The AI That Learns
To Build Itself
An autonomous coding system that doesn't just execute—it evolves.
Self-Improvement Loop
- Observe performance signals
- Generate and test refinements
- Promote proven upgrades
In a world where AI systems require constant human oversight, GC-Forged-Pylot takes a different path. Built on llama.cpp, it runs entirely on your hardware—no cloud dependencies, no API costs. What makes it remarkable isn't just what it does, but how it learns to do it better.
Built for Builders
Launch experiments, refactor systems, and architect new capabilities while your assistant evolves alongside you.
Local Mastery
Keep sensitive IP on your machines. Every insight the system gains stays with you and your team.
Always On
24/7 execution with adaptive scheduling. Tasks hand off seamlessly between planning, execution, and reflection phases.
Field Notes From Builders
Early adopters are already bending the curve on what autonomous engineering looks like in practice.
“GC-Forged-Pylot refactored a decade-old service in hours and left behind a plan for what to fix next. It feels like pairing with an elite teammate who never gets tired.”
“The local-first design gave us the confidence to use autonomous tooling on sensitive infrastructure projects. Zero external calls, complete transparency.”
“We used the self-improvement cycle to bootstrap internal tools. Every sprint, the agent left our human team better instrumentation than it started with.”
Beyond Conventional AI
Most AI systems are static. They're trained once, deployed, and remain unchanged. GC-Forged-Pylot breaks this paradigm.
Self-Improvement
The system analyzes its own performance, identifies gaps in its capabilities, and generates new code modules to address them. Each task makes it more capable than before.
Hardware Optimization
Automatically detects your CPU, GPU, and memory configuration, then optimizes compilation flags and runtime parameters. From laptops to workstations, it adapts.
Privacy First
Your code, your data, your hardware. No external API calls, no telemetry, no cloud dependencies. Complete sovereignty over your AI assistant.
Autonomous Operation
Provide a task description, set a confidence threshold, and let it work. The system plans, executes, evaluates, and iterates until the job is done right.
Continuous Learning
Each interaction enriches the system's memory. It learns from successes, failures, and user feedback, building a knowledge base that compounds over time.
OpenAI-Compatible API
Drop-in replacement for OpenAI's API. Existing tools and workflows work seamlessly, but now everything runs on your terms, on your infrastructure.
Elegantly Simple
Complexity hidden behind simplicity. One command to start, endless possibilities to explore.
# Launch the autonomous system
python run_autonomous.py "Create a REST API with authentication"
# With custom parameters
python run_autonomous.py "Optimize database queries" \
--cycles 5 \
--threshold 0.95 \
--notify email
# Continuous self-improvement mode
python run_autonomous.py "Monitor system security" --continuous
Intelligent Architecture
A modular design where each component contributes to the whole, creating emergent capabilities greater than the sum of parts.
The cycle repeats until the system achieves the desired confidence threshold, with each iteration building on the lessons of the previous one.
Perception & Context
Ingest natural language briefs, repo state, telemetry, and historical memory snapshots to frame intent.
Strategic Planning
Compose multi-stage execution plans, select tools, and align confidence targets before the first action fires.
Autonomous Delivery
Execute plans with intelligent branching, instant feedback loops, and telemetry-driven adjustments.
Self-Evolution
Harvest learnings, update heuristics, and propose code upgrades that are reviewed, tested, and promoted automatically.
Core Components
LLM Interface, Memory System, Execution Engine, Reasoning Module, Planning System—all working in concert to transform intent into reality.
Bridge Layer
Connects to external systems, processes feedback, manages notifications. The system's gateway to the broader world.
Self-Improvement Engine
The meta-layer that observes, learns, and modifies the system itself. Code that writes code, improving its own foundation.
The "Egg" Philosophy
A minimal seed that grows into something greater.
Deploy Anywhere
Windows, Linux, macOS, edge devices—wherever you have silicon, the system adapts and optimizes itself.
Evolve Continuously
Each task feeds the learning loop. Insights mature into reusable playbooks, tools, and heuristics.
Stay In Control
You decide when to ship upgrades. Review proposed changes, accept, or iterate together.
Like an egg containing all the instructions for life, it carries within it the capacity to adapt, to grow, to evolve. Provide the nutrients—tasks and feedback—and watch capability emerge.
Begin Your Journey
Three steps from curiosity to capability.
1. Clone & Install
git clone https://github.com/NickScherbakov/GC-Forged-Pylot.git
cd GC-Forged-Pylot
pip install -r requirements.txt2. Optimize Hardware
python optimize_llama.py \
--benchmark \
--model ./models/your-model.gguf3. Launch & Evolve
python run_autonomous.py \
"Your task description"For detailed documentation, visit the docs folder.
What's Possible?
The limitations aren't in the system—they're in what we imagine asking it to do.
Development Automation
Generate APIs, refactor codebases, write tests, optimize performance. Let the system handle the repetitive while you focus on the creative.
Research Assistant
Analyze data, generate reports, synthesize information. A tireless collaborator that gets smarter with each project.
System Administration
Monitor logs, detect anomalies, automate maintenance tasks. 24/7 vigilance without the burnout.
Learning Platform
Study AI systems from the inside. Modify the code, watch it adapt, understand how self-improvement works at a fundamental level.
Privacy-Critical Applications
Medical research, legal analysis, proprietary development—anything where data sovereignty is non-negotiable.
Edge Computing
Deploy AI capabilities in bandwidth-limited or offline environments. Intelligence that doesn't depend on the cloud.
Join the Evolution
Great systems aren't built in isolation. They emerge from communities of curious minds.
🌟 Star on GitHub
Show your interest and stay updated with developments
🐛 Report Issues
Found something that could work better? Share your discoveries
💡 Propose Features
Have ideas for capabilities the system should develop? Let's discuss
🔧 Contribute Code
Submit PRs. The system can self-improve, but it learns faster with help
📖 Improve Docs
Help others understand what you've figured out
🎓 Share Knowledge
Blog posts, tutorials, use cases—spread what you've learned
The question isn't whether AI will become more autonomous.
The question is: Will you be part of shaping how?
Start Contributing