ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: How To Learn To Code In 2026
กำลังสร้างบทเรียน...
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Learning to code is so different in 2026.
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So in this video, I shall explain to you how to learn to code in 2026.
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Starting off with what coding actually looks like today.
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It looks very different.
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Then I'll explain what you should be learning that is still relevant right now and for the future.
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And finally, some tips on how to supercharge your learning.
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Oh, by the way, just in case you have any doubt about whether you should still learn how to code in 2026, the answer is yes.
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There are still so many opportunities that are only available to people who know how to code.
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Plus, learning to code in 2026 faster than it used to be.
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So it's kind of a no brainer if you want to build stuff.
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Now with that out of the way, in case you don't know me, hi, my name is Tina.
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I used to be a data scientist at Meta and I have been coding for 10 years.
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Without further ado, let's go.
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A portion of this video is sponsored by Warped.
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On February 3rd, 2025, Andre Kaparthi, the previous director of AI at Tesla and the founding member of OpenAI, made a viral tweet defining a new term called vibe coding.
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He explains, There's a new kind of coding I call vibe coding, where you fully give in to the vibes, embrace exponentials, and I forget that the code even exists.
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It's not too bad for throwaway weekend projects, but still quite amusing.
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Then, almost exactly a year later, on February 5th, 2026, he made another post.
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except with more oversight and scrutiny.
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Now to differentiate this much more sophisticated, integral workflow from Vibe Coding, he said his current favorite term for it is agentic engineering.
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You have your terminal slash IDE open, where you have multiple coding agents coding furiously away for you.
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And you're like, build this, fix this.
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No, don't do that.
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Do this instead.
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Orchestrating your AI agents like a puppet master.
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But do not be fooled.
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It may look like you're just speaking
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engineer to even do that you need to know how to structure what it is
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that you want to build in a way
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that the ai agents can understand be able to provide context
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and documentation and in order to correct the ai and tell it
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that it's wrong you need to actually know what is right
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to be a good manager you need to know what it is
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that you're managing so that is why
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if you want to learn how to code in 2026 you still need to know the fundamentals of coding
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and engineering but instead of focusing so much on the details of the code implementation itself it's about
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and particular emphasis on security and privacy, because these are the things that AI coding agents tend to struggle the most with.
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Then you can actually move on to take advantage of the AI coding tools
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that we have available today and really do agentic engineering.
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So, should I tell you what it is that you need to learn?
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All right, I'm gonna list out the topics in a roughly chronological order that I think you should be learning, assuming you don't know anything, and also suggest some resources on these topics.
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Okay, so first thing that you want to learn is the basics of coding itself.
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Stuff like variables, types, if statements, loops, object -oriented programming, and APIs.
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Also, I'm using Python as the demo language to learn, which is particularly good if you want to be building AI agents and doing data stuff.
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But if you want to be doing something more like web stuff, like developing web apps, you might want to start off with a language like JavaScript.
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It's okay though, because I'm going to give you guys a prompt at the end of this section
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that you can use to So these are the basic basics.
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Like you really need to know these.
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Otherwise you wouldn't even be able to read the code that the AI generates.
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That sign can't stop me because I can't read.
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And I'm going to put on screen now some of my favorite resources on this topic.
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I personally still recommend going with these human -made resources because we know that they're really good.
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They've been vetted by thousands of engineers.
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Besides, all AI -generated code is based upon code that was written by human developers for decades.
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Next up, you need to learn about software architecture.
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How do you choose a tech stack?
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The system design?
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What kind of APIs should you be using?
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And how the data flows through your software and where is it being stored?
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What kind of databases are you using?
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And testing, testing is so important, especially if you're using AI coding agents.
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You need to understand how to write test cases and how to test different parts of your software.
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And finally, how does deployment work?
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Where are you going to be hosting your software?
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This is like the bread and butter of software engineering.
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And you really need to understand these high level things to
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be able to direct your AI to be building the things
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that you should be building and redirecting it when it's doing something wrong.
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You need to be able to conceptualize what the end product should look like based upon the requirements that you have.
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For example, at work, you might wanna build a software
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that stores a massive amount of information and have lots of people querying it all the time.
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So then you need to be thinking, what sort of database should I be using?
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That is cheap, safe, and fast.
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And how do I design a user interface that allows for multiple people querying at the same time?
