🚀 Add to Chrome – It’s Free - YouTube Summarizer
Category: Software Development Tutorial
Tags: Cursor AILarge Language ModelsPocket FlowPython ProgrammingYouTube Summarization
Entities: cursor AILexpocket flowYouTubeZach
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Hello, I'm Zach. Today I want to share with you a new development paradigm for large language model application.
So I recently built a YouTube summarization tool that uses a large language model to help you extract interesting topics and explain the questions and answers in a very friendly way. Now, the best part here is that I built the whole thing in just 1 hour,
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and so can you. The secret source behind it is pocket flow, a large language model framework in just 100 lines of code.
You heard that right—it's just 100 lines. But the simplicity is its power, because it allows AI assistants like cursor AI to build the application for you.
So in this video,
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I will show you a step-by-step tutorial on how I built such a YouTube summarization application using cursor AI plus pocket flow in just an hour. Let's dive right in.
So let me start by quickly motivating why I built this YouTube summarization tool. Lex just dropped
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a very nice podcast a few weeks ago on the topics of deep seek. I’ve already watched the first 1 hour, and it’s very, very interesting, but the whole video is 5 hours long, and I don’t want
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you know, how China’s AI models are changing the global race, why reasoning matters, how flow is. pocket flow is a large language model framework in just 100 lines of code,
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and you can see all the source code right here. It is essentially a Python library that represents the class of a node, a graph, and extension—and that’s it.
Building on top of all these graph representations, we can express all these popular design patterns like workflow,
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agents, retrieval augmented generation, and so on. Its minimalism is actually its power, because it is so simple that an AI assistant like cursor AI can easily pick it up.
So next I will show you how to set up pocket flow with cursor AI. We have already prepared
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a Python template for you, which includes the file structure for the most commonly used Python large language model application using pocket flow. You can simply use this template or download this template, and then upload it into your cursor projects and set it in the root directory. Now,
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the secret source here is this cursor root file, which is essentially the documentation of
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“Help me describe briefly about pocket flow,” and, as you can see here, because of the cursor root file, cursor AI already has pocket flow in its context, and it understands the framework,
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and it’s ready to help you build applications based on pocket flow. Now, in some cases, you may run into issues when cursor AI doesn’t have the context of pocket flow, so a foolproof way to make sure it works is, you know, simply go to the root files and copy-paste the whole documentation into the user rules.
In this case,
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asking cursor AI, “Help me build a project that summarizes YouTube videos. It takes a YouTube video as input, extracts interesting topics, and generates question–answers, then explains the above in a very friendly manner (as if I' m 5), and finally generates an HTML page to visualize the summary.” In just a few minutes, cursor AI has already drafted
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the project requirements, which look solid, and the different utility functions. We have YouTube video processing, audio transcription, large language model processing that chunks text, HTML generation, and file saving.
This is a good start, but usually, you know, we don’t really want
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to process the video and audio ourselves because YouTube should have already done that for us, and also I don’t think text chunking is necessary, because the transcript is not that long and can already fit well into the context of the current large language models. So let’s just say, “Hey, help us simplify the utility functions. Don’t process audio.
Just find a solution
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to get the transcript directly, and you don’t need to chunk the text.” So very quickly, cursor AI has helped us simplify the utility functions for only YouTube transcription, large language model, HTML, and file saving. This looks good.
Let’s just say,
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“Hey, finish this part for the design doc and write the code.” In a few more minutes, cursor has already finished the implementation. It starts by going through the file directory to explore the project. It then updates the design doc, so essentially filling in the requirements
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and describing the utility functions, and also designing the flow for us. Then it also implements the utility functions and updates the requirements, which is good.
For the utility functions, you usually want to test them out yourself, so we can just, you know,
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go through the terminals and study from the core model. We can see if this function works by just using it to ask a question about what’s the meaning of life, and it responds well.
Then let’s check the utility function. So here it generates a transcript given a YouTube link.
Oh,
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it also gives us a default YouTube link—let’s see what the video is for. Okay, we just got rick rolled by cursor AI, which is pretty nice, but let’s see if the function works or not.
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Unfortunately, it doesn’t work. There seem to be issues with this function, but let me figure out— All right, so I have fixed the issues for the YouTube transcription.
It turns out that the previous version was using pytube, which is a pretty old version that causes a lot of trouble,
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so I just removed the dependency and just used the YouTube transcript API, and it works pretty well. So we can check this out by just calling Python on the YouTube utilities functions, and it successfully gets the title of “Never Gonna Give Up” and the transcript.
Also, utility
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functions are usually the most unreliable part of the project because they depend on external APIs, and we have zero control over the other side, so you usually want to test these utility functions pretty well ahead of time to make sure they’re robust and reliable. Another thing I did here is also merge the file utility and the HTML utility into one
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single function so, you know, it can both generate the HTML and save it together. So
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the design of the workflow for the large model application. cursor AI has already helped us make an initial draft of the workflow: starting from the input YouTube video, it will extract the transcript and different topics, generate the summary and the questions, and finally generate
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and save the HTML code. This is a good starting point, but I think it misses a very important design pattern called map–reduce.
Here, the workflow generates a list of topics for us, but each topic is kind of independent, and we’re going to generate a summary and a question–answer
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for each of these topics. Instead of having one node that does the task for all the summary, what we want is a map process that branches based on topics.
We have one large language model call
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Okay, so now let’s implement such a map–reduce design pattern by simply asking cursor AI to do so. All right, in just a few minutes, we have cursor AI helping us update the design doc.
So we can take a look at the updated workflow: after we extract information from the video,
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we’re going to have this map–reduce processing. It will first identify all the interesting topics from the video, then it will process each individual topic one by one, and finally reduce the results by aggregating the topics, questions, and answers.
We can see
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that for the processing node, we’re going to have a batch node—essentially, it’s not a single call; it’s a batch of large model calls, one for each topic. Now, this design looks great.
Let’s finally go ahead and implement the design. The implementation is actually the easiest part,
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because we can just simply ask cursor AI, “Help me implement based on the design.” In around 5 minutes, cursor has finished the implementation of the workflow. As you can see, it starts by reading through the contextual file, then it populates the flow.py,
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and here’s how flow.py looks. It essentially implements all the nodes described in the design doc, starting from the input nodes, then extracting video information, and so on.
Finally, all these nodes are connected together into a workflow that is used to generate the final
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YouTube summary. We can test this out by just going through main.py and providing the Lex YouTube video as input.
It extracts the YouTube information and generates interesting topics,
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which will take a while because it’s quite a large transcript, but let’s wait a few minutes. So finally, in about 3 minutes, the script has finished, and we have the resulting
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summary in this HTML page. We can render this HTML and see a nice visualization of the YouTube title and thumbnail.
We also have a kid-friendly summary of these YouTube videos, some topics on AI models, open weights AI, and a few interesting questions under these topics
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like what’s the AI reasoning part, what’s the political implication, and why companies need electricity. There’s also a topic on hardware and megaclusters.
We can see that this is a much, much higher-quality summarization compared to the naive single-prompt approach.
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So this concludes this video tutorial. Hopefully, you are somehow convinced that this is the
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I plan to do more tutorials on different applications, so if there is any specific application you want to see me build, please let me know. See you next time.