everybody on your team who is making architectural decisions, those people must know performance and they must make decisions that will allow the other people downstream of them to use an architecture which can be optimized later. If you don't do that, you're just rolling the dice.
when you're working with a lot of data, the the difference can be massive if you structure it in one way versus structuring another way, right? Again, architectural decisions that have nothing to do with hotspots, they're how all the data is laid out and what the access pattern is, right?
If every uh software engineer knew to watch out for false serial dependency chains, things where they were creating series of dependent operations that could not be optimized away or other sorts of architectural problems like that that cannot be easily fixed, then the world would look more like just wait and optimize the hotspot, right?
Right now: llama3.2:3b appears to be the model for: is this email urgent?qwen3:8b appears to be the model for: summarize this 5000-word article. llama3.3:70b(q8) appears to be the model for: let’s write or debug some computer code.
Alternatively, if you don't want to use Qwen3.6, Cohere's North Mini Code is probably the most interesting, capable alternative at this size class right now.
And now suddenly people are building databases on top of object stores, for example. And now the replication happens at the object store level, no longer at the database level.
As it turned out, ollama was not able to fit Qwen3.5:35B onto my GPU, but llama-server could. Hence, from that day forward, I resolved to cease being a cave-dwelling noob, and use llama-server (via llama-swap to make model swapping easier).
none of us wanted to go through that extreme but lots of time when you are under a lot of pressure and no time to react other than just to survive that scale that keep on coming at you, you have to make uh decision that increase uh speed and velocity because speed and velocity allow us to build quick enough to survive
Because of how source survival works, or indeed, how architectural survival works, we are often encouraged to think of the past as a place populated entirely by the wealthy elite.
You can't build an infrastructure that's optimized for one model. If you do that what if you go fall behind? In fact, all the infrastructure you built will be a waste, right?
These models don't really have this distillation phase um of taking what happened, analyzing it, obsessively thinking through it, um basically doing some kind of a synthetic data generation process and distilling it back back into the weights
Thus, code is cheaper than ever, but I suspect that insight and good architectural design and understanding, at least for now, will become more valuable than ever.
o3's improvement over the GPT series proves that architecture is everything. You couldn't throw more compute at GPT-4 and get these results. Simply scaling up the things we were doing from 2019 to 2023 -- take the same architecture, train a bigger version on more data -- is not enough.
So, once you use terms “fascism,” and “Nazi,” and “denazification,” suddenly people, not just start listening, they just stop analyzing. And as a propaganda tool, this is, of course, very powerful tool.
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