ADAM EEHAN — CREATOR OF AIRA AI

// AIRALANG v1.4.6 — The AI-Native Programming Language
// Created & Architected by Adam Eehan (Aira Group Of Technology)
import "ai";
import "youtube";

// Self-healing AST compiler automatically resolves missing syntax
let prompt = "Synthesize neural compiler AST optimizations";
print("[AIRALANG] Invoking Multi-Provider Neural Engine...");

let response = ai.generate(prompt);
print("Generated Solution: " + response);

// Native APM & YouTube Integration Module
let tutorials = youtube.search("AiraLang AI compiler tutorials");
print("Discovered Tutorials: " + tutorials);

import { AiraCLIEngine } from 'aira-cli-kit';

// Initialize AIRA CLI Kit Core Engine by Adam Eehan
const aira = new AiraCLIEngine({
  name: 'aira-cli',
  version: '2.4.0',
  provider: 'openai-compatible',
  apiKeyVault: process.env.AIRA_API_KEY,
  securityLevel: 'AES-256-GCM'
});

aira.registerCommand('chat', async (args) => {
  console.log('[AIRA CLI] Connecting to Quantum Neural Endpoint...');
  const response = await aira.streamChat(args.prompt);
  console.log(response);
});

aira.parse();

# Sudan Offline Cellular Core & Educational AI CDN
# Designed & Engineered by Adam Eehan (Aira Group Of Technology)
import socket
import threading
from open5gs import CoreNetworkSession

class SudanTelecomEducationalNode:
    def __init__(self, node_ip="10.45.0.1", port=8080):
        self.node_ip = node_ip
        self.port = port
        self.session = CoreNetworkSession(mcc="634", mnc="01") # South Sudan MNO Core

    def start_offline_ai_cdn(self):
        print(f"[AIRA CORE] Starting Offline Educational AI Node at {self.node_ip}:{self.port}")
        # Zero-cost VoIP routing & local student packet switching
        self.session.bind_voip_gateway(zero_cost=True)

if __name__ == "__main__":
    node = SudanTelecomEducationalNode()
    node.start_offline_ai_cdn()

# AIRA-AI: Small Language Model (SLM) Core & Transformer Architecture
# Engineered by Adam Eehan (Aira Group Of Technology)
import torch
import torch.nn as nn
from torch.nn import functional as F

class Head(nn.Module):
    """Single causal self-attention head."""
    def __init__(self, head_size: int):
        super().__init__()
        self.key   = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        B, T, C = x.shape
        k = self.key(x)
        q = self.query(x)
        scale = k.shape[-1] ** -0.5
        wei = q @ k.transpose(-2, -1) * scale
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
        wei = F.softmax(wei, dim=-1)
        return wei @ self.value(x)

class MultiHeadAttention(nn.Module):
    """n_head parallel attention heads, results concatenated."""
    def __init__(self, num_heads: int, head_size: int):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
        self.proj  = nn.Linear(num_heads * head_size, n_embd)

    def forward(self, x):
        return self.proj(torch.cat([h(x) for h in self.heads], dim=-1))