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[5305.34 --> 5308.18] Jared and I really enjoy this hallway track series. |
[5308.18 --> 5308.58] We do. |
[5308.58 --> 5313.80] When we go to conferences like this, it really takes a lot out of us, but it also puts a lot |
[5313.80 --> 5314.80] right back into us. |
[5314.80 --> 5321.84] Because we get to do shows like this, to have an anthology episode like this with many voices, |
[5321.84 --> 5328.08] many perspectives on how to open source, how to maintain open source, how to support open |
[5328.08 --> 5332.82] source, how to love and support open source software maintainers. |
[5332.82 --> 5335.64] This is why we do what we do. |
[5335.98 --> 5343.06] Because like you, our lives depend on open source software and therefore open source software |
[5343.06 --> 5344.14] maintainers. |
[5344.42 --> 5353.34] So if you haven't yet, head to maintainermonth.github.com and find ways to participate and celebrate |
[5353.34 --> 5354.52] Maintainer Month. |
[5354.74 --> 5360.50] They've got news, a schedule, and a library of resources to tap into. |
[5360.50 --> 5363.86] Again, maintainermonth.github.com. |
[5363.86 --> 5370.62] And also thank you again to our friends at GitHub for helping us get to open source summit |
[5370.62 --> 5371.84] 2023 this year. |
[5372.04 --> 5373.78] It was an absolute blast. |
[5373.90 --> 5378.52] We met so many people and it was an awesome experience recording all these episodes. |
[5378.90 --> 5386.32] And once again, a big thank you to our friends at Fastly, Fly, and also Type Sense. |
[5386.78 --> 5387.90] But that is it. |
[5387.96 --> 5388.86] This show is done. |
[5388.86 --> 5390.86] We will see you on Friday. |
• The Linux Foundation's Open Source Summit North America 2023 in Vancouver, Canada, is discussed |
• Open source AI is the common denominator of conversations, featuring Byung Liu, Danny Lee, and Stella Biederman |
• Byung Liu's team at Sourcegraph is working on Cody, a completely open source model agnostic coding assistant |
• Databricks has released Dolly 2.0, the first open source instruction following LLM fine-tuned on human-generated instruction data |
• Stella Biederman gave a keynote address on generative AI and model training, and AI ethics |
• DevCycle's CTO and co-founder Jonathan Norris discusses the company's uptime reliability and feature flagging tool |
• Cody is a significant interest and a big deal, following Sourcegraph's relaunch as the intelligence platform |
• Sourcegraph's 10-year history and its mission to enable everyone to code |
• The company's early vision and how it has evolved over time |
• The role of large language models in changing the way developers find information and create code |
• The trade-offs between search engines like Google and large language models like ChatGPT |
• The limitations of ChatGPT, such as its reliance on outdated data and its lack of context in search results |
• Discussion of the capabilities of language models, specifically ChatGPT, with plugins enabled |
• Combination of large language models with code search engines |
• Benefits of this combination, including highly context-aware answers and code generation |
• History of the development of large language models, including the release of GPT-3 |
• The author's background in computer science and machine learning |
• The development of Cody, a tool combining large language models and code search engines |
• Potential impact of this technology on software development and engineering |
• Discussion of how humans learn and leverage technology |
• Chat interfaces as a simple, yet effective way to access powerful tools |
• The author's background as a programmer and his enthusiasm for textual input |
• Comparison of textual input and graphical input (e.g. mouse, 4K screens) |
• Importance of human agency in programming computers |
• Evolution of tools like Kodi to become rich REPLs (Read-Eval-Print Loops) |
• Future of Sourcegraph as a tool that integrates various sources of information |
• Discussion of the company's past decade of building advanced code understanding tools |
• The on-ramp to using powerful tools has historically been difficult due to the need for education and steep learning curves. |
• Language models have made it easier to provide powerful tools without requiring extensive education. |
• Sourcegraph is rethinking the user interaction experience to take advantage of language models. |
• Cody is the first iteration of this new user interaction, a conversational AI editor assistant. |
• Cody is an open-source extension available for download in the VS Code marketplace, with other editors to follow. |
• The extension provides features such as inline completions and chat functionality, using the language model's knowledge and the user's code base context. |
• Cody fetches context from local code and Sourcegraph |
• Cody gets intelligence from local codebase as an extension |
• Cody uses a language model to understand the codebase |
• Cody reads relevant pieces of documentation and source code from the codebase |
• Cody answers questions in real-time, typically within 1-2 seconds |
• Sourcegraph is fast and has no latency due to its classical CPU-based code |
• Privacy is extremely important to the company, including individual developers and enterprise customers |
• The company has zero retention policies with language model providers, ensuring data is never used as training data |
• The language model providers forget user data as soon as the request is complete |
• Sourcegraph has always taken user and code privacy seriously to serve enterprise customers. |
• Language models have increased value and sensitivity of user data |
• Users should have control and ownership over their data |
• Large language models can "memorize" user data and potentially be used for malicious purposes |
• Transparency and clear communication about data usage and privacy policies are essential |
• A clear terms of use agreement is presented to users, but its clarity and length are discussed |
• The company aims to be transparent and not hide important information from users |
• The possibility of language models being used for nefarious purposes is acknowledged, but also their potential benefits are highlighted |
• The company is planning to create specialized language models for various domains, including law and software creation. |
• Comparison of Cody and GitHub Copilot |
• Zero sum game vs non-competitive market |
• Features of Cody that set it apart from Copilot |
• Context window and model size of Copilot and Cody |
• Open source and model agnostic approach of Cody |
• Enterprise friendly and proxy friendly features of Cody |
• Legal concerns and lawsuits related to Copilot and Cody |
• Integration of models in Cody for different use cases |
• Cody is a code search engine that can also detect copied code and flag it for users |
• Cody is open-sourced to allow for integrations with various platforms and systems to pull in context |
• Cody is expanding to include more context and integrating with AI tools for code generation |
• The model layer of Cody is being explored for deeper capabilities, including chat-based completions |
• Cody can currently generate code based on natural language instructions, but limitations emerge when adding custom features |
• Future plans include allowing users to self-host Cody for large corporations with strict data retention policies |
• Developing an app without writing code using natural language |
• The challenges of coding with AI and the "whack-a-mole" problem |
• Adding new features to the app using AI |
• Predicting the future of AI and its potential impact on programming and society |
• The spectrum of AI capabilities, from glorified autocomplete to AGI |
• The potential for AI to empower more people to create software and change the way we think about programming |
• The future of coding will be more accessible and "flat", like reading and writing on Twitter, with many different forms of coding |
• The access to coding and AI will be democratized, allowing people to learn and level up quickly, regardless of background or experience |
• Code and AI will become like a "patient sidecar" that can have conversations and assist humans in creative and problem-solving tasks |
• This democratization of coding and AI will be a "fantastic thing" and will have a significant impact on society |
• The author compares language models to a "race car" or "rocket ship" that can greatly increase an individual's productivity and leverage |
• The growth of Cody has been "magical" and has addressed some of the challenges the company faced in getting programmers to adopt their product |
• The author attributes the success of Cody to its accessibility and ease of use, which has bridged the gap between programmers and non-programmers |
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