Skillget

the_ai_engineer_capstones

mcp
Guvenlik Denetimi
Uyari
Health Uyari
  • No license — Repository has no license file
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 5 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This project is an educational repository containing a four-week AI engineering curriculum. It progresses from foundational machine learning and deep learning concepts to an advanced final capstone that features an MCP-based agentic incident command system with telemetry and replay capabilities.

Security Assessment
The overall risk is rated as Low. An automated code scan of 12 files detected no dangerous patterns, hardcoded secrets, or requests for dangerous system permissions. Because the final week involves an MCP-based server/client workflow, it naturally relies on network communications. However, there are no indications of unauthorized data access or malicious background shell execution.

Quality Assessment
The project is very actively maintained, with the most recent code push occurring today. However, community visibility and trust are currently minimal, as the repository has only 5 GitHub stars. Additionally, the automated scan flagged a missing formal license file. While the README badge implies "Educational Use," the lack of a standard legal license (such as MIT or Apache) is a warning flag if you plan to reuse or modify the code extensively in a production environment.

Verdict
Safe to use for learning and experimentation, but use with caution regarding reuse rights due to the missing formal license.
SUMMARY

AI Engineer capstones progressing from optimization and deep learning to transformers and a final agentic MCP-based incident command system with telemetry and replay.

README.md

The AI Engineer

Weeks 1–3 are notebook-centered ML/DL deliverables.
Week 4 is an MCP-based agentic systems capstone whose primary submission path is the remote MCP server/client workflow, with a local deterministic mirror retained as supporting evidence for replay, debugging, and reviewer validation.

 TAE Logo

Weekly Capstones Overview

Week Capstone Primary artifact Access / delivery mode
1 Gradient Descent Optimization gd_capstone.ipynb Colab-ready notebook — Open in Colab
2 Backpropagation week02_master_capstone.ipynb Colab-ready master notebook — Open in Colab
3 Tiny Transformer week03_master_capstone.ipynb Colab-ready master notebook — Open in Colab
4 Agentic Incident Command demo_remote.py · mcp_client.py · remote_agent.py · mcp_server.py Remote MCP server/client workflow. The local deterministic runner is supporting evidence.

Week 04 is the primary capstone deliverable and represents the full agentic system: MCP + OPAL + telemetry + replay.

Repository Structure

the_ai_engineer_capstones/
├── assets/
├── capstones/
│   ├── week01_gd_optimization/   # GD optimization notebook + figures
│   ├── week02_backprop/          # Backprop notebook + saved weights
│   ├── week03_transformers/      # Tiny transformer training + checkpoint
│   └── week04_agentic_incident_command/
│       ├── 01_tool_harness/      # Warm-up: minimal MCP server/client
│       ├── 02_incident_command_agent/  # Primary capstone (graded)
│       ├── artifacts/            # Telemetry JSONL + sample summary
│       └── README_week04_capstone.md
├── pytest.ini
├── README.md
└── requirements.txt

Week 4 Verification Entry Points

From the repository root:

# Terminal A: start the MCP server
python capstones/week04_agentic_incident_command/02_incident_command_agent/mcp_server.py

# Terminal B: run the primary graded remote MCP path
python capstones/week04_agentic_incident_command/02_incident_command_agent/demo_remote.py

# Replay a telemetry trace
python capstones/week04_agentic_incident_command/02_incident_command_agent/cli.py --replay capstones/week04_agentic_incident_command/artifacts/telemetry.jsonl

# Supporting deterministic local run
python capstones/week04_agentic_incident_command/02_incident_command_agent/cli.py

For Week 4 details, telemetry, guardrails, and architecture notes, see the dedicated Week 4 README:
capstones/week04_agentic_incident_command/README_week04_capstone.md

Telemetry logs and incident summaries for inspection are stored in capstones/week04_agentic_incident_command/artifacts/.

Environment & Reproducibility

This repository uses a lightweight pip + venv workflow and targets Python 3.11.

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Notes:

  • Weeks 1–3 can be reviewed directly in GitHub and opened in Colab from the links above.
  • Week 4 is designed for a local Python environment because it depends on a live MCP server/client interaction and replayable telemetry artifacts.

License (Educational Use)

All content in this repository is provided for educational and illustrative purposes only.
No guarantees are made regarding correctness, performance, reliability, or suitability for any production environment.

© 2025 Francisco Salazar

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