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sorry if this is a dumb question, but how is this possible? what can cause the cache hit rate to change just by changing the model?

This is based on what we measure from the API - we see different endpoints depending on how they are served can have different cache hit rates (cache hit rate is defined as the % of cacheable tokens that actually come back as cache hits)

One possibility (just a guess): new arch produces a more compact cache, it becomes cost effective to hold more cache for longer.

He is probably referring to this : https://www.reuters.com/world/china/how-china-built-its-manh...

They hired old asml employees.


Also interesting: https://gizmodo.com/a-viral-new-bird-song-in-canada-is-causi...

White-throated sparrows across Canada are rapidly replacing their classic three-note song ending with a new two-note version, which originated in British Columbia and has spread eastward over the past two decades.


The whole page reads like it was vibe-written by an AI. If I'd built something as disruptive as this claims to be, I'd have spent at least fifteen minutes writing the announcement myself. Every time I see 'we' in an announcement like this, I picture one guy alone in his basement.


unfortunately all hand-written :( my chief-of-staff does unironically handwrite em dashes though


For what it’s worth, I didn’t get that impression, and even noticed a couple typos ;)


I really appreciated the hand-written release. Thank you.


I think that providing modern monitors that make sense is probably the easiest way to please employees. No matter how excellent the salary is, if they are stuck working on old, small, blurry, faded screens, they'll be unhappy. It also gives a bad impression of the company as a whole. I did L1 tech support a long time ago, and people were overwhelmingly happy to see me coming to replace their old CRTs with LCDs.


The slide at 14:06 say:

By june 11: Highly persistent experimental, internal-only model begins training.

I am not sure what that means. Are they preserving notes/memories and context between runs?


That's how I interpreted it, but now I'm wondering if they mean "this model gives up far less often"..


I'm thinking super long context length or something to that effect.

I can imagine schemes for instance where context is compressed into chunks and then chunks that are ranked highly relevant for the token are decompressed. Which would sort of be between a long context and a memory retrieval scheme...


Maybe they are doing it on purpose to demonstrate what it looks like when untrustworthy parties have access to frontier llm and that its development should be regulated to protect all of us. /tinfoil


It ought to be extremely easy now to use LLMs to transpile cobol and do the necessary reverse engineering to migrate off mainframes and aix servers. I think IBM will lose many customers.


It ought to be extremely easy now to use LLMs to transpile cobol and do the necessary reverse engineering to migrate off mainframes and aix servers. I think IBM will lose many customers.

The problem is that those COBOL systems have to be absolutely provably correct, for both financial and regulatory reasons. LLMs can't do that. They're designed to be variable.

You can't vibe code a bank transaction system. "Close enough" isn't good enough in some fields. A minor glitch in a video game may result in screen artifacts. A minor glitch in a banking system can crash the economy.


It’s also a solved problem combined with a risk averse industry.

COBOL on mainframes has worked reliably for decades. On the scale of what a bank will spend on operations the mainframe doesn’t really cost much.

Banks are sort of the canonical example of “no one ever got fired for buying ibm”.


Yep, if AI could do it so could out-sourcing it to India. AI is great for "almost" but not great at "exact"


but are you willing to spend the time or tokens to build it again for yourself ?


The Orville is excellent. I am a fan. Here is another show/episode that is very similar and relevant here : https://en.wikipedia.org/wiki/Nosedive_(Black_Mirror)


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