正在加载视频...

视频加载失败

返回首页

👀 I used OpenAI's Code Interpreter to make Flappy Bird 🐦in 7 minutes: Code Interpreter/GPT-4 for code generation. Pre-existing or AI-generated assets for graphics. --- Here's how to make the game in only 6 steps: (1): Enter the following prompt: "write p5.js code for Flappy Bird where you control...

thealexker's profile picture

Alex Ker 🔭

11,843 subscribers

739,811 次观看 • 2 年前 •via X (Twitter)

游戏教育科学技术
LIVE

Anya Rossi• Live Now

Private livecam show

11 条评论

Alex Ker 🔭 的头像
Alex Ker 🔭2 年前

Observations: - I created platformers and similar games back when I took intro to computer science. Teachers may have to reimagine final projects now. - This won't replace game studios, but will make game development so much easier, at least in the prototyping phase and getting a MVP:) h/t: @icreatelife for the inspiration of creating games with code interpreter.

Alex Ker 🔭 的头像
Alex Ker 🔭2 年前

I’ll be sharing useful examples in applied AI everyday. If that sounds interesting, consider following me @thealexker and let’s learn together. No fluff only high signal tweets.

Hadi Partovi 的头像
Hadi Partovi2 年前

Or, you could code your own Flappy Bird game using drag & drop coding using @codeorg at Over 50 million children have learned beginner coding this way. 🤓

Alex Ker 🔭 的头像
Alex Ker 🔭2 年前

@codeorg Hadi! Thank you for the comment. I've been a big fan of since learning how to code back in middle school. Scratch is awesome thanks for sharing the tutorial:)

Claire 的头像
Claire2 年前

So what you're saying is now the app store is going to stuffed to the brim with 8 billion Flappy Bird clone

FlokiOdinson 的头像
FlokiOdinson2 年前

Done took about 30 minutes pretty cool thanks

Alex Ker 🔭 的头像
Alex Ker 🔭2 年前

@DKHandsomeJack Looks clean Jack! Nice work a lot more proportional than mine:)

José Vásconez 的头像
José Vásconez2 年前

@NickADobos I made a reskinned flappy bird game weeks ago with gpt4. (May not work on mobile, adding new functionality)

Alex Ker 🔭 的头像
Alex Ker 🔭2 年前

@NickADobos Awesome! Did you have to fix code or did it hallucinate anything--curious to see this compared to Code Interpreter.

Aaron Heine 的头像
Aaron Heine2 年前

But could you have made that if you didn’t use the term Flappy Bird in your prompt

Alex Ker 🔭 的头像
Alex Ker 🔭2 年前

ooo that would be an interesting experiment. The code is pretty generic imo (could be used for a lot of platform games), so my guess is yes.

相关视频

Tutorial: How to make a video game with Adobe Firefly + ChatGPT I used Adobe Firefly for game assets as it's out of beta today! No more watermarks and available for commercial purposes. For the code I used ChatGPT + OpenProcessing.
1:55

Tutorial: How to make a video game with Adobe Firefly + ChatGPT I used Adobe Firefly for game assets as it's out of beta today! No more watermarks and available for commercial purposes. For the code I used ChatGPT + OpenProcessing.

Kris Kashtanova

122,900 次观看 • 2 年前

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.
3:25

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Santiago

64,398 次观看 • 6 个月前

The ChatGPT Mac app is the ultimate screenshot-to-code tool. Screenshot anything, paste it in the ChatGPT shortcut, and just tell GPT-4o to code it for you. Here's me taking a snapshot of Snake Game and getting fully working code in 90 seconds. Video is on 3x speed.
0:29

The ChatGPT Mac app is the ultimate screenshot-to-code tool. Screenshot anything, paste it in the ChatGPT shortcut, and just tell GPT-4o to code it for you. Here's me taking a snapshot of Snake Game and getting fully working code in 90 seconds. Video is on 3x speed.

