They used SO many words to say so little thing. At this point it seems pretty safe to say Mythos is purely PR stunt.
It reads like long winded version of paid advertising.
Project Glasswing: what Mythos showed us
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Re: Project Glasswing: what Mythos showed us
#132Re: Project Glasswing: what Mythos showed us
#133Earlier quoted context omitted.
Sounds different because it’s hidden advertisement not a regular blog post
But why would cloudflare advertise Anthropic? They are competing with Anthropic by hosting open weights models.
A corporation does not have its own will.
Their owners are invested in AI and need AI to do well. If this goal clashes temporarily with the goals set up for Cloudflare, so be it.
Re: Project Glasswing: what Mythos showed us
#134Earlier quoted context omitted.
Sounds different because it’s hidden advertisement not a regular blog post
But why would cloudflare advertise Anthropic? They are competing with Anthropic by hosting open weights models.
Not how I took it, but that this was a lot more marketing than content, while their other, older blogposts are more content than marketing. It is of course all content marketing, for Cloudflare. That doesn't mean it has to be bad (anemic on meaningful content). On the contrary, it being good is kind of the whole point.
Re: Project Glasswing: what Mythos showed us
#135Nice content marketing piece.
Re: Project Glasswing: what Mythos showed us
#136> Model refusals [..]
That even their model aimed at security research tries to be a pedantic better-than-thou annoys me much.
I build an agentic loop framework at work, and I need the model to test some boundaries and error-mechanisms, but Opus keeps whining that it's not ready to do these "bad" things and tells me to do it myself instead. Makes me roll my eyes...
Re: Project Glasswing: what Mythos showed us
#137Earlier quoted context omitted.
Sentence constructions like this definitely scream AI: "That's a reasonable bias for an exploratory tool. It's a ruinous one for a triage queue..." I will upgrade the "why it matters" to "and now AI output is part of the training data". A day is coming when the punched-up AI verbiage will be the norm and hard to distinguish unless you're from the previous generation. Sort of in the way that I miss some aspects of Use…
I had a dude in a conversation non-ironically use "load-bearing." I could only follow up with, "that is a genuine insight." Not a single person visibly flinched in pain.
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Re: Project Glasswing: what Mythos showed us
#138Earlier quoted context omitted.
Sentence constructions like this definitely scream AI: "That's a reasonable bias for an exploratory tool. It's a ruinous one for a triage queue..." I will upgrade the "why it matters" to "and now AI output is part of the training data". A day is coming when the punched-up AI verbiage will be the norm and hard to distinguish unless you're from the previous generation. Sort of in the way that I miss some aspects of Use…
I had a dude in a conversation non-ironically use "load-bearing." I could only follow up with, "that is a genuine insight." Not a single person visibly flinched in pain.
Re: Project Glasswing: what Mythos showed us
#139Earlier quoted context omitted.
yeah? it’s not that weird of a term
It’s weird when someone starts using terminology that is heavily over-indexed by LLMs out of the blue.
Is it weird? Pretty much everyone's writing and speech is influenced to some degree by what they've read and heard in conversation. For better or for worse, it's only getting harder to avoid exposure to LLM generated prose.
Re: Project Glasswing: what Mythos showed us
#140> The loudest reaction to Mythos Preview from other security leaders has been about speed - scan faster, patch faster, compress the response cycle. More than one team we have spoken with is now operating under a two-hour SLA from CVE release to patch in production [...] If regression testing takes a day, you cannot get to a two-hour SLA without skipping it, and the bugs you ship when you skip regression testing tend…
I don't know, but it always seems weird to me when people notice AI isn't performing super well and then they conclude that the solution to problem is to try using more AI
Reminds me of people adding more intervention and bureaucracy bc the last one did not do well, so we need more of it.
The problem is never the results of it. It is that we did not do well enough.