I read through the explanations and they’re all typical hand wavy LLM prose, full of jargon, and random tangential details. If you’re going to publicly present this as a learning tool, it would be great if you spent some actual human brain cycles writing and polishing it by hand, rather than just chucking the first thing that claude spits out over the fence. I can pretty much guess the prompt you used and probably get the same thing.
Not to mention that it's got all sort of bugs and traffic discontinuities. I just saw one of those "road train" things (a batch?!) on the central loop "crash" at the "Layer Counter Arch" while other cars just drove straight thru it.
The "Output Plaza" doesn't seem very outputtish - cars/embeddings seem to loop around back into the input.
Not sure why the Attention Plaza is occasionally shooting stuff down the storm drains/whatever in the center!
The sampler as centrifuge is a strange visualization!
So I guess the takeaway is that LLM are related to cars? Or maybe highways. Was this designed by Schmidhuber?
You can paste text and immediately see how it's split into tokens, along with token IDs, counts, and other details. It's useful for understanding why prompts cost what they do, why context limits behave the way they do, and why models sometimes split words in unexpected places.
I made it because I kept explaining tokenization to friends and realized there wasn't a simple, interactive tool that focused on learning rather than documentation.
I'd love feedback on:
Features that would make it more useful
UI improvements
Bugs or edge cases you've found
I read through the explanations and they’re all typical hand wavy LLM prose, full of jargon, and random tangential details. If you’re going to publicly present this as a learning tool, it would be great if you spent some actual human brain cycles writing and polishing it by hand, rather than just chucking the first thing that claude spits out over the fence. I can pretty much guess the prompt you used and probably get the same thing.
Or more gently...I think this is a great idea, but for the average person (me), dumbing it down a little more may go a long way
Thanks for the feedback! Will definitely look into simplifying it
[dead]
"This is not a loop in code: it is a stack of distinct blocks"
Proceeds to literally loop around 6 times.
I think you need to know a lot to understand this, so perhaps it should mention what you should already know in order for it to be comprehensible.
This would be really cool if it did 3Brown 1Blue's style of visualization - but interactively, and allowed you to explore at any stage.
I'd suggest the LLM visualiser (1) by Brendan Bycroft. I'm a visual learner and it helped me understand LLMs better than anything else ever could.
(1) https://bbycroft.net/llm
this doesnt help at all
Not to mention that it's got all sort of bugs and traffic discontinuities. I just saw one of those "road train" things (a batch?!) on the central loop "crash" at the "Layer Counter Arch" while other cars just drove straight thru it.
The "Output Plaza" doesn't seem very outputtish - cars/embeddings seem to loop around back into the input.
Not sure why the Attention Plaza is occasionally shooting stuff down the storm drains/whatever in the center!
The sampler as centrifuge is a strange visualization!
So I guess the takeaway is that LLM are related to cars? Or maybe highways. Was this designed by Schmidhuber?
You're absolutely right!
Congratulations you're now a senior AI engineer
Just softmax it over the KV cache dude!
I'd appreciate if you could make this mobile ready, with llms I expect this to be very easy
Can we filter out random vibe-coded projects like this from the homepage?
filtering functionality would be great
From this I learned LLMs are complex and compute-intensive.
interesting approach to describe this topic
[flagged]
You can paste text and immediately see how it's split into tokens, along with token IDs, counts, and other details. It's useful for understanding why prompts cost what they do, why context limits behave the way they do, and why models sometimes split words in unexpected places.
I made it because I kept explaining tokenization to friends and realized there wasn't a simple, interactive tool that focused on learning rather than documentation.
I'd love feedback on:
Features that would make it more useful UI improvements Bugs or edge cases you've found
Hope it's useful!
> I made it
*I asked a LLM to make it
Honestly this person's whole github pages screams tech grifter