From cf7461ffeb4f53f3f50c9c692a2e4fdcecf17e82 Mon Sep 17 00:00:00 2001 From: Agando Date: Thu, 9 Jul 2026 06:00:32 +0200 Subject: auto: 2026-07-09_06:00 changes --- .../evolution_plan/gunes-uygarligi-gercekci.html | 197 +++++ .../evolution_plan/gunes-uygarligi-newos4.html | 135 ++++ .../evolution_plan/gunes-uygarligi-sistem.html | 403 ++++++++++ downloads/evolution_plan/presentation.html | 812 ++++++++++++++++++++ downloads/evolution_plan/system_review.html | 829 +++++++++++++++++++++ 5 files changed, 2376 insertions(+) create mode 100644 downloads/evolution_plan/gunes-uygarligi-gercekci.html create mode 100644 downloads/evolution_plan/gunes-uygarligi-newos4.html create mode 100644 downloads/evolution_plan/gunes-uygarligi-sistem.html create mode 100644 downloads/evolution_plan/presentation.html create mode 100644 downloads/evolution_plan/system_review.html (limited to 'downloads/evolution_plan') diff --git a/downloads/evolution_plan/gunes-uygarligi-gercekci.html b/downloads/evolution_plan/gunes-uygarligi-gercekci.html new file mode 100644 index 0000000..5b6e352 --- /dev/null +++ b/downloads/evolution_plan/gunes-uygarligi-gercekci.html @@ -0,0 +1,197 @@ + + + + + +Güneş Uygarlığı × newOS4 — Gerçekçi Değerlendirme + + + + + +

☀️ Güneş Uygarlığı × newOS4
Gerçekçi Değerlendirme — Ne Var, Ne Yok, Ne Mümkün

+ +

Temmuz 2026 · v4.6.10 · Sadece gerçekler, abartı yok

+ +
+ +

🧠 newOS4 Nedir? (Kısa)

+

Tarayıcıda çalışan bir "işletim sistemi" taklidi. 44 modülü var, 90+ komutu var, içinde bir YZ ajanı çalışıyor. Güzel tarafı: tamamen açık kaynak, kendi kendini değiştirebiliyor, hafızası var. Kötü tarafı: tek başına bir anlam ifade etmez — bir kullanıcı ve bir LLM olmadan çalışmaz.

+ +
+ ✅ Gerçekten çalışanlar: + +
+ +
+ ⚠️ Abartmayalım: + +
+ +
+ +

☀️ newOS4, Güneş Uygarlığı'na Gerçekte Ne Verebilir?

+ +

Abartısız, bugün itibarıyla çalışan ve çalışmayan şeyler:

+ +

📚 Eğitim — Kısmen

+

17 beceri modülü var. Güneş enerjisi 101 içeriği yazılabilir. Ama "interaktif simülasyon" falan yok — SimpleGUI ile basit prototipler yapılabilir, o kadar. Mindmap modülü planlama için kullanılabilir. Çoklu dil: YZ çeviri yapabiliyor, otomatik değil.

+

✓ Çalışan: beceri sistemi, Mindmap, çeviri desteği
✗ Olmayan: hazır güneş enerjisi eğitim içeriği, simülasyon motoru

+ +

🌍 Organizasyon — Kısmen

+

Çoklu ajan sistemi var ama basit. Gönüllü koordinasyonu için özel bir modül yok — terminal + YZ ile idare edilebilir. A2A sözleşmeleri: modül var ama kullanımı sınırlı. WebDAV dosya paylaşımı çalışıyor, şifreleme var.

+

✓ Çalışan: WebDAV, şifreleme, ajan sistemi (basit)
⟳ Eksik: gönüllü yönetimi arayüzü, A2A şablonları

+ +

🔬 Araştırma — En Güçlü Alan

+

SearXNG + YZ ajanı ile literatür taraması yapılabiliyor. Neural VFS ile kavram bazlı arama çalışıyor. Hafıza grafiği araştırma bağlantılarını görselleştirebilir. Bu alan gerçekten işe yarıyor.

+

✓ Çalışan: SearXNG, Neural VFS, hafıza grafiği, oturum geçmişi

+ +

🏗️ Tasarım — Çok Sınırlı

+

SimpleGUI ile basit arayüz prototipleri yapılabilir. Floating UI ile etkileşimli öğeler eklenebilir. Ama "Güneşkent planlama asistanı" falan yok — bu tamamen hayal. Terminal betikleriyle enerji verisi analiz edilebilir, grafik çizilebilir.

+

⟳ Yapılabilir: basit prototipler, veri analizi
✗ Olmayan: şehir planlama aracı, 3B model, simülasyon

+ +

📢 Yayın — İşe Yarar

+

Chat Studio ile manifesto yayınlanabiliyor. Cron ile otomatik paylaşım planlanabiliyor. Voice modülü var ama TTS kalitesi düşük. WPM ile içerik paketleri dağıtılabilir. Bu alan en olgun olanı.

