Summary Exploring smol developer ft. swyx & Beyang Liu - YouTube (Youtube) www.youtube.com
6,075 words - YouTube video - View YouTube video
One Line
Swyx has launched the "smol developer" project, using GPT-4, Cody, and Copilot to boost productivity, gaining 8,000 stars on GitHub, with discussions covering its potential applications and the importance of leveraging existing code.
Slides
Slide Presentation (11 slides)
Key Points
- Sean Wing, also known as swyx, is a prominent figure in the react community and is known for his publications on various topics, including front-end JavaScript and AI.
- Smol developer is a project by Sean Wing that explores code generation using GPT-4 and aims to provide a developer's perspective on the possibilities of code generation.
- The project utilizes an AI coding assistant called Cody, developed by Sourcegraph, to enhance productivity and assist developers in generating code.
- The core of smol developer is a markdown file that serves as a prompt for generating code, allowing for an infinite level of nesting and effective communication with the AI.
- Cody, the AI coding assistant, is integrated with Sourcegraph and provides access to existing code context. It can answer high-level questions and translate prompts into different languages.
- Smol developer also includes a Chrome extension that can be used on any website, with the generated code scaffolding being editable by humans for customization and further development.
- The discussion around smol developer includes its potential applications in linguistics and brain studies, as well as the importance of writing a good readme file for its performance.
- Another project called "smol lag" is mentioned, which is a logging tool for tracking progress and system behavior. It offers flexibility and cost-effectiveness but may not be suitable for large-scale applications.
Summaries
95 word summary
Swyx, a prominent figure in the react community, has launched the "smol developer" project, utilizing GPT-4 for code generation and AI tools like Cody and Copilot to boost productivity. The project has gained 8,000 stars on GitHub. Swyx uses a markdown file to prompt code generation, resulting in a Chrome extension. Cody, an AI coding assistant integrated with Sourcegraph, can provide project-related answers. The discussion covers the concept of a "smol developer" and its potential applications, along with the logging tool "smol lag." The importance of leveraging existing code for insights and analysis is emphasized.
143 word summary
Sean Wing, also known as swyx, is a well-known figure in the react community and has recently started a project called "smol developer." Smol developer focuses on code generation using GPT-4 and aims to enhance productivity through AI tools like Cody and Copilot. The project has gained popularity with over 8,000 stars on GitHub. Swyx uses a markdown file as a prompt for generating code, which is then used to create a Chrome extension. Cody, the AI coding assistant, is integrated with Sourcegraph and can answer questions about the project. The discussion also explores the concept of a "smol developer" and its potential applications. Another project called "smol lag," a logging tool, is also discussed. The participants highlight the benefits of using smol developer and smol lag in different development scenarios and stress the importance of leveraging existing code for insights and analysis.
428 word summary
Sean Wing, also known as swyx, is a prominent figure in the react community and is known for his publications on various topics. He hosts a popular podcast called “latent spaces” and has recently embarked on a project called “smol developer.” Smol developer is an exploration of code generation using GPT-4 and 100,000 token context windows from a drop. The project aims to provide a developer's perspective on the possibilities of code generation and utilizes an AI coding assistant called Cody, developed by Sourcegraph. Smol developer has gained popularity with over 8,000 stars on GitHub.
Smol developer focuses on program synthesis and aims to enhance productivity through AI tools like Cody and Copilot. Swyx believes that developers should focus on smaller, achievable goals that can be useful in the present.
The core of smol developer is a markdown file that serves as a prompt for generating code. Swyx writes English instructions in the markdown file and uses bullet points and structure to define the sequence of events. The generated code from the prompt is used to create a Chrome extension that can be used on any website.
Cody, the AI coding assistant, is integrated with Sourcegraph and provides access to existing code context. It can answer high-level questions about the small developer project by reading documentation and relevant source files. Plans are underway to support longer windows for Cody's understanding of the prompt structure.
Smol developer and Cody aim to enhance developers' productivity by leveraging AI tools for code generation and assistance. The project encourages developers to explore the possibilities of program synthesis.
The discussion revolves around the concept of a “smol developer” and its potential applications in linguistics and brain studies. The participants mention the importance of writing a good readme file for the smol developer to perform well. They explore how the smol developer can generate a readme by analyzing the code and its prompts.
The conversation then shifts to another project called “smol lag,” which is a logging tool that allows developers to track their progress and analyze system behavior. The participants demonstrate how the tool can log information and save it in a JSON file for performance tracing. They also discuss converting the logs into a CSV file for further analysis.
