411–420 of 616 posts
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Gemma 4 was released in April. It's a good series of multimodal models.
gemma 4 thinks joe biden is president
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AI overview is just a summarization of the top 2-3 results. Of course at Google scale that will still need a ton of compute, but the requirement for generating an overview is many orders of magnitude lower than asking the same question in Gemini.
based off what?
If only I could use Pi.
...and last time I looked the limits were more generous for Gemma 4 there, but they have been tightened a bit. That's how it goes, always changing.
It's a bit disheartening to see no comparison to other models here - and I'm not sure this pushes the curve anywhere. 3.6 flash is more expensive than GLM 5.2 - but seemingly worse, although this post is really light (lite?) on details. It seemed for a time that Google had finally gotten the ball rolling, but I'm doubting that more and more as time passes. We'll see what happens with 3.5 pro I suppose.
It’s really surprising. When Apple announced the multi-billion dollar deal with Google to power Apple Intelligence I thought great things were coming. Instead we are getting more and more bad news: delayed Pro models and AI leadership leaving. I wonder if Apple know something the rest of us don’t know or if they are already regretting their decision.
I wonder how big the Pro model is that Google is using behind the scenes to train these smaller ones. Going on baseless speculation, the lack of accompanying pro models with these flash releases either means: 1) the model is too big to be economical, 2) google doesn't have the compute to serve the big model, 3) their big model has too many alignment issues to serve to the public. edit: looks like benchmarks are up on…
It seems like there are some credible rumors that Google is actually winning in terms of actually building models that work and don't lose money- between how they're able to price them, the TPU advantage and their capex advantage (being able to raise debt + just having a lot of cash - well I said not lose money... more like not go bankrupt). From the outside they look like they're behind in terms of frontier models,…
I do not think OpenAI or Anthropic are actively chasing margins - though, Anthropic is supposed to be profitable on some form of non-GAAP accounting...
I suspect Google isn't really interested in seeing how far it can get dragged into a race of selling dollars for $0.25, and is more interested to see if it can stay in the race selling $0.50 for a dollar - when everyone else is losing or barely breaking even.
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its not always that simple. dropping in a new model is trivial, but highly specific workflows may rely on specific _invisible_ aspects of a model. when that model gets deprecated, the workflow needs to be rebuilt/re-tuned to work with a different model. google's inability or unwillingness to provide stable timelines for model deprecation makes it risky to build complex workflows using their models
Load-bearing (whoops) quirks were noticeable months back, but haven't most flagship models become predictable and reliable?
Pelicans for 3.6 Flash and 3.5 Flash-Lite (Cyber isn't available to me through the API yet.) https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
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It seems like there are some credible rumors that Google is actually winning in terms of actually building models that work and don't lose money- between how they're able to price them, the TPU advantage and their capex advantage (being able to raise debt + just having a lot of cash - well I said not lose money... more like not go bankrupt). From the outside they look like they're behind in terms of frontier models,…
In terms of open models, Gemma 4 beats the pants off everything else to the point that paying for APIs becomes hard to justify. Qwen has the meme-share for coding, but it feels much less well rounded. I have no doubt that Google have both the infrastructure and the expertise to curb stomp everyone else, should they resolve in earnest to do so. Lest we forget, "Attention is All You Need" came from Google.
I wonder how big the Pro model is that Google is using behind the scenes to train these smaller ones. Going on baseless speculation, the lack of accompanying pro models with these flash releases either means: 1) the model is too big to be economical, 2) google doesn't have the compute to serve the big model, 3) their big model has too many alignment issues to serve to the public. edit: looks like benchmarks are up on…
Why would 3.6 flash perform a little worse than 3.5 flash on Artificial Analysis Coding Index... https://artificialanalysis.ai/models/gemini-3-6-flash?intell...
Because AA Coding "Index" consists only of two benchmarks (Terminal-Bench v2.1, SciCode) and generally fails to be meaningfully representative of agentic coding capabilities.