Luyện nói tiếng Anh bằng Shadowing qua video: How Great Software Engineers Are Moving Faster Than Ever
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The thing that got me excited about learning how to build software was the ability to solve a problem.
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This is Logan Kilpatrick, product lead for AI studio over at Google DeepMind.
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There's so much interesting innovation that's happening in this way that like was not happening three years ago.
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Every two weeks, every three months, every six months, what's possible changes.
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You have to reset your level of ambition.
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Somebody should build that company. Which skills really matter nowadays?
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The rules of software engineering have changed.
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Here's the full episode.
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I love your keynote yesterday.
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Thank you. I was very excited to see anti-gravity CLI. Yeah, yeah.
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And I feel like more and more engineers are moving towards CLI first.
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I do feel like we're trying to meet developers where they are.
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And I think this like story of like developer preference has also never been more.
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I think it's actually been hard to meet developers where their because it's expensive to build an SDK and the CLI and ID and an agent manager, and then also in a managed service.
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And I think it was cool to see the anti-gravity story come to life, literally with like the full breadth of that.
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You could literally use it in an IDE, you could use it in the agent manager, you can use the CLI, you can you could use it and through managed agents in the Gemini API, which is really cool.
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It was a unique, unique story that I don't think we've been able to pull off before.
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And I think it's actually based on feedback from developers who are like, give us a single product.
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We don't want to have to, you know, wade through the ecosystem of 50 different AI products that Google is offering for developers.
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And so you even like the consolidation of the Gemini CLI.
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Sort of moving over to the integrated CLI is all based on that feedback.
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Gotcha. The Gemini CI was open source, was open source.
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I noticed that the anti-gravity one is not.
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Yeah. This is an interesting thread.
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I think we'll all.
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I work super closely with the anti-gravity folks and actually also the Gemini CLI folks.
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It'll be interesting to see how this ends up shaking out over time.
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My sense is it's not like a deeply strategic choice right now.
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It's just like a you can move, there's a there's a cost to doing open source.
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Of course, you can move a lot faster.
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Actually right now. I think not doing it.
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And so I think if they decide to go open sourcing, they want to like actually lean in and do it.
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But my sense is it's not like there.
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I don't I don't think the headspace is that there's a bunch of like secret stuff that they're trying to do.
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I think it's mostly just like an operational thing.
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And like even does that team have like expertise and the desire to like, go and do that?
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I think the team who is working on Gemini Kelly and actually some of them are now working on a bunch of anti-gravity stuff.
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It's just like maybe less less the thing that they're that they've done before and have the expertise for. So it'll be interesting. We'll see.
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We'll see how it shakes out.
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But I definitely saw the feedback of folks wanting to have an open source variant.
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Yeah, I've seen a thread online as well that says, well, there is this kind of the ID is dying, right?
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Because if your agents are generating code and you if you have the right guardrails or if you have the right hardness capabilities, even engineering around your harness, then you don't necessarily need to look at the code that is generated.
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Right. You might do it in a pull request or even if that becomes a bottleneck.
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That's something we need to solve as engineers.
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But do people then.
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Do you see this narrative of the ID is dying or what's your take on that?
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I think my framing of it is not not necessarily the ID is dying.
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I think it's like the way that software is being built is evolving.
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And actually, in some sense, it's interesting that like, there's there's maybe even more tools that are involved in some sense, you know, there's there's less tools in some sense there's more tools.
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And actually your example where like, you spend a lot more time reviewing code in a pull request than historically you would before until like, I'd imagine there ends up being a lot more innovation and focus on like, how do you build great code review tools that maybe actually look different than the code review tools of the today era that were built with the assumption that it was a human writing code, not an agent writing code.
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So I think the the focus definitely moves.
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You still need an editor.
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And so actually for anti-gravity, like there is still an editor, you can actually hook it up to the editor of your choice now as well.
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So if you're like, hey, I love into your favorite editor, you can go and continue to use that.
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And I think developers will need to do like some.
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And this is, I think the biggest difference of like, you know, vibe coding versus a genetic engineering.
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And I think the engineer needs to be able to go into an IDE and edit code.
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In some cases, they'll probably maybe predominantly be using some sort of like agent manager, but you still need the ability to, when the time comes to go and edit code and look through and use a bunch of like, rich debugging tools that only exists in an editor and don't exist in the context of sort of an agent manager experience.
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So I don't think it's going to go away.
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I think developers will spend more time, though, probably outside the IDE.
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And to me that feels like a very natural thing that like developer tooling, continues to change.
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The developer tooling of today's era is very different than it was 10 or 15 years ago.
