Introducing SubQ - a major breakthrough in LLM intelligence.
It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA),
And the first frontier model with a 12 million token context window which is:
- 52x faster than FlashAttention at 1MM tokens
- Less than 5% the cost of Opus
Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention).
Only a small fraction actually matter.
@subquadratic finds and focuses only on the ones that do.
That's nearly 1,000x less compute and a new way for LLMs to scale.
is anyone using autonomous coding agents that are self serving? at an org scale? I know stripe does it and calls them "minions" lol .
i've found a couple of OSS solutions but they are a bit murky security-wise, i will patch them though.
i know Devin has a solution, but what's the fun in using cloud services, i'll raw dog self host the system.
hmu if you have any experience ( good or bad ) with this pls :)
another day, another one of my personal projects killed.
i'm starting to understand that connecting third party products that you don't own to these agents is not going to work as a product in itself. if the market is big enough, it's only a matter of time before the big guns are coming for you.
this loss feels personal, going back to the drawing board
Claude now connects to the tools creative professionals already use.
With the new Blender connector, you can debug a scene, build new tools, or batch-apply changes across every object, directly from Claude.
AI was supposed to be great at converting unstructured data into structured, actionable decisions. So the question is - are enterprises actually able to leverage AI at their scale, when all their knowledge is sitting inside large, not-so-well-formatted 2000s themed PDF files?
agents can't read your company's homework
Every time an enterprise brings in a new vendor, someone has to sit down and explain how everything works. Documents, processes, playbooks. Again. For every single vendor.
Even if vendors build this capability, they still need to cross-contextualize across thousands of documents, index them, link them semantically. It's a ton of work and frankly really uninteresting to build, when they can focus on improving their actual product. And if not done right, next month's AI bill eats into their lunch money - especially for small vendors.
How are founders today predicting what to build next? How do they know that there is enough demand for what they're planning?
Are there any tools that they use? If yes, then what tools?
Read somewhere that Linus Torvalds faced problems with the operating systems he used; and hence developed an entire OS himself.
I'm curious as to how people point out those flaws and actually start working on them, with me I just say "it is what it is" and move on with my day.