GlyphLang
GlyphLang replaces verbose keywords with symbols that tokenize more efficiently:
# Python
@app.route('/users/<id>')
def get_user(id):
user = db.query("SELECT * FROM users WHERE id = ?", id)
return jsonify(user)
# GlyphLang
@ GET /users/:id {
$ user = db.query("SELECT * FROM users WHERE id = ?", id)
> user
}
@ = route, $ = variable, > = return. Initial benchmarks show ~45% fewer tokens than Python, ~63% fewer than Java.
Before anyone asks: no, this isn't APL with extra steps. APL, Perl, and Forth are symbol-heavy but optimized for mathematical notation, human terseness, or machine efficiency. GlyphLang is specifically optimized for how modern LLMs tokenize. It's designed to be generated by AI and reviewed by humans, not the other way around. That said, it's still readable enough to be written or tweaked if the occasion requires.
It's still a work in progress, but it's a usable language with a bytecode compiler, JIT, LSP, VS Code extension, PostgreSQL, WebSockets, async/await, generics.
everlier•9h ago
For example see this prompt describing an app: https://textclip.sh/?ask=chatgpt#c=XZTNbts4EMfvfYqpc0kQWpsEc...
goose0004•9h ago
The approach with GlyphLang is to make the source code itself token-efficient. When an LLM reads something like `@ GET /users/:id { $ user = query(...) > user }`, that's what gets tokenized (not a decompressed version). The reduced tokenization persists throughout the context window for the entire session.
That said, I don't think they're mutually exclusive. You could use textclip.sh to share GlyphLang snippets and get both benefits.