GitHub, every mention

21 scenes (2023), the whole family · ← back to GitHub

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Verbatim, from the transcripts: the passages where GitHub comes up

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▶ 15:26 Yimei Fan Um, I want everyone walk away and thinking this is not GitHub co-pilot. 2 times in the scene
▶ 20:20 Cristina Pieretti So my, for example, the, the development team that has been working on this, I, I, they're, they're day in and day out using more of GitHub and looking at new technologies. 2 times in the scene

▶ 1:58 Eiso Kant In 2017, Jason had just become CTO of GitHub and GitHub made an acquisition offer for sourced. 5 times in the scene
▶ 2:14 Eiso Kant And we had a lot of the things running things that today that look like copilot where you could, you know, get code suggestions for based on natural language instructions.
▶ 38:55 Eiso Kant This was just like doing the work, like looking at GitHub's research papers, LinkedIn's like all ourselves, you know, and, and we went through about 3200 or 3200 people that we ended up putting on the list.

▶ 40:26 Sharon Zhou It's a very hard thing to be able to deploy these models quickly because it's a new piece of technology, but I will say all of the customers who do deploy fast, uh, essentially will reap the benefits of it because even if you launch…

▶ 4:43 Aravind Srinivas And, uh, GitHub Copilot
▶ 6:15 Aravind Srinivas The biggest hit at the time was GitHub copilot and Dolly two. 2 times in the scene

▶ 1:19 Mathew Lodge Uh, so if we think about generative AI, everyone's heard of ChatGPT and Copilot for the most part, which are large language model based approaches. 4 times in the scene
▶ 27:56 Matt Turck And how do people perceive the fact that it's, uh, fully autonomous, like versus again, uh, you know, copilot, uh, which, uh, as the name indicates, has more of that copilot, uh, kind of, uh, 2 times in the scene
▶ 34:26 Mathew Lodge And, and so, and that's, that's essentially what people are talking about with prompt engineering, because what, what Copilot is doing is basically doing, it's doing a lot of behind the scenes, doing a lot of work on the prompt to try and…

▶ 6:45 Nancy Xu People across the public web, so whether that's your Google Scholar page, your GitHub page, your LinkedIn page, various other pages that have information that might be relevant to whether or not someone is a good fit for an opportunity. 2 times in the scene
▶ 12:29 Nancy Xu Um, so for example, this might be data sources like Crunchbase, GitHub, Google Scholar, et cetera, aggregating all of that, um, feeding it into, um, putting it into various data stores.
▶ 42:13 Nancy Xu Um, but if you look at the use, where those stars are coming from, it's mostly hobbyists, and I'm sure you can monetize hobbyists, um, and sort of grow from there.

▶ 35:07 Stanislas Polu And eventually if you want to give more context to your assistance, you can connect, uh, your notion, connect your Slack, connect your GitHub, and we take care of everything, the kind of synchronization in real time, and obviously the…

▶ 49:32 Shreya Rajpal Uh, and then also check out, you know, the Guardrails website and the GitHub. 2 times in the scene

▶ 22:17 Munjal Shah And so whenever people are like, and math is that way too, like whenever people are like, and I think coding is that way too, because of GitHub, like people are like, oh, horizontal LLMs are much better than vertical LLMs.

▶ 0:48 Matt Turck Uh, so almost 19,000 GitHub stars, 227 contributors, uh, 25,000 Twitter followers, 8000 Discord members.

▶ 12:07 Gideon Mann You know, so, so far the most, um, widely deployed application of the large language models is probably Copilot. 6 times in the scene

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