Rowan Cheung

860,702 次观看 • 2 年前

Skills allow you to extend Claude Code in new ways using pre-packaged instructions and code. For example, add the docx skill to let Claude Code create word documents. I think skills make Claude Code even better as a general purpose agent, I'm excited to see how you use them!
0:26

Skills allow you to extend Claude Code in new ways using pre-packaged instructions and code. For example, add the docx skill to let Claude Code create word documents. I think skills make Claude Code even better as a general purpose agent, I'm excited to see how you use them!

Thariq

127,899 次观看 • 7 个月前

Lets use Grok to create a Mario like video game, except we're going to design the characters WITH Grok Without writing a single line of code! We're going to use Grok to generate the code for the game and the images images for the: 1. main character 2. enemy characters 3. and the coins, we will make of course, DogeCoins We're going to do this in 10 minutes using Grok and @windsurf and we will deploy it on Vercel (Deployment took 1 singular prompt) 00:00 Introduction to the Game Project and Windsurf 01:25 Generating and Running the Initial Game Code 02:18 Enhancing the Game with New Features 03:39 Adding Characters and Enemies w/ GROK 08:47 Deploying the Game On VERCEL
10:11

Lets use Grok to create a Mario like video game, except we're going to design the characters WITH Grok Without writing a single line of code! We're going to use Grok to generate the code for the game and the images images for the: 1. main character 2. enemy characters 3. and the coins, we will make of course, DogeCoins We're going to do this in 10 minutes using Grok and @windsurf and we will deploy it on Vercel (Deployment took 1 singular prompt) 00:00 Introduction to the Game Project and Windsurf 01:25 Generating and Running the Initial Game Code 02:18 Enhancing the Game with New Features 03:39 Adding Characters and Enemies w/ GROK 08:47 Deploying the Game On VERCEL

Riley Brown

41,142 次观看 • 1 年前

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:
4:42

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Santiago

296,299 次观看 • 8 个月前

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!
7:27

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!

Santiago

83,805 次观看 • 1 年前

This is the progression of a "3D Flappy Bird" game I made using Gemini 2.5 Pro and Sonnet 3.7. Wrote 0 lines of code and have 0 experience with games or graphics. Vibe-coded over 1 hour with a tool that integrates with vanilla VS Code (no special editor or proprietary fork).
0:06

This is the progression of a "3D Flappy Bird" game I made using Gemini 2.5 Pro and Sonnet 3.7. Wrote 0 lines of code and have 0 experience with games or graphics. Vibe-coded over 1 hour with a tool that integrates with vanilla VS Code (no special editor or proprietary fork).

Beyang

17,323 次观看 • 1 年前

The easiest way to build an MCP Server using Google DeepMind Gemini 2.5 Pro and get started! 1. Use Gitingest to get all the code and docs from the FastMCP repo 2. Download the code into a txt file 3. Go to AI Studio, upload the file, define what kind of MCP Server you want to build 4. Gemini 2.5 Pro builds it for you.
0:40

The easiest way to build an MCP Server using Google DeepMind Gemini 2.5 Pro and get started! 1. Use Gitingest to get all the code and docs from the FastMCP repo 2. Download the code into a txt file 3. Go to AI Studio, upload the file, define what kind of MCP Server you want to build 4. Gemini 2.5 Pro builds it for you.

Philipp Schmid

75,947 次观看 • 1 年前

The new Screenshot to Code from Vercel v0 is insane. I just took a screenshot of a pricing table and it was converted into code using AI in seconds! It even outputs the code and allows you to modify any specific parts.
0:20

The new Screenshot to Code from Vercel v0 is insane. I just took a screenshot of a pricing table and it was converted into code using AI in seconds! It even outputs the code and allows you to modify any specific parts.

