🧠 DeepThink AIOS

DeepThink AIOS is an enterprise-grade, fully local multi-agent AI Operating System that routes user queries across specialized neural pipelines for software engineering, theoretical mathematical reasoning, machine learning forecasting tournaments, 2-volume university textbook authoring, 3D physical semiconductor layout synthesis (from 180nm planar to 2nm GAAFET), and hardware-accelerated benchmarking — all running locally with dynamic hardware scaling from Intel iGPUs to NVIDIA H100s.

• 🎨 Human-Centered Modernized UI/UX — Production-grade glassmorphic interface engineered with Lucide SVG icons across all navigation and modal controls, a 30+ Design Token CSS architecture, a unified accessible <Modal> primitive, code blocks with syntax highlighting (vscDarkPlus) & line numbers, an interactive empty-state with 1-click starter prompt cards, dynamic "Jump to Section" heading navigation, and responsive mobile breakpoints (768px/900px).
• 🎓 Master 2-Volume Study Engine (🎓 Study) — Pedagogical textbook synthesis powered by DeepSeek-R1. Authors 15–20 page master reference books with centered display KaTeX formulas ($$ ... $$), embedded Mermaid architectural flowcharts, pedagogical alert callouts (> [!TIP], > [!IMPORTANT]), a 1-Page High-Yield Formula Cheat-Sheet, multi-dimensional comparison tables, a 10-Problem Solved Question Bank, and a Standardized University Mock Exam. Supports direct PDF/Slide ingestion.
• 🔮 Maximum Power Prediction Engine (🔮 Predict) — High-precision time-series forecasting across Financial Markets, Climate/Weather, Energy & Battery State-of-Health (SOH) degradation over 500 charge-discharge cycles, and Cloud Telemetry. Features a 14-Signal Alpha Feature Space, an 8-Algorithm Tournament with Bayesian Softmax Inverse-Loss Stacking ($\beta = 3.5$), and Conformal Prediction Probabilistic Uncertainty Bands ($80%$ & $95%$ corridors in Plotly).
• 🔬 Scientific Semiconductor EDA & 3D Physical Die Visualizer — Synthesizes synthesizable Verilog/SystemVerilog HDL and SPICE netlists across all process nodes (180nm Planar to 2nm RibbonFET / GAA Nanosheets with Backside Power Delivery). Supports 6 full silicon architectures with procedural WebGL/Three.js silicon die rendering and a live clock stepping toolbar.
• ⚡ Zero-Hallucination Category-Aware Mathematical Reasoning (⚡ Reason) — Solves complex theoretical derivations across General Relativity, Real/Complex Analysis, Quantum Mechanics, and General Mathematics. Features dynamic category prompt routing, publication-grade centered KaTeX display formulas ($$ ... $$), automatic line-break sanitization, and verified closed-form fallbacks (exact 9 Schwarzschild Christoffel symbols, Kretschmann scalar $K = \frac{48G^2M^2}{c^4 r^6}$, and the Bose-Einstein Riemann Zeta integral $\int_0^\infty \frac{x^3}{e^x - 1} dx = \frac{\pi^4}{15}$).
• 💻 Production-Grade Autonomous Coding Pipeline — Multi-phase software engineering with Big-O complexity optimization ($O(N)$ / $O(N \log N)$), automated AST Static Analysis Linting (SAST), strict type annotations, Google-style docstrings, C++17 shared mutex concurrency (std::shared_mutex, lock-free SPSC queues), and multi-language execution sandboxes.
• 🛡️ Enterprise Security & Air-Gap Hardening — Full sandbox protection with SSRF prevention (blocking private, loopback, and link-local CIDR ranges), Path Traversal guards in git workspace commits, Supply-Chain Auto-pip Allowlisting (restricted to ~35 vetted scientific packages), TarSlip/ZipSlip guards in archive extraction, and shell=False process safety.
• 📊 Benchmark Studio & Telemetry Dashboard — Parallel evaluation across 11 standard suites (HumanEval, MBPP, GSM8K, MATH, GPQA, AIME, MuSR, MMLU-Pro, SWE-bench Lite, SWE-bench Pro, SearchQA) with real-time scoring vs GPT-4o and Claude 3.5 Sonnet baselines, live throughput ($\text{tok/s}$), and JSON report exports.
• 🌐 100% Keyless Multi-Tier Web Search (🌐 Search & 🔬 Extreme) — Scrapes live financial quotes, real-time weather, and multi-source academic publications with deep synthesis without API keys.
• ⚡ Elastic VRAM Management (EVM) & DMA — Zero-cost dynamic model hot-swapping between System RAM and GPU VRAM with thread-safe progress locks and bounded SQLite vector memory recall (LIMIT 500).