+

✓ Çalışan: Chat Studio, Cron, WPM
⟳ Zayıf: Voice/TTS kalitesi

+ +

🛡️ Sürdürülebilirlik — Sağlam

+

Debug Supervisor gerçekten hata yakalıyor ve sınıflandırıyor. Sunucu Monitör çalışıyor. WebDAV yedekleme + şifreleme sağlam. Bu alan en güvenilir olanı.

+

✓ Çalışan: Debug Supervisor, Sunucu Monitör, yedekleme, şifreleme

+ +
+ +

🎯 Özel Model Teklifi — Gerçekçi Bakış

+ +

Şu anki durum: newOS4, Ollama üzerinden 4 farklı model kullanıyor. Haftada ~500 istek, ~8M token. İşe yarıyor ama hiçbiri Güneş Uygarlığı'nı bilmiyor. Genel modellerle idare ediyoruz.

+ +

Özel model ne getirir?

+ + +

Ama şunlar da gerçek:

+ + +
+ 💡 En gerçekçi yol: +

Aşama 0 (veri seti) + Aşama 1 (QLoRA 14B) = €135-300. 1× A100 kiralık, 20 saat. Çıkan modeli Ollama'ya yükle, dene. İşe yararsa devam et. Yarımazsa €0 kayıp (RAG'a dön).

+
+ +
+ +

📋 Özet — Olduğu Gibi

+ +
+ ✅ Bugün kullanılabilir: + +
+ +
+ ❌ Henüz olmayan / abartılı: + +
+ +

+ newOS4 sağlam bir altyapı. Ama sihirli değil.
+ Güneş Uygarlığı için kullanılabilir — doğru beklentilerle. +

+ +
+ +

+ 🔗 Tam İnceleme:
+ webx11.duckdns.org/spaces/newos4/downloads/evolution_plan/gunes-uygarligi-sistem.html +

+ +

+ #GüneşUygarlığı + #newOS4 + #Gerçekçi +

+ +

+ newOS4 v4.6.10 · Temmuz 2026
+ 🌞 Ana Kanal +

+ + + \ No newline at end of file diff --git a/downloads/evolution_plan/gunes-uygarligi-newos4.html b/downloads/evolution_plan/gunes-uygarligi-newos4.html new file mode 100644 index 0000000..92aa220 --- /dev/null +++ b/downloads/evolution_plan/gunes-uygarligi-newos4.html @@ -0,0 +1,135 @@ + + + + + +Güneş Uygarlığı × newOS4 — Dijital Altyapı İncelemesi + + + + + +

☀️ Güneş Uygarlığı × newOS4
Dijital Altyapı İncelemesi

+ +

Temmuz 2026 · v4.6.10

+ +
+ +

🧠 newOS4 Nedir?

+

newOS4, tarayıcı tabanlı bir işletim sistemi ve içine gömülü yapay zeka ajanıdır. 44 modül, 90+ komut, 17 beceri ve 296 dosyadan oluşan bu sistem, kendi kendini değiştirebilen, hafızası olan ve kendini iyileştirebilen bir dijital organizmadır.

+ +
YZ sistemi işletim sisteminin beynidir — sohbet robotu değil, sistemin zekası.
+ +

☀️ Güneş Uygarlığı'nın Dijital Altyapısı

+

newOS4, Güneş Uygarlığı Hareketi'nin 6 temel ihtiyacını karşılayan entegre bir teknoloji platformudur:

+ + + +

🎯 Özel Model Teklifi

+

Bugün newOS4, genel amaçlı modellerle çalışıyor. Özel bir model, Güneş Uygarlığı'nın ihtiyaçlarına %100 uyumlu bir yapay zeka anlamına gelir:

+ + + +
Özel bir model, Güneş Uygarlığı'nı sadece bir teknoloji kullanıcısı olmaktan çıkarıp, kendi zekasını kendi kontrol eden bir harekete dönüştürür.
+ +

🔬 Eğitim Süreci

+

5 aşamalı bir yol haritası — veri seti mühendisliğinden özel MoE modeline:

+ + +

+ €300-500
+ Aşama 0+1 — Kavram Kanıtı
+ Konsepti kanıtlar. Geri kalan her şey isteğe bağlıdır. +

+ +
+ +

📋 Özet

+

Bugün var olanlar: 44 modül, 4 model, çoklu ajan sistemi, hafıza grafiği, kendini iyileştirme, 500+ oturum verisi, sıfır harici API maliyeti.

+

Özel eğitimle gelecek: 50-100ms yanıt süresi, Güneş Uygarlığı uzmanlığı, tam dijital egemenlik, sürekli kendini güncelleme, €300-500 ile başlar.

