181–190 of 402 posts
This is the classic apple approach - wait to understand what the thing is capable of doing (aka let others make sunk investments), envision a solution that is way better than the competition and then architect a path to building a leapfrog product that builds a large lead.
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Well you can do a lot with 640k…if you try. We have 16G in base machines and very few people know how to try anymore. The world has moved on, that code-golf time is now spent on ad algorithms or whatever. Escaping the constraint delivered a different future than anticipated.
People get hung up on bad optimization. It you are the working at sufficiently large scale, yes, thinking about bytes might be a good use of your time. But most likely, it's not. At a system level we don't want people to do that. It's a waste of resources. Making a virtue out of it is bad, unless you care more about bytes than humans.
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similar vibes as "640k ought to be enough for anybody"
Well you can do a lot with 640k…if you try. We have 16G in base machines and very few people know how to try anymore. The world has moved on, that code-golf time is now spent on ad algorithms or whatever. Escaping the constraint delivered a different future than anticipated.
Gemma4 in my view is good enough to do things similar to Gemini 2.5 flash, meaning if I point it code and ask for help and there is a problem with the code it’ll answer correctly in terms of suggestions but it’s not great at using all tools or one shooting things that require a lot of context or “expert knowledge” If a couple more iterations of this, say gemma6 is as good as current opus and runs completely locally o…
By the time gemma6 allows you to do the above the proprietary models supposedly will already be on the next step change. It just depends if you need to ride the bleeding edge but specially because it's "intelligence", there's an obvious advantage in using the best version and it's easy to hype it up and generate fomo.
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Don’t underestimate the march of technology. Just look at your phone, it has more FLOPS than there were in the entire world 40 years ago.
Yeah, but that's the current state of the art after decades of aggressive optimizations, there's no foreseeable future where we'll ever be able to cram several orders of magnitude more ram into a phone.
Gemma4 in my view is good enough to do things similar to Gemini 2.5 flash, meaning if I point it code and ask for help and there is a problem with the code it’ll answer correctly in terms of suggestions but it’s not great at using all tools or one shooting things that require a lot of context or “expert knowledge” If a couple more iterations of this, say gemma6 is as good as current opus and runs completely locally o…
similar vibes as "640k ought to be enough for anybody"
I won't deny that the latest Claude models are fantastic at just one shotting loads of problems. But we have an internal proxy to a load of models running on Vertex AI and I accidentally started using Opus/Sonnet 4 instead of 4.6. I genuinely didn't know until I checked my configuration.
AI models will get to this point where for 99% of problems, something like Gemma is gonna work great for people. Pair it up with an agentic harness on the device that lets it open apps and click buttons and we're done.
I still can't fathom that we're in 2026 in the AI boom and I still can't ask Gemini to turn shuffle mode on in Spotify. I don't think model intelligence is as much of an issue as people think it is.
Gemma4 in my view is good enough to do things similar to Gemini 2.5 flash, meaning if I point it code and ask for help and there is a problem with the code it’ll answer correctly in terms of suggestions but it’s not great at using all tools or one shooting things that require a lot of context or “expert knowledge” If a couple more iterations of this, say gemma6 is as good as current opus and runs completely locally o…
But that difference atm is the difference between it being OK on its own with a team of subagents given good enough feedback / review mechanisms or having to babysit it prompt by prompt. By the time gemma6 allows you to do the above the proprietary models supposedly will already be on the next step change. It just depends if you need to ride the bleeding edge but specially because it's "intelligence", there's an obvi…
Do people actually build meaningful things like that?
It's basically impossible to leave any AI agent unsupervised, even with an amazing harness (which is incredibly hard to build). The code slowly rots and drifts over time if not fully reviewed and refactored constantly.
Even if teams of agents working almost fully autonomously were reliable from a functional perspective (they would build a functional product), the end product would have ever increasing chaos structurally over time.
I'd be happy to be proven wrong.
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A lot of people are making the mistake of noticing that local models have been 12-24 months behind SotA ones for a good portion of the last couple years, and then drawing a dotted line assuming that continues to hold. It simply.. doesn't. The SotA models are enormous now, and there's no free lunch on compression/quantization here. Opus 4.6 capabilities are not coming to your (even 64-128gb) laptop or phone in the pop…
Don’t underestimate the march of technology. Just look at your phone, it has more FLOPS than there were in the entire world 40 years ago.
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These are software/cloud features. You can install gemini on iphone if you want to talk about towers in Chicago. The only reason to care about it being OS integrated is to interact with functions of the OS, which siri does fine.
Apple's AI stuff also uses cloud features, though you can't use them on other platforms. The problem with Apple's new cloud features is that they generally just suck. I'm surprised iCloud works so well with how hard they're fumbling basic stuff like this.
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The only thing that we are sure can't be highly compressed is knowledge, because you can only fit so much information in given entropy budget without losing fidelity. The minimal size limits of reasoning abilities are not clear at all. It could be that you don't need all that many parameters. In which case the door is open for small focused models to converge to parity with larger models in reasoning ability. If that…
> and only calling out to large models when they actually need the extra knowledge When would you want lossy encoding of lots of data bundled together with your reasoning? If it is true that reasoning can be done efficiently with fewer parameters it seems like you would always want it operating normal data searching and retrieval tools to access knowledge rather than risk hallucination. And re: this discussion of lar…
There's is a problem though: we know that it is possible, but we don't know how to (at least not yet and as far as I am aware). So we know the answer to "what?" question, but we don't know the answer to "how?" question.