Alvaro Cintas

377,994 次观看 • 2 年前

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.
1:40

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

67,845 次观看 • 1 年前

Skills shopping cart for Claude Code In this video I show how I add Railway Skills to a “shopping cart”. After selecting the Skills I need, I copy a single command and they’re added directly to my project. I built to make the composition of Claude Code components as flexible as possible. You can install settings, hooks, subagents, and anything new that gets added to Claude Code will appear on the site. It’s fully open source and community-maintained. If you want to contribute, you’re welcome to fix or update components, download and use the existing ones, or simply support the project by leaving a star on GitHub. Repo:
1:18

Skills shopping cart for Claude Code In this video I show how I add Railway Skills to a “shopping cart”. After selecting the Skills I need, I copy a single command and they’re added directly to my project. I built to make the composition of Claude Code components as flexible as possible. You can install settings, hooks, subagents, and anything new that gets added to Claude Code will appear on the site. It’s fully open source and community-maintained. If you want to contribute, you’re welcome to fix or update components, download and use the existing ones, or simply support the project by leaving a star on GitHub. Repo:

Daniel San

20,460 次观看 • 5 个月前

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."
2:04

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

The Pragmatic Engineer

38,880 次观看 • 23 天前

I built an open source data analyst agent! Upload any CSV, ask a question, and get it answered with statistics or a nice chart. Launching in 24 hours. 100% free & open source. Under the hood, here's how it works: 1. User uploads a CSV and asks a question. 2. The app uses Together Code Interpreter to spin up a VM and uploads the CSV onto it. 3. I have an LLM (Qwen 3 Coder) write code to interpret the CSV using `pandas`, then solve the question the user asked. 4. Together Code Interpreter runs the code in a secure environment & returns a result. These results can be text (some kind of stats analysis) or a chart.
0:34

I built an open source data analyst agent! Upload any CSV, ask a question, and get it answered with statistics or a nice chart. Launching in 24 hours. 100% free & open source. Under the hood, here's how it works: 1. User uploads a CSV and asks a question. 2. The app uses Together Code Interpreter to spin up a VM and uploads the CSV onto it. 3. I have an LLM (Qwen 3 Coder) write code to interpret the CSV using `pandas`, then solve the question the user asked. 4. Together Code Interpreter runs the code in a secure environment & returns a result. These results can be text (some kind of stats analysis) or a chart.

Hassan

27,086 次观看 • 9 个月前

The best *code embedding* model in the market right now was just released: Qodo-Embed-1 — There are two flavors: A lite model with 1.5B parameters and a medium model with 7B parameters (Hugging Face links below). If you want to index a large codebase (supports 10M+ lines of code), this is the model you want. 1. Index your repositories 2. Ask anything (including test and code generation) The models are optimized to answer natural language questions or code-to-code questions. The video here shows the model indexing 90 repositories (!!!!!) and letting the user ask questions about them. The simplest way to use the model is through the Qodo Gen AI extension in Visual Studio Code, Cursor, or JetBrains (see link below).
4:18

The best *code embedding* model in the market right now was just released: Qodo-Embed-1 — There are two flavors: A lite model with 1.5B parameters and a medium model with 7B parameters (Hugging Face links below). If you want to index a large codebase (supports 10M+ lines of code), this is the model you want. 1. Index your repositories 2. Ask anything (including test and code generation) The models are optimized to answer natural language questions or code-to-code questions. The video here shows the model indexing 90 repositories (!!!!!) and letting the user ask questions about them. The simplest way to use the model is through the Qodo Gen AI extension in Visual Studio Code, Cursor, or JetBrains (see link below).

