791–800 of 1001 posts
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Imagine the metrics though. "this quarter we've had a 12% increase on people using AI solutions in their google drive".
Google Drive is one of the bigger offenders when it comes to “metrics-driven user-hostile changes”, in gsuite, and its Google Meet is one of its peers.
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after taking a walk for a bit i decided you’re right. I came to the wrong conclusion. Gemini 3 is incredibly powerful in some other stuff I’ve run. This probably means my test is a little too niche. The fact that it didn’t pass one of my tests doesn’t speak to the broader intelligence of the model per se. While i still believe in the importance of a personalized suite of benchmarks, my python one needs to be down wei…
Walks are magical. But also this reads partially like you got sent to a reeducation camp lol.
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After few more attempts longer animation with a story from my gamedev inspired mind: https://codepen.io/Runway/pen/zxqzPyQ PS: but yeah thats attempt #20 or something.
Wow looks like total shit and eventually very hard to take on and actually improve it, given the convoluted code it generated, YET people are impressed. What world are we living in...
> setTimeout(() => showSub("Ah, Earl Grey.", 2000), 1000);
> setTimeout(() => showSub("Finally some peace.", 2000), 3500);
> // Scene 2
> setTimeout(() => showSub("Armor Clanking", 2000), 7000);
> setTimeout(() => showSub("Heavy Breathing", 2000), 10000);
If we will lose our jobs to this dumb slop I'd rather be happy doing something else
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Ironically, OpenAI was conceived as a way to balance Google's dominance in AI.
Balance is too weak of a word. OpenAI was conceived specifically to prevent Google from getting AGI first. That was its original goal. At the time of its founding Google was the undisputed leader of AI anywhere in the world. Musk was then very worried about AGI being developed behind closed doors particularly Google, which was why he was the driving force behind the founding of OpenAI.
*closed doors that aren't his
Out of curiosity, I gave it the latest project euler problem published on 11/16/2025, very likely out of the training data Gemini thought for 5m10s before giving me a python snippet that produced the correct answer. The leaderboard says that the 3 fastest human to solve this problem took 14min, 20min and 1h14min respectively Even thought I expect this sort of problem to very much be in the distribution of what the mo…
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Gemini 3 somehow is able to give a list of mayors, including details on who got impeached, etc.
This should be a simple answer, because all the data is on wikipedia, that certainly the models are trained on, but somehow most models don't manage to give that answer right, because... it's just a irrelevant city in a huge dataset.
But somehow, Gemini 3 did it.
Edit: Just asked "Cool places to visit in Londrina" (In portuguese), and it was also 99% right, unlike other models, who just create stuff. The only thing wrong here, it mentioned sakuras in a lake... Maybe it confused with Brazilian ipês, which are similar, and indeed the city it's full of them.
It seems to have a visual understanding, imo.
I just gave it a short description of a small game I had an idea for. It was 7 sentences. It pretty much nailed a working prototype, using React, clean css, Typescript and state management. It event implemented a Gemini query using the API for strategic analysis given a game state. I'm more than impressed, I'm terrified. Seriously thinking of a career change.
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Wow looks like total shit and eventually very hard to take on and actually improve it, given the convoluted code it generated, YET people are impressed. What world are we living in...
Let me double down to get even more downvotes, here's a snippet of the code: > setTimeout(() => showSub("Ah, Earl Grey.", 2000), 1000); > setTimeout(() => showSub("Finally some peace.", 2000), 3500); > // Scene 2 > setTimeout(() => showSub(" Armor Clanking ", 2000), 7000); > setTimeout(() => showSub(" Heavy Breathing ", 2000), 10000); If we will lose our jobs to this dumb slop I'd rather be happy doing something else
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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
0: https://en.wikipedia.org/wiki/51st_G7_summit#/media/File:Pri...
Here are my notes and pelican benchmark, including a new, harder benchmark because the old one was getting too easy: https://simonwillison.net/2025/Nov/18/gemini-3/
They've been training for months to draw that pelican, just for you to move the goalposts.
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