SynapseOS: Potato-Grade Linux Distro & 7-Lab Native AI Workbench

Base OS: Debian 12 Bookworm (minbase, x86_64) + Xorg + Openbox + Tint2 + tty1 autologin (242 MB live idle RAM).
Synapse Studio & Synapse Kids Arcade: Native GTK3 + Cairo 60 FPS AI Workbench (62.9 MB full runtime RSS, 3.27 ms/frame at 1280x720, 0 KB texture bloat, 0.09s cold start) featuring:
- Full-Screen Synapse Kids Mode (workbench/kid_arcade_view.py): Gamified Scratch-style classroom UI with 1-click L1–L7 Level Selector Pills, interactive canvas clicks across all 7 levels, NEAT Moon Lander solar panels/callouts & Lunar Landing Pad touchdown physics, live swirling 2D Color Sorter Brain, waving Cortex-Phi3 Robot Mascot + Speech Bubble, live mission checkboxes, 3-star scoring, and 1-click English / French (EN ▸ FR) toggle.
- ⚙ Teacher / Pro Workstation Mode (workbench/studio_gui.py): Full 3-column engineering interface with 35 curated Lab Examples (5 per lab), 14 Classroom Tutorials, Direct Structural Brain Surgery (+ / - Neurons & Synapses), live FPS / PRIME C99 tok/s / Phi-3 tok/s & TTFT / RAM telemetry, 60 FPS Animated Phi-3 + PRIME Brain Visualizer, General Chat Mode, Safe Linux Command Runner, and Encrypted Remote Browser Tunnel (synapse-tunnel).
Three-Tier Local AI Engine:
1. Tier 1 (0.68 ms, 92 KB): SQLite FTS5 BM25 offline knowledge base (cortex_kb.sqlite).
2. Tier 2 (8,737 tok/s on BC-250 / 6,738 tok/s potato CPU, 262 KB constant $\mathcal{O}(1)$ RAM, zero AVX2 required): Native C99 PRIME Moment Sliding-Window Attention engine (libprime.so / prime-moment, integrated from batteryphil/PRIME-Moment-Attention).
3. Tier 3 (24.8 tok/s on AMD BC-250 / 11.6 tok/s 4-core CPU, 2.2 GB): Local cortex-phi3:latest (Phi-3-Mini-4K-Instruct Q4_K_M, num_ctx=1024).

1. Complete Tester Instructions: Download & Boot SynapseOS (`Full 2.6 GB LLM ISO` or `437 MB Lite ISO`)

1-Click Single-File Full 2.6 GB LLM .iso Download (Hugging Face — No Reassembly Needed): 👉 SynapseOS-Debian12-Potato-x86_64.iso (2.6 GB Direct Download) | Browse Hugging Face Repo | SHA256 Checksum
GitHub Release Page (v1.0-k12): 👉 https://github.com/batteryphil/SynapseOS/releases/tag/v1.0-k12

Option A: Linux / macOS — 1-Command Full LLM ISO Download & Boot (`2.6 GB`)

Includes everything 100% offline: Debian 12 + Openbox + the fine-tuned cortex-phi3:latest LLM (2.2 GB GGUF) + Ollama daemon + C99 prime-moment engine + 7 AI Labs + all 325 K–12 lessons + offline espeak-ng Read-Aloud TTS.

# 1. Clone the repository
git clone https://github.com/batteryphil/SynapseOS.git
cd SynapseOS

# 2. Download, reassemble, verify SHA256, and boot in QEMU (4 GB RAM, 2 CPU Cores):
./scripts/download_and_boot_full_iso.sh

# Or download & verify the .iso file only (for VirtualBox, VMware, or flashing to USB):
./scripts/download_and_boot_full_iso.sh --download
# Output file: output/SynapseOS-Debian12-Potato-x86_64.iso

Option B: Windows (PowerShell) — Download & Combine the Full 2.6 GB LLM ISO

Because GitHub Releases caps individual files at 2.0 GB, the 2.6 GB Full ISO is split into part-aa (1.85 GB) and part-ab (706 MB). Run this in PowerShell to download and join them into SynapseOS-Debian12-Potato-x86_64.iso:

# 1. Download both parts and checksum from GitHub Releases
curl.exe -L -O https://github.com/batteryphil/SynapseOS/releases/download/v1.0-k12/SynapseOS-Debian12-Potato-x86_64.iso.part-aa
curl.exe -L -O https://github.com/batteryphil/SynapseOS/releases/download/v1.0-k12/SynapseOS-Debian12-Potato-x86_64.iso.part-ab
curl.exe -L -O https://github.com/batteryphil/SynapseOS/releases/download/v1.0-k12/SynapseOS-Debian12-Potato-x86_64.iso.sha256

