Pratica di Shadowing: 12 Important Concepts In the Age of AI Software Development - Impara a parlare inglese con i video
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Hey guys, so obviously things are changing.
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They have changed a lot over the past couple years, especially the past year, and it's only speeding up.
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And we've gone from kind of a standard workflow of writing
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code to using different AI tools with more attention on things like planning, reading, reviewing, and testing code.
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Now with that said, just because you won't be writing as much code anymore, or maybe not even writing any code,
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it doesn't mean that you shouldn't understand what's going on and what the AI is generating.
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I think being able to read code is now one of the most important skills.
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So I think the focus is shifting a bit.
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Fundamentals like functions, loops, conditionals, data types, that's all still a must.
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You have to learn those.
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Without those, you're pretty much going to be lost.
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But it's less important to memorize every function or method in a language or a library or framework.
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Most developers were looking that stuff up before anyway.
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I think what really matters now is why it belongs there, the data that it's working with,
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and just how it fits into the rest of the project.
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So in this video, I want to go over the concepts
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that I think are most important to understand if you're going to be using AI as a developer.
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And this is not a full lesson on each one.
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It's more of a checklist of things to pay attention to when AI gives you code.
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So let's get into it.
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So before we dive in, this video is sponsored by CodeRabbit.
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You know that feeling when a PR comes in and there's 47 files changed, GitHub just throws them at you alphabetically.
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There's no sense of what depends on what, there's no logical order.
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And now that AI is writing more of our code, these PRs are only getting bigger.
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So CodeRabbit review was built specifically for this.
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It reorganizes the diff into groups of related changes with ordered layers
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so that Foundational stuff like data models come before the code
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that depends on them and every layer has an AI summary
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and inline diagrams You can also drop inline comments You can navigate with keyboard shortcuts
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and you can submit your github review without ever leaving the tab It's also opt -in per reviewer
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so your teammates who prefer the default github experience can just ignore it entirely
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And it's free to try right now during early access comment.
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You can read more about it in the link in the description below.
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All right, guys, so we're going to cover a bunch of very important concepts, and I'm not going to just define what that concept is, but I'm going to relate it to agentic coding.
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And of course, these are important regardless, even, you know, long before AI, but I'm going to, like I said, relate them to coding with AI.
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Now, the first three are very closely related, and they're about the flow of your program.
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So number one is control flow.
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And this is the order that your code runs in.
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So what happens first, what happens next, what happens if something is true, and so on.
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And this includes things like if statements, loops, try catch blocks.
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And this matters because AI can generate code that looks clean, but the path through that code could do things like return too early,
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or maybe it skips validation, or maybe the error handling is in the wrong place.
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So if you can't follow the flow, then you can't really debug the program.
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Now, number two, as I said, is related to number one, it's data flow.
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So data flow is how much, sorry, not how much, but how data moves through your application, and where does it come from, where does it end up,
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and what changes along the way.
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So for instance, a very common data flow pattern that we see in is where it starts from a user, right?
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Usually within a form, within a web page, it gets sent to an API, it gets stored in the database, comes back as JSON, and then it shows up in the UI.
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Now, this matters because AI might generate multiple files for one feature.
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And if you can trace the data from that initial user action to the database and back to the screen,
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that whole feature starts And this is what happens when something goes wrong.
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So a request fails, a value is missing, a user enters bad data, a database query throws an error.
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There's a lot of different things obviously that can cause errors and a lot of beginners
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they only think about the happy path, right?
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But real software has, it has to handle failure.
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And AI can generate code that works when everything's perfect, but it all falls apart when something unexpected happens.
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And I see this over and over again.
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So you really need to ask, where can this fail?
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And what happens if it does?
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And does the user or the developer get a useful message to work with?
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All right.
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So you got control flow, data flow, and error flow, So the next one is scope.
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And scope is where things like variables and functions and other values, it's where they're available in your code.
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So if a variable is created inside of a specific function, can you use it outside of that function?
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And the answer is usually no. That's scope.
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And this matters because AI might create a variable in one place and try to use it somewhere else.
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Or you might move code around and suddenly something breaks because that variable doesn't exist in that part of the program.
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this value created where can it be accessed and who's allowed to change it Number five is input and output.
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I would say most useful pieces of code, they usually take something in and give something back.
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So if you think about a function, for example, it might take in a user ID and it might return the entire user.
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An API endpoint might take form data and return a success message.
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A component might take props and render something on the screen.
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you can check if the function expects the right arguments, if it returns the right data, and if it fits in with the rest of the app.
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Number six is state.
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So state is data that changes over time.
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And this could be something like a logged in user, a shopping cart, a form input, a loading status, or just data stored in a database.