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These are all like design questions that you need to be able to understand.
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I'm gonna put on screen now some resources that you can check out to learn about these topics.
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Next up is version control and GitHub.
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I mean a system that's able to track the changes in your files and code over time.
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So you can track the history, revert back if you need to, and collaborate simultaneously with other people as well.
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There are actually a lot of different types of version control software out there.
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The most popular one is called Git.
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So people would use Git in order to do version control and then put their code on something called GitHub.
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GitHub is the industry standard for displaying your code, sharing it, and collaborating with other people.
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This is especially important if you're intending to do agentic engineering, because you want to be tracking the changes that your AI coding agent is doing right?
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And in case it makes a mistake, you can revert it and redirect it without like losing your entire code base,
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which by the way, this happened to a lot of Vibe coders who did not know the fundamentals of coding and engineering.
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I'll put on screen out some resources for you to learn about version control and GitHub.
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All right, next up is security and privacy.
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So this is a topic that was not particularly important when somebody is learning how to code like a few years back.
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Like obviously security and privacy is important, right?
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But it's sort of something that just gets
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Like how does authentication work and how do you like integrate authentication into your apps?
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But the reason why I'm separating this out as its own like little topic here is
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because I think it's particularly important in this day
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and age to learn about security and privacy because it's usually one of the blind spots of AI coding agents.
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So you as the human can't just kind of like hope
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that the AI agent is just going to take care of it.
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You do need to be quite explicit in incorporating these principles.
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Don't worry, this is honestly not like a really big topic.
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It's just really important to know.
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to engineer maybe i'm going to put on screen now a few different resources
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that i suggest you take a look at and learn about privacy
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and security all right
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so at this point you would have covered pretty much the fundamentals of coding
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and software engineering now you have now earned your stripes to
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be able to harness the power of agentic engineering by using ai coding agents yay
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but with that being said i do want to throw in a bonus little topic which is debatable whether it's
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but I personally think it's pretty important, and you're probably gonna have to learn about it anyway, and that is microservices, otherwise known as the concept of containerization.
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This is a software deployment method that packages an application's code together with all of the files, libraries, and dependencies that it needs in order to run that piece of code.
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So it's sort of like little packages, little containers of pieces of software.
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The purpose of this is to isolate the application from its surroundings, so it's not interacting with the rest of your computer, and is able to run consistently across different computer environments,
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different types of clouds.
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This is usually more of an advanced topic
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and people usually learn about this when they're building stuff or like on the job.
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But the reason why I'm putting this here as like an optional
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but maybe you should learn about this kind of topic is as another precaution if you're using AI coding agents.
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You can imagine if you're letting your coding agent or even like multiple coding agents just do whatever it wants, right?
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It can end up messing up your development environment entirely
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or like building things in a way that cannot be properly deployed and used by other people.
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I would be just left like really frustrated because you can't figure out why it's
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take the precaution to containerize you're going to end up with something more reliable
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and if your ai agent does run amok
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and starts doing crazy things it's going to be in its own little environment doing it
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so worst case scenario just shut off
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that environment let me know in the comments of these topics
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i have just covered how many of these topics do you already know
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which ones do you still need to learn curious where you
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guys at on the learning journey as promised i'm also going
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to put a prompt on screen also probably link in description too which you can put into whatever you want.
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And it will help adapt this learning plan
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and resource list to fit the goals of your learning
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and also help you determine which coding language makes the most sense for you to learn.
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Yay, amazing.
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You have now earned your stripes to be able to tackle this new world, this brave new world of agentic engineering.
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Now, agentic engineering itself.
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Gosh, agentic engineering does not sound as cool as vibe coding, it's just not as catchy.
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It's fine.
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Agentic engineering, okay.
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Agentic engineering itself is also a skill.
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It involves how to set up your project and how to set up your AI coding agents, how to communicate what it is that you wanted to build to your coding agents, and how to monitor them correctly, among many other things.
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For agentic engineering, there are different options for which coding agents to use Me personally, I really like using Warp,
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who is also the sponsor of this portion of the video, because it's a pro developer tool that covers the full spectrum of AI coding.
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Getting started is so simple.
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I built this entire project using Warp, but as it scaled, I needed to move faster, and that's where the magic begins.
