Master Development Timeline

Tracking the engineering progression and feature milestones of the AdenLab Home AI Assistant framework.

DEPLOYED September 2026

Milestone 0: Multi-Node Infrastructure Base

Deploying the foundation network, server hardware, and public web hosting pipeline layout files entirely from scratch.

  • Configure isolated TrueNAS 2 TB storage pools and secure SMB network shares.
  • Install clean Proxmox VE hypervisor routing kernels on dedicated Alienware host targets.
  • Launch the Git pipeline code engine to auto-deploy changes smoothly to Cloudflare Pages.
  • Configure secure Cloudflare Tunnels on the [Original Proxmox Host] to map remote subdomains for Firefly III, Uptime Kuma, Trilium Notes, and Stirling PDF.
  • Implement Cloudflare Access Zero Trust OTP validation alongside custom Service Token bypass headers to authenticate mobile apps securely.
DEPLOYED October 2026

Milestone 1: The Design Workspace Interface

Constructing a comprehensive, modern visual front-end dashboard canvas for the home assistant engine.

  • Write semantic HTML5 layout panes to split chat zones from hardware grids.
  • Code responsive CSS Flexbox and Grid cards to represent smart devices.
DEPLOYED Target: Phase 1

Milestone 2: The Interactive Client Engine

Using vanilla JavaScript (ES6+) to build a frontend state engine driving active dashboard UI updates.

  • Capture real-time user keystrokes and dynamic append formatted chat bubble strings.
  • Implement asynchronous fetch logic loops to stream mock data packets with realistic network latency.
DEPLOYED Target: Phase 2

Milestone 3: The Home Assistant Local Proxy

Spinning up local smart home services and scripting a dedicated Python 3.12+ data collection runtime agent.

  • Deploy a Home Assistant container OS instance within an isolated Proxmox environment.
  • Write Python network scripts using modern package management (uv) to scrape device states via REST APIs.
  • Securely authenticate and append JSON telemetry logs directly to the mapped TrueNAS SMB share.
FUTURE FEATURE Target: Phase 2

Milestone 4: The IoT Analytics Data Cruncher

Constructing an automated data-cleansing pipeline to analyze network tracking history.

  • Leverage Pandas and NumPy to clean raw, unstructured text datasets mounted from network storage.
  • Compute room power metrics and render dynamic analytics reporting tables straight onto a web route.
FUTURE FEATURE Target: Phase 3

Milestone 5: The Edge Intent Classification Pipeline

Training and deploying your first core localized Machine Learning model completely from scratch.

  • Convert text inputs into mathematical token blocks using Scikit-Learn feature vectorizers.
  • Train a Logistic Regression classification model to route text commands into distinct logical pathways.
FUTURE FEATURE Target: Phase 4

Milestone 6: The Autonomous Full-Stack AI Assistant Workspace

The flagship capstone assembly. Orchestrating UI, hardware networks, and local accelerated intelligence models.

  • Build a fast asynchronous FastAPI backend server web route to link frontend views to backend logic models.
  • Configure NVIDIA GPU pass-through across the hardware hypervisor kernel to accelerate open-source LLMs via CUDA.
  • Implement background cron-scheduler logic loops (APScheduler) paired with smart plug telemetry to detect incomplete habits (slow cooker, chores, self-care routines).
  • Deploy behavioral psychology system prompting vectors to output proactive, anti-procrastination mobile push notifications based on live house states.
  • Deploy custom Python automation tools alongside Cloudflare Access sandboxing rules to shield live house circuits.
FUTURE FEATURE Target: Phase 5

Milestone 7: Autonomous Agentic Research & Voice Interface

Expanding local assistant capabilities with autonomous "Teach-First" deep research loops, ETA estimations, and voice interaction.

  • ReAct Search & Tool-Use Pipeline: Implement autonomous search loops via self-hosted SearXNG for real-time knowledge gap resolution.
  • Universal "Teach-First" Mentor Loop: Enforce FastAPI system prompts to generate architectural breakdowns before producing code artifacts.
  • Research ETA Estimator: Dynamic metadata generation to calculate and display live countdown timers during background web research.
  • Wyoming Protocol Voice Stack: Integrate local openWakeWord, Faster-Whisper, and Piper using modular adapters.