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And so it doesn't feel that part's not super surprising to me.
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Yeah. Same here.
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I feel like the bottleneck will be will requests very, very soon.
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Right. I feel this already inside of Google.
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Like I write code all the time now and it's like the code is actually reasonable.
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The the burden is now on a bunch of engineers across Google who have to review the code that I'm writing and give feedback and all that stuff.
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So like, it's I think it's it's quickly shifted into a bunch of different places.
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And I think we can speed up code review and then the, the, the actually, Jeff Dean made this comment to me a couple of weeks ago, but like the burden in a lot of cases actually moved to like tool execution.
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And so as models get faster, even as you sort of maybe if you had a human who could review your code right away instantly, there's still like the CI time maybe becomes, you know, you have to run a whole set of tests and maybe you have a lot of tests because you're a great agent engineer and you're being really thoughtful about it, but like that becomes the burden.
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So I think the burden, the sort of bottleneck actually.
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So the bottleneck is going to be sort of all over the place.
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And I think it'll actually be interesting to see how developers and how companies are measuring this, like where in the process of creating software is the bottleneck moved to.
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And I think Google's actually had historically a great culture.
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And like a ton of instrumentation that helps us answer this question internally because we want to, you know, remove the bottleneck for engineers building software.
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Yeah, it's really a puzzle that it's more on my day to day to solve.
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How do we measure kind of productivity?
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Because that's the the marketing, right.
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You become more effective. Engineers feel that.
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But the costs are also rising.
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That's why specifically with 3.5 flash being very cost efficient, I was super excited.
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If this is where we can go.
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For example, Cloud Code has opus plan where you reason and you create, for example, your plan in a higher end model, and then you execute in a more lightweight model.
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And I see the ambition with 3.5 is to just use the same model and do both.
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That's the goal.
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Yeah. And I love I love feedback.
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As far as where it lands, I think this is this is definitely like the first iteration of the 3.5 family.
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And I feel like also the first iteration where we've done this model product harness symbiosis.
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So the model is being trained at the same time that we're using the harness, at the same time that we're thinking about like, how is that actually impact?
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There's lots of feedback historically about the Gemini models, sort of like looking great on paper and not being great for real world use cases.
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And sort of we've taken that feedback super seriously over the last, probably more than a year now to to try to make sure that they're as representative in the products themselves as they are in benchmarks.
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And so it's cool to see that flywheel actually spinning now and all of the all the progress happening.
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Yeah, for me, the at least in my community, for the engineer that's listening, for example, it could be someone that is very on the higher end, but it could also be someone that's kind of not even dabbled their feet in there.
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And skills are evolving for the person that's listening.
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Which skills really matter nowadays from your perspective?
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It's a good question.
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I think it depends what you want to do and like what your what your actual job is. I think there's, there's there's definitely like evergreen skills.
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I mean, I think, you know, taste I think is important if you're, if you're an engineer who, you know, can sort of see a user bug, report it and know, like, oh, this is how I should go and solve that problem, and this is how I should get my agent to solve that problem.
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Whatever it is.
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I think there's alpha in that because I think there's, there's there's so much software to be built that as much as engineers can sort of take the burden of building that software and just go and run with it.
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I think so much of the and actually, again, this is like goes back to measuring like where all the bottlenecks so much of the bottleneck is in this like human communication loop that happens like someone reports an issue or says they want x, Y and z feature.
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There's this like long ping pong game of humans talking about the something before somebody makes a plan and then actually goes and builds it and then brings it to users.
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And like most of the ping pong, probably in some cases it's really useful.
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And like the taste aspect is like knowing when you need to play ping pong.
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And if you if you sort of know in a lot of cases and you have a high degree of accuracy of knowing when you need to play ping pong and knowing when you don't need to, it speeds up the process so much, and there's like a huge amount of alpha in in being able to do that and like actually the best, the best sort of engineers I work with have a great sense of being able to answer that question.
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Yeah, I like that a lot.
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I wonder how long it takes to kind of develop this taste if you start out or if you're already here kind of working with a generic tooling, because it is a I think it's a loss in kind of productivity initially.
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Right? Whenever we try something new, there's an investment there and the payoff could be three months, it could be six months.
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It really depends also on the curiosity and the eagerness and the drive, but it varies.
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What is like the minimum investment that you think people should do?
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Yeah it does. It varies.
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And I think it also varies depending on what your team has done to set up.
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And so I think we've had a lot of conversations with the studio engineering team as an example.
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And one of the conversations and I told them, I told the engineering team this, I was like, hey, I want to do the minimal amount of setup humanly possible to get like full context of all of the engineering information that I need for my agent to go and make a bunch of changes in the studio code base.