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Instead of tackling one thing at a time, I deployed multiple coding agents simultaneously.
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Here's one that's adding a backend feature, one on documentation, and one that's writing tests.
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This is all powered by Oz, which is a cloud -based agent orchestration platform The Oz dashboard gives you a real -time view of all your agents.
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At a glance, you can see what they're working on, their status, and any issues that they may have.
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And because it's cloud -based, each agent runs in its own isolated environment, meaning that they won't interfere with each other and my poor MacBook Air can stay alive.
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That is not all.
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You can actually incorporate Oz within your project itself.
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For example, this is a scheduled agent that updates my microsite every day.
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It goes in there, takes the AI news of that day, which is what I care about, and summarizes it and displays that information for me.
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into AI coding agents, integrate them directly with Slack and linear, and tap into a ton of other features that support team workflows.
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700 ,000 engineers are already using it, and 97 % of code diffs get accepted, which honestly is pretty impressive and tracks from my experience too.
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So if you want to get started, you can get started for free at the link linked below.
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Thank you so much for sponsoring this portion of the video.
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Now back to the video.
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Unfortunately, there are not that many good resources on agentic engineering, probably because it's such a new topic.
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But I'm going to put on screen now a couple of videos that I've done and some other resources as well.
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If you guys want, I can also make a video that specifically covers how to do agentic engineering.
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You know what's crazy is that to learn all of these topics that we just discussed, higher to AI, it would have taken you years.
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Like people go to school for this and then work multiple years to get to this level.
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Or even if they're attending like a fast track boot camp, we're still talking like at least 10 to 12 months completely full time.
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good learning techniques and with AI to help with your learning, you can reduce the learning time down by so much.
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I think that if you really focus and if say you're doing it like completely full time, I would estimate maybe like six months to be able to learn all these topics very solidly.
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So in the next section, I'm actually going to share some of my favorite learning techniques that can help you learn faster.
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For those of you who've watched my channel for a while, you probably also know that learning things fast is like my favorite activity.
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So very exciting.
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Let's not talk about tips to supercharge your learning.
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I'm going to divide these tips up into two different categories.
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The first is how to use your resources better, like the lectures, the books.
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And the second is how to use projects for learning
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and specifically how to use an AI coding agent to help you build a project and learn at the same time.
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Two birds with one stone.
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Okay, let's first talk about how to use resources.
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If your resource happens to be like a video or like some text or like readings and stuff like that, I would really recommend using something like Notebook LM and put all that information in there
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generate questions that will guide you along the work.
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This is called primate and can really speed up your learning process
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because you'll understand what are the things that you need to be learning and how they fit together with each other.
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As you're going through the lectures, the text, and like code snippets and whatever, if there's anything you don't understand,
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you can also just poppy paste that and put that into AI to help you explain what's going on.
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When it comes to code and logic, at least of the filming right now, the best AI model to do this would be clod.
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I personally But if you don't want to pay for it and you run out of the free trial, honestly, most modern AI models would be able to help explain code.
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You can even use the free ones, the open source ones, like Quen, Kimmy, or DeepSeek.
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Another way that I really like using AI models to help me learn is by taking a concept
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and asking it to give me examples and analogies, because this will really allow it to solidify in my mind to understand the concept.
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Like, for example, if you're learning about object -oriented programming, you can ask it, explain object -oriented programming to me using an analogy and provide examples.
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Much clearer, I think.
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how to code is to take the code
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and the snippets from the resources that you have like other people's code
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and then ask AI to help you understand the code that's there to explain to you what the code is doing.
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This is so powerful and I'm kind of salty that I did not have this
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option when I was learning how to code.
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To do this you can also just like copy paste
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or like upload the code into an AI model
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or this could be a good opportunity for you to set up an AI coding agent.
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Just give it the code and it would explain to you the structure of the code how the pieces are fitting together, what different lines of code mean, line by line if you want.
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And speaking of projects, this is a good transition to the second category of tips that I have, which is for projects.
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First of all, I want to just emphasize that projects are the best way for you to learn how to code.
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Hands down.
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You can watch like so many lectures, read so many things and whatever, and you think that you know these things.
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Yeah, but you don't unless you actually start building with it.
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And the way you do that is by doing projects.
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So I encourage you to do projects continuously as Make a project in which you use an API or multiple APIs.
