AIForge — Autonomous AI Engineering Workspace

Software development involves much more than generating code. Developers need to understand requirements, plan the implementation, work across multiple files, run tests, debug failures, verify security, and prepare the final application for deployment. Managing all of these steps manually can be slow and repetitive, especially for students, small teams, and developers working on rapidly changing projects. AIForge is our attempt to bring this complete workflow into one engineering workspace. Instead of asking an AI only for a piece of code, users can give AIForge a complete software requirement in natural language. The autonomous engineering agent first understands the requirement and existing project context, identifies relevant files, creates an implementation plan, and then executes the changes across the project. It can run tests, detect failures, perform bounded automated repairs, and repeat verification before considering the task complete. The platform also includes project generation, existing-project modification, security and secret scanning, application startup verification, API verification, ZIP packaging, and independent artifact verification. This means the goal is not simply to produce code, but to produce a project that has actually gone through an engineering validation process. AIForge also provides a multi-LLM architecture. Gemini is supported alongside OpenAI and Anthropic Claude, allowing users to configure and switch AI providers instead of being permanently dependent on a single model. API credentials are handled through secure configuration and are not exposed to generated projects or frontend responses. Beyond software generation, AIForge includes a Dataset & ML workspace for inspecting datasets, training machine-learning models, evaluating metrics such as accuracy, precision, recall and F1 score, and experimenting through an inference playground. A technical Report Intelligence workspace turns the development run into a structured report containing architecture, process telemetry, testing, repair activity, security checks, and artifact information. Our target users are developers, students, researchers, startups, and engineering teams who want to move from an idea to a validated software project faster while keeping visibility into what the AI is actually doing. What makes AIForge different is the focus on the complete engineering loop rather than code generation alone: Requirement → Understanding → Planning → Implementation → Testing → Repair → Security → Verification → Packaging → Report We designed the interface as a scientific/cyberpunk engineering workspace so that users can see the AI agent's progress, project files, test results, provider status, ML experiments, and verification information instead of treating AI development as a black box.