Cosines New AI Software Developer GENIE Surprises Everyone! (AI Software Engineer)

Summary

Cosign Genie, a fine-tuned version of GPT-4, has achieved a 3.8% score on the SW Bench, setting a new benchmark in software engineering. Unlike traditional AI models, Genie is designed to mimic human software engineers, using unique data sets to understand and solve coding problems. It can fetch issues from GitHub, write and debug code iteratively, and even open pull requests. CoSign's approach to AI development focuses on human-like reasoning, with plans to expand Genie's capabilities across programming languages and frameworks.

Takeaways

• 🚀 Cosign Genie is a new, state-of-the-art fine-tuned version of GPT-4 designed for software engineering tasks.
• 🏆 Genie achieved the highest score on the SW Bench, a software engineering benchmark, with a 3.8% performance rate.
• 🧠 The development approach for Genie was unique, focusing on emulating human reasoning by training on real examples of software engineers' work.
• 🔍 Genie can be prompted with natural language, such as GitHub issues, and it iteratively solves problems by fetching relevant code examples and writing new code.
• 💻 Genie's process includes planning, retrieval, code writing, and code running, all performed in a manner that mimics human software engineers.
• 🔧 Genie has the ability to edit code in place, a task that foundational models often struggle with.
• 🔄 The model is trained using a self-improvement loop, where it learns from its mistakes and corrects them in subsequent training iterations.
• 📈 There's a significant potential for improvement in AI models, as shown by the rapid increase in scores on the SW Bench.
• 🌐 Cosign plans to refine Genie's capabilities, expand its proficiency to more programming languages and frameworks, and create different sizes of AI models for various tasks.
• 📖 The future of Genie includes open-sourcing, pre-training, and the ability to specialize in specific code bases or programming languages.

Q & A

What is CoSign Genie and how does it relate to software development?

CoSign Genie is a state-of-the-art, fine-tuned version of GPT-4 designed to perform software engineering tasks. It is capable of autonomously solving coding problems by emulating human reasoning and decision-making processes.

What is the significance of CoSign Genie's 3.8% performance on the SW Bench?

CoSign Genie's 3.8% performance on the SW Bench signifies its high capability in software engineering tasks, outperforming other models and showcasing its advanced problem-solving abilities in real-world coding scenarios.

How does CoSign Genie's approach differ from other AI models in software engineering?

CoSign Genie is trained on real examples of software engineers' work, focusing on human reasoning and step-by-step decision making. This differs from other models that use base models and prompting, allowing Genie to tackle problems more like a human.

What are the unique data techniques CoSign used to train Genie?

CoSign used techniques that represent perfect information lineage, incremental knowledge discovery, and step-by-step decision making, all of which are designed to mimic how a human engineer logically approaches problem-solving.

How does Genie interact with a real coding problem from a repository?

Genie can be prompted with a natural language description, such as a GitHub issue. It then iteratively fetches relevant files, writes and tests code, and uses debugging tools until it successfully solves the problem.

What advantages does CoSign Genie's data-first approach provide over foundational models?

The data-first approach gives Genie a deep understanding of how software engineers break down and triage issues. It can edit code in place efficiently and has a long context window, allowing it to try multiple solutions without losing information.

How quickly was CoSign Genie able to solve a real problem from an unknown repo?

CoSign Genie solved a real problem from an unknown repository in just 84 seconds, which is significantly faster than what a human could typically achieve.

What does CoSign Genie do after solving a problem?

After solving a problem, Genie writes a pull request (PR) title and body, and opens the PR on the linked GitHub repository through the CoSign web platform, where it can respond to comments and reviews as if it were a human colleague.

What is the future outlook for CoSign Genie according to the script?

The future outlook includes refining the data set to enhance Genie's capabilities, broadening its proficiency in more programming languages and frameworks, and creating different sizes of AI models for various tasks. There's also a plan for an open-source model and pre-training to improve generalization.

How does CoSign Genie's training process involve self-improvement?

CoSign Genie's training process involves using the model's initial attempts to solve problems, correcting its mistakes, and incorporating these corrections into the training data for subsequent versions, leading to iterative improvement.

What are the implications of CoSign Genie's ability to understand specific code bases?

CoSign Genie's ability to understand specific code bases allows it to be tailored to a company's unique programming languages and practices, making it an expert in a company's 'dialect' of code and enhancing its practical utility in real-world software development.