fnbr

MoEs have a lot of technical complexity and aren't well supported in the open source world. We plan to release a MoE soon(ish).

I do think that MoEs are clearly the future. I think we will release more MoEs moving forward once we have the tech in place to do so efficiently. For all use cases except local usage, I think that MoEs are clearly superior to dense models.

(I’m a researcher on the post-training team at Ai2.)

Where did you try this? On the Ai2 playground?

(I’m a researcher on the post-training team at Ai2.)

7B models are mostly useful for local use on consumer GPUs. 32B could be used for a lot of applications. There’s a lot of companies using fine tuned Qwen 3 models that might want to switch to Olmo now that we have released a 32B base model.

Like what? All the examples people have said are where either

1) the company has Nx preferences, for N >1, in which case the company has essentially failed to fundraise or

2) the company sells for less than they raised, which again, is a polite form of failure.

This is why I will never work somewhere with a short post termination exercise period (PTEP). If it’s not at least 5 years, ideally 10, they don’t seriously consider equity something that employees are owed.

Can you explain? In most cases, preferences won’t come into play, assuming you raise at a standard 1x preference and sell for more than you have raised. In that case, owning 0.5% should roughly translate into $5M (modulo dilution).

I hate how every company that I place an order with treats that as permission to send a constant drip of marketing emails. I send them straight to spam.

They raised ~$200M total, and $150M at the series A. So if they're just paying back investors, they'd "only" need $500M.

do we know they were profitable? I doubt it, if they pulled this. I think they had high DAU/MAU but low paid users.

this is basically Inflection 2.0.

Yes, many AI startups pay more than FAANG for ML talent.

What's the dating app?