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  • As someone who is not a software developer (there are some of us here, lurking around because we learn things) but who runs a business and spends his days doing said 'knowledge work', how and why would something like this convince me to move the company away from using Claude (as a desktop app)? I'm sure these models are cheaper to use than our Team subscription with Claude, but it feels like it would be a massive headache for me to work out how to select and implement some harness for this and set it up for people, and the space is moving so quickly I'd feel I need to keep considering when to move to some new model, introducing risk. The safest bet for us and most companies I expect is to stick with what you know works and pay a premium for that. Thoughts very welcome please.
  • Since there is not a lot of hope for me to reasonably run DS V4.1 on my 2 x DGX Spark setup (I know, spoiled), both the “readable prose” and the fewer tokens per task claims (relative to the base GLM-5.3-Flash) are most welcome.

    I’ll absolutely be giving this a go once weights land (a QAT FP4 experts release would be most appreciated)

  • I'm curious to try it out, how many specialized models will it take to run a company.

    Going to try it out for some bookkeeping, e.g. point it to a folder and tell it to book the invoices and point it to a csv file holding a year of private transactions and see them being processed, categorized and then ask about certain trends.

  • What kind of harness does one use for these?

    They say it can create office documents (if I understand correctly), but how does it do that?

    What's the thing one needs to plug this into to say "give me a slide deck with last quarter's sales figures"?

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    https://www.paperinstruments.com/feather

    According to their Twitter video, it might be this thing?

  • I’m confused this seems exactly like a skinned glm 5.3 - flash with a few context injection tricks and guardrails - whats going on with these “startups”?
  • As an aside, I do not like unnecessarily misleading graphs. Own your strengths and weaknesses.

    In the "Results" section, they choose to compare Ultramarine only to models that it's better than on every metric. They ignore models like Fable 5.1, GPT-6 Astra, Claude Opus 5.5, etc.

    Then, in the "Score against cost per task" graph, which is focused on price, suddenly all of those models that perform better than Ultramarine come roaring back because they are also more expensive.

    Just list yourselves as the second best sometimes. Your thing is impressive. Don't let the idiot marketing guy hide that behind stupid tricks.

  • > We present Ultramarine 1 Flash, an agentic model for professional knowledge work. Ultramarine applies large-scale reinforcement learning to a strong open base model, GLM-5.3 Flash, and improves on it across all six knowledge-work benchmarks we evaluate [...]

    In case anyone was curious what it's based on. (No judgement either way from me, IMO depends how well they did the post-training, which I can't tell).

  • Put it on OpenRouter… I don't really want to sign up for the first-party API

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