# 2. Combine into a single bootable .iso file (binary copy)
cmd /c "copy /b SynapseOS-Debian12-Potato-x86_64.iso.part-aa + SynapseOS-Debian12-Potato-x86_64.iso.part-ab SynapseOS-Debian12-Potato-x86_64.iso"

# 3. Verify SHA256 hash matches SynapseOS-Debian12-Potato-x86_64.iso.sha256
Get-FileHash .\SynapseOS-Debian12-Potato-x86_64.iso -Algorithm SHA256
Get-Content .\SynapseOS-Debian12-Potato-x86_64.iso.sha256

Option C: Boot in VirtualBox, VMware, or from a Physical USB Stick

VirtualBox / VMware / Proxmox:
- Create a new Linux (Debian 64-bit) VM with 4096 MB (4 GB) RAM and 2 CPU Cores (works down to 1 CPU core). No virtual hard disk is required.
- Attach SynapseOS-Debian12-Potato-x86_64.iso as the Optical CD/DVD drive and boot.
Flash to a Physical USB Drive (Boot on Any PC/Laptop):
- Windows / macOS: Use BalenaEtcher or Rufus (select DD Image mode if prompted) and flash SynapseOS-Debian12-Potato-x86_64.iso.
- Linux:
```
sudo dd if=output/SynapseOS-Debian12-Potato-x86_64.iso of=/dev/sdX bs=4M status=progress oflag=sync
```

Option D: Fast Lite ISO (`437 MB`, No LLM — Single File or In-Repo)

If you want a fast 437 MB download without the 2.2 GB LLM weights (still includes the C99 prime-moment engine, cortex_kb.sqlite, 7 AI Labs, and all 325 K–12 lessons):

Direct Single-File Download: SynapseOS-Lite-x86_64.iso (437 MB on GitHub Releases)

Or Decompress From Git Repo:

./iso/decompress_iso.sh
qemu-system-x86_64 -enable-kvm -m 2048 -smp 2 -cdrom iso/SynapseOS-Lite-x86_64.iso

What Happens on Boot & Tester Checklist

Auto-Login: Boots directly into X11 + Openbox (~148 MB idle OS RAM) as user synapse (password: synapse, passwordless sudo) and launches Synapse Studio automatically.
Synapse Kids Mode (Default View):
- Click L1–L7 level pills at the top to play the 7 interactive AI simulations (Moon Lander, Lidar Car, GridWorld, CartPole, Neural Net, Predator-Prey Swarm, PRIME Attention).
- Click ♪ Read (or click Lumi the Robot) to hear offline speech synthesis read the mission aloud.
- Click EN ▸ FR to toggle everything between English and French, or ❖ Student ↻ to switch student profiles.
Teacher / Pro Workstation & Full K–12 School Curriculum:
- Click ⚙ Teacher in the top-right corner to switch to the 3-column Pro/School Workstation.
- Open the 🎓 School tab on the right: pick any grade from Kindergarten (Yr K) to Grade 12 (Yr 12) (325 complete textbook lessons across Math, Science, ELA, History, and AI).
- Answer the multiple-choice check using the Quiz: [A] [B] [C] [D] bar at the bottom of the textbook reader, or click ▶ Load Lab & Code to run and auto-grade the Python worksheet.
- Open the 📋 Report tab and click ✎ Export to generate a printable HTML + TXT K–12 Report Card & Honors Certificate.
Built-In Fine-Tuned cortex-phi3 LLM + C99 PRIME Copilot:
- Type any question into the AI Copilot Chat bar at the bottom of the window (or open a terminal and run ollama run cortex-phi3 or cortex ai "explain neuroevolution").

ISO Artifact	Uncompressed Rootfs	SquashFS	Bootable `.iso` Size	How to Get It	Contents
`SynapseOS-Debian12-Potato-x86_64.iso` (Full LLM)	`3,998.0 MB`	`2,546.1 MB` (`zstd`)	`2.6 GB`	`./scripts/download_and_boot_full_iso.sh` or Release `v1.0-k12`	Everything in Lite + pre-baked `2.2 GB` `cortex-phi3:latest` GGUF + Ollama runtime + `espeak-ng` TTS
`SynapseOS-Lite-x86_64.iso` (Lite)	`1,561.8 MB`	`376.5 MB` (`xz -Xbcj x86`)	`437 MB`	Direct Download or `./iso/decompress_iso.sh`	Debian 12 + Openbox + 7 AI Labs + 325 K–12 Lessons + `cortex_kb.sqlite` + C99 `prime-moment` (`libprime.so`)

2. 7 Native GTK3 + Cairo AI Labs (`workbench/`) — 35 Presets (`5 per Lab`)

