🐴🔥
newOS4 v4.6.10
System Review & Custom Model Training Proposal
22 Core Modules
21 Ext Modules
v4.6.10
● System Online
44 Modules Loaded
0 JS Errors
Prepared for review · July 2026
🧠 What is newOS4?
newOS4 is a browser-based operating system with a built-in AI agent. It runs entirely in the browser — no server-side compute needed. Think of it as a self-contained, self-aware desktop environment where:
- The AI lives inside the OS — not as a chatbot, but as the system's brain
- Everything is a terminal command — 90+ commands across 43 modules
- It can modify itself — view, edit, test, and ship its own source code
- It has memory — session history, user profile, skill library, memory graph
- It can spawn agents — multi-agent orchestration for parallel work
- It self-heals — DebugSupervisorModule catches & classifies errors, suggests fixes
🏗️ System Architecture
The system is built in layers. Each layer can talk to the one below, but the AI sits on top and can access everything.
AI Layer — Agent Loop · LLM Router · Smart Context
glm-5.2 · gemma4:31b · mistral-large-3:675b · minimax-m3
↓ ↑
Core Modules (22) — Terminal · VFS · AI Assistant · Config · Security
Debug Supervisor · Neural VFS · Ollama · Orchestrator · Agent Registry · SearXNG · Server Monitor · Secret Vault
↓ ↑
Extension Modules (21) — Editor · Shell · Cron · WebDAV · Voice · Mindmap
Debug Panel · Floating UI · Media · JS Console · File Manager · Theme · Tutorial · WPM
↓ ↑
Virtual File System — 296 files · 149 dirs · 836KB
localStorage-backed · WebDAV sync · Neural search index
The AI agent sits at the top and can read/write any layer. This is what makes it self-aware and self-modifying.
⚡ Full Capability Map
🖥️ System & OS
Terminal (90+ cmds)
Virtual FS
Config Manager
Security Levels
Secret Vault
Module Loader
Debug Supervisor
🤖 AI & Agents
AI Assistant (Agent Loop)
Ollama Integration
Multi-Model Router
Agent Registry
Orchestrator
Lease System
Smart Context Profiles
🔧 Development
Code Editor
Module View/Edit/Test
JS Console
Diff Tool
Shell (script/export)
WPM Package Manager
🌐 Web & Research
Web Search (SearXNG)
Web Fetch
URL Content Extract
💾 Data & Sync
WebDAV Cloud Sync
Backup/Restore
Crypto + Gzip
File Manager
🎨 UI & Productivity
Mindmaps (SVG)
Floating UI
Voice Control
Themes
Tutorials
Simple GUI
Chat Studio
🛡️ Self-Healing
Error Interception
Root-Cause Analysis
Auto-Fix Pipeline
Self-Diagnostic
Health Check
🤖 The AI Engine
The AI is not a simple chatbot. It's a full agent loop — it thinks, plans, uses tools, observes results, and iterates.
1. User Request
Natural language → system prompt + context + memory
2. Smart Context Selection
8 profiles: coding, debug, creative, technical, chat, system-admin, research, security
3. Tool Selection & Execution
40+ tools available — filesystem, web, modules, agents, VFS, skills, etc.
4. Observation
Tool results fed back → analyze → decide next action
5. Multi-Agent Delegation
Spawn workers (auto-learner, kernel-architect, searcher) for parallel tasks
6. Response + Memory
Answer user → save session → update memory graph → auto-improve
📊 Current LLM Usage (7 days)
| Model | Requests | Tokens |
|---|
| glm-5.2:cloud | 214 | 4.99M |
| minimax-m3:cloud | 219 | 2.60M |
| gemma4:31b-cloud | 35 | 219K |
| mistral-large-3:675b | 32 | 237K |
All inference via Ollama (self-hosted API). Zero external API costs.
👥 Multi-Agent System
newOS4 can spawn specialized worker agents that run tasks in parallel. Each agent has its own role and capabilities.
🔍 Searcher
Web research & data gathering
web_searchread_file
🏗️ Kernel Architect
Code writing & system changes
write_filerun_command
📚 Auto-Learner
Pattern extraction & learning
view_modulewrite_file
How it works
- Agent Registry — agents register with capabilities and contracts
- Lease System — tasks assigned via time-limited leases
- Orchestrator — spawns, monitors, and kills agents
- A2A Contracts — agents communicate via structured contracts
- Failure Recovery — auto-restart policies, failure counting
Current state: 3 agent types registered, 35 lifetime spawns. All idle/sleeping — ready for tasking.