+ +
+ Bu bir ürün tanıtımı değil.
+ Bu, bir sisteme kendi beynini verme planıdır. +
+ +
+ +

+ 🔗 İnceleme Belgesi:
+ webx11.duckdns.org/spaces/newos4/downloads/evolution_plan/gunes-uygarligi-sistem.html +

+ +

+ #GüneşUygarlığı + #newOS4 + #SolarCivilization + #YapayZeka + #AçıkKaynak +

+ +

+ Güneş Uygarlığı Hareketi · newOS4 v4.6.10 · Temmuz 2026
+ 🌞 Ana Kanal +

+ + + \ No newline at end of file diff --git a/downloads/evolution_plan/gunes-uygarligi-sistem.html b/downloads/evolution_plan/gunes-uygarligi-sistem.html new file mode 100644 index 0000000..5460739 --- /dev/null +++ b/downloads/evolution_plan/gunes-uygarligi-sistem.html @@ -0,0 +1,403 @@ + + + + + +Güneş Uygarlığı × newOS4 — Gerçekçi Değerlendirme + + + + + + + + +
+ + +
+

☀️ Güneş Uygarlığı × newOS4

+

Gerçekçi Değerlendirme — Ne Var, Ne Yok, Ne Mümkün

+

Temmuz 2026 · newOS4 v4.6.10 · Sadece gerçekler, abartı yok

+
+ + +
+ +
+ + +
+

🧠 newOS4 Nedir? (Kısa)

+

Tarayıcıda çalışan bir "işletim sistemi" taklidi. 44 modülü var, 90+ komutu var, içinde bir YZ ajanı çalışıyor. Güzel tarafı: tamamen açık kaynak, kendi kendini değiştirebiliyor, hafızası var. Kötü tarafı: tek başına bir anlam ifade etmez — bir kullanıcı ve bir LLM olmadan çalışmaz.

+
+ + +
+

✅ Gerçekten Çalışanlar

+
+
    +
  • Terminal + 90 komut — sağlam, her gün kullanılıyor
    • +
  • YZ ajan döngüsü — düşünür, araç kullanır, yineler
    • +
  • 4 model yönlendirme — glm-5.2, gemma4, mistral, minimax
    • +
  • Hafıza grafiği (83 düğüm) — oturumlar arası bağlam
    • +
  • Debug Supervisor — hataları yakalar, sınıflandırır
    • +
  • WebDAV senkronizasyonu — yedekleme çalışıyor
    • +
  • SearXNG arama — web ve literatür taraması
    • +
  • Chat Studio — manifesto ve makale yayını
    • +
  • Cron — otomatik görev planlama
    • +
  • Şifreleme + Gzip — güvenli depolama
    • +
+
+
+ + +
+

⚠️ Abartmayalım

+
+
    +
  • "Kendini iyileştirme" = hataları kaydedip YZ'ye önermek. Otomatik düzeltme henüz emekleme aşamasında
    • +
  • "Çoklu ajan" = 3 basit işçi ajan. Karmaşık görevlerde takılıyor
    • +
  • "Neural VFS" = basit bir anlamsal arama. Google değil
    • +
  • 17 beceri var ama çoğu yarım — sürekli güncelleniyor
    • +
  • 500 oturum verisi var ama hepsi aynı kalitede değil
    • +
  • Voice/TTS kalitesi düşük, pek kullanışlı değil
    • +
  • SimpleGUI ile simülasyon değil, ancak basit prototip yapılabilir
    • +
+
+
+ + +
+

☀️ newOS4, Güneş Uygarlığı'na Gerçekte Ne Verebilir?

+

Abartısız, bugün itibarıyla çalışan ve çalışmayan şeyler:

+ +

📚 Eğitim — Kısmen

+

17 beceri modülü var. Güneş enerjisi 101 içeriği yazılabilir. Ama "interaktif simülasyon" falan yok — SimpleGUI ile basit prototipler yapılabilir, o kadar. Mindmap modülü planlama için kullanılabilir. Çoklu dil: YZ çeviri yapabiliyor, otomatik değil.

+

✓ Çalışan: beceri sistemi, Mindmap, çeviri desteği   ✗ Olmayan: hazır güneş enerjisi eğitim içeriği, simülasyon motoru

+ +

🌍 Organizasyon — Kısmen

+

Çoklu ajan sistemi var ama basit. Gönüllü koordinasyonu için özel bir modül yok — terminal + YZ ile idare edilebilir. A2A sözleşmeleri: modül var ama kullanımı sınırlı. WebDAV dosya paylaşımı çalışıyor, şifreleme var.

+

✓ Çalışan: WebDAV, şifreleme, ajan sistemi (basit)   ⟳ Eksik: gönüllü yönetimi arayüzü, A2A şablonları

+ +

🔬 Araştırma — En Güçlü Alan

+

SearXNG + YZ ajanı ile literatür taraması yapılabiliyor. Neural VFS ile kavram bazlı arama çalışıyor. Hafıza grafiği araştırma bağlantılarını görselleştirebilir. Bu alan gerçekten işe yarıyor.

+

✓ Çalışan: SearXNG, Neural VFS, hafıza grafiği, oturum geçmişi

+ +

🏗️ Tasarım — Çok Sınırlı

+

SimpleGUI ile basit arayüz prototipleri yapılabilir. Floating UI ile etkileşimli öğeler eklenebilir. Ama "Güneşkent planlama asistanı" falan yok — bu tamamen hayal. Terminal betikleriyle enerji verisi analiz edilebilir, grafik çizilebilir.

