Analyze the Request:

• Role: Technical Writer.
• Subject: Silvio Chessari.
• Output: GitHub README.md markdown file.
• Requirements:
  • Output only the markdown (no preamble/explanation).
  • Style: English, technical, precise, no fluff.
  • Technical Depth: Focus on LLM inference research (EAGLE, speculative decoding, MoE routing), running LLMs on unsupported GPUs (Tesla P40 sm_61, CUDA workarounds), self-built AI infrastructure, embedded systems.
  • Structure:
    1. Centered header.
    2. 3-line technical bio.
    3. Compact stack table.
    4. 4 Project sections (LLM Research / AI Infrastructure / Tools & Automation / Embedded & Hardware).
    5. GitHub stats (image placeholder).
  • Project Format: [name](https://github.com/chessarisilvio/slug) + 1-line technical description.
  • Repo Slugs: Derived from project names (lowercase, hyphens).
  • Emojis: Max 2 (header only).
  • Length: 90–120 lines total.
• Source Data: Provided "vault data" (Obsidian notes style) containing project lists, taxonomy, and descriptions.

Process Source Data & Extract Key Info:

• Bio Keywords: AI infrastructure, LLM inference, Tesla P40, CUDA compatibility, speculative decoding, embedded systems, automation.

• Stack: C++, Python, ESP32, CUDA, Node.js, Flask, GGUF, EXL2.

• Projects Mapping:

  • LLM Research:

    • add-video-input-support-to-llamacpp-mtmd: Video input for llama.cpp (CMake, OpenCV).
    • automated-exl2-conversion-validation-pipeline: EXL2 conversion, Qwen3.5 MoE, mixed quantization.
    • auto-quantization-pipeline-gguf: Automated GGUF quantization, benchmarking on P40/RTX3050.
    • bias-personalizzato-per-whisper-locale: Whisper bias injection via JSON/TSV.
    • qwen-image-edit-inpainting-workflow: ComfyUI workflow for Qwen.

  • AI Infrastructure:

    • ai-dashboard: Web dashboard (port 9190), AGENDA.md, 35B local generation.
    • automazione-bootwatchdog-ai-avanzato: Watchdog for llama-stack, VRAM monitoring, Telegram notifications.
    • git-mcp-server-per-agenti-ai: MCP server for Git operations.
    • openclaw: Node.js Ollama gateway.
    • nllb-self-hosted-per-testo-multilingua: Offline NLLB-200, OCR+MT pipeline.

  • Tools & Automation:

    • sviluppati/sistema-di-benchmarking-automatizzato-per-nuovi-mo: GGUF benchmarking on P40/RTX3050.
    • reddit-monitor: Subreddit monitoring, feeding AGENDA.md.
    • mtop-htop-per-monitoraggio-llm-locale: htop-like LLM monitor, real-time GPU/token metrics.
    • silvioprint: Telegram content bot.
    • cache-plan-logic-for-recurring-queries: Semantic caching for LLM plans.

  • Embedded & Hardware:

    • controller-termico-proattivo-esp32: Proactive fan control for Tesla P40, ESP32.
    • ai-model-selector-physical-controller: ESP32 rotary encoder, OLED, HID/Gateway integration.
    • digital-thermal-lcd: Thermalright LCD, HID interface.
    • cuda-sm61-compatibility-shim: Pascal (P40) compatibility.

Drafting Content - Section by Section:

• Header: Center <h1> with max 2 emojis.

  • "Silvio Chessari 🧠 ⚙️" (Brains + Gears fits the tech/infra vibe).

• Bio: 3 lines.

  • L1: Focus on LLM inference optimization and proprietary hardware workarounds (Tesla P40/sm_61).
  • L2: Building self-hosted AI infrastructure with automation and real-time monitoring pipelines.
  • L3: Developing embedded interfaces and tools for local model deployment and hardware control.

• Stack Table: Compact.

  • Languages: C++, Python, Bash.
  • Hardware/Infra: Tesla P40, ESP32, CUDA, GGUF, EXL2.
  • Tools: Ollama, Node.js, Flask, OpenCV, ComfyUI.

