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humanscript

A truly natural scripting language

humanscript is an inferpreter. A script interpreter that uses a large language model to infer the meaning behind commands written in natural language. Human writeable commands are translated into code that is then executed on the fly. There is no predefined syntax, humanscripts just say what they want to happen, and when you execute them, it happens.

The humanscript inferpreter supports a wide range of LLM backends. It can be used with cloud hosted LLMs like OpenAI's GTP-3.5 and GPT-4 or locally running open source LLMs like Llama 2.

Example

This is a humanscript called bitcoin-poem.

#!/usr/bin/env humanscript

write a poem

print the poem

get the latest bitcoin blockhash from the mempool api

hash the poem and the blockhash together

print the blockhash, and the combined hash

It can be executed like any other script.

$ ./bitcoin-poem
Poem: Roses are red, violets are blue. Bitcoin is volatile, and that is true
Bitcoin Blockhash: 0000000000000000000413b966555eee6794dac502ac66ec88d7e752ffec8a4b
Combined hash: ad69015c2f43d86b2d3247b78c81d9bb8f38e453a05d6fd264f42c44d74390e4

The LLM inferpreted the humanscript into the following bash script at runtime.

#!/usr/bin/env bash

poem="Roses are red, violets are blue. Bitcoin is volatile, and that is true"

echo "Poem: $poem"

blockhash=$(curl -s https://mempool.space/api/blocks/tip/hash)

combined_hash=$(echo -n "$poem$blockhash" | sha256sum | cut -d ' ' -f1)

echo "Bitcoin Blockhash: $blockhash"
echo "Combined hash: $combined_hash"

The code is streamed out of the LLM during inferpretation and executed line by line so execution is not blocked waiting for inference to finish. The resulting code is cached and will be executed immediately next time the humanscript is executed, bypassing the need for reinferpretation.

Usage

Install humanscript

You can run humanscript in a sandboxed environment via Docker:

docker run -it ghcr.io/lukechilds/humanscript

Alternatively you can install it natively on your system with Homebrew:

brew install lukechilds/tap/humanscript

Or manually install by downloading this repository and copy/symlink humanscript into your PATH.

Be careful if you're running humanscript unsandboxed. It can sometimes do weird and dangerous things. If you're brave enough to run unsandboxed it's a good idea to run humanscripts initially with HUMANSCRIPT_EXECUTE="false" to eyeball the resulting code.

Write and execute a humanscript

humanscript is configured out of the box to use OpenAI's GPT-4, you just need to add your API key.

We need to add it to ~/.humanscript/config

mkdir -p ~/.humanscript/
echo 'HUMANSCRIPT_API_KEY="<your-openai-api-key>"' >> ~/.humanscript/config

Now you can create a humanscript and make it executable.

echo '#!/usr/bin/env humanscript
print an ascii art human' > asciiman
chmod +x asciiman

And then execute it.

./asciiman
  O
 /|\
 / \

Configuration

The following environment variables can be added to ~/.humanscript/config to be globally applied to all humanscripts.

HUMANSCRIPT_API

Default: https://api.openai.com

A server following OpenAI's Chat Completion API.

Many local proxies exist that implement this API in front of locally running LLMs like Llama 2. LM Studio is a good option.

HUMANSCRIPT_API="http://localhost:1234"

HUMANSCRIPT_API_KEY

Default: unset

The API key to be sent to the LLM backend. Only needed when using OpenAI.

HUMANSCRIPT_API_KEY="sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

HUMANSCRIPT_MODEL

Default: gpt-4

The model to use for inference.

HUMANSCRIPT_MODEL="gpt-3.5"

HUMANSCRIPT_EXECUTE

Default: true

If the humanscript inferpreter should automatically execute the generated code on the fly.

If false the generated code will not be executed and instead be streamed to stdout.

HUMANSCRIPT_EXECUTE="false"

HUMANSCRIPT_REGENERATE

Default: false

If the humanscript inferpreter should regenerate a cached humanscript.

If true the humanscript will be reinferpreted and the cache entry will be replaced with the newly generated code.

HUMANSCRIPT_REGENERATE="true"

License

MIT © Luke Childs