flowchart TD
    %% ── TOP-LEVEL INGESTION ──
    USER([User Prompt / Image / PDF]) --> MODE_CHECK{"Pipeline Mode Selected?"}
    
    MODE_CHECK -->|🎓 Study| STUDY_PIPE["Study Pipeline: 2-Volume Master Curriculum + Mermaid + 10 Problems + Exam"]
    MODE_CHECK -->|🔮 Predict| PREDICT_PIPE["Predict Pipeline: 14-Signal Alpha Features + 8-Model Bayesian Tournament"]
    MODE_CHECK -->|🔬 Extreme| EXTREME_PIPE["Extreme WebSearch: Multi-Source Academic Survey"]
    MODE_CHECK -->|🌐 Search| SEARCH_PIPE["Simple Search: Live Real-Time Web Data"]
    MODE_CHECK -->|📊 Benchmark| BENCH_PIPE["Benchmark Studio: 11-Suite Parallel Worker Evaluation"]
    MODE_CHECK -->|Auto / Prompt| ROUTER["Fast-Path & Router Intent Classifier"]

    %% ── Intent Classification Branches ──
    ROUTER --> PATH_CODING["1. CODING (AST Linter + O(N) Complexity + Type Hints)"]
    ROUTER --> PATH_REASONING["2. REASONING (SymPy CAS Grounding & Kretschmann Scalar)"]
    ROUTER --> PATH_CHIP["3. CHIP DESIGN (2nm GAAFET, BSPDN & 3D Live Clock)"]
    ROUTER --> PATH_VISION["4. VISION & OCR"]
    ROUTER --> PATH_SIMPLE["5. DIRECT / CONVERSATIONAL"]

    %% ── Execution Pathways ──
    STUDY_PIPE --> STUDY_OUT["Master Textbook + 1-Page Cheat Sheet + 10 Solved Problems + Mock Exam"]
    PREDICT_PIPE --> PREDICT_OUT["8-Model Bayesian Stacking + 80%/95% Conformal Fan Chart"]
    BENCH_PIPE --> BENCH_OUT["Real-Time Throughput / Accuracy Telemetry vs Baselines"]
    PATH_CODING --> CODE_SB{"Execution Sandbox"} --> CODE_PASS["Verified Working Polyglot Code"]
    PATH_REASONING --> PAL_SB{"SymPy / Math Sandbox"} --> PAL_PASS["Verified KaTeX Proof ($$ ... $$)"]
    PATH_CHIP --> EDA_SB{"Icarus / Yosys / SPICE"} --> CHIP_OUT["Verilog Module + Interactive 3D Die Visualizer"]

git clone https://github.com/Arpit104147/DeepThink-AIOS.git
cd DeepThink-AIOS

# Launch servers (Backend on :8000, Web UI on :5173)
./start.sh

Open http://localhost:5173 in your browser.

Paste and run this complete Python script inside a single Kaggle Notebook cell:

# =========================================================================
# 🚀 DEEPTHINK-AIOS: KAGGLE BACKEND + CLOUDFLARE TUNNEL (CONTINUOUS RUNNER)
# =========================================================================

import os, subprocess, time, re, sys

# 1. Clone or Auto-Update Repository
if os.path.exists("/kaggle/working/DeepThink-AIOS"):
    os.chdir("/kaggle/working/DeepThink-AIOS")
    subprocess.run(["git", "pull", "origin", "main"], check=True)
else:
    os.chdir("/kaggle/working")
    subprocess.run(["git", "clone", "https://github.com/Arpit104147/DeepThink-AIOS.git"], check=True)
    os.chdir("/kaggle/working/DeepThink-AIOS")

# 1.5 Install System EDA Chip Design Tools (Icarus Verilog, Yosys, NGSPICE, KLayout)
print("🔬 Installing System EDA Chip Design Tools (iverilog, yosys, ngspice, klayout)...", flush=True)
subprocess.run(["apt-get", "update", "-y", "-q"], check=False)
subprocess.run(["apt-get", "install", "-y", "-q", "iverilog", "yosys", "ngspice", "klayout"], check=False)