• Project Sections:

  • LLM Research & Inference

    • Auto-Quantization Pipeline GGUF -> Automated GGUF quantization, benchmarking, and validation on Tesla P40 and RTX 3050.
    • Automated EXL2 Conversion Pipeline -> Toolchain for Qwen3.5 MoE EXL2 conversion, quantization tuning, and validation.
    • Video Input to llama.cpp -> Implementation of video input via CMake patches and OpenCV integration for multimodal inference.
    • Cache Plan Logic -> Semantic caching for LLM action plans to reduce latency on local 35B models.
    • Whisper Bias Module -> Customizable JSON/TSV bias injection and contextual correction for local Whisper transcription.

  • AI Infrastructure & Monitoring

    • AI Dashboard -> Local web interface (port 9190) for GPU monitoring, AGENDA management, and worker orchestration.
    • Advanced AI Watchdog -> Systemd watchdog monitoring llama-stack VRAM and TPS with Telegram failover notifications.
    • Git MCP Server -> Model Context Protocol server enabling Git operations for local AI agents.
    • mtop: LLM Monitor -> Real-time htop-like terminal utility for local LLM token throughput and GPU utilization.

  • Tools & Automation

    • GGUF Benchmarking System -> Automated benchmarking suite for GGUF models across legacy P40 and consumer GPUs.
    • Reddit Monitor -> Script that scans AI/tech subreddits to populate the AGENDA.md with fresh ideas every 3 hours.
    • Bot Short -> C++ Telegram bot generating AI-driven vector graphics (SVG/EPS) via serverless pipeline.
    • OpenClaw -> Node.js gateway providing standardized access to local Ollama instances.

  • Embedded & Hardware

    • Proactive Thermal Controller -> ESP32-based fan controller for Tesla P40 utilizing predictive algorithms and sensor data.
    • AI Model Selector -> Rotary encoder and OLED interface for physical local model switching via HID/gateway.
    • CUDA sm_61 Compatibility Shim -> Compatibility layer to enable modern LLM runtimes on legacy Pascal architecture GPUs.
    • Digital Thermal LCD -> HID interfaced LCD display for real-time thermal readings of hardware components.

• GitHub Stats:

  • Standard layout: ![stats](...), ![streak](...)

Review and Refine:

• Check Constraints:
  • Markdown only? Yes.
  • English/Technical? Yes.
  • Deep tech shown? Yes (Pascal, EXL2, GGUF, MoE, ESP32 HID).
  • Structure correct? Yes.
  • Slugs correct? Yes.
  • Line count check:
    • Header: 3
    • Bio: 3
    • Stack: 1 (table) + header = 3
    • Projects: ~15 items lines + headers = ~30
    • Stats: 2
    • Spacing: ~20
    • Total: ~60-70 lines? A bit short. I should add more detail to the descriptions or ensure the formatting (list spacing) fills it out visually to hit the 90-120 range without adding fluff text. I will ensure standard markdown spacing or add a brief "Connect" section if needed, but the requirement implies dense content.
    • Let's maximize the line count by keeping the list format clean but utilizing the full project list provided in the vault.
    • Adjustment: The prompt asks for Max 2 emojis. I used 2. It asks for 4 project sections. I have 4. It asks for 90-120 lines. I need to make sure I don't compress the list too much. I will put each project on its own line in the markdown source.

Final Polish of Descriptions:

• Make them sound sharper.
• Instead of "Toolchain for...", "C++ core and Python CLI for fixing Qwen3 architecture and converting to EXL2."
• "Watchdog that monitors..."

Constructing the Markdown:

<div align="center">
  <h1>Silvio Chessari 🧠 ⚙️</h1>
</div>

Focused on LLM inference optimization, speculative decoding, and speculative execution on unsupported architectures (Tesla P40/sm_61).
Engineering self-hosted AI infrastructure featuring automated pipelines, CUDA workarounds, and real-time monitoring embedded systems.
Developing local-first solutions for MoE routing, multimodal inputs, and hardware-controlled model orchestration.

### Technical Stack

| **Core** | **Hardware** | **Infra** | **AI/ML** |
| :--- | :--- | :--- | :--- |
| C++, Python, Bash | Tesla P40, ESP32, | Docker, Systemd, | GGUF, EXL2, |
| Node.js, CMake | CUDA (sm