Lab	Paradigm	Interactive Parameters & Brain Surgery	Headless CPU Speed (`12 gens`)
1. NEAT Rocket Moon Lander	Neuroevolution (`NEAT`)	Gravity, Crosswind, Thruster Power, Safe Landing Speed, Pop Size, Add-Neuron/Synapse Rate, Mutation Power, Target Hidden Neurons	`24.1 ms`
2. NEAT Self-Driving Lidar Car	Autonomous Control (`NEAT`)	Lidar Ray Range, Max Speed, Steering Agility, Track Width, Pop Size, Mutation, Target Hidden Neurons	`189.4 ms`
3. RL Hazard GridWorld	Tabular RL (`Q-Learning` & `SARSA`)	Learning Rate ($\alpha$), Discount ($\gamma$), Exploration ($\epsilon$), Lava Penalty, Goal Reward, Slippery Wind	`8.2 ms`
4. CartPole Balance	Neuro-Policy Control	Gravity, Pole Length, Motor Force, Wind Gusts, Pop Size, Mutation Power, Target Hidden Neurons	`31.6 ms`
5. Neural Network Backprop	Deep Learning (`MLP`)	Dataset (`XOR`, `Circles`, `Moons`, `Spiral`), Hidden Neurons, LR ($\eta$), L2 ($\lambda$), Noise, Batch Steps	`64.5 ms`
6. Predator-Prey Swarm	Co-Evolutionary Boids	Prey Flock Size, Predator Count, Vision Radius, Max Speeds, Cohesion Drive, Food Density	`42.8 ms`
7. PRIME Moment Attention	Hybrid Sliding Window + $\mathcal{O}(1)$ C99 Memory	Context Tokens ($N$), Local Window ($W$), PRIME Moments ($K$), EMA Decay ($\gamma$), Recall Temp, Threads	`14.2 ms` (`8,737 tok/s` C99)

3. CLI, Remote Browser Tunnel & Test Commands

# Launch the 7-Lab GTK3 + Cairo AI Workbench GUI (starts in Synapse Kids mode by default; set SYNAPSE_INITIAL_MODE=pro for Teacher Mode)
./synapse studio

# Start encrypted HTTPS Remote Browser Tunnel (noVNC + Cloudflare Quick Tunnel)
./bin/synapse-tunnel synapse-ai

# Run headless benchmark across all 7 AI Labs
./synapse studio --benchmark --gens 12

# Run the native C99 PRIME-Moment O(1) Attention benchmark (10,000 tokens)
./cortex/c_prime/bin/prime

# Query the Three-Tier Cortex Copilot (FTS5 + C99 PRIME Moment + Ollama)
./synapse cortex cmd "compress directory with zstd"
./synapse cortex ai "prime moment attention"

# Rebuild the Lite ISO (385 MB, no LLM) or Full ISO (2.5 GB, with LLM)
./distro/build-iso.sh --lite
./distro/build-iso.sh --all

# Browse & manage the Complete K-12 Rolling Curriculum (Kindergarten + Grades 1-12)
./synapse pack list-years
./synapse pack set-year K            # Install Kindergarten (year_00)
./synapse pack set-year 1            # Install Grade 1 (year_01), wipes old year files, keeps Transcript
./synapse curriculum y00_math        # Run Kindergarten Math + AI Lab
./synapse curriculum y08_science_q2  # Run Grade 8 Science Quarter 2 (Winter Unit) + AI Lab
./synapse pack advance-year          # Graduate to next School Year (wipes old year, saves Report Card)

# Run all automated unit & integration tests
python3 -m unittest discover -s tests

4. Complete K–12 Rolling Year-by-Year School Curriculum (`curricula/year_00.json` – `year_12.json`)

Every grade from Kindergarten (year_00 / Grade K) through Grade 12 (year_12 / Senior Capstone) (13 complete grades) includes 5 Full-Year Subject Courses + 20 Quarterly Unit Lessons (Q1 Fall, Q2 Winter, Q3 Spring, Q4 Capstone) = 325 total K–12 lessons + 91 bilingual EN/FR visual levels:

📐 Mathematics (Counting & Shapes $\rightarrow$ Arithmetic $\rightarrow$ Fractions/Ratios $\rightarrow$ Algebra & Geometry $\rightarrow$ Pre-Calculus, Calculus & Linear Algebra)
🔬 Science (5 Senses & Weather $\rightarrow$ Forces & Ecosystems $\rightarrow$ Genetics, Waves & Chemistry $\rightarrow$ Physics, Thermodynamics & Computational Neuroscience)
📖 Reading & Language Arts (ELA) (Alphabet Phonics $\rightarrow$ Reading Comprehension & Grammar $\rightarrow$ Claim-Evidence-Reasoning (CER) $\rightarrow$ Rhetoric & Technical Peer Review)
🌍 Social Studies & History (Classroom Civics & Maps $\rightarrow$ Ancient Civilizations & U.S./World History $\rightarrow$ Constitution & Economics $\rightarrow$ AI Ethics, Public Policy & TCO)
🤖 Connected AI & Coding Assignment (Hands-on simulation + auto-graded Python worksheet tied directly to that grade's Math, Science, ELA, and History lessons)