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And they were like, yep, okay.
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And they literally got a like a whole suite of skills set up.
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And I literally ran one command and then I not actually, but then I like had as much content.
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My agent had as much context as every engineer on the team should have.
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Not fully, of course. It's definitely loss here.
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And then literally that same day, I was making production code changes in the AI studio code base.
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So I think in some cases it actually can be months depending on what your setup looks like.
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In some cases it's like days or hours.
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And I think that's the that's the interesting thing is also like just trying to identify which of the situations you're in, like, and maybe there's a, you know, I'm sure there's lots of great engineers everywhere.
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So maybe it's not that we have the smartest engineers who are just really good at doing it.
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Maybe it's our tech stack actually just like works really well.
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We already had Agent Rails, sort of.
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We had the governance structure set up so that we could do the things we needed to add the AI tooling we actually wanted, and maybe that's harder at other companies, but it could be the best cases, like literally ours, where you're able to actually get that.
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And I felt that as somebody who was doing a ton of software engineering work before I joined Google, joined Google on the product ladder originally, and like actually stopped doing a lot of engineering work because two years ago it was really like Google's internal infrastructure is actually really different than the rest of the world's developer infrastructure.
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And so the learning curve is really hard.
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It's sort of a lot more difficult to use some of the tools because they're designed to scale to like billion user products.
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And even if you're not using a billion user product, it's sort of it's sort of difficult.
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And this AI coding era has brought me back into like making real production software changes, which I've appreciated so much because I'm a I'm a software engineer and I love building software.
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Yeah. So I think in that sense I got lucky.
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But it's been it's been cool to see it happen.
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And again, the best case is like literally they built the skills that day and I was able to benefit from them and start sending code changes.
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Yeah, the best engineers that I know, the organization trusts them with kind of overarching ventures, right.
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Bigger changes across multiple teams.
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So the ability to fly in a team, kind of do your research or make impact is incredibly valuable.
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I think we give those stakes when the stakes are high to the best engineers we have within organizations.
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And then part of that, for me, something I've been recently thinking about is going to be agent experience.
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So within a code base or within a team, can we measure some form of experience?
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Right. If I come in and I have my stack, how well can I indeed familiarize myself and what in the repository allows for that to be able to be productive quite quickly?
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I frame this to our team as I was thinking about this, and I'm curious actually what others think, and I would love to.
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I'll read the comments on this video and see what people's reaction is to this.
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But around sort of this idea of like code, code test coverage has existed for a long time.
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You actually want the same thing for agents.
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It's like agent coverage.
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Like how much context?
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How can you measure the amount of context that your agent has on the systems that it's it's orchestrating over?
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The impressive thing is oftentimes these models can like, do a bunch of really, really smart things without having any of that context.
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But if you give it the context, here's why this system was built this way.
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Here's the chat threads where the engineers talked about, you know, the design trade offs or the meeting notes from the from the design review for this UI feature.
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So you have that context.
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The more of that context and the more coverage that exists for the actual software, the faster you can run.
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And the best case is you actually need a system that's like continually updating that context that's being created about the software.
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And I think it's a new I think I don't think there's a ton of startups thinking about this, but it's an interesting area of like, somebody should build that company.
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I feel like it's very it'd be very interesting and it needs to live in the code too.
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That's the other thing, because the agent needs to go, I'm, I think it needs to actually go there and like one, one really quick example of this is like as agents go and create more software, the artifact in like in anti-gravity.
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And we use antigravity internally, and I use it outside of Google to the artifact that gets created is like this plan and this walk through and all this like checklist of like, here are the 50 things I did.
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Here are the test that I ran.
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When you actually commit the code, all that goes away.
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So like I as somebody reviewing even going back to like where's the bottleneck?
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Move to the code review process looks completely different than the fact that I just created all these artifacts that actually show the the proof of work is now different.
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The proof of work for developers used to be creating code, passing the set of tests that were running at CI, and then that was like sort of it.
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And I think the proof of work is now different because the bottleneck is not creating code.
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Yeah, I've seen people experiment with spectrum and development frameworks where they then in the end, they trust the process and the guardrails they've built around not just agent, but also built around the harness capabilities, specifically making making it explicit some of the things they want.
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And then in the review, reviewing the spec that is then also committed as part of the change, which I think is fascinating, I love it, I feel like it's the, the, my main takeaway is like there's there's so much interesting innovation that's happening in this way that like was not happening three years ago.
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I feel like we all sort of like were, you know, this is how software gets built, whatever the, whatever the way is that the company was operating.
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That's how software gets built.
