Andrej Karpathy: Best IDE for programming | Lex Fridman Podcast Clips

Summary

这段视频脚本讨论了理想的计算机设置和编程环境，强调了使用大型显示屏和远程Linux集群进行深度学习任务的重要性。提到了VS Code作为当前最受欢迎的集成开发环境（IDE），并讨论了GitHub Copilot的集成，以及它如何帮助提高编程效率和发现新的API。同时，也表达了对未来编程自动化和程序合成发展的担忧，特别是在错误监控和验证方面。

Takeaways

• 🖥️ 个人计算机设置偏好因人而异，有人喜欢多屏幕，有人喜欢单一大屏幕。
• 💻 笔记本电脑和大屏幕是受访者熟悉的高效工作组合。
• 🖥️🐧 操作系统选择上，受访者平时主要使用macOS，但在进行深度学习任务时会使用Linux。
• 🔍 开发工作通常通过SSH远程连接到集群，而实际文件操作在远程位置进行。
• 📝 编辑器选择上，Visual Studio Code（VS Code）是受访者目前最喜欢的编辑器，因为它有大量的扩展和GitHub Copilot集成。
• 🤖 GitHub Copilot受到Python创始人Guido van Rossum的喜爱，它对编程有很大帮助。
• 🧠 与GitHub Copilot的交互需要学习如何判断其建议的有用性，以便更有效地利用它。
• 🔄 GitHub Copilot在代码补全和API建议方面特别有用，尤其是在模式识别和发现不熟悉的API时。
• 🔍 对于Copilot生成的代码，建议通过搜索引擎进一步验证其功能和用法。
• 🚀 程序合成的未来可能会发展为更加自动化，减少人为干预，可能会出现更多针对bug检测的自动化工具。
• 🌐 随着自动化编程工具的发展，程序员的数量和角色可能会发生变化，但目前看来，人类程序员仍不可替代。
• 🔄 语言模型的发展仍处于初级阶段，未来可能会有更多创新的工作方式出现，如与程序进行对话式编程。

Q & A

你认为理想的计算机设置是什么样的？

理想的计算机设置因人而异，但根据这段话，一个常见的设置包括一个大屏幕，例如27英寸的显示器，以及一台笔记本电脑。这样的设置可以提高工作效率，尤其是在进行深度学习等复杂任务时。

为什么在深度学习任务中通常使用Linux操作系统？

在深度学习任务中，Linux操作系统因其稳定性和对各种硬件和软件的广泛支持而受到青睐。此外，Linux提供了丰富的开源工具和库，这对于深度学习开发非常有用。

VS Code是如何与远程文件夹通过SSH进行连接的？

VS Code可以通过SSH远程连接到服务器上的文件夹，这样开发者就可以在本地编辑器中直接操作远程文件。这需要在VS Code中配置SSH连接，然后挂载远程文件夹到本地项目中。

你认为VS Code是目前最好的IDE吗？为什么？

是的，根据这段话，VS Code被认为是目前最好的IDE之一，因为它拥有大量的扩展插件，并且与GitHub Copilot集成，这为开发者提供了极大的便利。

GitHub Copilot对于编程有哪些帮助？

GitHub Copilot可以帮助开发者自动完成代码，提供API建议，甚至帮助发现不熟悉的函数或库。它可以提高编程效率，尤其是在复制粘贴和模式匹配方面。

如何正确使用GitHub Copilot以提高编程效率？

正确使用GitHub Copilot需要开发者学会判断何时应该接受其建议，何时应该忽略。例如，在模式清晰的情况下，Copilot在完成代码模式方面非常擅长；而在发现新API时，它可以提供学习的机会。但开发者应该对Copilot生成的代码进行验证，以确保其正确性。

你认为编程的未来会是怎样的？

编程的未来可能会更加自动化，AI将在编程中扮演更重要的角色。例如，程序合成技术将使得编写更复杂的程序成为可能。同时，可能会出现新的工具来帮助发现和修复bug，以及进行代码审查。

自动化编程会对程序员的数量产生什么影响？

尽管自动化编程可能会改变程序员的工作方式，但并不意味着会大幅度减少程序员的数量。相反，程序员的角色可能会发生变化，他们需要更多地与AI合作，提供指导和监督，确保生成的代码符合要求。

