u/Substantial_Set5836

im making a program that compiles scratch projects to text and the text back to scratch
so now you can write projects in text
compile to sb3
and later decompile that sb3 back to text
in C

its an AI hunter using machine learning to chase the player

trained in python on T4 GPU using colab with reinforcement learning

it might be slow on scratch.

its a prototype im working on the game

also it only works on this maze.

https://scratch.mit.edu/projects/1364037879/

it can sing

its the same model but now it actually replies to messages

https://scratch.mit.edu/projects/1298961147/

Upgraded from an RNN to an LSTM!

Project: https://scratch.mit.edu/projects/1351859837/

Note: It's too large to run in the Scratch, so you'll need to use TurboWarp (instructions and link are on the project page).

This doesn't mean it's better it's mostly an experiment and a base for future versions. Right now its replies are funnier, but overall I'd say it's worse than my RNN.

Some things it does better:

• Almost no spelling mistakes.
• Knows really obscure words.
• Has a bizarre habit of going into "thesaurus mode."

Examples:

Prompt: banana
Response: bondage bondage not servitude servitude not thralldom thralldom not captivity captivity not imprisonment imprisonment not confinement confinement not...

Prompt: apple orange banana grape
Response: how can i stay motivated remember that every great journey starts with a small step keep moving forward feeling low any motivational words believe in yourself for you are capable of achieving incredible things.

Prompt: ok
Response: you too friend you are smart thanks but i am dum i am happy now glad to make you happy lets talk more yes i love talking what is hello hello means hi friend what is bye bye means goodbye i like summer

Try asking it:

• say hi
• hi

Specs:

• Architecture: 28 → 512 → 28 (character-level LSTM)
• Parameters: 2,116,616 (~2.1 million)
• Estimated IQ: 1.1 :P

The next version will probably use fewer neurons and should do a better job of actually answering the question instead of wandering off into thesaurus mode. Not sure about the hallucinations yet.

Huge thanks to u/quadcat for the main LSTM engine his Scratch AIs are much larger and more advanced than mine.

This model was trained on about 20k training pairs.

If anyone wants it, I can also release a Scratch project containing the dataset I used for my recent models.

so till now my RNN was learning to spell and talk
it was just learning and was TINY (for comparison chatgpt and most other models have trillions of neurons) now this has just 150 neurons, less neurons than an ant and it spells.

here is a reply from it
i am nou sure i doing helli hin chat is you how are you doing today i am fine thank you i am not sure i day ure to ng not sure i doing helli hin chat is you how are you doing today i am fine thank you
i wasnt able to tune it well but ive solved the scratch inaccuracy problem with tanh

please view the project and follow to support
https://scratch.mit.edu/projects/1298961147/
some of you have said that others have made bigger AIs than mine
ive checked them and theyre great! but rn im doing more with less
its still character by character but im in the middle of training a much BIGGER token by token model (UGRNN)

I built a bad word detector for Scratch that uses actual AI so it dosent require a blacklist or whitelist.
https://scratch.mit.edu/projects/1328096270/

its light enough to be added to any scratch project

scratch banned chats because it dosent have moderation
and blacklists were banned bc well, they are a list of bad words kids can click see inside and learn

whitelists are too for some reason

How it works

It's a bigram classifier — it looks at every pair of adjacent letters in a word and scores them using 676 learned weights. The score goes through a sigmoid function and if it crosses a threshold, the word is flagged. There's no list of bad words anywhere in the project. The weights are just 676 numbers that mean nothing to a human reading them.

technical stuff

- Architecture: bigram bag-of-features → linear layer → sigmoid

- Parameters: 676 weights + 1 bias = 677 total

- Training data: ~1000 bad words (3 open source profanity datasets + handcrafted game chat insults) + ~1000 clean words (Google 10k English corpus)

- Accuracy: 83.2% on held-out validation set

- F1 score: 0.829

- Trained in Google Colab using PyTorch, exported as JSON

- Inference runs fully inside Scratch with no extensions or internet

Why bigrams?

I originally tried a char-level RNN (128 hidden units) but Scratch's 32-bit float precision caused error accumulation across hidden states that killed accuracy. Bigrams sidestep this entirely — it's just integer index lookups and addition, with one sigmoid at the end. Much more stable.

limitations

- Some false positives

- Obscure slang and non-English words are sometimes missed

- Very short words (under 3 chars) get minimal signal

next

Working on a trigram version (26³ = 17,576 weights) which should significantly reduce false positives since 3-char sequences are much more distinctive.

Hope this helps enable cloud chat in Scratch projects. Enjoy.