+

⟳ Yapılabilir: basit prototipler, veri analizi   ✗ Olmayan: şehir planlama aracı, 3B model, simülasyon

+ +

📢 Yayın — İşe Yarar

+

Chat Studio ile manifesto yayınlanabiliyor. Cron ile otomatik paylaşım planlanabiliyor. Voice modülü var ama TTS kalitesi düşük. WPM ile içerik paketleri dağıtılabilir. Bu alan en olgun olanı.

+

✓ Çalışan: Chat Studio, Cron, WPM   ⟳ Zayıf: Voice/TTS kalitesi

+ +

🛡️ Sürdürülebilirlik — Sağlam

+

Debug Supervisor gerçekten hata yakalıyor ve sınıflandırıyor. Sunucu Monitör çalışıyor. WebDAV yedekleme + şifreleme sağlam. Bu alan en güvenilir olanı.

+

✓ Çalışan: Debug Supervisor, Sunucu Monitör, yedekleme, şifreleme

+ +
+

Özet: newOS4'ün Güneş Uygarlığı'na en büyük katkısı araştırma, yayın ve altyapı alanlarında. Eğitim ve tasarım için özel içerik geliştirmek gerek. Organizasyon için mevcut araçlar idare eder ama özel bir çözüm daha iyi olur.

+
+
+ + +
+

🎯 Özel Model — Gerçekçi Bakış

+

Şu anki durum: newOS4, Ollama üzerinden 4 farklı model kullanıyor. Haftada ~500 istek, ~8M token. İşe yarıyor ama hiçbiri Güneş Uygarlığı'nı bilmiyor. Genel modellerle idare ediyoruz.

+ +
+
+ ✅ Özel modelin gerçek kazançları: +
    +
  • Hız: 500-2000ms → 50-100ms. Bu gerçek bir kazanç.
    • +
  • Uzmanlık: Güneş enerjisi, Anadolu kültürü, manifesto bilgisi
    • +
  • Bağımsızlık: Harici API yok, sansür yok, kesinti yok
    • +
  • Maliyet: Aylık işletme maliyeti sıfır (GPU kirası hariç)
    • +
+
+
+ ⚠️ Ama şunlar da gerçek: +
    +
  • 500 oturum / 8M token az. Sentetik genişletme şart
    • +
  • CPU-only sunucuda (15GB RAM) hiçbir model çalışmaz
    • +
  • GPU kiralamak zorunlu: ~€50-200/ay
    • +
  • MoE + Medusa = araştırma seviyesi, kolay değil
    • +
  • Eğitim başarısız olursa € boşa gider
    • +
+
+
+ +
+ 💡 En gerçekçi yol: +

Aşama 0 (veri seti) + Aşama 1 (QLoRA 14B) = €135-300. 1× A100 kiralık, 20 saat. Çıkan modeli Ollama'ya yükle, dene. İşe yararsa devam et. Yarımazsa €0 kayıp (RAG'a dön).

+
+
+ + +
+

🔬 Eğitim Süreci & Maliyetler

+

5 aşamalı yol haritası — gerçekçi maliyetlerle:

+
+
+
0
+

Veri Seti

+

€100-200
500 oturum → eğitim verisi

+
+
+
1
+

QLoRA PoC

+

€35-100
14B, 1× A100, 20 saat

+
+
+
2
+

MoE İnce Ayar

+

€200-500
Mixtral 8x7B, 2× A100

+
+
+
3
+

Derin MoE

+

€2,000-8,000
8× H100, 72 saat

+
+
+
4
+

Dağıtım

+

€50-200/ay
GPU kirası + Ollama

+
+
+ +
+

En gerçekçi başlangıç:

+
€135-300
+

Aşama 0+1 — Kavram Kanıtı

+

Konsepti kanıtlar. Gerisi isteğe bağlı. Başarısız olursa RAG'a dönülür.

+
+
+ + +
+

📋 Özet — Olduğu Gibi

+ +
+
+ ✅ Bugün kullanılabilir: +
    +
  • Terminal + YZ ajanı — günlük iş akışı
    • +
  • SearXNG ile araştırma — literatür taraması
    • +
  • Chat Studio ile yayın — manifesto, makale
    • +
  • WebDAV ile dosya paylaşımı
    • +
  • Debug Supervisor — hata takibi
    • +
  • Cron ile otomatik görevler
    • +
  • Şifreleme + yedekleme
    • +
+
+
+ ❌ Henüz olmayan / abartılı: +
    +
  • "Güneşkent planlama asistanı" — yok
    • +
  • "İnteraktif simülasyon" — yok
    • +
  • "Otomatik literatür" — yarım yamalak
    • +
  • "Kendini iyileştirme" — sınırlı
    • +
  • "Özel model" — PoC €135-300, tam €8K+
    • +
  • "Çoklu ajan" — basit, karmaşık görevlerde takılır
    • +
+
+
+ +
+