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And I feel like now that there's so much disruption happen, people are like, oh, let's try ten different things and we'll see.
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And like maybe it is spec driven development that's going to be the thing.
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Maybe it's something else.
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So it's cool to see the level of experimentation.
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I have so much fun.
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Just like reading about what teams and are doing to to try out things.
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Yeah, I feel like a kid in a candy store and like you like a couple years ago I was responsible for product.
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I always wanted that role, being responsible more from a vision, more high over perspective.
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And then things just accelerated and I got the biggest FOMO I have to go back to hands on.
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And now I have a new role, which is more kind of productivity based.
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And how do we level up our whole engineers entirely?
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And then there are different challenges, right?
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Some people are they don't really mind as much.
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They like it even.
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And some people really take pride in the craft that they have built in this, which is also the the writing code is a big component of that.
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And that's slowly going away. Yeah.
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To the to them I say, well, like there's still the part that is personal hobby projects where you can always do that, get that fulfillment.
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But there is kind of a strong narrative.
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That generation is just really what agents are good at and the engineering capabilities, they are kind of going more high over, and we are stacking things to make this work right.
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We have agents, we have harnesses, we are building around that.
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So it's a fascinating time. It is fascinating.
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I feel this myself too.
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I used to get the thing that got me excited about learning how to build software was like the ability to solve problems.
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And I think if I go back to, you know, like computer science education, I think like independent of like clicking a bunch of keys on a keyboard is, is about sort of like a way of it's like a way of seeing the world.
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Actually, I draw this analogy to like the, the legal system when you if you haven't studied the legal system and like have a deep understanding of how law works, you see the world in a way.
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They're like, oh, there's a bunch of laws in a vacuum, and maybe I follow a speed limit here and there, and I have to pay my taxes, otherwise something's going to happen.
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But you sort of don't have this depth of appreciation of like, actually, how does how do all of these complex systems work?
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I think about and when you have that appreciation, you go about the world in a specific way.
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You see the world in a specific way.
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Software is the same thing, I think, like because I study computer science, because I understand how systems work, I see the world in a specific way.
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The fact that now, today, I'm not the one who's like typing the keys.
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I think doesn't actually diminish the the fact that there's alpha and seeing the world in that way in a lot of cases.
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And so the abstraction layer changes.
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But actually the skill of solving problems and actually very acutely, like the art of building software is the art of, you know, the code doesn't run, there's a problem, there's a bug, this thing, it's like almost like a building resilience in software engineers who are like trying to tackle all these really complicated problems and thinking about things in an interesting way is like the art of thinking, the art of, you know, again, solving problems.
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That's such a useful skill.
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Like, it's so, so I'm very I'm very bullish for like both like computer science education, but also like people who have this ability in the world.
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Because if you can solve problems, you can do the world is filled with problems to be solved, which is exciting.
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Yeah, I, I recently had this thought and it was because of one of the demo booths.
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Right. The demo was you've got your own game, and I asked a lot of questions on kind of what happened under the hood, and they said they're going to release it in a month or so.
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Open source, I can play around with it.
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But in essence, I could just put my thoughts on paper and a game rolled out.
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It took a little bit of a back and forth, but the outcome was there.
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And then I felt this kind of like, I'm happy with how my career flew.
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I'm happy with where I'm at now, but I am a little bit jealous about this newer generation because I feel like I think in constraints.
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Right. And as a kid I had that less and less.
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So if all of a sudden I can, for example, my little brother or my cousins, I can put them in front of the system and say, let's design a game.
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I am sure they will be way more creative than I can be, because I now think in constraints, this is what you have to reset.
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And I feel like this.
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This is this is the hardest thing as humans and I fall, I'm I'm saying this to you to remind myself that I need to do this.
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But like the every two weeks, every three months, every six months, what's possible changes.
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And I frame this to people and like you have to reset your level of ambition and I've there's a double edge of this and I'll give this for like personal projects.
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I've reset my level of ambition on personal projects so much that I'm like, my bar is now.
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This is like I'm basically building a company.
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Like if I can only companies everywhere, mini company that I'm going to go after.
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And so I'm like, okay, if this isn't a mini company, like I probably shouldn't do this because like, it's not ambitious enough, but like that is that is the level of ambition that I think you can actually have.
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And like the crazy thing is, I was always personally somebody who, like, wanted to who like you, I'm sure, who's like wanted to be able to build something ambitious.
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And I was like, I just couldn't like, I never had enough time.
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I couldn't actually, you know, I wasn't good enough software engineer.
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I didn't have the right team, whatever it was.
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And that's now completely changed.
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And it is like it feels very empowering.
