do well. It seems one of the things humans do much better at the moment is distill the big picture across a long period of time.
Re: Gemini 3
#957Earlier quoted context omitted.
If we've learned anything so far it's that the parlor tricks of one-shot efficacy only gets you so far. Drill into anything relatively complex with a few hundred thousand tokens of context and the models all start to fall apart roughly the same. Even when I've used Sonnet 4.5 with 1M token context the model starts to flake out and get confused with a codebase of less than 10k LoC. Everyone seems to keep claiming thes…
>documented problem in Home Assistant this evening. All it would take is 3-5 lines of YAML. The model failed miserably. I think we're all still safe. This is mostly because HA changes so frequently and the documentation is sparse. To get around this and increase my correction rate, I give it access to the source code of the same version I'm running. Then instructions in CLAUDE.md on where to find source and it must u…
Feel like sharing that prompt? I have a feeling that the phrasing on the "must use source code" part needs to be just right.
Re: Gemini 3
#958I love it that there's a "Read AI-generated summary" button on their post about their new AI. I can only expect that the next step is something like "Have your AI read our AI's auto-generated summary", and so forth until we are all the way at Douglas Adams's Electric Monk: > The Electric Monk was a labour-saving device, like a dishwasher or a video recorder. Dishwashers washed tedious dishes for you, thus saving you…
> I can only expect that the next step is something like "Have your AI read our AI's auto-generated summary"
That's basicaly "The Washing Machine Tragedy" by Stanislav Lem in a nutshell.
Re: Gemini 3
#959Earlier quoted context omitted.
This is exactly the kind of task that LLMs are good at. They are good at transforming one format to another. They are good at boilerplate. They are bad at deciding requirements by themselves. They are bad at original research, for example developing a new algorithm.
> They are good at transforming one format to another. They are good at boilerplate. You just described 90% of coding
Maybe 90% of the actual typing part of coding, but not 90% of the JOB of coding.
Re: Gemini 3
#960Earlier quoted context omitted.
There's a good chance Gemini 3 was trained on ARG-AGI problems, unless they state otherwise.
Its almost certain that it was, but the purpose of this puzzle benchmark is that it shouldn't really be possible just to be memorized by the amount of variations that can be created and other criteria detailed in it.
Sure, but the types of pattern in these problems do repeat, so I don't think it'd be too hard to RL train on these, whether public samples, or a privately generated more-of-the-same dataset, to improve performance a lot.
Every company releasing new models leads with benchmark numbers, so it's hard to imagine they are not all putting a lot of effort into benchmark-maxxing.