prolog-ninja 1.0.0

Prolog-powered system optimizer - uses logical reasoning to optimize your system

prolog-ninja

Prolog-powered system optimizer - uses logical reasoning to optimize your computer for specific tasks.

The "wheel within a wheel" - a reasoning engine inside a Python package that analyzes your hardware and generates smart optimization recommendations.

How It Works

1. Detects your system hardware (CPU, RAM, GPU, disk, power status)
2. Converts hardware specs into Prolog-style facts
3. Reasons using rules to determine suitability and optimizations
4. Recommends (or applies) settings for your chosen task

Your Hardware → Facts → Prolog Engine → Rules → Recommendations
     ↓              ↓                      ↓
  CPU: 8 cores   cpu_cores(8)    suitable_for(gaming, good) :-
  RAM: 16GB      ram_gb(16)          has_dedicated_gpu(true),
  GPU: RTX 3080  gpu_vendor(nvidia)  ram_gb(R), R >= 8.

Installation

pip install prolog-ninja

Quick Start

# See your system info
prolog-ninja info

# List available optimization tasks
prolog-ninja list

# Get recommendations for gaming
prolog-ninja optimize gaming

# Check if your system can handle ML training
prolog-ninja check ml_training

# See the reasoning behind recommendations
prolog-ninja explain gaming

# Interactive mode
prolog-ninja interactive

Available Tasks

Task	Description
gaming	Optimize for gaming performance
video_editing	Optimize for video editing (Premiere, DaVinci)
programming	Optimize for software development
ml_training	Optimize for machine learning training
streaming	Optimize for live streaming (OBS)
3d_modeling	Optimize for 3D modeling (Blender, Maya)
audio_production	Optimize for audio production (DAWs)
compiling	Optimize for compiling large projects
virtualization	Optimize for running VMs
data_analysis	Optimize for data analysis (pandas, R)
office	Optimize for office work
web_dev	Optimize for web development
battery_saver	Maximize battery life
quiet	Minimize fan noise
balanced	Balanced performance

Usage Examples

Check System Info

$ prolog-ninja info

==================================================
SYSTEM INFORMATION
==================================================

OS: Linux 6.6.87
    (Running in WSL)

CPU: AMD Ryzen 7 5800X
    Cores: 16 (8 physical)
    Tier: HIGH

RAM: 32.0 GB total (24.5 GB available)
    Tier: HIGH

GPU: NVIDIA GeForce RTX 3080 (10.0 GB VRAM)
    Dedicated: Yes
    Tier: HIGH

Disk: NVME - 500 GB total (234 GB free)
    Tier: HIGH

Power: Desktop (AC)

Get Optimization Recommendations

$ prolog-ninja optimize gaming

============================================================
OPTIMIZATION REPORT: GAMING
============================================================

Task: Optimize for gaming performance

--- System Summary ---
  CPU: 16 cores (high)
  RAM: 32.0 GB (high)
  GPU: nvidia GeForce RTX 3080 (high)
  DISK: nvme (high)

Suitability: EXCELLENT

--- Recommended Optimizations ---
  1. Set power plan to High Performance for maximum speed
  2. Set GPU to prefer maximum performance
  3. Disable unnecessary background applications
  4. Set application priority to High

Run Prolog Queries

$ prolog-ninja query "suitable_for(X, excellent)"

Query: suitable_for(X, excellent)
----------------------------------------
Found 3 solution(s):

  1. X = gaming
  2. X = programming
  3. X = compiling

Explain the Reasoning

$ prolog-ninja explain ml_training

Reasoning Explanation: ML_TRAINING
==================================================

1. System Facts (detected from your system):
----------------------------------------
   cpu_cores(16)
   ram_total_gb(32.0)
   gpu_vendor(nvidia)
   gpu_vram_gb(10.0)
   has_dedicated_gpu(True)
   ...

2. Suitability Query: suitable_for(ml_training, Rating)?
----------------------------------------
   → Rating = excellent

3. Optimization Query: optimize(ml_training, Setting, Value)?
----------------------------------------
   → set_power_mode = high_performance
   → set_cuda_visible_devices = 0
   → set_process_priority = high

4. Warning Query: warning(ml_training, Type, Message)?
----------------------------------------
   → No warnings

Python API

from system_sage import (
    detect_system, get_system_facts,
    PrologEngine, Fact, Rule,
    optimize_for, list_tasks, get_recommendations
)

# Detect your system
system = detect_system()
print(f"CPU: {system['cpu']['cores']} cores")
print(f"GPU: {system['gpu']['vendor']} {system['gpu']['model']}")

# Get recommendations
recs = get_recommendations("gaming")
print(f"Suitability: {recs['suitability']}")
for opt in recs['optimizations']:
    print(f"  - {opt['description']}")

# Use the Prolog engine directly
engine = PrologEngine()
engine.add_fact("likes", "alice", "python")
engine.add_fact("likes", "bob", "prolog")

# Query
from system_sage.reasoner import var, fact
X = var("X")
for solution in engine.query(fact("likes", X, "python")):
    print(f"Who likes Python? {solution['X']}")

The Prolog Engine

prolog-ninja includes a lightweight Prolog-style reasoning engine with:

• Unification - Pattern matching with variables
• Backtracking - Find all solutions via Python generators
• Rules - Define complex relationships
• Built-in predicates - >=, <=, >, <, ==, !=, member, not

from system_sage.reasoner import PrologEngine, var, fact, rule

engine = PrologEngine()

# Add facts
engine.add_fact("parent", "tom", "bob")
engine.add_fact("parent", "tom", "liz")
engine.add_fact("parent", "bob", "ann")

# Add rules
X, Y, Z = var("X"), var("Y"), var("Z")
engine.add_rule(
    fact("grandparent", X, Z),
    fact("parent", X, Y),
    fact("parent", Y, Z)
)

# Query
for sol in engine.query(fact("grandparent", "tom", var("Who"))):
    print(f"Tom is grandparent of: {sol['Who']}")
# Output: Tom is grandparent of: ann

Support

If you find this project useful, consider supporting its development:

• Buy Me a Coffee
• Ko-fi

License

MIT License