跟讀練習: 24h Inside $5M Silicon Valley AI Startup Building Personal Super Intelligence - 通過影片學習英語口語

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this stack of chicken bake boxes reaches the ceiling.

We just gotta go give up and find jobs and get mad because we can't keep doing this.

It's doing everything right.

Like not just content and not just like the ads and not just like the onboarding, but it has to all work.

Most venture capital funded AI startups are building for engineers and enterprises.

And almost no one is building for regular people because consumer is one of the most brutal categories you can touch.

But on this trip, I met a team taking a more creative approach to consumer AI than most startups have come across so far.

That's our definition of personal super intelligence.

It is one that understands your goals and is unequivocally for you.

What's really special about Tomo is

that it's really trying to inspire everyone to pursue their life's work in one aspect or another.

This is a huge space.

Our team is the entire world.

We are excited to build something that will go large and big and go beyond the beta.

Today I'm going behind the scenes with Justin and the team from Tomo, a personal AI that lives in your text messages,

learns your goals, and is now starting to build custom software around your life.

They've just raised a five million dollar seed round led by Bain Capital Ventures

and already have over 500,000 people texting Tomo to run their lives.

There's like such wide range of interfaces we can create that really motivate people, which is what I'm particularly excited about.

A lot of developers and founders try to overcomplicate.

But when you actually look at what consumers want and how hard it is to develop just the most simple features, a company like Tomo is building just for the simple stuff,

for the everyday people stuff.

It's a category so new, it's almost impossible to put a number on.

So the real question I want to answer is whether a

small team can actually win in the one space most of Silicon Valley is too afraid to build for.

Oh my god, walking up a hill.

I feel so unfit.

I am going behind the scenes with Justin and the team from Tomo.

They've raised their seed round from some pretty big names

and they are only a month away from sending this live and launching their first proper product to market.

Tomo is heavily centered as more of like an AI agent.

Infrastructure for the average human being and I've always been skeptical about these kind of plays in AI, but Justin gave me access to the product,

full access the other day, and I've actually used it every day since.

So if I'm a good indication of using a product like this and something that's actually sticky and defensible,

I'd hope that's a good litmus test on whether something's actually pretty cool.

So we're going behind the scenes with them.

We're gonna go to their house first because I'm pretty sure they just signed the lease for a new office.

So we're gonna go check out where it all began, and then we're gonna go to the new place.

So, let's go see him.

This is where I sat up until like basically yesterday for the last two and a half years.

Over there is where Ray has sat for the last year or so.

And then Varun about a couple months ago started sitting over there.

Yeah, this has been the 100 square feet that we've been working out of.

Wild.

Yeah, so what's the story of the chicken bag boxes?

What is that about?

We had worked on a lot of different products for consumer.

For the vast majority, almost everything, we didn't charge, but our op-max was quite high.

But it got to the point where we just looked at each other and said, OK, if this stack of chicken bag boxes reaches the ceiling, we just got to go give up and find jobs again,

man, because we can't keep doing this.

And super luckily, in that sort of idea maze, there's like about a box and a half of clearance that you'll see before, yeah, we would've had to go and like, sort of call it quits.

Good timing in that there's just enough juice left before things really started working.

Literally.

Yeah, stuff really was not working for the better half of two years.

It's like, maybe a box and a half of clearance, but...

That's crazy.

You can probably tell by the expiration dates of the boxes when like, things were really rough, and then we were like, okay, we can afford to eat like, a little better from this week.

This is insane.

Oh my god, that's so close, man.

Yeah, but one of the, maybe it's fate, but one of the cool things about the new office is

that it's also like a five minute walk from Costco.

So we can go straight to the source and we don't have to get the frozen stuff anymore.

So dude, real quick, if you were to sum it up in kind of one or two sentences, like really layman's terms, like basic, what is Tomo?

Yeah, so Tomo is personal AI for real people.

And what that means is that it's not AI necessarily for doing the work in the way that we, in Silicon Valley, perceive doing the work,

but it is AI that learns your goals and will do whatever it takes to help you get there.

And practice for anyone that oftentimes wants to go and lose weight

or go to the gym more often

or wants to keep track of their finances or wants to go

and do the childhood hobby and maybe they used to go and sing and write together.

That's what a lot of people are using Tomo for.

Today I'm going through a couple of sort of like meetings that you have to launch product-wise.

I made some changes yesterday on Google chats in an effort to make it counter-intuitively less proactive so it feels more assisted.

That's the feedback that we've seen.

My focus as with the rest of this week has been app building.

one that I talked to me about is it's hard to actually iterate on like the

or even use the end-to-end system right now of like testing

that building without example context of like here's an example chat

of like oh this is the goal they care about whether it's like calorie tracking

or financial things and it's actually like thus build like a proper app on top of

so going to be working on a way to just like implement ten different scenarios

that we can we use for the sake of future evals, but primarily now for app building.