# 2. Install dependencies & Pre-compile CUDA llama-cpp-python for Kaggle GPU
print("⚡ Installing requirements & pre-compiling CUDA llama-cpp-python for Kaggle GPU...", flush=True)
subprocess.run([sys.executable, "-m", "pip", "install", "-r", "requirements.txt", "-q"], check=True)

try:
    import torch
    if torch.cuda.is_available():
        print("🔥 Pre-installing CUDA-accelerated llama-cpp-python for Kaggle GPU...", flush=True)
        env = os.environ.copy()
        env["CMAKE_ARGS"] = "-DGGML_CUDA=on"
        env["FORCE_CMAKE"] = "1"
        subprocess.run([
            sys.executable, "-m", "pip", "install",
            "llama-cpp-python", "--force-reinstall", "--no-cache-dir", "-q"
        ], env=env, check=False)
except Exception as e:
    print(f"⚠️ CUDA setup note: {e}")

# 3. Download Cloudflare Tunnel binary
subprocess.run(["wget", "-q", "https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64", "-O", "/tmp/cloudflared"], check=True)
subprocess.run(["chmod", "+x", "/tmp/cloudflared"], check=True)

# 4. Launch FastAPI Backend
print("⏳ Launching FastAPI Backend on Port 8000...", flush=True)
backend_proc = subprocess.Popen([sys.executable, "-m", "uvicorn", "backend.app:app", "--host", "0.0.0.0", "--port", "8000"])

# 5. Create Cloudflare Tunnel
print("🌐 Creating Secure Cloudflare Tunnel...", flush=True)
tunnel_proc = subprocess.Popen(
    ["/tmp/cloudflared", "tunnel", "--url", "http://localhost:8000"],
    stdout=subprocess.PIPE,
    stderr=subprocess.STDOUT,
    text=True
)

time.sleep(4)

# 6. Extract & Print Public URL
public_url = None
for line in tunnel_proc.stdout:
    match = re.search(r"https://[a-zA-Z0-9-]+\.trycloudflare\.com", line)
    if match:
        public_url = match.group(0)
        print("\n" + "="*72, flush=True)
        print("🎉 YOUR KAGGLE BACKEND PUBLIC URL:", flush=True)
        print(f"👉 {public_url}", flush=True)
        print("="*72, flush=True)
        print("📌 COPY the URL above and paste it into your local Laptop Frontend!", flush=True)
        print("="*72 + "\n", flush=True)
        break

# 7. Continuous Heartbeat to keep Kaggle alive overnight
start_time = time.time()
print("⚡ Backend is ACTIVE & serving requests continuously...", flush=True)

try:
    while True:
        time.sleep(120)
        elapsed_min = int((time.time() - start_time) // 60)
        print(f"💓 [HEARTBEAT - {elapsed_min}m elapsed] DeepThink-AIOS Backend Running | URL: {public_url}", flush=True)
except KeyboardInterrupt:
    print("Stopping server...", flush=True)
    backend_proc.terminate()
    tunnel_proc.terminate()

Copy the printed https://xxxx.trycloudflare.com URL, open http://localhost:5173 in your local browser, click Settings (⚙️), and paste the URL into Server URL.

The system includes automated pre-flight audit suites that test every Python file, frontend build, mathematical post-processor, and router fast-path:

# 1. Run 6-Stage Deep Line-by-Line System Audit
python3 backend/test_deep_line_by_line_audit.py

# 2. Run 7-Subsystem End-to-End Project Audit
python3 backend/test_full_project_audit.py

# 3. Test React 18 / Vite Production Bundle
npm run build --prefix frontend

• Backend: FastAPI, Uvicorn, Python 3.10+, PyTorch, Vulkan SDK, llama-cpp-python, ChromaDB, PyPDF, Scikit-Learn, Icarus Verilog, Yosys, NGSPICE, SymPy, NumPy, Pandas
• Frontend: React 18, Vite 8, Lucide React (lucide-react), React Syntax Highlighter (react-syntax-highlighter), KaTeX Typography, Plotly.js, Three.js / WebGL, CSS Design Tokens
• Hardware Acceleration: Vulkan Compute (NVIDIA, AMD, Intel iGPU/dGPU), NVIDIA CUDA, Apple Metal MPS, Multi-Core CPU Fallback
• Security: Safe SAST Execution Sandbox, SSRF Defense, Path Traversal Defense, Supply-Chain Package Allowlisting, TarSlip/ZipSlip Guards

GNU Affero General Public License v3.0 (AGPL-3.0) — see LICENSE for details.

This project is licensed under the GNU AGPLv3. Any modification, redistribution, or remote network interaction (e.g., deploying or running DeepThink AIOS over a computer network, demoing as a service, or participating in hackathons/competitions) strictly requires providing access to the complete Corresponding Source code of the modified version to all users under the exact same AGPL-3.0 terms, with all original copyright and attribution notices preserved.