跟读练习: The Art Of Winning In Tech - 通过视频学习英语口语
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There has never been a better time to be a software engineer.
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I know everyone's scared about the whole AI wave, but if you're anything like me, you got interested in software because of the ability to make something real.
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Something another person could actually use.
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Something that could blow up and change the world, all while just needing a laptop and an idea.
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And that has never been more achievable than now.
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What used to take a team of five can now take one person.
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What used to take years of learning syntax and documentation and writing every single line by hand, you can now do in a fraction of the time.
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And trust me, I know everywhere you look and all over the comments of this video, people are gonna say software developers will be completely wiped out.
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But the ones getting wiped out by the AI wave aren't losing because they're bad developers, they're losing because that world of software is fading out.
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But the people winning, you know that one guy in your classes with five internships and a job lined up?
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Or the guy who built something real enough that a company just hired them for it?
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Or the back-end engineer at a normal tech company who became
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so productive with AI that they now own systems they would have never touched?
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Or the random developer charging $50 a month for a tool they built in a weekend?
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or the founders chasing a vision that they actually believe in with a team of two instead of twenty, they're all winning for the same reason.
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They understood the new rules faster, about what to build, how to build it, and how to use every resource available to close the gap between where they are and where they want to be.
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So instead of getting wiped out by this new wave of software engineering...
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Picture two types of people.
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The first person is the spectator.
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For whatever reason, they never fully leaned into the AI wave, and honestly, part of it's probably because the entire space feels overwhelming.
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They hear LLMs, RAG, Agents, GBT, Claude, OpenAI, Anthropic, and after a certain point, it just all starts blending into one giant cloud of AI words.
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Or they've been told that using AI somehow makes them a worse programmer, so they keep their distance from it.
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But on the other hand, you have the surfer.
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The surfer understands that this is a field built on abstraction.
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Nobody calls you a fake programmer for using frameworks or libraries, stack overflow, random open source code where you don't even know how it works.
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So instead of resisting the wave, they learn how to move with it.
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They use AI to learn fundamentals faster, to experiment faster, to close the gap between what they can imagine and what they can actually build.
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Maybe you're building web apps and you use AI to scaffold an entire dashboard in a day.
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Or maybe you're building a game engine
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and using AI to help explain some brutal graphics concept that would have taken you hours to decode from documentation alone.
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The leverage looks different depending on the field, but the surfers, regardless, are experimenting.
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They're shortening the distance between idea and execution.
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They have an idea and immediately test it.
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They automate boring parts of their workflow.
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They use AI to understand unfamiliar code bases faster.
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To research faster.
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To prototype faster.
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To debug faster.
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To learn faster.
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Their feedback loops are insanely short.
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And that matters because software engineering is becoming more and more of a creativity game.
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In the sense that the people that are winning are the people noticing things.
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Connecting things.
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Building weird tools that nobody even thought of before.
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And following curiosity fast enough before the excitement disappears.
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Because a few years ago, if you wanted to test an ambitious idea, the barrier was enormous.
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Now some random student can lock in for a weekend and accidentally build something that thousands of people use.
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And I think a lot of developers still haven't adjusted to how insane this shift actually is.
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They're still waiting until they know enough or until they master
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that one language or framework or just completely distancing themselves from this new wave of technology.
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Meanwhile, the surfers moving the fastest are already in the water, building, testing, learning, shipping, adjusting,
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because programming is becoming less about manually typing every brick yourself and more about what should exist in the first place.
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The bottleneck is no longer can you write the code, it's can you think clearly, can you recognize good ideas, can you move on them quickly, can you learn fast enough to keep up with your own ambition.
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Because AI doesn't remove the need for builders, if anything it raises the ceiling for the ones that actually build.
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But adapting to this new wave isn't just about using AI, it's about learning how to think clearly while using it.
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Which is why you need to- You can't just build in silence anymore.
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The people getting opportunities right now, like internships, referrals, startup roles, even research positions, are often the ones who are most visible.
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They're building their projects and they're also posting it on LinkedIn.
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Or they're doing short demo videos on Twitter.
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Or maybe they're just writing about random things that they learned from a class, or a bug, or a failed project, or a random opinion that they have about programming.
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Not because they're influencers, but it signals that this person is actively building.
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And in this new era of software engineering, especially at companies that are literally shaping this wave like OpenAI or Anthropic,
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the people who are reviewing talent here are looking for builders who are already in motion, not just people who say that they're interested.
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So you don't need a perfect portfolio, you just need a visible one.
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And the third part of this is that you have to stop over-filtering what you're allowed to build.
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People get stuck because they keep asking, is this idea even good enough for my resume or for my portfolio?
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Or they might ask, am I skilled enough or should I wait until I know more?
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But the people who are actually improving are just using everything available to them and just building it anyway.
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So using AI, libraries, templates, random GitHub repos, documentation, they're trying to brute force learning through building.
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Even if it feels not good enough or maybe it feels too good
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and it feels ambitious or if it feels messy or something that one person shouldn't be able to build.
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That's exactly the point.
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Because the fastest way to become that kind of programmer, the one who just builds things and makes it work, is to stop negotiating with yourself every time that you get an idea.
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You build it, ship it, show it, iterate, and slowly your identity starts to catch up with your behavior.