I've been looking into some of how our ping backoffs happen

and some of the messaging around what Tomo is saying when it decides to back off.

And instead of merely today backing off and kind of disappearing into the void,

reframing that as an opportunity to improve what the user was originally trying to do with Tomo.

I took a look and I think I have like a much cleaner version of this

which uses a more improved model and also

cleaner structure that separates first into the first message being like hey I noticed you did this thing

and then the second message is more actionable and offers like three options for you to re-engage.

It's kind of related to what we talked about yesterday where

smoothness is actually not necessarily conducive to reduction

and some of the like the entropy

that you might expect within like a real conversation of even

some of the sharpness can be helpful obviously you don't know what the backoff is going to be like

but there's some level of delight from the kind of like

wabi-sabi of like a message that's like hey like what's like why aren't you coming back

or like hey what about this thing

that you promised me yeah some people like this this contrast in the sharpness

100

and if it came to like a real-life human relationship

101

which there's like push and pull and it's not just like constantly pull like like most other sort of language model products.

102

What are we doing now?

103

We are going to Costco for lunch but then more importantly to make sure

104

that Varun and Jessie get their employee benefit which is a Costco executive membership. And Jackie.

105

Let's go. And Jackie.

106

Very important.

107

I've been waiting for this.

108

I've been telling Tomo this like every day.

109

And getting his badge of adulthood which is a Costco membership card.

110

I'm excited.

111

I appreciate it.

112

He's locked in.

113

That's the one that's the one.

114

Jesse won honestly.

115

Wow.

116

Nice sample.

117

Good job.

118

Oh here it is man.

119

The company would not exist without these guys.

120

Okay, you asked me if there's a Costco in China.

121

Tomo says yes, definitely.

122

They opened the first one in Shanghai.

123

Oh, they have many?

124

Yeah, they have a lot.

125

Oh shit!

126

I'm gonna ask Tomo how many calories I think all of this is.

127

Mmmmm.

128

I don't recall.

129

Holy, you guys really went for it.

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The spread is actually insane.

131

Just for the whole table, you're looking at at least 6,000 to 7,000 calories easy.

132

Wait, how many thousand?

133

6, 6, 7.

134

One fun thing is like unreleased but

135

Komo is starting to go and generate interfaces for people and

136

so it knows I've been as you know like eating a lot of these chicken bakes

137

and it's like oh it'd be fun to keep track of exactly how many outside of the boxes

138

and so it's developed this chicken bake app for me and so if I go in

139

You can see that it is like 36 this year

140

and I guess the month just started so zero and

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so I can take a photo and text it like hey

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and then it'll increment or if it's just easier

143

because you know Sometimes you don't want to go and text with these like AI agents.

144

I can go into this like fun interface

145

and be like oh I just ate one today and then we'll like add on Number of chicken bakes per month.

146

Yeah, but then also Tomo obviously has like comments about About this.

147

Oh, and he's reminding me that like I have to go in fixed or like

148

So it's like actually I need you to do a real thing right now Yeah, I just like okay like after you finish like get back

149

to work like this is what you have to do or what you told me But yeah, that's like the sort of like fun sort of chapter two

150

that we're excited to release pretty soon Where it's based on everything that you're sharing with your Tomo just texting.

151

Yeah What is that personal operating system look like?

152

What are games that we can create based off of this?

153

And you were saying like you've got you've got really like strong API connections with some pretty cool stuff So like yeah,

154

I'm talking about like budget tracking like cuz you got plaid like fitness health like all that stuff Yeah, it's stuff that we've only fitness and health that you'll need after eating this.

155

Yeah.

156

Yeah, especially after this My tone will probably send me a ping about getting back in track.

157

So today we're going to work through everything, like how the communication will go, what is the project like, and also connect a little bit more information from you guys that we needed.

158

So this is gonna be the page that we will share with you.

159

It's kind of our main source of truth.

160

Here you will find all the information about the project.

161

This timeline is in line where the kind of hard launch that we're calling it, that Jackie's helping us with.

162

But we ultimately want to have products that we can get in the hands of friends quite a bit earlier than that.

163

You know, even as soon as the next couple weeks, like you've seen even the couple days last week, we're already cooking and thinking through these different experiences.

164

Yeah, yeah, and it keeps us also like early testing, so that's great.

165

So we can also use it on users immediately.

166

So how did that meeting go for you?

167

Like, and actually probably more important question, why are you engaging in an agency from Amsterdam?

168

Yeah, yeah, yeah, really interesting kickoff meeting, like Justin said.