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Yeah. For me, the the thing that will give people an edge, right.
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All of a sudden, if you're looking for a job or you're looking to level up specifically a portfolio right now, doing really cool things is what will give you an edge.
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I feel like more easily your foot in the door.
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Do you also see that from a hiring perspective or the things you've been involved in?
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Yeah, I love this proof of work set up, which has always made hiring a little bit easier when you have this proof of work.
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And I think like actually open source historically to the original thread of our conversation, I think is a was always a great example.
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So I love the open source community and why I spent like so much of my early career doing open source, because it was it was permissionless in many ways, like getting a job is like or an internship.
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Your first internship doing software engineering is like convincing someone to let you change to like, contribute to their code base.
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And the coolest thing in the world is open source.
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I could just show up to anyone's code base and ideally in a non annoying way, like contribute something meaningful and actually become a part of this thing without needing them to like hire me.
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And I still think that's like such an empowering force.
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And now it's even more significant than that is like, I don't even need to go to it.
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You still could go to an open source project, but like, you can actually just go build the project yourself and like, you could be in the driver's seat of the whole thing and the story and the narrative and the problem you want to solve and the way of bringing that thing to the market.
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It's very interesting to see that that transition.
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And it does feel it does feel empowering.
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Yeah. The how you do that for me is incredibly crucial, right?
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Because I feel like I learn a lot from it.
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But I'm so curious, like, I want to know intricacies and I ask questions and that's where I get my knowledge.
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You can also just go autopilot. No, no.
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You build something, you don't know what it is.
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You can go autopilot.
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And then there's the best quote, the best sort of the quote of the era, which I'm going to get on a t shirt or something or put up a poster, will end with that.
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The mascot of the poster in my room, which is sort of you can you can outsource intelligence, but you can't outsource understanding.
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And so there's so many things.
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There's a there's a, there's nuance of a line to walk of in what cases, what can you build where like it's actually fine to outsource both of those.
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But like you go and build a product and you put real users and then you don't understand how it works.
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Like you probably it's probably a recipe for bad things to happen.
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So you could, you know, take advantage of the fact that you can outsource intelligence, don't outsource understanding.
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I think there's a ton of alpha still on that.
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Logan, thanks so much for coming on. This is a blast.
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Thank you for coming to IO and having this conversation.
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Thanks for having me, man. This was a blast.
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I love it. Cool. That's it.
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Các cấu trúc ngữ pháp & cách diễn đạt trong ngữ cảnh
Dưới đây là một số cấu trúc ngữ pháp và cách diễn đạt quan trọng mà Logan sử dụng trong video:
- “I think”: Câu mở đầu này giúp người nói thể hiện ý kiến cá nhân một cách tự tin, rất hữu ích trong giao tiếp hàng ngày.
- “It was cool to see”: Cấu trúc này giúp mô tả một trải nghiệm tích cực, có thể dùng khi chia sẻ cảm nhận về một sự kiện hay sản phẩm.
- “Based on feedback from”: Cách diễn đạt này rất quan trọng khi thảo luận về ý tưởng, sản phẩm hoặc dịch vụ, giúp bạn thể hiện sự chú ý và tôn trọng ý kiến người khác.
- “There’s a cost to doing”: Một cách diễn đạt giúp diễn tả sự phân tích chi phí và lợi ích trong các quyết định kinh doanh hay kỹ thuật.
Việc nắm vững các cấu trúc này sẽ giúp bạn phản xạ nhanh hơn khi giao tiếp và mở rộng vốn từ vựng liên quan đến công việc hoặc sở thích cá nhân.
Các bẫy phát âm thường gặp
Khi luyện nói tiếng Anh qua video, người học có thể gặp phải một số từ ngữ hoặc âm điệu khó. Dưới đây là những điểm cần lưu ý:
- “AI studio”: Phát âm “AI” có thể gây nhầm lẫn; hãy chú ý để không phát âm sai.
- “cost”: Từ này thường bị phát âm không chính xác, hãy luyện phát âm đúng để nâng cao kỹ năng giao tiếp của bạn.
- “feedback”: Chú ý đến âm tiết và ngữ điệu, tránh nhấn sai trọng âm khi nói.
Bằng cách luyện với video và chú ý đến những bẫy phát âm này, bạn sẽ cải thiện khả năng nói của mình thông qua những bài học luyện nói tiếng anh thực tiễn.
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| “Used to” used to + động từ — thói quen hoặc tình trạng trong quá khứ nay không còn | used to be · used to get |
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | have changed · has also never been · we've been |
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | is generated · being built · are involved |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | API, which is · somebody who was · role, which is |
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.