401–410 of 609 posts
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And Google themselves obviously believe that too as they happily insert AI summaries at the top of most serps now.
Or maybe Google knows most people search inane, obvious things?
They probably use old Flash Lite model, something super small, and just summarize the search...
I hate adding -preview to my model environment variable
My main issue with Gemini is that business accounts can't delete individual conversations. You can only enable or disable Gemini, or set a retention period (3 months minimum), but there's no way to delete specific chats. I'm a paying customer, prices keep going up, and yet this very basic feature is still missing.
Feels like Google is really pulling ahead of the pack here. A model that is cheap, fast and good, combined with Android and gsuite integration seems like such powerful combination. Presumably a big motivation for them is to be first to get something good and cheap enough they can serve to every Android device, ahead of whatever the OpenAI/Jony Ive hardware project will be, and way ahead of Apple Intelligence. Speakin…
Apple Intelligence is going to be Gemini https://www.macrumors.com/2025/11/05/apple-siri-google-gemin...
I so want to like Gemini. I so want to like Google, but beyond their history of shuttering products they also tend to have a bent towards censorship (as most directly seen with Youtube)
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The problem with publicly disclosing these is that if lots of people adopt them they will become targeted to be in the model and will no longer be a good benchmark.
This thought process is pretty baffling to me, and this is at least the second time I've encountered it on HN. What's the value of a secret benchmark to anyone but the secret holder? Does your niche benchmark even influence which model you use for unrelated queries? If LLM authors care enough about your niche (they don't) and fake the response somehow, you will learn on the very next query that something is amiss. No…
The reason I don't disclose isn't generally that I think an individual person is going to read my post and update the model to include it. Instead it is because if I write "I ask the question X and expect Y" then that data ends up in the train corpus of new LLMs.
However, one set of my benchmarks is a more generalized type of test (think a parlor-game type thing) that actually works quite well. That set is the kind of thing that could be learnt via reinforcement learning very well, and just mentioning it could be enough for a training company or data provider company to try it. You can generate thousands of verifiable tests - potentially with verifiable reasoning traces - quite easily.
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If they told you, it would be picked up in a future model's training run.
Don't the models typically train on their input too? I.e. submitting the question also carries a risk/chance of it getting picked up? I guess they get such a large input of queries that they can only realistically check and therefore use a small fraction? Though maybe they've come up with some clever trick to make use of it anyway?
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OpenAI made a huge mistake neglecting fast inferencing models. Their strategy was gpt 5 for everything, which hasn't worked out at all. I'm really not sure what model OpenAI wants me to use for my applications that require lower latency. If I follow their advice in their API docs about which models I should use for faster responses I get told either use GPT 5 low thinking, or replace gpt 5 with gpt 4.1, or switch to…
Hardware is a factor here. GPUs are necessarily higher latency than TPUs for equivalent compute on equivalent data. There are lots of other factors here, but latency specifically favours TPUs. The only non-TPU fast models I'm aware of are things running on Cerebras can be much faster because of their CPUs, and Grok has a super fast mode, but they have a cheat code of ignoring guardrails and making up their own world…
Where are you getting that? All the citations I've seen say the opposite, eg:
> Inference Workloads: NVIDIA GPUs typically offer lower latency for real-time inference tasks, particularly when leveraging features like NVIDIA's TensorRT for optimized model deployment. TPUs may introduce higher latency in dynamic or low-batch-size inference due to their batch-oriented design.
https://massedcompute.com/faq-answers/
> The only non-TPU fast models I'm aware of are things running on Cerebras can be much faster because of their CPUs, and Grok has a super fast mode, but they have a cheat code of ignoring guardrails and making up their own world knowledge.
Both Cerebras and Grok have custom AI-processing hardware (not CPUs).
The knowledge grounding thing seems unrelated to the hardware, unless you mean something I'm missing.
I don't want to say OpenAI is toast for general chat AI, but it sure looks like they are toast.