.ml .ee

Starts with “L,” ends with “bann.”

UniversalMonk potentially

Honesrly: tokenboomer likely is all those accounts as well.

You will notice that anytime you respond to boomer with disagreement, multiple accounts will begin flooding you, and they all brave in and out of the conversations as if they are the ones who said things said by others.

You wouldn't expect a paid professional to be so sloppy, but on the other hand, if he was good at his job, he wouldn't be working for Hamas.

I don't rule it out. The prior era of "reddit alternatives" in the Voat era was quickly overrun too even though they were very small. The key to the internet has always been first mover advantage. If they have enough power to manipulate the top sites, it would take very little to hedge bets on budding platforms. They risk losing their advantage if a replacement platform establishes itself without them. That's pretty much the whole history of modern tech. To actively seek and snuff out your competitors.

Someone always has to be king shit of whatever hill they're on.

Okay so now that everyone is here, the above comment is precisely what to look for when checking for signs of stroke.

On to .ml for the rest of our addled brains tour. Stay with you buddy and the group!

And continues to do so. You've grown since 2013

So ask it about a made up or misspelled word - "how many r's in the word strauburrry" or ask it something with no answer like "what word did I just type?". Anything other than, "you haven't typed anything yet" is wrong.

Llms look for patterns in their training data. So like if you asked 2+2= it would look its training and finds high likelihood the text that follows 2+2= is 4. Its not calculating, its finding the most likely completion of the pattern based on what data it has.

So its not deconstructing the word strawberry into letters and running a count... it tries to finish the pattern and fails at simple logic tasks that arent baked into the training data.

But a new model chatgpt-o1 checks against itself in ways i dont fully understand and scores like 85% on international mathematic standardized test now so they are making great improvements there. (Compared to a score of like 14% from the model that cant count the r's in strawberry)

Over simplification but partly it has to do with how LLMs split language into tokens and some of those tokens are multi-letter. To us when we look for R's we split like S - T - R - A - W - B - E - R - R - Y where each character is a token, but LLMs split it something more like STR - AW - BERRY which makes predicting the correct answer difficult without a lot of training on the specific problem. If you asked it to count how many times STR shows up in "strawberrystrawberrystrawberry" it would have a better chance.