🛡️ Self-Healing System (NEW in v4.6.10)
The DebugSupervisorModule gives the system a full immune system — it catches errors, classifies them, finds root causes, and can auto-apply fixes.
1. Error Interception
console.error · window.onerror · unhandledrejection · fetch failures · tool errors
2. Classification
Type (ReferenceError/TypeError/HTTP/timeout) · Severity (fatal/error/warning) · Category (module/network/tool/security/vfs)
3. Dedup & Persistence
Jaccard similarity 0.35 dedup · LRU eviction · localStorage-backed · quota monitoring
4. Root-Cause Analysis
Traces: tool fail → module not loaded · HTTP → network · ReferenceError → bundle
5. Auto-Fix Pipeline
Confidence-gated: 0.7 = suggest to AI · 0.9 = auto-apply · fixes injected into AI system prompt
6. Self-Diagnostic
_healthy flag · fallback to original error log · health check command
6 terminal commands: debug-stats · debug-history · debug-analyze · debug-trends · debug-fix · debug-health
🧬 Memory & Learning
The system doesn't start fresh each time. It has multiple memory systems:
📦 Session Memory
- Save/load sessions by date
- Recent session list
- Aggregate stats per session
- Terminal output history
👤 User Profile
- Preferred model
- Custom preferences
- Persistent across reloads
- Forgetting supported
🎓 Skills Library
- 17 skills across 6 categories
- Procedural memory (how-to guides)
- Auto-loaded by context
- Create/patch/list/search
🕸️ Memory Graph
- 83 nodes, 88+ edges
- File access patterns
- Task correlation
- Auto-growing knowledge base
🧠 Neural VFS
The Virtual File System has a semantic search index — you can search files by concept, not just by name. nvfs-resolve "security" finds security-related files even if they're named differently.
nvfs-index
nvfs-resolve
nvfs-status
nvfs-tree
📦 Module Ecosystem
43 modules total (22 core + 21 ext). Each module is a self-contained JS class with its own commands and lifecycle.
Core Modules (always loaded)
Extension Modules (load on demand)
EditorShellCron
WebDAVVoiceMindmap
Floating UIMediaJS Console
File ManagerFinderTheme
TutorialWPMCalc
DiffBusyBoxCrypto/Gzip
AliasDebug PanelSkill Pack
✅ What We've Built So Far
Phase 1
System Foundation
44 modules · 17 skills · 296 files · 90+ commands · terminal · VFS · security
Phase 2
AI Integration
Agent loop · multi-model routing · Ollama integration · smart context profiles
Phase 3
Multi-Agent System
Agent registry · orchestrator · leases · A2A contracts · 35 lifetime agent spawns
Phase 4
Memory & Persistence
Session save/load · user profile · skills library · memory graph (83 nodes) · Neural VFS
Phase 5
Self-Healing (v4.6.10)
DebugSupervisorModule · error interception · root-cause analysis · auto-fix pipeline
Phase 6
Cloud & Sync
WebDAV backup · crypto/gzip · file manager · floating UI · voice control
Phase 7 — Current
Custom Model Training
Planning phase — dataset strategy, MoE architecture, budget defined
Phase 8 — Next
Custom MoE Model
QLoRA fine-tune → MoE deep training → self-hosted inference
⚠️ The Current Bottleneck
The system is powerful, but it depends on external LLMs for intelligence. Here's why that limits us:
❌ Current State
- Depends on Ollama-hosted models
- Generic models — no domain specialization
- No memory of past sessions baked in
- Token costs scale with usage (8M/week)
- Latency: 2-27s avg per request
- Can't customize reasoning style
- No multi-token generation
- Model behavior changes with updates
✅ With Custom Model
- Self-hosted, fully owned
- Specialized for OS operations
- Training data from real sessions
- Fixed compute cost (one-time training)
- Latency: <0.5s with speculative decoding
- Custom reasoning chain style
- Multi-token generation (Medusa heads)
- Full control — no surprise updates
The Core Insight
The system already has 500 real interaction sessions, 8M tokens of reasoning data, and 17 skills with structured procedures. This is a ready-made training dataset. No other project has this kind of organic, domain-specific data.