Overall, the discussion highlights the benefits of using smol developer and smol lag in different development scenarios. The participants emphasize the importance of leveraging existing code and file systems to generate useful insights and facilitate analysis.
Beyang Liu asks for feedback on how to improve Cody and what features or improvements the speaker
859 word summary
Sean Wing, also known as swyx, is a prominent figure in the react community and is known for his publications on various topics, including front-end JavaScript and AI. He hosts a popular podcast called "latent spaces" and has recently embarked on a project called "smol developer." Smol developer is an exploration of code generation using GPT-4 and 100,000 token context windows from a drop. The project aims to provide a developer's perspective on the possibilities of code generation and utilizes an AI coding assistant called Cody, developed by Sourcegraph. The project started as a side project and has gained popularity with over 8,000 stars on GitHub.
Smol developer focuses on program synthesis and aims to enhance productivity through AI tools like Cody and Copilot. The project encourages developers to build their own apps and extensions to maximize productivity. Swyx believes that instead of aiming for complex projects like self-driving cars, developers should focus on smaller, achievable goals that can be useful in the present.
The core of smol developer is a markdown file that serves as a prompt for generating code. Swyx writes English instructions in the markdown file and uses bullet points and structure to define the sequence of events. He also includes CSS and code snippets within the markdown file. Swyx explains that this approach allows for an infinite level of nesting and helps the AI understand the requirements more effectively. He also uses the markdown file as a logbook for debugging purposes.
The generated code from the prompt is used to create a Chrome extension that can be used on any website. Swyx demonstrates the functionality of the extension in a demo video. He emphasizes that the scaffolding generated by the AI can be edited by humans, allowing for manual customization and further development.
Cody, the AI coding assistant, is integrated with Sourcegraph and provides access to existing code context. It can answer high-level questions about the small developer project by reading documentation and relevant source files. Cody's understanding of the prompt structure is based on the contents of the files it reads. While it currently has limitations in terms of context window size, plans are underway to support longer windows.
In addition to English, Cody can also understand other languages and translate prompts accordingly. It showcases a command of diverse languages, despite most of its training corpus being in English.
Overall, smol developer and Cody aim to enhance developers' productivity by leveraging AI tools for code generation and assistance. The project encourages developers to explore the possibilities of program synthesis and emphasizes the importance of starting
The discussion revolves around the concept of a "smol developer" and its potential applications in linguistics and brain studies. The participants mention the importance of writing a good readme file for the smol developer to perform well. They explore how the smol developer can generate a readme by analyzing the code and its prompts. They also discuss its limitations in understanding implementation details.
The conversation then shifts to another project called "smol lag," which is a logging tool that allows developers to track their progress and analyze system behavior. The participants demonstrate how the tool can log information and save it in a JSON file for performance tracing. They also discuss the ability to convert the logs into a CSV file for further analysis using spreadsheets. The participants acknowledge that while the tool is useful, it may not be suitable for large-scale applications and suggest using professional logging platforms. Nonetheless, they appreciate the flexibility and cost-effectiveness of smol lag.
Overall, the discussion highlights the benefits of using smol developer and smol lag in different development scenarios. The participants emphasize the importance of leveraging existing code and file systems to generate useful insights and facilitate analysis. They also appreciate the simplicity and affordability of these tools for individual developers.
Beyang Liu asks for feedback on how to improve Cody and what features or improvements the speaker would like to see. Speaker C suggests that Cody needs better environment awareness and should pay special attention to package manifest and requirements. They also mention that sometimes the questions asked by Cody are wrong and they would like pushback on their questions. They give an example of a server function they were working on and how Cody didn't realize that a file would not be present in the server function when the app is deployed. Beyang Liu agrees with Speaker C's feedback and compares Cody's current capabilities to level 1 self-driving cars, suggesting that Cody needs to have a semantic understanding of the code structure. Speaker C mentions that they are working on a rewrite of small developer to include a loop that does multi-shot and hopes to launch it soon. They discuss the importance of balancing work and communication in AI development. Beyang Liu expresses excitement for the next iteration of small developer. Speaker C talks about "small talk," an open-source code interpreter, and demonstrates its capabilities of generating code and performing data analysis. They mention that small talk will be integrated into small developer's menu bar. Beyang Liu expresses enthusiasm for small talk and thanks Speaker C for sharing their work.
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Source: https://www.youtube.com/watch?v=aFOnU4g07Cs
Page title: Exploring smol developer ft. swyx & Beyang Liu - YouTube
Meta description: We ask our AI coding assistant, Cody, to give us a tour of smol developer, a new tool for generating scaffolding for new code projects.https://cody.devhttps:...