Podcast Summary
GitHub's Arctic Code Vault: Storing Open-Source Code for Thousands of Years and Enabling Real-Time Code Autocompletion: GitHub's Arctic Code Vault stores open-source code on silver film to preserve for thousands of years. OpenAI has used this to build machine learning models for real-time code autocompletion, which has led to challenges with the launch of GitHub Copilot.
GitHub has created an Arctic Code Vault which preserves open-source code on silver film for thousands of years.This snapshot of public repositories has been given to OpenAI, which has built machine learning models to autocomplete code for engineers in real-time as they work.One of the products this has incubated is GitHub Copilot, which has been launched with surprising ethical, scaling, and business model challenges.The Copilot project emerged from a small research and development team in Microsoft, teaching how big bets can be brought from prototype to Microsoft scale.AG1 from Athletic Greens is a product which absorbs 75 vitamins, minerals, probiotics, and adaptogens.
The Power of Creativity in Software Development: Ryan Salva, VP of Product at GitHub, believes in the power of creativity to bring people together. He moved from Microsoft to GitHub to help people create and collaborate on code through repositories, making a significant impact on the software development community.
Ryan J.Salva, VP of Product at GitHub, has a background in philosophy and English.He is interested in how people communicate through creativity and how to convey our experiences of the world to others.He got into software development and product management because he wanted to be involved in the business of creativity.He made the move from Microsoft to GitHub because he saw the potential of GitHub in bringing developers together to collaborate on code through repositories.He is motivated by helping people create and saw that working at GitHub would give him the largest impact.
Introducing GitHub Copilot: The AI-Powered Code Companion: GitHub Copilot revolutionizes software development with AI-driven autocomplete suggestions that save time and boost productivity. It uses CodeX analysis to generate code templates, enabling developers to focus on creativity instead of typing.
GitHub Copilot is an AI-powered tool that offers multi-line autocomplete suggestions to developers while they code, helping them stay in flow and focus on creating instead of typing.It uses an AI model called CodeX, which analyzes variables, class names, and methods to infer what the developer intends to create and provide a code template for them to riff on.Copilot is a huge improvement from simple, intelligent autocomplete and saves time for developers who no longer have to constantly check documentation or look up tutorials.Copilot is a game-changer for software development and has become one of the most magical and addictive products to use.
AI-powered Copilot simplifies coding, assists with unfamiliar languages.: Copilot provides an AI-powered solution to common coding challenges like working with unfamiliar languages and code bases. With its mental map and contextual insight, it makes coding simpler and more accessible.
The use of Copilot, an AI-powered tool, is making it easier for people to learn to code by providing guidance in building real solutions to problems.Copilot is particularly helpful in wading into unfamiliar code bases and working with programming languages that individuals may not be familiar with.Copilot collects context on the code base and provides a mental map for the user.The conceptions for Copilot began as an extension of Microsoft and OpenAI's collaboration on large language models, which are being used to expand the capabilities of AI to include programming languages.
OpenAI collaborates with GitHub to create Copilot for inline auto-complete.: OpenAI used data from GitHub's Arctic Code Vault to create Copilot, which provides inline autocomplete for developers. This collaboration between OpenAI and GitHub has resulted in an efficient user experience after 16 months of experimentation.
OpenAI cloned GitHub's repositories to harvest data, which is a legitimate practice but had consequences for GitHub due to the high amount of requests.So, GitHub provided OpenAI with a data snapshot of public repositories that they had preserved in an Arctic Code Vault.OpenAI used this data to create large language models that allowed for translation and predictive text in programming languages.After experimenting with different user experiences, they came up with Copilot, which provides inline autocomplete for developers.This process took about 16 months, from OpenAI almost taking down GitHub to the creation of the user experience of Copilot.
Developing Copilot: Experimenting with Parameters and Prompt Crafting: The team behind Copilot spent time experimenting with parameters to optimize developers' workflow. They found a sweet spot of 200 milliseconds for model suggestions and developed "prompt crafting" to prompt useful responses.
The team behind Copilot had to experiment with thousands of parameters to create a model that would optimize developers' workflow.One of the key factors they had to consider was the amount of time a user would have to wait for the model's suggestions, with the team finding that 200 milliseconds was the sweet spot.Additionally, they had to experiment with how to prompt the model to return useful responses, which led them to develop what they call "prompt crafting." The team working on Copilot was part of GitHub's "Next" team, which focuses on creating second and third horizon projects that may not yield meaningful results for several years.
Creating Space for Innovation at GitHub: To drive big innovations in a company, create a dedicated team for exploring new ideas. Allow them the freedom to experiment and identify novel solutions, then gradually transition viable projects to the larger organization.
