One thing i've got to wonder. Would this always remain the case, at what point should society seriously consider the "personhood" of an AI (as a noun).
Sol and 5.5 pro are in parity at $5 input / $30 output. What I'm inferring from this is that:
- model weight size didn't change, and this is mostly a result of better model architecture and scaled up RL
- better hardware utilization and and they're making better margins OR
- worse hardware utilization and they're okay with digging into their margins.
"We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity."
This seems like it would be the largest and first closed-source model Cerebras has offered till date
I certainly hope so, it's cheaper than ever to write native code, but if sub-trillion dollar valuated companies like OpenAI have done the math and still use electron for the codex app, one has to ponder what was considered.
Awesome project, love how straight to the point your landing page is.
I was building something similar to linkedrecords but on the internet computer (https://internetcomputer.org), i went about things naively compared to this, and decided working on the problem wasn't worth as much effort as completely reformulating it (different topic)
Yeah i feel separating compute from data is one of the best directions the industry can move towards, it means less lock-in, more flexibility and user agency as well as more personal compute utilisation (increasingly important with the cloud compute pricing-in the RAM supply crisis).
Could i ask, what made you decide to work on this ? personal and/or enterprise ?
I’m building https://design.withfudge.com, a Prolog-backed design search engine that lets designers/agents query structured design knowledge from real websites. It uses data from my other startup, https://fontofweb.com, to help designers find concrete inspiration e.g fonts, colors, layouts, screenshots, and patterns, so they can make better design decisions.
"The api pricing for mimo-v2-pro and mimo-v2-omni remain unchanged" could we presume this means the discount isn't from hardware improvement or availability ?
My opinion is that we're using the wrong paradigms for LLMs. We should be leaning more on declaratively specifying behaviour.
If there's any hope for reliability, auditability, predictability to be had it lies in contraining and LlMs grammar whilst delegating freeform behavior to a more passive substrate.