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If you're learning about testing, write some tests.
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Try and understand the differences between different tech stacks.
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Try them out yourself, especially with the help of AI coding agents.
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It is so easy to do this.
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You can ask the coding agent something like build me a music storage software with like X by Z specs, built with a Django backend,
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a Next .js frontend, and store the data in a SQLite database, for example.
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engineering.
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I previously mentioned other people's projects as well.
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Another great way for you to learn is to take other people's projects and then adapt them, add on more stuff to them, change around the code and see how the results change.
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This is such a great way to learn and a staple amongst engineering students everywhere.
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Another tip, as you're coding, you can also ask AI to give you multiple suggestions for how to implement something.
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This way you can expand the skill of your knowledge, which is going to make you a much better coder.
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And final little tip, documentation.
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Like, I hate it so much.
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I don't think I've read any documentation since like
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2024
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because i will ask ai to help me understand documentation to
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be clear i still need to understand the documentation i just don't like reading it
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so this is a great way to understand documentation faster
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and incorporate them into your projects i'm not going to go
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into vibe coding slash agentic engineering specific tips right now
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because there's just like way too much and there's a whole other video
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that i can make on this topic
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so i'm just gonna put on screen some of my favorite little vibe coding tips
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if that's helpful for you guys just know that this is not everything and it
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if you never had any experience at all.
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Let me know in the comments if you want me to actually make like a fully fledged video for that topic.
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All right, we have now come to the end of this video.
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Thank you so much for watching until the end.
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I know that was a lot.
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I really hope this is helpful to you.
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I'm so glad that you're learning how to code in 2026.
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That's the luck in your coding journey.
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And I will see you guys in the next video or live stream.
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สิ่งที่คุณจะได้เรียนรู้
ในวิดีโอนี้ คุณจะได้เรียนรู้เกี่ยวกับการเขียนโค้ดในปี 2026 ซึ่งมีการเปลี่ยนแปลงอย่างมาก โดยเฉพาะสิ่งที่คุณควรทราบเกี่ยวกับพื้นฐานของการเขียนโค้ด เช่น ตัวแปร ประเภทข้อมูล การใช้ if statements และ loops นอกจากนี้ยังมีการเน้นที่การเขียนโปรแกรมเชิงวัตถุและการใช้ APIs ซึ่งทั้งหมดนี้จะช่วยปรับปรุงการออกเสียงภาษาอังกฤษของคุณได้อีกด้วย
ฟังเสียงเหล่านี้
ในบทสนทนานี้ คุณจะได้ยินการเชื่อมเสียงและการลดเสียง ซึ่งเป็นสิ่งที่สำคัญในการเรียนรู้การพูดภาษาอังกฤษให้มีประสิทธิภาพ การสังเกตเสียงที่เชื่อมโยงกันจะช่วยให้คุณเข้าใจการพูดแบบธรรมชาติของเจ้าของภาษาได้ดีขึ้น โดยเฉพาะการออกเสียงที่เน้นจุดสำคัญ ซึ่งจะทำให้คุณสามารถฝึกพูดภาษาอังกฤษได้อย่างมั่นใจ
พูดให้เหมือนเจ้าของภาษา
หากคุณต้องการทำให้การพูดภาษาอังกฤษของคุณเป็นธรรมชาติเหมือนเจ้าของภาษา ลองใช้วิธีการ shadowing โดยการเลียนแบบจังหวะและความเครียดในการพูดของผู้พูดในวิดีโอ การฟังและพูดตามจะช่วยให้คุณปรับปรุงการออกเสียงภาษาอังกฤษได้อย่างรวดเร็ว คุณจะรู้สึกถึงความก้าวหน้าในการสื่อสารและเพิ่มความมั่นใจในตัวเอง นอกจากนี้ การเรียนรู้จากวิดีโอยังเป็นแนวทางที่ดีในการฝึกพูดภาษาอังกฤษจากยูทูปที่คุณสามารถทำได้ทุกที่ทุกเวลา
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| “used to” used to + กริยา — นิสัยหรือสภาพในอดีตที่ตอนนี้ไม่เป็นแล้ว | used to be |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | is sponsored · be fooled · They've been vetted |
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | They've been vetted · have now earned · have just covered |
| Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ | people who know · learn, which is · topic which is |
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