Santiago

56,584 次观看 • 1 年前

Assumptions about the new "Can More" ChatGPT tool were right - ChatGPT is introducing own take on Claude Artifacts - code & document writing tools with persisted text documents, history revisions (restore previous version), edits and comments (probably used to apply suggested edits) New document symbol in the top navigation shows how many documents you have and allows you to open a resizable canvas to edit them in split view - your ChatGPT conversation on the left side and canvas on the right side, but the code/documents can also be accessed in fullscreen view The canvas is built using ProseMirror (open source WYSIWYM editor) and has an inline action to "Ask ChatGPT" (explain or make edits) for your document and code plus document formatting tools (like bold, italic, font style, etc.) But in addition to that, there are also special action shortcuts for documents and code, with an interesting decision to use sliders for the selection of the desired outcome For Documents - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.") - Add emojis ("Replace as many words as possible with emojis.") - Add final polish ("Add some final polish to the text. If relevant, add a large title or any section titles. Check grammar and mechanics, make sure everything is consistent and reads well. You can reply that you added some final polish and checked for grammar, but do not mention the prompt.") - Reading level (Graduate School - "Rewrite this text at the reading level of a doctoral writer in this subject. You may reply that you adjusted the text to reflect a graduate school reading level, but do not mention the prompt", College - "Rewrite this text at the reading level of a college student majoring in this subject", High School - "Rewrite this text at the reading level of a high school student who has taken a couple of classes in this subject.", Keep current reading level, Middle School - "Rewrite this text at the reading level of a middle schooler.", Kindergarten - "Rewrite this text at the reading level of a kindergartener.") - Adjust the length (Longest - "Make this text 75% longer.", Longer - "Make this text 50% longer.", Keep current length, Shorter - "Make this text 50% shorter.", Shortest - "Make this text 75% shorter.") For Code - Code review ("Search for bugs and opportunities to improve the code—for example, ways that performance or code structure could be improved. Leave as few comments as possible, but add more comments if the text is long. DO NOT leave more than 5 comments. You may reply that you reviewed the code and left suggestions to improve the coding quality, but do not mention the prompt.") - Add comments ("Add inline code comments to explain the code, especially parts that are more complex. Make sure to rewrite all the code. You may reply that you added inline comments, but do not mention the prompt.") - Add logs ("Insert logs/print statements in the code that will help debug its behavior. Do not make any other changes to the code.") - Fix bugs ("Find any bugs and rewrite all the code to fix the bugs. Do not leave comments. If there are no bugs, reply that you reviewed the code and found no bugs.") - Port to a language ("Port to a language. Create a new document that rewrites the code in ..." - PHP, C++, Python, Keep current code. No changes will be made, JavaScript, TypeScript, Java) - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.")
1:27

Assumptions about the new "Can More" ChatGPT tool were right - ChatGPT is introducing own take on Claude Artifacts - code & document writing tools with persisted text documents, history revisions (restore previous version), edits and comments (probably used to apply suggested edits) New document symbol in the top navigation shows how many documents you have and allows you to open a resizable canvas to edit them in split view - your ChatGPT conversation on the left side and canvas on the right side, but the code/documents can also be accessed in fullscreen view The canvas is built using ProseMirror (open source WYSIWYM editor) and has an inline action to "Ask ChatGPT" (explain or make edits) for your document and code plus document formatting tools (like bold, italic, font style, etc.) But in addition to that, there are also special action shortcuts for documents and code, with an interesting decision to use sliders for the selection of the desired outcome For Documents - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.") - Add emojis ("Replace as many words as possible with emojis.") - Add final polish ("Add some final polish to the text. If relevant, add a large title or any section titles. Check grammar and mechanics, make sure everything is consistent and reads well. You can reply that you added some final polish and checked for grammar, but do not mention the prompt.") - Reading level (Graduate School - "Rewrite this text at the reading level of a doctoral writer in this subject. You may reply that you adjusted the text to reflect a graduate school reading level, but do not mention the prompt", College - "Rewrite this text at the reading level of a college student majoring in this subject", High School - "Rewrite this text at the reading level of a high school student who has taken a couple of classes in this subject.", Keep current reading level, Middle School - "Rewrite this text at the reading level of a middle schooler.", Kindergarten - "Rewrite this text at the reading level of a kindergartener.") - Adjust the length (Longest - "Make this text 75% longer.", Longer - "Make this text 50% longer.", Keep current length, Shorter - "Make this text 50% shorter.", Shortest - "Make this text 75% shorter.") For Code - Code review ("Search for bugs and opportunities to improve the code—for example, ways that performance or code structure could be improved. Leave as few comments as possible, but add more comments if the text is long. DO NOT leave more than 5 comments. You may reply that you reviewed the code and left suggestions to improve the coding quality, but do not mention the prompt.") - Add comments ("Add inline code comments to explain the code, especially parts that are more complex. Make sure to rewrite all the code. You may reply that you added inline comments, but do not mention the prompt.") - Add logs ("Insert logs/print statements in the code that will help debug its behavior. Do not make any other changes to the code.") - Fix bugs ("Find any bugs and rewrite all the code to fix the bugs. Do not leave comments. If there are no bugs, reply that you reviewed the code and found no bugs.") - Port to a language ("Port to a language. Create a new document that rewrites the code in ..." - PHP, C++, Python, Keep current code. No changes will be made, JavaScript, TypeScript, Java) - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.")