你认为未来的开发环境会有哪些变化？

未来的开发环境可能会更加智能化和交互式。开发者可能需要与AI进行更多对话式交互，不仅仅是编写代码，还包括与AI讨论和调整程序逻辑。同时，开发环境的用户体验和交互设计将变得更加重要。

在自动化编程的趋势下，如何保证代码的质量和安全性？

保证代码质量和安全性需要开发者和AI工具的共同努力。开发者需要对AI生成的代码进行仔细审查，同时，可能会出现新的自动化工具来帮助检测和修复bug。此外，建立严格的测试和验证机制也是确保代码质量的关键。

Outlines

00:00

💻 个人计算机设置与开发环境

本段讨论了个人计算机设置的偏好，包括屏幕数量和类型、操作系统选择以及IDE的使用。提到了Linux在深度学习中的重要性，以及VS Code作为当前最受欢迎的IDE，特别是其扩展性和GitHub Copilot集成。还讨论了编程语言的选择，如Python和C++，以及对编程未来的看法，包括自动化和程序合成的发展。

05:01

🤖 编程自动化与未来趋势

这段内容探讨了编程自动化的未来发展，特别是像GitHub Copilot这样的工具如何改变编程工作。讨论了程序员数量的增长趋势，以及自动化是否会导致程序员数量减少。还提到了如何与AI系统交互，包括指导、审计和验证AI生成的代码。最后，提出了未来编程可能变得更加多语言和灵活，以及UI/UX设计在开发环境中的重要性。

Keywords

💡电脑设置

电脑设置指的是个人或专业使用者为了提高工作效率和满足特定需求而对电脑硬件和软件进行的配置和调整。在视频中，讨论了使用多个屏幕和笔记本电脑的个人电脑设置，以及操作系统的选择，这些都是为了优化编程和深度学习等工作流程。

💡操作系统

操作系统是管理计算机硬件与软件资源的系统软件，它为计算机上运行的应用程序提供服务，并管理如内存、处理器和存储设备等硬件资源。在视频中，提到了macOS和Linux两种操作系统，其中macOS是用于日常任务的主要操作系统，而Linux则用于深度学习等专业领域。

💡集成开发环境（IDE）

集成开发环境（IDE）是一种软件应用程序，它为编程语言和软件开发提供便利的编写、调试和测试代码的环境。IDE通常包括代码编辑器、编译器和调试器等工具。在视频中，VS Code被提及为当前最佳的IDE选择，因为它具有丰富的扩展和与GitHub Copilot的集成。

💡GitHub Copilot

GitHub Copilot是一个由人工智能驱动的编程助手，它可以自动生成代码片段并提供编程建议。它通过分析大量开源代码库来学习编程模式，并在开发过程中为程序员提供帮助。视频中提到了对GitHub Copilot的评价和使用体验，以及它如何帮助提高编程效率。

💡编程语言

编程语言是一种用于创建计算机程序的形式化语言，它规定了如何通过一系列指令来控制计算机的行为。在视频中，讨论了多种编程语言，如Python和C++，以及它们在机器学习应用中的使用。

💡机器学习

机器学习是人工智能的一个分支，它使计算机系统能够通过经验自动改进性能。它通常涉及算法和统计模型的使用，通过数据训练来使计算机能够识别模式并做出决策。视频中提到了深度学习，这是机器学习的一个子集，它使用神经网络来模拟人类大脑处理信息的方式。

💡远程工作

远程工作指的是员工在非传统办公环境下进行工作，如在家或其他地点通过互联网连接到工作场所。这种方式在现代工作场所越来越普遍，特别是在技术行业。视频中提到了通过SSH远程连接到集群进行深度学习工作，这是远程工作的一个例子。

💡自动化

自动化是指使用技术来控制过程和系统，而无需人类直接参与。在编程和软件开发领域，自动化可以提高效率，减少重复性工作，并允许开发者专注于更复杂的问题。视频中提到了GitHub Copilot和其他自动化工具，它们可以自动生成代码，帮助程序员更快地编写程序。

💡人工智能

人工智能（AI）是指让机器模拟人类智能行为的技术和科学领域，包括学习、推理、自我修正和理解语言等能力。视频中多次提到了人工智能，尤其是关于编程助手GitHub Copilot的讨论，它是一个基于人工智能的编程工具。