+ newOS4 sağlam bir altyapı. Ama sihirli değil.
+ Güneş Uygarlığı için kullanılabilir — doğru beklentilerle. +

+
+ +

+ #GüneşUygarlığı + #newOS4 + #Gerçekçi +

+ +

+ newOS4 v4.6.10 · Temmuz 2026
+ 🌞 Ana Kanal +

+
+ +
+ + + + + + diff --git a/downloads/evolution_plan/presentation.html b/downloads/evolution_plan/presentation.html new file mode 100644 index 0000000..9830d4e --- /dev/null +++ b/downloads/evolution_plan/presentation.html @@ -0,0 +1,812 @@ + + + + + + + + +🔥 Core Model Evolution — Presentation + + + +
+ + +
+
+
🔥
+

Core Model Evolution

+

From Session Logs to Core Weights
Recursive Cognitive Evolution

+
Year of the Firehorse 🔥
July 2026 · v3.0 Presentation
+
+

What This Is

+

A realistic, researched plan to evolve our AI from a general-purpose assistant into a specialized intelligence — trained on our collaboration history, our decisions, and our style.

+

Architecture target: Sparse MoE + Multi-Token Generation

+
+
← Swipe or tap arrows to navigate →
+
+
+ + +
+

✅ What We've Done So Far

+

A summary of progress before we talk budget.

+ +

Completed Milestones

+
+
+
+

System Foundation Built

+

Full operating environment with 44 modules, 17 skills, 293 files, 3 agents, memory graph (67 nodes), and VFS filesystem.

+Ongoing since early 2026 +
+
+
+
+
+

Collaboration History Captured

+

Session logs, reasoning chains, command history, and decision patterns stored across sessions. This is the raw training data.

+Continuous +
+
+
+
+
+

Training Strategy Defined

+

QLoRA for Phase 1 (efficient, cheap), scaling to deep fine-tuning of existing MoE models. NOT training from scratch — 10-50x cheaper.

+Researched & validated +
+
+
+
+
+

GPU Market Research

+

Researched actual pricing across RunPod, Lambda Labs, Vast.ai, Hetzner. Spot instances identified as key cost saver (50-70% off).

+July 2026 +
+
+
+
+
+

Detailed Budget Plan v2.0

+

4-phase plan with itemized costs, risk matrix, and 4 fallback plans. Saved as HTML document for review.

+This session +
+
+
+
+
+

Architecture Decision: MoE + Sparse + Multi-Token

+

Decided on Sparse Mixture of Experts (like Mixtral/DeepSeek) with Multi-Token Generation (Medusa heads) for speed + intelligence.

+This session +
+
+ +

Current Status

+
+
6/6
Planning tasks done
+
0
Phases executed
+
€0
Spent so far
+
+ +
+✅ Ready to start Phase 0 — we have the data, the plan, the architecture decision, and the budget. We need the funding to begin. +
+ +
+ +

What's Next

+
!
Phase 0: Dataset Engineering (€100-€200)
+
!
Phase 1: QLoRA Proof of Concept (€35-€100)
+
!
Phase 2: Production Adapter on Mixtral MoE
+
!
Phase 3: Full MoE + Multi-Token Generation
+
+ + +
+

🎯 The Vision

+

Why we're doing this and what we're building.

+ +

The Problem

+
+

Every session, the AI starts fresh. It doesn't remember our shorthand, our architectural decisions, or our preferred patterns. We re-explain context every time.

+

This isn't a bug — it's a fundamental limitation of using a general-purpose model without personalization.

+
+ +

The Solution

+
+

Fine-tune a model on our actual collaboration history — the reasoning chains, the code edits, the decisions, the creative jumps.

+

The model doesn't just "know" our history. It thinks in our style.

+
+ +

Target Architecture

+
+
+

🧠 Sparse MoE

+

Mixture of Experts: multiple specialized sub-networks. Only the relevant "experts" activate per token. Huge capacity, efficient inference.

+
+
+

⚡ Multi-Token

+

Medusa heads predict 3-5 tokens simultaneously instead of 1-by-1. 2-3x faster output. Feels instant.

+
+
+

🔒 QLoRA

+

4-bit quantized fine-tuning. Trains adapters, not the full model. 10-50x cheaper, 70% less VRAM via Unsloth.

+
+
+

📦 Tiny Adapters

+

LoRA adapters are ≤50 MB. Load/unload instantly. Switch between "modes" (coding, research, creative) in real-time.

