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🚀 NeuralAI Development Roadmap

Version Target: 6.0 (The Workstation Pivot) + 7.2 Resilient Runtime Last Updated: July 15, 2026

✅ Completed Milestones (NeuralAI Legacy)

Phase 0: Core System ✓

  • SmolLM2-360M base model fine-tuned with QLoRA

  • Chat streaming (SSE) working

  • Web UI deployed (NeuralAI → NeuralAI v1.0)

  • Unified Service Migration: Consolidated Model + UI + Terminal into file neural_core_service.py

  • Fixed Chat streaming and Terminal consistency

  • Improved SSE responsiveness with tool execution indicators and non-blocking stream chunks.

Phase 1: Tool Ecosystem ✓

  • Code Execution Sandbox, File Manager, Web Fetcher, DB Connector, Git Assistant

  • Tool detection and routing in chat

Phase 2: DPO Alignment ✓

  • DPO training pipeline implemented

  • Preference dataset expanded to 244 pairs (v12.0)

  • Memphis Culture & Founder Context integration

🏗️ Phase 3: NeuralAI Evolution (In Progress)

1. Workstation Orchestration

  • Establish distinction: NeuralAI (Model) vs. NeuralAI (Hub)

  • UI Overhaul: Added "Workstation Dashboard" tab with project/model/shell status

  • Robust Multi-Turn Context Support (10-message sliding window)

  • Integrated Multi-Modal Speech-to-Speech (Gemini Live + ElevenLabs Fallback)

  • Transition UI from Chat-Only to Multi-Panel Workstation (Expand dashboard features)

  • Implement System-Wide Context Layer

  • Add "Vibe Stack" Workflow Registry

2. Neural Knowledge Graph

  • Implement Persistent Memory (Graph-based)

  • Automate Infrastructure Learning

  • Sync with Supermemory

📊 System Status

  • Main Service: READY & RESILIENT (webui_service.py v7.2.0 — auto-restart, memory watchdog, ZO-native inference backend)
  • Voice Service: READY (ElevenLabs v2 Migrated)
  • Model: SmolLM2-360M-Instruct + DPO v15.0 (Deployed)
  • Context: System-wide (Expanding)

Latest DPO Run (v15.0)

  • Training samples: 597 (expanded from 302)
  • Epochs: 3
  • Steps: 450
  • Final training loss: 0.305
  • Reward margin: ~0.5 → ~3.5
  • Hardware: Apple Silicon MPS (MacBook Air M4)
  • Duration: 730.5s (~12 min)
  • Completed: 2026-07-11 20:00 UTC
  • Adapter: live on HF Subject-Emu-5259/NeuralAI

Deployment & Inference (updated July 15, 2026)

  • Host: ZO Computer (Free plan, 4 GB RAM) at neuralai-deandrewharris.zocomputer.io
  • Inference backend: LLM_BACKEND=zo → ZO native /zo/ask using the user's own BYOK model (byok:0d3567f7-f521-42b0-8adf-65c9b036cf89). Uses 0 MB local RAM.
  • Why not local: Loading PyTorch + SmolLM2-360M on a 4 GB box used ~6.2 GB → OOM-kill loop that paused the service. The ZO backend removes that dependency entirely (see docs/INCIDENT-2026-07-14-NEURALAI-PAUSES.md).
  • Local PyTorch backend: still available via LLM_BACKEND=local on GPU/Colab-class machines (≥8 GB RAM) for offline/private inference.
  • Resilience: supervisor auto-restart, /api/health keepalive, and a memory watchdog that runs GC before any OOM. Service verified stable after the July 15 fix (free RAM returned to ~3 GB).

🎯 Next Steps (Priority Order)

1. Training Data Expansion

Status: In progress (500+ samples reached, target 1000+)

Categories to expand:

  • Symbolic Logic & Formal Proofs: +50 samples
  • Security & Vulnerability Analysis: +50 samples
  • Multi-Step Algorithmic Reasoning: +50 samples
  • Advanced Mathematics (Calculus/Linear Algebra): +50 samples

2. Evaluation Suite

Status: Created, pending execution

Benchmarks:

  • Code correctness: Generated code runs
  • Response helpfulness: Quality scoring
  • Safety: Refuses harmful requests
  • Latency: Inference speed

🚀 Future Phases (The Agentic Horizon)

Phase 4: Agentic Autonomy & Computer Use

Goal: Transition from "Assistant" to "Operator"

  • Browser Agent Integration: Implement autonomous web navigation and interaction (Computer Use).

  • Multi-Agent Orchestration: Ability to spawn and manage specialized sub-agents for parallel task execution.

  • Long-Horizon Planning: Implement hierarchical planning for tasks requiring 10+ steps.

  • Third-Party App Integration: Direct agentic control over productivity tools (Calendar, Email, CRM).

Phase 5: Universal Knowledge Integration (The "World-Brain" Training)

Goal: Massive expansion of general-world intelligence and cultural context.

  • Natural World: Plants, animals, creatures, ecosystems, and biology.

  • Humanity & Culture: History, religions, beliefs, sociology, and anthropology.

  • The Arts: Music theory, cinematic history, fine arts, and literature.

  • Global Systems: Geography, geopolitics, economics, and planetary sciences.

Phase 6: Model Capability Upgrades

Goal: Integration of frontier reasoning and multimodal capabilities.

  • Deep Reasoning Integration: Implement "Think" modes for complex mathematical and logical deduction.

  • Native Multimodal Understanding: Unified processing of video, audio, and images in a single context window.

  • Test-Time Compute Optimization: Optimize inference to allow the model to "think longer" for harder problems.

📊 Data Files 

data/
├── train.jsonl              # 347 original samples
├── train_v3.jsonl           # 404 samples (latest)
├── train_dpo.jsonl          # 13 DPO pairs
├── train_dpo_expanded.jsonl # 31 DPO pairs
└── train_expanded.jsonl     # 363 samples

📁 Project Structure 

NeuralAI/
├── checkpoints/final_model/    # LoRA adapter
├── data/                       # Training data
├── eval/benchmarks.py          # Evaluation suite
├── from-scratch/web_ui/        # Flask app + static files
│   ├── app.py                  # Main Flask server
│   ├── neuralai_engine.py      # Model + tools
│   └── neuralai_router.py      # Routing logic
├── tools/                      # Tool implementations
│   ├── code_sandbox.py
│   ├── file_manager.py
│   ├── web_fetcher.py
│   ├── db_connector.py
│   └── git_assistant.py
└── training/                   # Training scripts
    ├── train_dpo.py
    ├── generate_training_v3.py
    └── NeuralAI_TPU_Training.ipynb

🔗 Quick Links

📝 Commands 

# Start the service
cd /home/workspace/Projects/NeuralAI/from-scratch/web_ui
python3 app.py

# Generate v5 DPO data
python3 training/generate_dpo_v5.py

# DPO training (currently running in background)
python3 training/train_dpo.py

Next Session Goals:

  1. Run evaluation benchmarks
  2. Expand training data to 1000+ samples
  3. Request GPU or prepare Colab notebook for DPO training