💡用户界面和用户体验（UI/UX）

用户界面（UI）和用户体验（UX）是指设计和优化软件应用程序的外观、感觉和互动方式，以提高用户的满意度和效率。在视频中，讨论了与AI和自动化编程相关的UI/UX问题，如何与智能系统交互，并确保开发过程对人类用户友好和可审计。

Highlights

讨论了个人计算机设置的偏好，包括屏幕尺寸和操作系统的选择。

提到了在深度学习任务中通常使用Linux操作系统并通过SSH连接到集群。

强调了使用Visual Studio Code（VS Code）作为当前最受欢迎的集成开发环境（IDE）。

提到了Python之父Guido van Rossum对GitHub Copilot的喜爱。

讨论了GitHub Copilot的实用性，尤其是在代码补全和API建议方面。

提到了GitHub Copilot在学习曲线和如何有效利用其输出方面的考量。

探讨了编程自动化的未来，包括程序合成和自动化程度的提高。

预测了未来可能出现的自动化编程工具，如自动生成编译器和代码检查器。

讨论了程序员数量的增长以及自动化对程序员职业未来的影响。

强调了在与AI系统交互时，UI/UX设计的重要性。

提出了与编程语言模型进行对话的可能性，如将代码转换为不同的编程语言。

讨论了开发环境的复杂性，包括硬件、环境变量和自动化脚本的集成。

提出了未来编程可能需要的多语言能力和在不同编程范式间转换的能力。

强调了程序员与AI系统交互时的迭代提示和对话的重要性。

讨论了自动化编程对软件质量和潜在bug问题的担忧。

预测了未来可能出现的AI系统，用于发现和修复代码中的错误。

讨论了语言模型的发展阶段，以及未来可能的发展方向。

Transcripts

what's your computer setup what uh

what's like the perfect are you somebody

that's flexible to no matter what laptop

four screens yeah uh or do you uh prefer

a certain setup that you're most

productive um I guess the one that I'm

familiar with is one large screen uh 27

inch

um and my laptop on the side water

operating system I do Max that's my

primary for all tasks I would say OS X

but when you're working on deep learning

everything is Linux your SSH into a

cluster and you're working remotely but

what about the actual development like

that using the IDE so you would use uh I

think a good way is you just run vs code

um my favorite editor right now on your

Mac but you are actually you have a

remote folder through SSH

um so the actual files that you're

manipulating are on the cluster

somewhere else so what's the best IDE

a code what else did people so I use

emacs still that's cool uh so it may be

cool I don't know if it's maximum

productivity

um so what what do you recommend in

terms of editors you worked with a lot

of software Engineers editors for

python C plus plus machine learning

applications I think the current answer

is vs code currently I believe that's

the best

um IDE it's got a huge amount of

extensions it has a GitHub copilot

um uh integration which I think is very

valuable what do you think about the the

co-pilot integration I was actually uh I

got to talk a bunch with Guido and

Rossum who's the creator of python and

he loves Coppola he like he programs a

lot with it yep uh do you

yeah he's copilot I love it and uh it's

free for me but I will pay for it yeah I

think it's very good and the utility

that I found with it was is in is it I

would say there is a learning curve and

you need to figure out when it's helpful

and when to pay attention to its outputs

and when it's not going to be helpful

where you should not pay attention to it

because if you're just reading its

suggestions all the time it's not a good

way of interacting with it but I think I

was able to sort of like mold myself to

it I find it's very helpful number one

in uh copy paste and replace some parts

so I don't um when the pattern is clear

it's really good at completing the

pattern and number two sometimes it

suggests apis that I'm not aware of so

it tells you about something that you

didn't know so and that's an opportunity

to discover and you it's an opportunity

to see I would never take copilot code

AS given I almost always uh copy a copy

this into a Google Search and you see

what this function is doing and then

you're like oh it's actually actually

exactly what I need thank you copilot so

you learned something so it's been part

of search engine apart maybe getting the

exact syntax correctly that once you see

it yep it's that NP hard thing to say

once you see it you know yes exactly

correct exactly you yourself you can

struggle you can verify efficiently but

you you can't generate efficiently and

copilot really I mean it's it's

autopilot for programming right and

currently is doing the link following

which is like the simple copy paste and

sometimes suggest uh but over time it's

going to become more and more autonomous

and so the same thing will play out in

not just coding but actually across many

many different things probably but

coding is an important one right like

writing programs yeah what how do you

see the future of that developing uh the

program synthesis like being able to

write programs that are more and more

complicated because right now it's human

supervised in interesting ways yes like

what it feels like the transition will

be very painful

my mental model for it is the same thing

will happen as with the autopilot uh So

currently is doing link following is

doing some simple stuff and eventually

we'll be doing autonomy and people will

have to intervene less and less and

there could be like you like testing

mechanisms

