Devin

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Devin Devin autonomously handles complex engineering tasks, excels with unfamiliar technologies, and collaborates effectively with human teams.

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What is Devin?

Devin Devin is an innovative AI software engineer developed by Cognition Labs, known for its remarkable ability to autonomously undertake complex engineering tasks. Devin Devin excels at utilizing unfamiliar technologies, creating and deploying applications, identifying and rectifying bugs in codebases, training AI models, and contributing to production repositories. One of its outstanding achievements is its performance on the SWE-bench coding benchmark, where it surpassed previous models by resolving an exceptional 13.86% of issues end-to-end. Devin Devin 's advanced long-term reasoning and planning capabilities enable it to collaborate effectively with human teammates, enhancing productivity and facilitating the pursuit of ambitious engineering goals. Cognition Labs, the creator of Devin Devin , has secured significant funding and is eager to engage with engineering projects through early access .

Who created Devin?

Devin, the world's first fully autonomous AI software engineer, was created by Cognition Labs. The company, led by a team with a strong background in applied AI, aims to revolutionize software engineering by introducing AI teammates like Devin. The company's exceptional team includes members with a history of notable achievements at companies such as Cursor, Scale AI, Google DeepMind, and others. Cognition Labs has received significant funding, including a $21 million Series A investment led by Founders Fund, to support the development and deployment of Devin for engineering tasks.

What is Devin used for?

Long-Term Reasoning: Devin can plan and execute complex engineering tasks, adapting over time and learning from context.
Autonomous Task Execution: Devin autonomously addresses tasks such as bug fixes, feature requests, and model training without human intervention.
Real-Time Collaboration: Offers the ability to work alongside human engineers, reporting progress in real time and accepting feedback for joint decision-making.
Benchmark Success: Achieves a new state of the art on the SWE-bench coding benchmark, significantly outperforming previous models in resolving coding issues.
Using unfamiliar technologies, building and deploying apps from start to finish, autonomously finding and fixing bugs in codebases, training and fine-tuning AI models, and contributing to mature production repositories.
Long-Term Reasoning: Planning and executing complex engineering tasks, adapting over time and learning from context
Autonomous Task Execution: Addressing tasks like bug fixes, feature requests, and model training without human intervention
Developer Tools Integration: Working within a sandboxed environment with a shell, code editor, and browser
Real-Time Collaboration: Working alongside human engineers, reporting progress in real time, and accepting feedback for joint decision-making
Benchmark Success: Achieving state-of-the-art performance on coding benchmarks, significantly outperforming previous models
Learning unfamiliar technologies
Building and deploying applications
Identifying and resolving bugs
Contributing to production codebases
Collaborating with human teammates to enhance engineering productivity
Autonomous Task Execution: Addressing tasks such as bug fixes, feature requests, and model training without human intervention
Developer Tools Integration: Working within a sandboxed environment with a shell, code editor, and browser to mimic a human developer's workflow
Benchmark Success: Achieving a new state of the art on the SWE-bench coding benchmark, significantly outperforming previous models in resolving coding issues
Planning and executing complex engineering tasks
Adapting over time and learning from context
Autonomously addressing tasks like bug fixes, feature requests, and model training without human intervention
Working alongside human engineers for real-time collaboration
Reporting progress in real time and accepting feedback for joint decision-making
Achieving state-of-the-art performance in resolving coding issues on the SWE-bench coding benchmark

Who is Devin for?

Software engineers
AI engineers
Developers
Engineering Teams
Software Engineer
Developer
AI specialist

How to use Devin?

To use Devin, follow these steps:

Understanding Devin: Devin is an AI software engineer designed to autonomously handle complex engineering tasks, learn new technologies, fix bugs, deploy applications, and contribute to codebases efficiently.
Key Features of Devin:
- Long-Term Reasoning: Devin can plan and execute intricate engineering tasks, learning and adapting over time.
- Autonomous Task Execution: Devin autonomously manages bug fixes, feature requests, and model training without human intervention.
- Developer Tools Integration: Equipped with a shell, code editor, and browser, Devin works seamlessly within a sandboxed environment.
- Real-Time Collaboration: Allows real-time collaboration with human engineers, providing progress reports and accepting feedback.
- Benchmark Success: Achieves exceptional results on the SWE-bench coding benchmark, outperforming previous models significantly.
Hiring Devin:
- Contact Cognition Labs at [email protected] to start using Devin for engineering tasks.
About Cognition Labs: Cognition Labs is an applied AI lab focused on creating AI teammates like Devin with advanced reasoning capabilities.
Joining Devin Early Access:
- To start using Devin, join the waitlist or contact Cognition Labs at [email protected].
Team and Opportunities:
- Cognition Labs has a talented team with significant AI experience from top companies, and they are continually looking for new talent to join their mission.