Every lesson contains 6 Complete Textbook Sections:

Section 1: Core Textbook Lesson & National Standards Alignment (CCSS Math, NGSS Science, CCSS ELA, NCSS C3, CSTA CS)
Section 2: Full 36-Week / 4-Quarter Course Syllabus (Q1 Weeks 1–9, Q2 Weeks 10–18, Q3 Weeks 19–27, Q4 Weeks 28–36)
Section 3: Academic Vocabulary Glossary (Terms & Definitions)
Section 4: Step-by-Step Worked Textbook Examples
Section 5: Traditional Practice Worksheet & Self-Check Quiz (with Answer Key)
Section 6: Teacher Guide & 100-Point Mastery Rubric (40 pts Core Subject + 30 pts Quiz + 30 pts AI Lab & Python Code)
1-Year-at-a-Time Potato Storage Wiper: Only one school year's curriculum package resides in active classroom storage (~/.config/synapse-os/active_pack/current_pack.json). Advancing a student (./synapse pack advance-year or clicking ★ Yr X ↻ in the Teacher Report tab) wipes the old year's pack and scratch files while permanently preserving the student's Cumulative Multi-Subject Report Card Transcript, Gold Trophies, Coins, and High Scores in student_profile.json.

5. Repository Structure

synapse-os/
├── synapse                                # Unified CLI entrypoint
├── SYNAPSE_OS_SPECS.txt                   # Full technical architecture & benchmark specification
├── curricula/
│   ├── index.json                         # Master manifest of all 13 K-12 School Year Curricula (K-12)
│   └── year_00.json .. year_12.json       # Hosted Kindergarten + Grades 1-12 complete textbook packages
├── bin/
│   ├── synapse-studio                     # Studio desktop launcher
│   └── synapse-tunnel                     # Encrypted HTTPS noVNC + Cloudflare remote classroom tunnel
├── iso/
│   ├── SynapseOS-Lite-x86_64.iso.zst.part-* # 5 chunks (<90 MB each, 374 MB total) of the 385 MB Lite ISO
│   ├── SynapseOS-Lite-x86_64.iso.sha256   # SHA256 checksum for verification
│   └── decompress_iso.sh                  # 1-command ISO reassembly + zstd decompression + SHA256 check
├── cortex/
│   ├── engine.py                          # Three-tier AI engine (FTS5 + C99 PRIME Moment + Ollama + Safety Shield)
│   ├── prime_window.py                    # ctypes binding to libprime.so + O(W+K) context compressor
│   ├── c_prime/                           # Native C99 PRIME-Moment & Sim kernels (GCC + Cosmopolitan + Fil-C)
│   ├── knowledge/cortex_kb.sqlite         # 92 KB SQLite FTS5 BM25 database
│   └── forge/
│       ├── Modelfile.phi3                 # Fast-TTFT (num_ctx=1024) Ollama Modelfile
│       ├── cortex_phi3_train.jsonl        # Curated instruction-tuning dataset
│       ├── hybrid_prime_attention.py      # PyTorch causal-mask Transformer surgery for Phi-3 & Qwen2
│       └── train_phi3_qlora.py            # 4-bit NF4 QLoRA trainer with GPU hardware guardrail
├── workbench/
│   ├── studio_gui.py                      # GTK3 + Cairo 60 FPS 7-Lab GUI + K-12 Teacher Report Card
│   ├── kid_arcade_view.py                 # Full-screen Synapse Kids gamified view (EN/FR toggle)
│   ├── k12_curricula.py                   # 12-Year K-12 Traditional Subjects + AI Curriculum generator
│   ├── pack_manager.py                    # 1-Year-at-a-Time Wiper & Persistent Student Transcript Vault
│   ├── labs/prime_attention_lab.py        # Lab 7: Interactive O(N^2) vs O(W+K) PRIME Moment simulator
│   ├── curriculum.py                      # 35 Lab Examples (5 per lab) & classroom missions
│   └── sandbox_runner.py                  # Subprocess Python sandbox (8s timeout)
├── distro/
│   └── build-iso.sh                       # Debian 12 Live ISO builder (--lite for 385 MB, --all for 2.5 GB)
└── tests/                                 # Automated unit & integration test suite (32 tests)

SynapseOS: Potato-Grade Linux Distro & 7-Lab Native AI Workbench

1. Complete Tester Instructions: Download & Boot SynapseOS (Full 2.6 GB LLM ISO or 437 MB Lite ISO)

Option A: Linux / macOS — 1-Command Full LLM ISO Download & Boot (2.6 GB)