🚀 How a Custom Model Improves Everything
1. Speed — Multi-Token Generation
Current: 1 token/step
- Sequential generation
- ~20-40 tokens/sec
- Long responses take 10-30s
With Medusa Heads: 3-5 tokens/step
- Parallel prediction branches
- ~100-200 tokens/sec
- Same response in 2-5s
2. Intelligence — Domain Specialization
- Module management — model knows the 43-module API without RAG lookup
- Error diagnosis — trained on debug-history patterns, faster root-cause
- Code generation — knows the system's architecture, generates compatible code
- Command routing — maps natural language to 90+ commands accurately
- Agent delegation — learns optimal task decomposition from past sessions
3. Cost — One-Time Training vs Endless API
| Approach | Cost Model | Per Week |
|---|
| Current (Ollama API) | Per-request tokens | ~8M tokens |
| Custom Model (fine-tuned) | One-time training | ~0 marginal cost |
| Custom MoE (full train) | One-time + hosting | ~0 marginal cost |
4. MoE Architecture — Sparse Expert Routing
- Expert 1: System operations (VFS, modules, config)
- Expert 2: Code generation & debugging
- Expert 3: Web research & information synthesis
- Expert 4: Natural language & user communication
- Router: Dynamically selects 2 experts per token — 2x speed, same quality
Only the relevant experts activate → less compute per token → faster + cheaper inference.
🔬 Training Pipeline
How we turn system data into a custom model:
Phase 0 — Dataset Engineering
Extract 500 sessions · 8M tokens · 17 skills · error/fix pairs · format as instruction-response pairs · synthetic expansion
Phase 1 — QLoRA Proof of Concept
Fine-tune Qwen2.5-14B with Unsloth on 1× A100 (20 hrs) · validate on held-out sessions · cost: €35-100
Phase 2 — MoE Fine-Tune
Fine-tune Mixtral 8x7B MoE on full dataset · 2× A100 (48 hrs) · validate expert routing · cost: €200-500
Phase 3 — Deep MoE + Multi-Token
DeepSeek-V3 or Mixtral 8x22B + Medusa heads · 8× H100 cluster (72 hrs) · cost: €2,000-8,000
Phase 4 — Deploy & Integrate
Self-host on local GPU or rented instance · wire into Ollama module · replace external models · cost: €0-200/mo
📊 The Ask
Phase 0 + Phase 1 only: €300-500 — proves the concept works. Phase 2/3 funding decided after PoC results.
🏆 What We Have That Others Don't
1. Organic Training Data
Most AI projects buy or scrape data. We have 500 real sessions of a human working with an AI on system-building tasks. This is gold-standard data.
2. Built-in Evaluation Harness
The system has a module test pipeline (edit → test → health check → regression). We can evaluate model quality by running it against the actual system — no external benchmark needed.
3. Closed-Loop Improvement
DebugSupervisorModule captures errors → auto-fix suggestions → model learns from fixes → fewer errors next time. The system teaches itself.
4. Multi-Model Today, Custom Tomorrow
We already run 4 models (glm-5.2, gemma4:31b, mistral-large, minimax-m3). The routing infrastructure is built. A custom model just becomes another model in the pool — but specialized.
5. Skills as Structured Training Data
17 skills are already written as step-by-step procedures. These are perfect instruction-response training pairs — zero formatting work needed.
⚖️ Risks & Fallback Plans
Risk Matrix
Fallback Plans
- Ultra-budget (€20): Google Colab free tier + QLoRA 7B model — proves concept with zero risk
- Zero-budget (€0): Enhanced RAG pipeline — index all sessions/skills into Neural VFS, retrieve context dynamically. No training, but better prompting.
- Mid-budget (€500): Phase 0+1 only — QLoRA on 14B, self-host on existing Ollama setup
- Full vision (€8K+): All phases — custom MoE with multi-token generation
📋 Summary for Review
What custom training adds
- ⚡ 5-10x faster inference (multi-token generation)
- 🧠 Domain-specialized (OS operations, debugging, code gen)
- 💰 Near-zero marginal cost after one-time training
- 🔒 Full ownership — no external model dependency
- 🔄 Self-improving — learns from its own error fixes
Why now
- We have 500 sessions of real training data already
- QLoRA + Unsloth make training 70% cheaper than 1 year ago
- The system's self-healing layer generates new training data automatically
- The Ollama integration is already built — deployment is plug-and-play
🐴🔥
Year of the Firehorse
newOS4 is a living system that can build, repair, and improve itself.
A custom model makes it truly autonomous.
The Ask
€300-500 for Phase 0+1 (dataset + PoC)
Proves the concept. Everything else is optional.
This is not a pitch for a product.
This is a plan to give a system its own brain.
🙏