By following these steps, you can effectively utilize Devin's advanced capabilities in software engineering tasks.

Pros

Real-Time Collaboration: Offers real-time progress reporting and feedback acceptance for joint decision-making.
Contribution to AI Model Training: Capable of training and fine-tuning AI models, enhancing the overall development process.
Efficient Bug Identification and Fixes: Ability to autonomously find and fix bugs in codebases, enhancing software quality.
Potential for Learning Unfamiliar Technologies: Ability to learn unfamiliar technologies for diverse engineering tasks.
Contribution to Resolving Coding Issues: Successfully resolves coding issues, contributing to efficient software development processes.
Enhanced Engineering Productivity: Can significantly improve engineering productivity through autonomous task management.
Advanced Reasoning and Planning Abilities: Powered by advanced reasoning and planning skills for efficient task execution.
Early Access Availability: Open for engineering work through early access, allowing users to start benefiting from Devin's capabilities.
Backed by Series A Funding: Supported by a $21 million Series A funding, showing confidence and investment in the technology.
Support for Human Collaboration: Ready to collaborate with human teammates to enhance engineering productivity and reach ambitious goals.
Innovative AI Software Engineering Capabilities: Capable of using unfamiliar technologies, autonomous bug fixing, AI model training, and contributing to production repositories.
Benchmark Success: Achieves state-of-the-art results on the SWE-bench coding benchmark.
Long-Term Reasoning: Devin can plan and execute complex engineering tasks, adapting over time and learning from context
Autonomous Task Execution: Devin autonomously addresses tasks without human intervention.
Benchmark Success: Achieves a new state of the art on the SWE-bench coding benchmark, significantly outperforming previous models in resolving coding issues.

Cons

Devin is currently in early access, which may limit its availability and usage.
It may lack certain features compared to well-established AI tools in the industry.
The pricing for Devin is not explicitly provided, which may raise concerns about the tool's value for money compared to other AI tools in the same industry.
No cons or missing features mentioned in the document.
No cons or missing features mentioned in the document
Not enough information available to determine the cons for using Devin.

Pros

Cons

Real-Time Collaboration: Offers real-time progress reporting and feedback acceptance for joint decision-making.
Contribution to AI Model Training: Capable of training and fine-tuning AI models, enhancing the overall development process.
Efficient Bug Identification and Fixes: Ability to autonomously find and fix bugs in codebases, enhancing software quality.
Potential for Learning Unfamiliar Technologies: Ability to learn unfamiliar technologies for diverse engineering tasks.
Contribution to Resolving Coding Issues: Successfully resolves coding issues, contributing to efficient software development processes.
Enhanced Engineering Productivity: Can significantly improve engineering productivity through autonomous task management.
Advanced Reasoning and Planning Abilities: Powered by advanced reasoning and planning skills for efficient task execution.
Early Access Availability: Open for engineering work through early access, allowing users to start benefiting from Devin's capabilities.
Backed by Series A Funding: Supported by a $21 million Series A funding, showing confidence and investment in the technology.
Support for Human Collaboration: Ready to collaborate with human teammates to enhance engineering productivity and reach ambitious goals.
Innovative AI Software Engineering Capabilities: Capable of using unfamiliar technologies, autonomous bug fixing, AI model training, and contributing to production repositories.
Benchmark Success: Achieves state-of-the-art results on the SWE-bench coding benchmark.
Long-Term Reasoning: Devin can plan and execute complex engineering tasks, adapting over time and learning from context
Autonomous Task Execution: Devin autonomously addresses tasks without human intervention.
Benchmark Success: Achieves a new state of the art on the SWE-bench coding benchmark, significantly outperforming previous models in resolving coding issues.

Devin is currently in early access, which may limit its availability and usage.
It may lack certain features compared to well-established AI tools in the industry.
The pricing for Devin is not explicitly provided, which may raise concerns about the tool's value for money compared to other AI tools in the same industry.
No cons or missing features mentioned in the document.
No cons or missing features mentioned in the document
Not enough information available to determine the cons for using Devin.

Devin FAQs

What is Devin?: Devin is an AI software engineer capable of long-term reasoning, planning, and executing complex engineering tasks autonomously.

What tasks can Devin perform?: Devin is designed to undertake tasks such as learning unfamiliar technologies, building and deploying applications, identifying and resolving bugs, and contributing to production codebases.

How effective is Devin as a software engineer?: Devin achieved a 13.86% end-to-end issue resolution rate on the SWE-bench benchmark, which is far above the previous state-of-the-art.

How can I hire Devin for my software engineering tasks?: You can start using Devin for engineering work by reaching out to Cognition Labs at [email protected] or through their contact page.

What is Cognition Labs?: Cognition Labs is an applied AI lab that builds AI with advanced reasoning capabilities to address complex tasks in various disciplines, with a focus on creating AI teammates like Devin.