The StarkNet AI Transaction Simulator is a web-based application that uses advanced AI models to replicate StarkNet network behavior and simulate user-defined transactions. It forecasts gas usage, potential reverts, and contract state changes by interpreting transaction parameters through machine learning. Users can iteratively adjust inputs, explore alternative execution paths, and verify expected outcomes before deployment. The platform supports multi-step contract interactions, token transfers, and DeFi operations, providing detailed logs and AI-driven recommendations to streamline development and audit processes.

Agentless is a lightweight, agent-free framework designed to streamline AI-driven code automation workflows. By integrating directly with large language models via API calls, it generates, executes, and validates code in real time across diverse environments. Developers define tasks in YAML or JSON workflows and extend functionality through a plugin architecture supporting multiple programming languages. Agentless eliminates the overhead of dedicated agent processes, simplifying deployment and monitoring. It offers built-in connectors for GitHub Actions, Jenkins, and other CI/CD systems, plus automated testing modules for code review, unit test generation, and static analysis to ensure high-quality output.

The Java AgentSpeak Consistency Checker loads AgentSpeak source files, analyzes belief bases, goal definitions, and plan structures, then runs a suite of consistency and validity tests. It identifies conflicting plans, unreachable goals, and inconsistent belief updates, generating detailed reports. By integrating into build processes or CI pipelines, it helps developers catch design flaws early, maintain robust BDI agents, and accelerate multi-agent application development.

Copilot Chat is an AI-powered conversational tool designed to assist developers in generating code using a test-driven development approach. It allows users to specify test cases that represent their coding intentions. Copilot Chat interacts with a large language model (LLM) to create, validate, and iteratively refine the code until it passes all test cases. This tool aims to simplify the coding process, reduce debugging time, and improve code quality by ensuring that generated code meets predefined test criteria.