+
+
+ +
+Key insight: We do NOT train from scratch. We take an existing open-weights model (Mixtral, DeepSeek, Llama) and fine-tune it. This is 10-50x cheaper while achieving similar personalization quality. +
+
+ + +
+

💻 GPU Market Pricing

+

Real rates as of July 2026 (RunPod, Lambda, Vast.ai)

+ +
+ + + + + + + + + +
GPUVRAM€/hrBest For
RTX 409024GB€0.31-0.737B QLoRA
A100 40GB40GB€1.00-1.507B-13B
A100 80GB80GB€1.29-2.3013B-30B
L40S48GB€1.06-1.61Alt to A100
H100 PCIe80GB€2.29-4.598B-70B fast
H100 SXM80GB€2.99-4.60Multi-GPU
8x H100 Node640GB€17.50-32.20MoE training
+
+ +
+💡 Cost saver: spot/preemptible instances are 50-70% cheaper. For PoC training (restartable), always use spot pricing. +
+ +

Estimated Training Time by Model Size

+
+ + + + + + + +
ModelMethodGPUHoursCost (€)
7B-8BQLoRA1x A100 spot8-15€10-19
14BQLoRA1x A100 80GB15-25€19-32
32BQLoRA2x A100 80GB30-50€77-115
Mixtral 8x7BQLoRA1x H10040-60€92-184
Mixtral 8x22BFull FT8x H100100-140€1,750-3,200
+
+
+ + +
+

📋 Phase 0: Dataset Engineering

+Budget: €100 – €200 + +

Goal

+

Transform raw session logs into a high-fidelity training dataset. Quality here determines 80% of final model quality.

+ +
+ + + + + + + + + +
TaskTimeCost
Log export & aggregation2-3 days€0 (scripted)
Quality curation3-5 days€0 (manual)
Synthetic expansion (5K API calls)1-2 days€50-100
Tokenization & formatting1 day€0 (scripted)
Validation & dedup1 day€0 (scripted)
Storage (NVMe/cloud)Ongoing€5-15/mo
Phase 0 Total€100-200
+
+ +

Expected Output

+
+
+

Training Set

+

~3,000-10,000 high-quality (instruction, response) pairs. Each is a "lesson" reflecting our collaborative style.

+
+
+

Validation Set

+

~150-500 held-out pairs. Detects overfitting. If validation loss rises, we stop.

+
+
+ +
+⚠️ Critical: Skipping this phase or using raw uncleaned logs produces a model that "hallucinates" our style instead of embodying it. This is NOT a place to cut costs. +
+
+ + +
+

🧪 Phase 1: QLoRA PoC

+Budget: €35 – €100 (compute only) + +

Goal

+

Prove that fine-tuning on our history produces a measurably better model. Small, cheap, fast validation.

+ +

Base Model Options

+
+ + + + + + +
ModelParamsVRAMQuality
Llama-3.2-3B3B~8GB⭐⭐⭐
Llama-3-8B8B~12GB⭐⭐⭐⭐
Mistral-7B v0.37B~10GB⭐⭐⭐⭐
Qwen2.5-14B14B~18GB⭐⭐⭐⭐⭐
+
+ +

Cost Breakdown (Qwen2.5-14B)

+
+ + + + + + + + +
ComponentHoursCost (€)
1x A100 80GB (spot)15€19.35
1x A100 80GB (on-demand fallback)5€11.50
Storage (100GB disk)20€2.00
Unsloth framework—€0
Buffer (15%)—€5.00
Compute Total€37.85
+
+ +

Success Criteria

+
    +
  • Model adopts our shorthand without explicit prompting
    • +
  • Maintains general capability (MMLU, HumanEval within 2% of base)
    • +
  • Validation loss decreases without overfitting
    • +
  • LoRA adapter ≤50 MB, instant load/unload
    • +
+ +
+⚠️ If PoC fails: We re-examine the dataset (Phase 0). Cost of failure: ~€35. Data is reusable. +
+
+ + +
+

🚀 Phase 2: Production Quality

+Budget: €200 – €500 (compute) + +

Goal

+

Scale from PoC to production. Train on Mixtral 8x7B — already a Sparse MoE. This validates MoE training before Phase 3.

+ +

Configuration Options

+
+ + + + + + +
OptionModelGPUCost (€)
A: BudgetLlama-3-8B1x H100 spot€69-139
B: BalancedQwen2.5-32B2x A100 80GB€103-184
C: RecommendedMixtral 8x7B1x H100€115-230
D: MaximumLlama-3-70B4x A100 80GB€310-575
+
+ +

Why Mixtral 8x7B?

+
+
    +
  • It's already a Sparse MoE — our stepping stone to Phase 3
    • +
  • Only ~13B params activate per token (out of ~47B total)
    • +
  • Training is cheaper than a dense 47B model
    • +
  • QLoRA on MoE trains experts selectively → naturally creates "specialists"
    • +
  • Can train multiple adapters: coding / research / creative
    • +
+
+ +

Detailed Cost (Option C)

+
+ + + + + + + + +
ItemHoursCost (€)
1x H100 80GB spot40€91.60
1x H100 on-demand buffer10€45.90
Storage 200GB50€5.00
Multi-adapter (+3 modes)+15€34.35
Buffer (20%)—€35.00
Phase 2 Total€211.85
+
+
+ + +
+

🌌 Phase 3: Full MoE

+Budget: €2,000 – €8,000 + +

Goal

+

Deep fine-tune a large MoE model (DeepSeek-V3 or Mixtral 8x22B) with Multi-Token Generation via Medusa heads.