GitHub has a team dedicated to exploring and working on horizon two and horizon three projects that are separate from EPD (engineering, product, and design).This team has the freedom to be creative and experiment without the pressure of meeting fundamental requirements upfront.When they identify an idea that solves a problem in a novel way, they begin market testing and gradually transition it to the EPD team.Companies looking to invest in big moonshots within a larger organization should attract smart people, give them the opportunity to be creative, and only transition their ideas to be monetized when there is clear evidence of solving a customer problem.
Ensuring a Smooth and Balanced Integration for R&D and Product Teams: In order to smoothly transition R&D projects into operational products, it is important to ensure that the entire team is involved and that the roadmap is delegated to the product team. It is also essential to maintain a balance between engineering fundamentals and research visions, and to consider ethical and legal challenges when dealing with AI. One helpful approach is to view AI as an AI pair programmer, with two developers working together to solve problems.
To successfully transition an R&D project into an operational product, it is important to bring the entire team along and delegate the roadmap to the product team.Moving researchers back into their R&D team should be based on the replacement in seat, who has picked up all the necessary skills.Engineering fundamentals are critical and a mix of engineers who can maintain the service and researchers who can bring vision to the idea is important.When dealing with AI, ethical and legal challenges must also be considered.Framing AI as an AI pair programmer can be useful, with two developers working together to solve a problem.
Developing Effective AI for a Positive Programmer Experience: The Copilot AI programmer should enhance, not distract from a developer's work. AI models are evolving to accurately detect and edit out offensive content, creating a better work environment for developers.
The Copilot AI pair programmer needs to be appropriate and not distracting to developers.The product team creates principles or guidelines for the developer experience by having conversations with legal, privacy and security champions, and actual developers.They created a block list of words to edit out offensive content, but this can be tricky and not always effective.The team partnered with the Azure Department of a Responsible AI to create better AI models that can detect offensive content more accurately than a simple block list.The team aims to create an experience that benefits developers and does not cause any offensive instances like the negative bot incident from Microsoft.
The Emergence of AI in the Development Process.: AI is being integrated into the development process to reduce the repetitive task of coding. Copilot is just the beginning, with AI managing build queues and summarizing changes. This will allow developers to focus on creative problem-solving.
AI is becoming increasingly integrated into the development stack and may eventually alleviate the drudgery associated with coding.Currently, Copilot represents just the beginning of a trend in which AI is relied upon for managing build queues or summarizing commit message changes, allowing developers to focus on creative acts.Though there are challenges with the disruption of supply chains for the rare and unique GPUs Copilot requires, the vision for AI integration in the development process is to create a layer of abstraction and provide more opportunities for people to become developers while experienced developers can focus on larger problems and creativity.
Building trust through dialogue and skepticism: Copilot, GitHub's AI tool, is meant to augment developers' work, not replace them. Engaging with the community and prioritizing responsible and ethical use is critical for AI technology.
GitHub's AI tool, Copilot, has faced challenges in gaining community trust due to concerns over ethical use and potential harm.The product team has had to scale up to address these concerns and have engaged in dialogue with the community to ensure responsible use of the technology.The team emphasizes that Copilot is not meant to replace developers or other measures for producing quality code, but rather to augment their work and enable them to focus on more creative tasks.The importance of dialogue and skepticism, as emphasized in the product manager's education in philosophy and literature, plays a critical role in building trust and ensuring ethical use of AI technology.
Balancing Innovation and Incremental Progress in Team Management: To achieve a balance between innovative projects and existing product improvements, allocate roughly 5-10% of resources to bold research and 25-30% to operations, focusing the remainder on iterative improvements. "Make It So," "Brief Interviews with Hideous Men," "The Memory Palace," and "Arrival" offer valuable insights.
When managing a team, it's important to balance bold and experimental research with incremental progress on existing products.Roughly 5-10% of the team's capacity should be reserved for uncertain bets, while 25-30% should be dedicated to operations and the remainder towards iterative improvements on end market products.At startups, the focus is on that one big bet.As for book recommendations, "Make It So" explores user experience through sci-fi references and "Brief Interviews with Hideous Men" offers a collection of short stories about villainous characters.Ryan J.Salva recommends "The Memory Palace" podcast for great storytelling, and the movie "Arrival" for its exploration of language and memory.
The Icebreaker Question That Helps Product Managers Hire Top Candidates: During interviews, product manager Ryan J. Salva asks candidates to teach him something new in one minute, grading them on completeness, complexity, and clarity. His most interesting hire taught him about 18th century art's connection to religious trends.
Ryan J.Salva, a product manager, asks an interesting icebreaker question during interviews to early to mid career candidates, asking them to teach him something new in one minute.He grades them based on three criteria: completeness, complexity, and clarity.The most interesting thing someone taught him was about 18th century art and its connection to religious trends at the time, and he eventually hired her.Ryan is affiliated with Copilot, which offers a 60-day free trial, and he is eager to receive feedback from users on their experience.You can find him on Twitter, GitHub and LinkedIn, under Ryan J.Salva.
