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Comment Re:Slack off in mission-critical ... (Score 1) 36

Kind of hilarious that they passive-aggressively blame "business requirements" for the problem:

" Regardless of the business need for near instantaneous consistency of the data globally (i.e. quota management settings are global), data replication needs to be propagated incrementally"

Comment Re: was this (Score 1) 36

This is pretty good for an external report.

No it's not lol.

The problem here is that the code path with the bug was cut it abruptly rather than gradually;

Your sentence doesn't make sense, it's not grammatical. Furthermore the problem was not abrupt: the code was merged in May, the outage was in June.

The report is bad.

Comment Re:was this (Score 2) 36

The article is written in a way to CYA of the people involved. It spreads the blame around without being too specific, so no one knows what was going on. For example, it talks about "the null pointer" but that's the only mention of it. At best that doesn't make sense gramatically.

They also talk about "This policy data contained unintended blank fields" but leave it at that. Why did it contain unintended blank fields? How did they get in there? It doesn't say. Is it a problem? Are they hand-writing this policy data?

Are they going to take measures to ensure that the "unintended blank fields" don't happen again? No, they are not; based on what is written in their document.

Was the report written by AI? It's probably too short and concise, but the grammar and disconnectedness makes it feel that way.

Comment Re:say what? (Score 1) 106

I've written things in Java and in Swift, and you aren't going to see an 85% difference. Swift has a lot of its own weird verboseness going on.

In an extremely heavy allocate/free environment, GC in Java will take something like 10 minutes out of every hour. Which is significant, but you shouldn't see a 40% increase in throughput from that either.

Someone else suggested they were probably parsing JSON/XML repeatedly or something similar, which is possible considering 'parsing' is just a simple function call.

Comment Re:ChatGPT is not a chess engine (Score 1) 132

LLMs are mostly composed of regular old fully connected ANNs, and the remainder, the transformers, are also ANNs.

You're talking theoretical things here, not practical reality.

ANNs certainly can learn the rules of chess, and you can train one to play chess at a level that is generally regarded as superhuman.

No one has ever made an ANN that plays chess at a level that is superhuman. AlphaZero is still primarily a tree searching algorithm with an ANN used to evaluate every node of the tree.

There's also a proof that any 2+ layer ANN of sufficient size can learn any IO function.

To remove the tree search, the ANN would need to be VERY big.

The fact that they don't, or don't do so very well, means that the way they are trained is an inefficient way to learn chess.

LLMs are trained in an inefficient way to learn anything. How many billions of pages did you need to be trained how to read?

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