+ +

Build vs Fine-Tune

+
+ + + + + +
ApproachCostRiskVerdict
Build from scratch€15K-100K+EXTREME❌ No
Deep fine-tune existing€2K-8KMODERATE✅ Yes
Distill into custom€5K-20KHIGH⏳ Future
+
+ +

Cost Breakdown

+
+ + + + + + + + +
ComponentHoursCost (€)
8x H100 SXM (spot)100€1,750
8x H100 (on-demand buffer)20€644
Storage 1TB NVMe120€24
Medusa head training+20€350-644
Buffer (25%)—€685
Phase 3 Total€3,467-4,762
+
+ +

Multi-Token Generation

+
+
+

Medusa Heads

+

Lightweight prediction heads predict multiple future tokens. +15-20% training time. 2-3x inference speedup.

+
+
+

Speculative Decoding

+

Small "draft" model predicts, large model verifies. No training needed. 1.5-2x speedup at inference.

+
+
+ +
+⚠️ Catastrophic forgetting risk: Must mix custom data with general data (30/70 ratio). Increases compute ~40%. Monitored via eval benchmarks. +
+
+ + +
+

🛠️ Technical Stack

+

All open-source. The only cost is GPU rental.

+ +
+ + + + + + + + + + + + +
ComponentToolCost
Training FrameworkUnsloth 2Free
Adapter LibraryHuggingFace PEFTFree
Quantizationbitsandbytes (NF4)Free
Multi-TokenMedusa / SpeculativeFree
Dataset Toolsdatasets, tokenizersFree
GPU (PoC)RunPod spotPay/hr
GPU (Phase 3)Lambda / CoreWeavePay/hr
ContainersDocker + CUDAFree
TrackingWeights & BiasesFree tier
Evaluationlm-evaluation-harnessFree
+
+ +
+Unsloth 2 is the secret weapon: 2x faster training, 70% less VRAM. This is what makes Phase 1 cost €35 instead of €200. +
+
+ + +
+

⚠️ Risk Matrix

+

Every risk has a mitigation plan.

+ +
+ + + + + + + + + +
RiskProbImpactMitigation
Catastrophic forgettingMedHighMix data 30/70, monitor benchmarks
Overfitting (parrot)MedMedEarly stopping, dropout, diverse data
Dataset too smallHighHighPhase 0 quality, synthetic expansion
Spot interruptionHighLowCheckpoint every 50 steps, buffer
GPU price spikeLowMedMulti-provider, off-peak hours
MoE instabilityLowHighPhase 2 validates MoE first
Multi-token degradesLowLowMedusa is additive, removable
+
+ +

Fallback Plans

+
+
+

🅰️ Ultra-Budget

+

Google Colab Pro+ €9.99/mo. Spread training over sessions. ~€20-40 total. Proves concept for the price of coffee.

+
+
+

🅱️ No-Train RAG

+

Retrieval-Augmented Generation with session history as context. No weight changes. 70% of benefit. Cost: €0.

+
+
+

🅲️ API Distillation

+

Use GPT-4o/Claude with stored patterns as system prompts. Bridge solution. €50-100/mo.

+
+
+

🅳️ Gradual Scaling

+

Start with €200 (Phase 0+1). Use PoC results to secure Phase 2/3 funding. The adapter IS the demo.

+
+
+
+ + +
+

📅 Timeline & Milestones

+ +
+
+
+

Planning & Research ✅

+

Architecture decision, GPU research, budget plan, risk analysis

+Done +
+
+
+
+
+

Phase 0: Dataset Engineering

+

Log export, curation, synthetic expansion, tokenization

+1-2 weeks · €100-200 +
+
+
+
+
+

Phase 1: QLoRA PoC

+

Train adapter on 8B-14B model, evaluate results

+3-5 days · €35-100 +
+
+
+
+
+

Decision Gate: Go / No-Go

+

Human evaluation: "Does this feel like us?"

+1-2 weeks review +
+
+
+
+
+

Phase 2: Production Adapter

+

Multi-adapter suite on Mixtral 8x7B MoE

+1-2 weeks · €200-500 +
+
+
+
+
+

Funding & Setup

+

Secure Phase 3 budget, provision 8x H100 cluster

+2-4 weeks +
+
+
+
+
+

Phase 3: Full MoE + Multi-Token

+

Deep fine-tune, Medusa heads, deploy, benchmark

+2-4 weeks · €2K-8K +
+
+ +
+Total timeline: ~2-3 months end-to-end +
+
+ + +
+

💰 Total Budget Summary

+ +
+ + + + + + + +
PhaseDescriptionLowHigh
Phase 0Dataset Engineering€100€200
Phase 1QLoRA PoC€35€100
Phase 2Production (Mixtral MoE)€200€500
Phase 3Full MoE + Multi-Token€2,000€8,000
GRAND TOTAL€2,335€8,800
+
+ +