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And the one thing under all of that, the thing that lets you keep up with your own ambition is to never stop learning, which is why I genuinely got excited when I saw that Brilliant Today sponsor just dropped something game-changing.
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This is the new Brilliant.
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It's a personal tutor for math and coding, and it's the fastest tool I've used to actually get good at this stuff.
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It sits on your screen with you, adapts to how you think, and makes hard concepts feel like puzzles that you actually want to solve.
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The curriculum covers everything from the fundamentals all the way through college level and above in subjects like math and coding, built by people from MIT,
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Harvard, and Stanford, so you know you're getting the best of the best.
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And for where we're at in software right now, the coding side is specifically focused on the things that actually matter, such as logical reasoning, debugging instincts, thinking clearly under complexity.
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It's also there every day.
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No scheduling, no friction, you can just open it and go.
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So if you want to level up as a developer and try Brilliant, click the link below or scan the QR code to get started with Brilliant's tutor for free.
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You can upgrade to Premium to unlock all courses
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and right now lattice viewers can save 20 off an annual subscription at brilliant.org slash lattice
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so thank you once again to brilliant for sponsoring this video
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and thank you to everyone for watching i'm really trying to up the levels like editing better uploading more
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and all that stuff so subscribe like comment share everything thank you
FOR YOU
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本课的词汇与口语要点
这节 B2 级别的口语课以视频“The Art Of Winning In Tech”为素材。 视频中反复出现的词有：build, wave, faster, software, random。 这段视频共有 89 个句子、1446 个单词可供跟读。 讲话部分时长为 6:24。 说话人语速较快，每分钟约 226 个词，会出现较多连读和弱读。 83% 的单词属于英语最常用的 3,000 词，其余的词建议在练习前先学一下。
视频中的重点词汇
视频中最难的 15 个单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| programmer 名词 | /ˈpɹoʊɡɹæmɚ/ | 程序員 /程序员, 程式員 /程式员 |
| wipe 动词 | /waɪp/ | 擦, 擦拭 |
| debug 动词 | /diːˈbʌɡ/ | 調試 /调试, 除錯 /除错 |
| lattice 名词 | /ˈlæt.ɪs/ | 晶格 |
| internship 名词 | /ˈɪntɜːnʃɪp/ | 實習 /实习 |
| tutor 名词 | /ˈtjuːtəː/ | 私人教師 /私人教师, 家庭教師 /家庭教师 |
| builder 名词 | /ˈbɪl.dəɹ/ | 建築工人 /建筑工人, 建築工 /建筑工 |
| ambition 名词 | /æmˈbɪʃ.ən/ | 奢望, 抱負 /抱负 |
| ambitious 形容词 | /æmˈbɪʃ.əs/ | 有雄心, 遠大 /远大 |
| adapt 动词 | /əˈdæpt/ | 適應 /适应 |
| sponsor 名词 | /ˈspɒn.səː/ | 保證人 /保证人 |
| adjust 动词 | /əˈd͡ʒʌst/ | 調整 /调整 |
| mathematics 名词 | /mæθ(.ə)ˈmæt.ɪks/ | 數學 /数学, 算學 /算学 |
| achievable 形容词 | /əˈt͡ʃivəbəl/ | 可完成的, 可實現的 /可实现的 |
| automate 动词 | /ˈɔ.təˌmeɪt/ | 自動化 /自动化 |
视频中出现的短语动词
| 单词 | 释义 |
|---|---|
| blow up 动词 | 炸掉, 爆破 |
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| “used to” used to + 动词 — 过去的习惯或状态，现在已不再如此 | used to take |
| 定语从句 who / which + 从句 — 补充说明人或事物 | guy who built · ones who are · people who are |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | has never been · they've been told · haven't adjusted |
需要注意的发音
说话人用了 30 次缩略和弱读形式，例如 they're, you're, don't。请按听到的简短形式来说。
- “sh” 和 “zh” 音: documentation /ˌdɑkjəmɛnˈteɪʃən/, internship /ˈɪntɜːnʃɪp/, ambition /æmˈbɪʃ.ən/, ambitious /æmˈbɪʃ.əs/, abstraction /æbˈstɹæk.ʃn̩/
- 长单词——注意重音位置: developer /dɪˈvɛləpɚ/, documentation /ˌdɑkjəmɛnˈteɪʃən/, portfolio /pɔːtˈfəʊ.liˌəʊ/, mathematics /mæθ(.ə)ˈmæt.ɪks/, achievable /əˈt͡ʃivəbəl/
中文母语者容易读错的音：
- /v/ — 不要读成 /w/，上齿轻触下唇: developer /dɪˈvɛləpɚ/, achievable /əˈt͡ʃivəbəl/, overflow /ˌəʊvəˈfləʊ/, overwhelm /ˌoʊ.vɚˈwɛlm/, viewer /ˈvju.ɚ/
- 词尾辅音 — 要读清楚，后面不要加元音: wipe /waɪp/, debug /diːˈbʌɡ/, lattice /ˈlæt.ɪs/, internship /ˈɪntɜːnʃɪp/, ambitious /æmˈbɪʃ.əs/
- /r/ 和 /l/ 的区别: builder /ˈbɪl.dəɹ/, influencer /ˈɪnflu.ənsə(ɹ)/, referral /ɹɪˈfɜː.ɹəl/, unfamiliar /ˌʌnfəˈmɪli.ə(ɹ)/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 programmer, wipe, debug 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。