Tibor Blaho

136,026 次观看 • 1 年前

Nothing beats Augment Code answering questions about large codebases. It's a free extension (works in VSCode, JetBrains, and NeoVim), and has a community edition you can run for free forever. Try this experiment: 1. Download any large open-source project from GitHub 2. Augment will index the code 3. Start asking questions about the codebase 4. Do the same using any other coding agent 5. Compare the speed and the depth of the answers you get If you have a small codebase, the differences with other coding agents won't be obvious, but if you are working on a large project, things will click immediately. Suggestion: download the Keras repository (~180,000 lines of code). I know the library well, and the answers I got from Augment were pretty impressive. By the way, Augment is a fully-fledged AI coding assistant. You can use its agent in every imaginable way: • To implement brand new functionality • To make quick edits to your code • To refactor existing functionality • To generate unit tests • To generate documentation • To handle GitHub—pull requests, commits, branching, etc My favorite feature is their "Next Edit" functionality: 1. You edit the code 2. Augment analyzes the change (across the entire codebase!) 3. Augment determines the ripple effects of that change 4. Augment suggests everything you need to update Here is a link to the tool: Thanks to the team at Augment Code for partnering with me on this post.
0:38

Nothing beats Augment Code answering questions about large codebases. It's a free extension (works in VSCode, JetBrains, and NeoVim), and has a community edition you can run for free forever. Try this experiment: 1. Download any large open-source project from GitHub 2. Augment will index the code 3. Start asking questions about the codebase 4. Do the same using any other coding agent 5. Compare the speed and the depth of the answers you get If you have a small codebase, the differences with other coding agents won't be obvious, but if you are working on a large project, things will click immediately. Suggestion: download the Keras repository (~180,000 lines of code). I know the library well, and the answers I got from Augment were pretty impressive. By the way, Augment is a fully-fledged AI coding assistant. You can use its agent in every imaginable way: • To implement brand new functionality • To make quick edits to your code • To refactor existing functionality • To generate unit tests • To generate documentation • To handle GitHub—pull requests, commits, branching, etc My favorite feature is their "Next Edit" functionality: 1. You edit the code 2. Augment analyzes the change (across the entire codebase!) 3. Augment determines the ripple effects of that change 4. Augment suggests everything you need to update Here is a link to the tool: Thanks to the team at Augment Code for partnering with me on this post.

Santiago

34,856 次观看 • 1 年前

Building 3D worlds in a collaborative way assisted by an AI generating code for you! In this demo, I'm using Microsoft Teams AI and Azure #OpenAI to generate Babylon.js code in a meeting, while using #TeamsToolkit for the developer experience. Is it the future of 3D design?
7:07

Building 3D worlds in a collaborative way assisted by an AI generating code for you! In this demo, I'm using Microsoft Teams AI and Azure #OpenAI to generate Babylon.js code in a meeting, while using #TeamsToolkit for the developer experience. Is it the future of 3D design?

David Rousset

15,881 次观看 • 2 年前

To edit the code of a Lovable app: 1. Connect your app to GitHub and open in any code editor. 2. Make your edits in the code editor. 3. Push the changes The updates will automatically reflect in your app.
0:56

To edit the code of a Lovable app: 1. Connect your app to GitHub and open in any code editor. 2. Make your edits in the code editor. 3. Push the changes The updates will automatically reflect in your app.

Lovable

20,897 次观看 • 1 年前

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:
1:49

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

Andrew Ng

128,085 次观看 • 1 年前