StarCoder vs. Cody

StarCoder and StarCoderBase are sophisticated Large Language Models crafted for coding tasks, built from freely available data sourced from GitHub, which includes an extensive array of over 80 programming languages, along with Git commits, GitHub issues, and Jupyter notebooks. Similarly to LLaMA, these models were developed with around 15 billion parameters trained on an astonishing 1 trillion tokens. Additionally, StarCoderBase was specifically optimized with 35 billion Python tokens, culminating in the evolution of what we now recognize as StarCoder. Our assessments revealed that StarCoderBase outperforms other open-source Code LLMs when evaluated against well-known programming benchmarks, matching or even exceeding the performance of proprietary models like OpenAI's code-cushman-001 and the original Codex, which was instrumental in the early development of GitHub Copilot. With a remarkable context length surpassing 8,000 tokens, the StarCoder models can manage more data than any other open LLM available, thus unlocking a plethora of possibilities for innovative applications. This adaptability is further showcased by our ability to engage with the StarCoder models through a series of interactive dialogues, effectively transforming them into versatile technical aides capable of assisting with a wide range of programming challenges. Furthermore, this interactive capability enhances user experience, making it easier for developers to obtain immediate support and insights on complex coding issues.

Cody is a sophisticated AI coding assistant created by Sourcegraph to improve software development's efficiency and quality. It works effortlessly within popular Integrated Development Environments (IDEs) such as VS Code, Visual Studio, Eclipse, and various JetBrains tools, offering features like AI-enhanced chat, code autocompletion, and inline editing, all while preserving existing workflows. Tailored for both solo developers and collaborative teams, Cody focuses on maintaining consistency and quality throughout entire codebases by leveraging extensive context and shared prompts. Moreover, it broadens its contextual insights beyond mere code by integrating with platforms like Notion, Linear, and Prometheus, thus creating a comprehensive picture of the development landscape. By utilizing advanced Large Language Models (LLMs), including Claude 3.5 Sonnet and GPT-4o, Cody provides customized assistance that can be fine-tuned for various applications, striking a balance between speed and performance. Users have reported notable increases in productivity, with some indicating time savings of around 5-6 hours weekly and a doubling of their coding efficiency when utilizing Cody. As developers continue to explore its features, the potential for Cody to transform coding practices becomes increasingly evident.