169

I think they're really great cultural people, so I'm excited to get like their perspectives on especially like what are actually meaningful products

170

and apps for again like real people out there not those that are, you know, kind of in sort of the tech bubble that, I mean, obviously we see it live in here.

171

Yeah, I think Kickoff's great.

172

Looking forward to working with them over the next few weeks.

173

You wanna see this?

174

There's like one thing that you get, it's like Jesse is not an engineer.

175

He is our head of growth, but he runs more coding agents than anybody at the company combined.

176

Run me through, what's the setup here?

177

Overall, I am actually really bad at typing, so...

178

The main thing I use a lot, of course, is Whisperflow.

179

Um...

180

Yeah, yeah, you interviewed them recently.

181

Yo, Hector.

182

Yeah.

183

So something funny that I do is that I have my backspace, so that it deletes by words instead of by character.

184

So, it's like...

185

Oh, gangster, that's cool.

186

Yeah, it's a bit weird, but I...

187

But that's because you're speaking into it.

188

So Flo will already correct your words.

189

So you're just faster, right?

190

Yeah, it's just like speed of editing, essentially.

191

Nice.

192

All I do actually all day, I feel like a salesperson, but all I do all day is talk.

193

Tell me about version 2

194

because like we talked a little about version 1 we understand how it works

195

but like personal OS literally custom building software is based on your preferences like tell me about

196

that how does that work? this thought really arrived from all the data

197

that we saw

198

which is like it's crazy how many people were sending things they cared about to Tomo their preferences some context

199

but even more

200

so just like images like things they care about for example like tracking like the food they eat

201

and sort of like calories or like macros associated with it.

202

The fact that they were like going to the gym to work out every time and laying to know.

203

And it's sort of like this realization

204

that like this UX was pretty suboptimal with like iMessage or really just like any raw chat interface.

205

You want to be able to just like talk to this like entity

206

and have it have that context and give you like correct answers about like what to do.

207

But what's even better is like what

208

if you can actually see like visuals of like here's my

209

progress here's how I'm doing another example is like what

210

if I am like trying to save enough money to like

211

buy this car next month reality is like there's actually like

212

a lot of ways you can create like budgeting apps

213

and use like other sort of like finance tracking apps out there

214

but they're not really like optimized for this scenario what would really be cool is like what

215

if it was just like a widget

216

that was like a progress bar to like a car emoji like that's a lot like simpler more motivating

217

and just like clear like here's my goal

218

and here's my progress to reach out rather than like uploaded like budget tracking app

219

so I'm super excited to sort of like bring this like dynamic like interface world to life.

220

One of the things that's really important to understand here is

221

the capability of an app like this actually really simple for the end user I'll show you a simple feature I know.

222

Hey Tomo I'm getting pretty hungry you know how I like healthy foods you've got my location

223

so can you ping me maybe like five of the closest restaurants near here where I kind of like grab

224

and go get something takeaway like I don't want to sit

225

into a fine dining restaurant find something that's like pretty easy

226

also relatively cheap I send it in I'm gonna say on

227

it let me find you some good healthy spots nearby

228

so it's like got a bit of attitude it's got a bit of its own like language

229

and it's just hitting me with locations

230

and the cool part is I can say to it okay cool could you remind me at 5 p.m

231

because I'm a bit busy now

232

but can you remind me to head out of the office

233

and actually go eat something I'll send that to it

234

and then it's going to save that it's going to message me

235

and say yeah no worries but it's a simple thing

236

so it's like help me find recommendations help me go to

237

the gym help me drink water help me do really basic stuff

238

and it's funny when you get into this how hard it is to actually just do basic stuff

239

and to not overcomplicate this especially in consumer world

240

because consumers are very very very difficult to bring on consumer software is one of the most cutthroat categories

241

that you can jump into in consumer okay real quick it is 5.02 pm

242

and right on time tomo just messaged me Wild. So cool.

243

The thing we do on the channel is we ask one piece of advice from founder to founder.

244

So you're in the thick of it right now, right?

245

Like raising a seed here.

246

So is there any bit of advice you'd give for a

247

founder that's maybe just a little bit earlier on in their journey right now?

248

Yeah, I think it's really two things.

249

They say it a lot, but don't die.

250

Like really, really do not die.

251

There are a lot of moments where it's just like we were pretty close to death, but it's that that scar tissue becomes burned insights that someone starting out doesn't have.

252

And the other thing is as part of that, like you can be the smartest person in the world, but I really do subscribe to the belief that you just gotta get stuff out there.

253

And if you aren't, you are doing a disservice to your team and your company and your investors.

254

And so if you aren't dying, and you're getting in front of real people and building real product for real people, your success is guaranteed.