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And a lot of bugs happen when the program thinks that the state is one thing, but it's actually something else.
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So AI might put state in the wrong place, it might it.
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It might update one thing but forget to update something else.
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So you want to pay attention to where state lives, who can change it, and what should happen when it changes.
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Now the next three concepts are closely related to each other
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and probably the most important in my opinion
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because they all relate to just how files are created and put together in an efficient way.
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So number seven, the first one of these three is abstraction.
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So abstraction is hiding complexity behind something simpler to use.
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So for example, a function called create user, it might validate input, it might hash a password, save a record to the database, and return the new user.
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But from the outside, you just see a function called create user.
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So AI will create abstractions constantly, helpers, services, hooks, middleware, utilities, all that.
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And good abstraction makes code easier to use and files just to figure out what's happening.
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So there is a fine line between, you know, too little abstraction and too much.
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Now, modularity number eight is closely related to abstraction.
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So once you start hiding logic behind functions and components and services,
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the next question is where those pieces should live and what each one should be responsible for.
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So instead of having Routes, controllers, models, components, utilities.
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I could go on and on.
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Config files.
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The idea is simple, though.
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It's each piece should have a job, right?
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So you have routes.
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They handle requests.
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Controllers or handlers, they decide what happens.
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Models deal with data.
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Components deal with UI.
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Utilities, they handle reusable helper logic.
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So good modularity makes the code base much easier to navigate,
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much easier to read, much easier to have multiple team members work on it and so on.
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Now, number nine, architecture is the overall structure of the application.
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So where does the front end live?
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Where does the back end live?
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The database?
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How do they communicate?
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What files are responsible for what?
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And you don't need to be a software architect, but you should understand the basic shape of what you're building.
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And this matters because with AI, you need to know where to point it.
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You know, if you want to add an endpoint, where should that go?
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If you want to change how data is displayed, is that the front end, is that the back end, or is it both?
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You don't need every detail memorized, but you should understand the map.
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So just to kind of recap those three concepts, you have abstraction, which is mostly about simplifying pieces of logic,
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modularity, which is about organizing those pieces, as a whole and how everything fits together.
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Now, the reason that this stuff is more important than ever, at least in my opinion, is because AI can generate a lot of code very quickly,
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but it doesn't always understand the long -term shape of your project, no matter how much context you give it.
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It might solve the immediate problem, but put the logic in the wrong place or create an unnecessary abstraction, duplicate something that already exists.
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And that's the difference between agentic coding and vibe coding.
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is basically saying build this feature and you just accept whatever comes back
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and just hope it works and doesn't wreck something else.
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Where a more serious AI workflow is being able to say, you know, this logic belongs in a service, not in a component, or this helper is unnecessary.
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That's why when I use AI, I use very specific workflows along with skills and agents, sub -agents that let me make all those decisions.
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I'm going to be using in my next video to really show you what, what I think a good AI workflow looks like.
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So next we have side effects and side effects are when code changes something outside of itself.
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So returning a value from a function is one thing, but five other things may happen as a result of that.
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So things like updating a database, writing to a file, sending an email, modifying state, those can all be side effects.
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So a function that has no side effects and just simply takes in and give something back, that's called a pure function.
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And this matters because side effects are where a lot of bugs happen, right?
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So they make code harder to predict because the function isn't just giving something back, it's changing the world around it in some way.
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And AI will often mix pure logic and side effects together.
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So it helps to be able to look at the code and ask, is this just calculating a value or is it changing something somewhere else that I need to know about?
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Next we have the request response cycle and this one is really geared more toward obviously web development.
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So in a browser a user will click a button, the browser sends a request, the server receives it, runs some code, maybe talks to a database and then sends a response back.
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So if you understand that cycle then frameworks like Express,
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Laravel, Django, Rails or whatever else they start to make a lot more sense and different frameworks have different syntax
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But a lot of them are solving the same basic problem and doing the same thing, which is receiving a request, doing some work and returning a response.
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And I would also lump in just like APIs or REST APIs in this as well.
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So knowing HTTP status codes, knowing the different methods and so on.
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So next is concurrency.
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So concurrency is when multiple things are happening during the same period of time.
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So maybe you have multiple API requests running, users editing data at the same time, background jobs processing, things like that.
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And this can get tricky because bugs can happen based on timing, right?
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Something works fine once but breaks when two things happen at nearly the same time.
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of video conversions, so basically videos to articles, and one job might be processing, another might be waiting, and another might fail,
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and then another might finish right as the UI is trying to update.
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So I had a Big issue with that that I worked on for a week straight.
