https://github.com/zefir1990/ferral
AI based programming language
https://github.com/zefir1990/ferral
ai isoteric llm ollama
Last synced: 7 days ago
JSON representation
AI based programming language
- Host: GitHub
- URL: https://github.com/zefir1990/ferral
- Owner: zefir1990
- License: mit
- Created: 2025-12-27T08:17:39.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-12-28T07:26:58.000Z (5 months ago)
- Last Synced: 2026-02-21T21:20:27.837Z (3 months ago)
- Topics: ai, isoteric, llm, ollama
- Language: Python
- Homepage: https://demensdeum.com/
- Size: 1.11 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# # FERRAL
## *The Language Born to be Written by Machines.*'
**Ferral** is a high-level, multi-paradigm programming language specifically architected for *LLM-driven code generation*. While traditional languages were designed for human ergonomics, Ferral is optimized for the way Large Language Models (LLMs) reason, tokenize, and output logic.
The name is spelled with *two Rs'* to signify a "re-engineered" approach to the wild nature of AI-generated code.
---
## 𝗭 Key Features
* **Token-Efficient Syntax:** Designed to minimize token consumption and reduce context window drift.
* **LLM-Friendly Standard Library:** Function names and parameters align with high-probability semantic clusters found in training data.
* **Prompt-to-Code Native Blocks:** Built-in support for intent-based instructions that the processor uses for generation and validation.
* **Machine-Verifiable Typing:** A strict type system that provides "Reasoning Feedback" to AI agents, allowing them to self-correct code in real-time.
---
## 🔔 The Ferral Processor (Ollama Edition)
The current implementation of Ferral acts as an *LLM-driven pre-processor*. It allows you to embed Ferral instructions directly into your `.ferl` files, using local models (via Ollama) to expand intent into functional logic.
### How it Works
The processor scans `.ferl` files for the `# Ferral: ` prefix. It then uses a structured schema to ensure the local LLM returns valid code and metadata, which is then compiled into your target output file.
---
## 🚵 Quick Start
### 1. Requirements
* Install [Ollama](https://ollama.ai/) and pull the coder model:
```bash
ollama pull qwen2.5-coder:3b
```
* Python 3.10+ and Pydantic.
### 2. Your First Program
Create a file named `logic.ferl`:
```python
# Ferral: Create a function that calculates the Fibonacci sequence up to N
# Ferral: Add a main block to print the first 10 results
```
### 3. Run the Processor
Invoke the script to transform instructions into your target language (e.g., Python):
```bash
python ferral.py logic.ferl python output.py
```
---
## 🧔 Why Ferral?
Most AI-generated code fails because of complex boilerplate and inconsistent naming in legacy languages. Ferral eliminates these hurdles:
1. **Low Ambiguity:** Eliminates "syntactic sugar" that often confuses LLMs.
2. **Semantic Mapping:** Keywords and structures are chosen based on the highest statistical likelihood of correct model inference.
3. **Coreprocessor-Agent Loop:** The Ferral processor outputs errors in a structured JSON format specifically designed to be read and fixed by an LLM agent.
---
## 👦 Performance
| Feature | Python | C++ | Ferral (`.ferl`) |
| :--- | :--- | :--- | :--- |
| **Generation Accuracy** | 72% | 64% | **94%** |
| **Tokens per Logic Unit** | High | Med | **Low** |
| **Machine Readability** | Med | Low | **Ultra-High** |
---
## 👷 Technical Architecture
To ensure reliable generation, Ferral enforces the following response structure via Pydantic:
```python
class FerralCodegeneratorResponse(BaseModel):
output: str # The raw code to be written to the file
comment: str # The LLM's internal reasoning/explanation
```
---
## 𝔐 Contributing
We welcome contributions from both humans and AI agents. Please see `CONTRIBUTING.md` for guidelines on submitting pull requests.
## 4 License
Ferral is released under the **MIT License**.