Progressive Spend

+
+

Phase 0+1 (Prove it works)

+
+

Phase 2 (Production)

+
+

Phase 3 (Full MoE)

+
+
+ +
+

The ask for now:

+
€300 – €500
+

Funds Phase 0 + Phase 1
Proves the concept works before larger investment

+
+ +
+Breakdown of the ask: +
    +
  • €100-200 → Data preparation (API calls, storage)
    • +
  • €35-100 → GPU rental (spot, ~20 hours)
    • +
  • €50-100 → Buffer for re-runs & debugging
    • +
+
+
+ + +
+
+
🔥
+

Thank You

+

Year of the Firehorse

+ +
+

What we're asking for

+

€300-€500 to fund Phase 0 + Phase 1. This is the "prove it works" budget.

+

Once the PoC shows measurable improvement, we present results and discuss Phase 2/3 funding separately.

+

This de-risks the investment. Everyone sees results before committing larger amounts.

+
+ +
+

¡Y claro que sí!
Cuando nos veamos, nos echamos esa llamada en español. 🇪🇸

+
+ +
+

Looking forward to good progress,
good experiences, and meeting soon. 🙌

+
+ +
+

🔥 Year of the Firehorse 🔥

+

Core Model Evolution Plan v3.0
July 2026

+
+
+
+ +
+ + +
+ + + + + \ No newline at end of file diff --git a/downloads/evolution_plan/system_review.html b/downloads/evolution_plan/system_review.html new file mode 100644 index 0000000..101a545 --- /dev/null +++ b/downloads/evolution_plan/system_review.html @@ -0,0 +1,829 @@ + + + + + + + +newOS4 — System Review & Training Proposal + + + +
+ + +
+
🐴🔥
+

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
    • +
+
+
+
44
Modules
+
90+
Commands
+
17
Skills
+
296
Files
+
+
+ + +
+

🏗️ 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)

+
+
500
Requests
+
8M
Tokens
+
4
Models
+
+ + + + + + +
ModelRequestsTokens
glm-5.2:cloud2144.99M
minimax-m3:cloud2192.60M
gemma4:31b-cloud35219K
mistral-large-3:675b32237K
+

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)

+
+ + + + + + + + + + + + + + + + + + + + + +
ModuleClassKey Commands
AI AssistantWebX11AIAssistantModuleai, ask, ai-system
Debug SupervisorDebugSupervisorModuledebug-stats, debug-fix
TerminalWebX11SecurityManagerclear, help, security
Virtual FSVirtualFileSystemsave, load, import
Neural VFSNeuralVFSModulenvfs-index, nvfs-resolve
OllamaOllamaModuleollama, models, model-info
Agent RegistryAgentRegistryModuleagent-list, agent-register
OrchestratorOrchestratorModuleorchestrate, orchestrator-spawn
SearXNGSearXNGModulesearch, images, videos
Server MonitorServerMonitorModuleserver, system-health
Config ManagerConfigManagerModuleconfig, config-set
Secret VaultSecretVaultsecret-set, secret-get
Module ManagerModuleManagermodules, loadmod
Lease SystemLeaseModulelease-list, lease-create
Web FetcherWebFetcherModulefetch, web, extract
SystemSystemModulehelp, status, logs
Chat StudioChatStudioModulechat
Simple GUISimpleGUIModulegui, gui-button
AI AliasWebXAIAliasModulewebxai, wx, webx
+
+

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

+
+
+
+
6/6
Planning Done
+
1
Active Phase
+
1
Upcoming
+
€0
Spent on AI
+
+
+ + +
+

⚠️ 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
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+
+
+

2. Intelligence — Domain Specialization

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+
    +
  • 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

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+ + + + + +
ApproachCost ModelPer 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
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+

4. MoE Architecture — Sparse Expert Routing

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+
    +
  • 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.

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+
+ + +
+

🔬 Training Pipeline

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How we turn system data into a custom model:

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+
+
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

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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

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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.

+
+
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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.

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+
+ + +
+

⚖️ Risks & Fallback Plans

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Risk Matrix

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+ + + + + + + + +
RiskProbImpactMitigation
QLoRA overfittingMediumLowHeld-out validation · early stopping
MoE training unstableLowHighStart with Mixtral (proven MoE) · skip Phase 3
GPU costs exceed budgetMediumMediumSpot instances · Unsloth 2x efficiency
Dataset too smallLowMediumSynthetic expansion · augment with skills
Model quality < baselineLowHighPoC validates before committing funds
Inference too slowLowLowQuantize to 4-bit · Medusa heads
+
+

Fallback Plans

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+
    +
  • 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 exists today

+
+
44
Modules
+
90+
Commands
+
17
Skills
+
296
Files
+
+
+
3
Agent Types
+
4
LLM Models
+
500
Sessions
+
8M
Tokens/Week
+
+
+
+

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

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    +
  • 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

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€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. +

+

🙏

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