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🤯 The Problem: LLM Token Inflation

Current AI Coding Agents (Claude 3.5, GPT-4o, Gemini Pro) spend 90% of their output tokens writing:

• Hundreds of lines of repetitive import requests, BeautifulSoup boilerplate.
• Tedious HTML boilerplate, <!DOCTYPE html>, Tailwind classes strings, and nested loop syntax.
• Verbose CSS selector traversals and error-handling blocks.

The result? Higher API bills, sluggish response times, context window exhaustion, and higher chances of hallucinated syntax.

💡 The Solution: AeCode

AeCode compresses complex web workflows into high-density mathematical & functional primitives. An AI model writes 10 lines of AeCode; the engine compiles and executes it as 200+ lines of production Python + Tailwind CSS.

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🥊 Real Code Comparison

Task: Scrape 20 Products and Render a Live Dashboard

❌ The Python + HTML Traditional Way (285 Tokens)

import csv, requests
from bs4 import BeautifulSoup

resp = requests.get("http://books.toscrape.com")
soup = BeautifulSoup(resp.text, 'html.parser')
books = []
for pod in soup.select(".product_pod"):
    t = pod.select_one("h3 > a")["title"]
    p = pod.select_one(".price_color").text
    books.append({"title": t, "price": p})

html = """<!DOCTYPE html><html><head><script src="https://cdn.tailwindcss.com"></script></head><body class="bg-gray-900 text-white p-8"><div class="grid grid-cols-4 gap-6">"""
for b in books:
    html += f"""<div class="bg-gray-800 p-4 rounded-xl border border-gray-700"><h3 class="font-bold">{b['title']}</h3><p class="text-amber-400 font-bold">{b['price']}</p></div>"""
html += "</div></body></html>"
with open("view.html", "w") as f: f.write(html)
import webbrowser; webbrowser.open("view.html")

✅ The AeCode Way (32 Tokens — 89% Ahorro)

DATA = SCRAPE("http://books.toscrape.com", ".product_pod", [title: "h3 > a" @ "title", price: ".price_color" @ "text"])
VIEW = GRID 4 > CARD(title, price) FROM DATA
RENDER VIEW

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📊 Benchmarks

Task	Traditional Python	AeCode	Token Savings
Basic Web Scraping	~180 tokens	48 tokens	-73%
Multithreaded Parallel FlatMap	~280 tokens	60 tokens	-78%
Auto-Pagination (Crawl)	~320 tokens	52 tokens	-84%
Tailwind Landing Page	~500 tokens	25 tokens	-95%
Live Scrape-to-UI Dashboard	~600 tokens	35 tokens	-94%

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⚡ Quick Start

1. Installation

pip install aecode

2. Verify Your System

aecode doctor

3. Run Your First Flow

aecode flow examples/dashboard.ae

Voilà! It automatically scrapes live data, builds an interactive Tailwind CSS UI, and opens it directly in your default browser.

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🧭 Three Engines in One DSL

1. 🌐 AeWeb (Scraping & Data Extraction)

doc = GET("http://books.toscrape.com")
items = doc > ".product_pod"
rows = items | λi. {title: (i > "h3 > a") @ "title", price: (i > ".price_color") @ "text"}
rows => "libros.csv"

aecode scrape examples/scraper.ae

2. 🎨 AeUI (Declarative Web Layouts)

PAGE /home
  NAV [MiMarca, Links(Productos, Precios, Contacto)]
  HERO "Bienvenido al futuro" center
  GRID 3 > CARD(title, desc, price)
  BTN "Empezar ahora"
  FOOTER

aecode build examples/landing.ae --output index.html

3. ⚡ AeFlow (End-to-End Scraping to Web App)

DATA = SCRAPE("http://books.toscrape.com", ".product_pod", [title: "h3 > a" @ "title", price: ".price_color" @ "text"])
VIEW = GRID 4 > CARD(title, price) FROM DATA
RENDER VIEW

aecode flow examples/dashboard.ae

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📐 Syntax Reference

Operator / Primitiva	Descripción	Ejemplo
GET(url)	Petición HTTP + Parseo HTML	doc = GET("https://...")
>	Selector CSS	items = doc > ".product_pod"
@	Extracción de atributo o texto	(el > "a") @ "href" o @ "text"
|	Map funcional sobre colecciones	items | λi. { ... }
|?	Filtro funcional booleano	items |? λi. num(i @ "text") > 50
|*	FlatMap concurrente multihilo	urls |* λu. (GET(u) > ".pod")
CRAWL(url, fn, max)	Auto-paginación secuencial segura	CRAWL(url, λd. d > "li.next > a" @ "href", 5)
SCRAPE(...)	Extracción masiva estructurada	SCRAPE(url, selector, [fields])
GRID n > CARD(...)	Cuadrícula responsiva Tailwind	GRID 4 > CARD(title, price)
RENDER view	Compilación y apertura web	RENDER VIEW

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🧠 Philosophy: Mathematical Brevity > Verbosity

Programming languages designed in the 1990s were made for human keyboards. In 2026, code is generated by AI agents collaborating with humans.

• Tokens are compute.
• Tokens are latency.
• Tokens are cost.

AeCode treats code like mathematical equations: dense, declarative, composable, and crystal clear.

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📈 Star History

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🤝 Community & Support

• 🌟 Star the repository on GitHub
• 💬 Discuss on HackerNews and Reddit (r/Python)

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📄 License

Distribuido bajo la Licencia MIT. Consulta LICENSE para más detalles.