701–710 of 722 posts
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>because we can no longer find tasks that are feasible for normal humans but unsolved by AI. "Answer "I don't know" if you don't know an answer to one of the questions"
Gpt5.2 can answer i don't know when it fails to solve a math question
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The term “model” is one of those super overloaded terms. Depending on the conversation it can mean: - a product (most accurate here imo) - a specific set of weights in a neural net - a general architecture or family of architectures (BERT models) So while you could argue this is a “model” in the broadest sense of the term, it’s probably more descriptive to call it a product. Similarly we call LLMs “language” models e…
I'm pretty sure only the second is properly called a model, and "BERT models" are simply models with the BERT architecture.
I probably won’t even assume it’s the OG BERT. It could be ModernBERT or RoBERTa or one of any number of other variants, and simply saying it’s a BERT model is usually the right level of detail for the conversation.
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I read somewhere that Google will ultimately always produce the best LLMs, since "good AI" relies on massive amounts of data and Google owns the most data. Is that a based assumption?
No.
Great output is a good model with good context… at the right time.
Google isn’t guaranteed any of these.
Arc-AGI-2: 84.6% (vs 68.8% for Opus 4.6) Wow. https://blog.google/innovation-and-ai/models-and-research/ge...
https://arcprize.org/leaderboard $13.62 per task - so we need another 5-10 years for the price to run this to become reasonable? But the real question is if they just fit the model to the benchmark.
Is it me or is the rate of model release is accelerating to an absurd degree? Today we have Gemini 3 Deep Think and GPT 5.3 Codex Spark. Yesterday we had GLM5 and MiniMax M2.5. Five days before that we had Opus 4.6 and GPT 5.3. Then maybe two weeks I think before that we had Kimi K2.5.
I'm having trouble just keeping track of all these different types of models. Is "Gemini 3 Deep Think" even technically a model? From what I've gathered, it is built on top of Gemini 3 Pro, and appears to be adding specific thinking capabilities, more akin to adding subagents than a truly new foundational model like Opus 4.6. Also, I don't understand the comments about Google being behind in agentic workflows. I know…
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Out of curiosity, did you give a test for them to validate the code? I had a test failing because I introduced a silly comparison bug (> instead of <), and claude 4.6 opus figured out it wasn't the test the problem, but the code and fixed the bug (which I had missed).
There was a test and a very useful golang error that literally explain what was wrong. The model tried implementing a solution, failed and when I pointed out the error most of them just rolled back the "solution"
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Suggestion: run the identical prompt N times (2 identical calls to Gemini 3.0 Pro + 2 identical calls to GPT 5.2 Thinking), then running some basic text post-processing to see where the 4 responses agree vs disagree. The disagreements (substrings that aren't identical matches) are where scrutiny is needed. But if all 4 agree on some substring it's almost certainly a correct transcription. Wouldn't be too hard to get…
Look what they need to mimic a fraction of [the power of having the logit probabilities exposed so you can actually see where the model is uncertain]
Google is absolutely running away with it. The greatest trick they ever pulled was letting people think they were behind.
Their chat feels similar. It just runs off like a wild dog.
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Strange, because I could not for the life of me get Gemini 3 to follow my instructions the other day to work through an example with a table, Claude got it first try.
Claude is king for agentic workflows right now because it’s amazing at tool calling and following instructions well (among other things)
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Even before this, Gemini 3 has always felt unbelievably 'general' for me. It can beat Balatro (ante 8) with text description of the game alone[0]. Yeah, it's not an extremely difficult goal for humans, but considering: 1. It's an LLM, not something trained to play Balatro specifically 2. Most (probably >99.9%) players can't do that at the first attempt 3. I don't think there are many people who posted their Balatro p…
> . I don't think there are many people who posted their Balatro playthroughs in text form online There are * tons * of balatro content on YouTube though, and it makes absolutely zero doubt that Google is using YouTube content to train their model.