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So you don't need to be a concurrency expert right away, but you should understand that software doesn't always just run in a perfect neat line.
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And AI can handle the simple case, but you still need to think about what happens when that timing gets messy.
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All right.
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So I know that that was a lot, but the point of this is not to have you master all of this before touching AI.
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Nobody masters all this overnight.
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I certainly haven't even come close.
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The point of this is that there are concepts
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that you want to keep learning as you build
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and as you learn because AI can generate code but you still need to understand the shape of what it's generating.
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And the better you understand these concepts,
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the better you can just getting AI to write code,
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but knowing whether that code actually makes sense.
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All right, so hope you guys enjoyed it, and I will see you next time.
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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione di livello B2 si basa sul video “12 Important Concepts In the Age of AI Software Development”. Le parole che ritornano più spesso: code, function, happen, user, flow. Questo video contiene 164 frasi e 2352 parole da ripetere con lo shadowing. Il parlato dura 14:00. Chi parla ha un ritmo naturale di circa 168 parole al minuto, vicino a una conversazione quotidiana. Il 84% delle parole rientra nelle 3.000 più comuni dell’inglese; conviene studiare le altre prima di iniziare.
Vocaboli chiave di questo video
Le 15 parole più avanzate del video, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| abstraction sostantivo | /æbˈstɹæk.ʃn̩/ | astrazione |
| workflow sostantivo | /ˈwɝkfloʊ/ | flusso di lavoro, procedura |
| variable aggettivo | [ˈvæɹ.i.ə.bl̩] | variabile, regolabile |
| helper sostantivo | /ˈhɛlpɚ/ | aiutante, assistente |
| modularity sostantivo | modularità | |
| developer sostantivo | /dɪˈvɛləpɚ/ | sviluppatore, sviluppatrice |
| endpoint sostantivo | estremo | |
| memorize verbo | /ˈmɛm.əˌɹaɪ̯z/ | memorizzare, imparare a memoria |
| navigate verbo | /ˈnæv.ɪ.ɡeɪt/ | seguire, dirigere |
| controller sostantivo | /kənˈtɹoʊlɚ/ | controllore, revisore |
| unnecessary aggettivo | /ʌnˈnɛs.əˌsɛɹ.i/ | innecessario, superfluo |
| depend verbo | /dɪˈpɛnd/ | dipendere |
| alphabetically avverbio | alfabeticamente | |
| reorganize verbo | riorganizzare | |
| reusable aggettivo | riutilizzabile |
I phrasal verb che sentirai
| Parola | Pronuncia | Significato |
|---|---|---|
| come back verbo | tornare | |
| end up verbo | andare a finire, finire | |
| fall apart verbo | cadere a pezzi | |
| figure out verbo | scoprire, rendersi conto | |
| speed up verbo | /spiːdˈʌp/ | stringere i tempi |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | be lost · is sponsored · was built |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | have changed · we've gone · haven't even come |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | abstraction, which is · modularity, which is · thing, which is |
Pronuncia a cui fare attenzione
Chi parla usa 25 contrazioni e forme ridotte, come doesn't, I'm, don't. Pronunciale nella forma breve, così come le senti.
- I suoni “sh” e “zh”: abstraction /æbˈstɹæk.ʃn̩/, shortcut /ˈʃɔːtkʌt/, conditional /kənˈdɪʃ.ə.nəl/, hash /ˈhæʃ/, validation /ˌvæl.əˈdeɪ.ʃən/
- Parole lunghe — attenzione all’accento: concurrency /kəŋˈkʌɹənsi/, developer /dɪˈvɛləpɚ/, unnecessary /ʌnˈnɛs.əˌsɛɹ.i/, conditional /kənˈdɪʃ.ə.nəl/, validation /ˌvæl.əˈdeɪ.ʃən/
I suoni difficili per chi parla italiano:
- /h/ — va soffiata, non è muta: helper /ˈhɛlpɚ/, handler /ˈhænd.lə/, hash /ˈhæʃ/
- Consonante finale — senza aggiungere una vocale dopo: agentic /eɪˈd͡ʒɛn.tɪk/, memorize /ˈmɛm.əˌɹaɪ̯z/, navigate /ˈnæv.ɪ.ɡeɪt/, depend /dɪˈpɛnd/, checklist /ˈt͡ʃɛklɪst/
- /æ/ — più aperta della “e”: abstraction /æbˈstɹæk.ʃn̩/, variable [ˈvæɹ.i.ə.bl̩], navigate /ˈnæv.ɪ.ɡeɪt/, validate /ˈvæl.ɪ.deɪt/, handler /ˈhænd.lə/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come abstraction, workflow, variable.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.