like if it writes a function and that

function looks pretty damn correct but

how do you know it's correct because

you're like getting lazier and lazier as

a programmer like your ability to

because like little bugs but I guess it

won't make a little no it will it

copilot will make uh off by one subtle

bugs it has done that to me but do you

think future systems will or is it

really the off by one is actually a

fundamental challenge of programming in

that case it wasn't fundamental and I

think things can improve but uh yeah I

think humans have to supervise I am

nervous about people not supervising

what comes out and what happens to for

example the proliferation of bugs in all

of our systems I'm nervous about that

but I think there will probably be some

other copilots for bug finding and stuff

like that at some point because there

will be like a lot more automation for

uh oh man

so it's like a program a co-pilot that

generates a compiler for one that does a

linter yes one that does like a a type

Checker yes

it's a committee of like a GPT sort of

like and then they'll be like a manager

for the committee yeah and then there'll

be somebody that says a new version of

this is needed we need to regenerate it

yeah there were 10 gpts that were

forwarded and gave 50 suggestions

another one looked at it and picked a

few that they like a bug one looked at

it and it was like it's probably a bug

they got re-ranked by some other thing

and then a final Ensemble uh GPT comes

in it's like okay given everything you

guys have told me this is probably the

next token you know the feeling is the

number of programmers in the world has

been growing and growing very quickly do

you think it's possible that it'll

actually level out and drop to like a

very low number with this kind of world

because then you'll be doing software

2.0 programming

um and you'll be doing this kind of

generation of copilot type systems

programming but you won't be doing the

old school

software 1.0 program I don't currently

think that they're just going to replace

human programmers

um

it's I'm so hesitant saying stuff like

this right because this is going to be

replaced in five years and no it's going

to show that like this is where we

thought because I I agree with you but I

think we might be very surprised

right like what are the next

I I what's your sense of where we stand

with language models like does it feel

like the beginning or the middle or the

end the beginning 100 I think the big

question in my mind is for sure GPT will

be able to program quite well

confidently and so on how do you steer

the system you still have to provide

some guidance to what you actually are

looking for and so how do you steer it

and how do you say how do you talk to it

how do you um

audit it and verify that what is done is

correct and how do you like work with

this and it's as much not just an AI

problem but a UI ux problem yeah um so

beautiful fertile ground for so much

interesting work uh for vs code plus

plus where you're not just it's not just

human programming anymore it's amazing

yeah so you're interacting with the

system so not just one prompt but it's

iterative prompting yeah you're trying

to figure out having a conversation with

the system yeah that actually I mean to

me that's super exciting to have a

conversation with the program I'm

writing

yeah maybe at some point uh you're just

conversing with it it's like okay here's

what I want to do actually this variable

maybe it's not even that low level as

variable but you can also Imagine like

can you translate this to C plus plus

and back to python yeah that already

kind of exists no but just like doing it

as part of the program experience like I

think I'd like to write this function as

C plus plus

or like you just keep changing for

different uh different programs because

they're different syntax maybe I want to

convert this into a functional language

yeah and so like you get to become

multilingual as a programmer and dance

back and forth efficiently yeah I mean I

think the UI ux of it though is like

still very hard to think through because

it's not just about writing code on a

page you have an entire developer

environment you have a bunch of hardware

on it uh you have some environmental

variables you have some scripts that are

running in a chrome job like there's a

lot going on to like working with

computers and how do these uh systems

set up environment flags and work across

multiple machines and set up screen

sessions and automate different

processes like how all that works and is

auditable by humans and so on is like

massive question at the moment