Continuum vs Kel: Which is better?

Continuum is an AI-powered tool designed to aid in software development and coding via natural language interaction. Its core functionality revolves around making coding more intuitive by enabling developers to converse with their codebase, implement new features, refactor, and understand how their own code functions. Possessing the capability to work with large codebases swiftly, Continuum also ensures full encryption and secure handling of all sensitive files, offering optional data security reports without third-party data retention. Its flexibility is eminent in its ability to generate complex code changes, debug exceptions, assist in feature ideation, and explain implementations, grounded fully in the context of your own codebase. Continuum stands different from other codegen tools, like Copilot, by creating a comprehensive model of your codebase, enabling interaction with it in natural language. Despite its sophisticated capabilities, Continuum is designed to be a tool and not an autonomous agent, aiming to increase developer productivity without undermining their decision-making. Enterprise integration options are available with various data sources and communication channels, such as Slack, Trello, Jira, and Asana. For additional security, there is the possibility of self-hosting and single-tenant arrangements for enterprise customers. Continuum supports multiple programming languages and is essentially language-agnostic, but it offers enhanced support for languages like Python, Java, JavaScript, C, C++, Go, and Julia.

Kel is an AI-based assistant embedded in your Command Line Interface (CLI). It is designed to augment productivity by automating repetitive tasks and enhancing the overall efficiency of your CLI. Kel is equipped with multiple Languages for Learning Machines (LLMs) support including OpenAI, Anthropic, and Ollama, whilst also supporting OpenAI Assistants. It further allows users to engage in unique interactions such as 'chatting' with their artifacts by uploading their files to the assistant. Developers can personalize their assistant using their own API keys for OpenAI or Anthropic, a feature that might soon extend to other LLMs.