AI Coder

Please check out the neural sketch related code :

https://github.com/bhagvank/AI-Coder/

AI Coder is an AI bot that generates code in PHP, Ruby on Rails, Django, Java, and Python — including Python/Django and other frameworks. Rather than requiring developers to write code by hand, AI Coder is given basic information about the entities involved in the application, and it produces the corresponding code.

AI Coder is built on neural sketch learning: an artificial neural network trained to recognize code-level patterns across different software by "reading" large volumes of human-written code and repository content. From this training it learns to generate code based on an application's components.

Key capabilities include:

Generating application code from entity, rule, tool, and protocol definitions
Reading from a code repository and generating code based on existing components
Creating sample code and unit tests for software frameworks
Generating test data
Extensibility to support additional software APIs for code generation
How It Works
Neural Sketch Learning
Code generation is driven by a syntax and semantics specification supplied as input. The framework is trained by being shown applications that are already configured within it (a supervised learning approach). Training input consists of a set of applications configured in terms of their views, entities, and actions; metadata from these configurations teaches the framework the application's semantics — its data types, methods, control flow, exceptions, and entity types.

The neural sketch framework combines neural learning and combinatorial search to identify patterns and render applications, while enforcing the constraints defined in the configuration file. Internally, the source code can build tree-based syntactic models ("sketches") of a program.

A sketch corresponds to a class, entity, or method — including its arguments, return values, and control flow.

Application Framework
The framework follows a metadata-driven architecture, with metadata stored and modeled using the object repository pattern. Metadata is read to render applications assuming a microservices-based architecture with the following layers:

Service layer
Services client (mobile)
Persistence layer
Data access layer
Database layer
Each layer is implemented using language SDK– and platform-specific patterns and practices, with metadata attributes configured per layer. Applications built with the framework are configurable and extensible through events, delegates, templates, services, and classes, and support customization without code changes (with custom modules loadable when code changes are needed).

This approach targets native, hybrid, and web-based mobile applications, and can be used across iOS, Android, and Windows, along with their respective programming language SDKs. Because the approach is not tied to a specific technology stack, it can be upgraded by changing the framework's implementation in the relevant layer.

Application Architecture
The application architecture is designed to be extended with commercial packages and software, using only the functionality required, and includes:

Security & licensing enforcement
Auditing of inserts, edits, and deletions
Multi-tiered, entity-context sensitive security for data and actions across user roles and groups
Admin-level metadata management through screens, plus developer-level configuration of metadata and programmatic updates