481–490 of 609 posts
Only if I could figure out how to use it. I have been using Claude Code and enjoy it. I sometimes also try Codex which is also not bad. Trying to use Gemini cli is such a pain. I bought GDP Premium and configured GCP, setup environment variables, enabled preview features in cli and did all the dance around it and it won't let me use gemini 3. Why the hell I am even trying so hard?
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> Don’t let the “flash” name fool you I think it's bad naming on google's part. "flash" implies low quality, fast but not good enough. I get less negative feeling looking at "mini" models.
Interesting. Flash suggests more power to me than Mini. I never use gpt-5-mini in the UI whereas Flash appears to be just as good as Pro just a lot faster.
Mini - small, incomplete, not good enough
Flash - good, not great, fast, might miss something.
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Or maybe Google knows most people search inane, obvious things?
Google AI Overview a lot of times write wrong about obvious things so... lol They probably use old Flash Lite model, something super small, and just summarize the search...
Don’t let the “flash” name fool you, this is an amazing model. I have been playing with it for the past few weeks, it’s genuinely my new favorite; it’s so fast and it has such a vast world knowledge that it’s more performant than Claude Opus 4.5 or GPT 5.2 extra high, for a fraction (basically order of magnitude less!!) of the inference time and price
Can confirm. We at Roblox open sourced a new frontier game eval today, and it's beating even Gemini 3 Pro! ( Previous best model ). https://github.com/Roblox/open-game-eval/blob/main/LLM_LEADE...
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> GPUs are necessarily higher latency than TPUs for equivalent compute on equivalent data. 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…
I thought it was generally accepted that inference was faster on TPUs. This was one of my takeaways from the LLM scaling book: https://jax-ml.github.io/scaling-book/ – TPUs just do less work, and data needs to move around less for the same amount of processing compared to GPUs. This would lead to lower latency as far as I understand it. The citation link you provided takes me to a sales form, not an FAQ, so I can't s…
I don't see any latency comparisons in the link
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…
Pulling ahead? Depends on the usecase I guess. 3 turns into a very basic Gemini-CLI session and Gemini 3 Pro has already messed up a simple `Edit` tool-call. And it's awfully slow. In 27 minutes it did 17 tool calls, and only managed to modify 2 files. Meanwhile Claude-Code flies through the same task in 5 minutes.
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I don't think tricky niche knowledge is the sweet spot for genai and it likely won't be for some time. Instead, it's a great replacement for rote tasks where a less than perfect performance is good enough. Transcription, ocr, boilerplate code generation, etc.
The thing is, I see people use it for tricky niche knowledge all the time; using it as an alternative to doing a Google search. So I want to have a general idea of how good it is at this. I found something that was niche, but not super niche; I could easily find a good, human written answer in the top couple of results of a Google search. But until now, all LLM answers I've gotten for it have been complete hallucinat…
I know without the ability to search it's very unlikely the model actually has accurate "memories" about these things, I just hope one day they will acutally know that their "memory" is bad or non-existing and they will tell me so instead of hallucinating something.
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That's too bad. Apple's most interesting value proposition is running local inference with big privacy promises. They wouldn't need to be the highest performer to offer something a lot of people might want.
Apple’s most interesting value proposition was ignoring all this AI junk and letting users click “not interested” on Apple Intelligence and never see it again. From a business perspective it’s a smart move (inasmuch as “integrating AI” is the default which I fundamentally disagree with) since Apple won’t be left holding the bag on a bunch of AI datacenters when/if the AI bubble pops. I don’t want to lose trust in App…
Did you forget all the Apple Intelligence stuff? They were never "ignoring" if anything they talked a big talk, and then failed so hard.
The whole iPhone 16 was marketed as AI first phone (including in billboards). They had full length ads running touting AI benefits.
Apple was never "ignoring" or "sitting AI out". They were very much in it. And they failed.