255

Like there's actually no way, given a long time horizon that you fail.

256

And so we've seen the good and the bad and the ugly of that and I'm excited for other people, especially building consumer while outside of WFOS to build some cool stuff for real people.

257

Nice, man.

258

Appreciate you.

259

All right.

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詞彙遊戲邊玩邊學

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本課的詞彙與口語要點

這節 B2 級別的口語課以影片“24h Inside $5M Silicon Valley AI Startup Building Personal Super Intelligence”為素材。這段影片共有 258 個句子、3220 個單詞可供跟讀。講話部分時長為 17:05。說話人語速較快，每分鐘約 189 個詞，會出現較多連讀和弱讀。 87% 的單詞屬於英語最常用的 3,000 詞，詞彙容易理解。

影片中的重點詞彙

影片中最難的 15 個單詞，附發音和釋義：

單詞	發音	釋義
interface 名詞	/ˈɪn(t)ɚˌfeɪs/	介面
calorie 名詞	/ˈkæləɹi/	卡路里, 卡
bake 動詞	/beɪk/	烤, 烘
ping 動詞	/pɪŋ/	ping
motivate 動詞	/ˈmoʊ.tɪ.veɪt/	激發
startup 名詞	/ˈstɑɹtˌʌp/	初創公司 /初創公司, 新創企業 /新創企業
stack 名詞	/stæk/	堆
preference 名詞	/ˈpɹɛf(ə)ɹ(ə)ns/	優選 /優選, 選擇 /選擇
ceiling 名詞	/ˈsilɪŋ/	天花板, 天棚
scenario 名詞	/sɪˈnɛəɹioʊ/	指令碼 /指令碼, 場景 /場景
actionable 形容詞	/ˈæk.ʃə.nə.bəl/	可行動的 /可行動的
conducive 形容詞	/kənˈdjuːsɪv/	有助於 /有助於
entropy 名詞	/ˈɛntɹəpi/	熵
litmus 名詞	/ˈlɪtməs/	石蕊
optimize 動詞	/ˈɑptɪmaɪz/	最佳化

影片中出現的短語動詞

單詞	發音	釋義
give up 動詞		投案, 自首
check out 動詞		退房
come back 動詞		回來 /回來, 返回
go through 動詞		通過 /通過
look forward to 動詞		期待, 嚮往
work out 動詞	/ˌwɝk ˈaʊt/	計算 /計算, 算出

值得反覆跟讀的句子

影片中簡短而完整的句子，可以直接用在日常對話裡：

That's our definition of personal super intelligence.
That's the feedback that we've seen.
What are we doing now?
I've been waiting for this.
Run me through, what's the setup here?

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
現在完成進行時 have/has been + -ing — 以前開始、現在仍在繼續的動作	we've been working · I've been looking · I've been waiting
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	have come · They've just raised · They've raised
定語從句 who / which + 從句 — 補充說明人或事物	people, which is · benefit which is · adulthood which is
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	is based · is guaranteed

需要注意的發音

說話人用了 79 次縮略和弱讀形式，例如 I'm, we're, don't。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: actionable /ˈæk.ʃə.nə.bəl/, expiration /ˌɛk.spɚˈeɪ̯.ʃən/, realization /ˌɹɪə.laɪˈzeɪ.ʃən/, indication /ɪndɪˈkeɪʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/
長單詞——注意重音位置: actionable /ˈæk.ʃə.nə.bəl/, defensible /dɪˈfɛn.sɪ.bəl/, expiration /ˌɛk.spɚˈeɪ̯.ʃən/, suboptimal /sʌbˈɑptɪməl/, unequivocally /ˌʌnɪˈkwɪvək(ə)li/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: motivate /ˈmoʊ.tɪ.veɪt/, conducive /kənˈdjuːsɪv/, disservice /dɪ(s)ˈsɝvɪs/, proactive /pɹoʊˈæk.tɪv/, unequivocally /ˌʌnɪˈkwɪvək(ə)li/
詞尾子音 — 要讀清楚，後面不要加母音: interface /ˈɪn(t)ɚˌfeɪs/, bake /beɪk/, motivate /ˈmoʊ.tɪ.veɪt/, startup /ˈstɑɹtˌʌp/, clearance /ˈklɪɚ.əns/
/r/ 和 /l/ 的區別: calorie /ˈkæləɹi/, realization /ˌɹɪə.laɪˈzeɪ.ʃən/, importantly /ɪmˈpɔɹ.tənt.li/, brutal /ˈbɹuːtəl/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 interface, calorie, bake 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: 24h Inside $5M Silicon Valley AI Startup Building Personal Super Intelligence - 通過影片學習英語口語

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