| # | 原则 | 官方的意思 | 反例（官方点名） |
|---|---|---|---|
| 1 | Be human, not corporate | 投资人想听你本人说话，讲真实经历、露个性 | 「Changing the world, one step at a time.」 |
| 2 | Show your work every week | 势能靠「看得见」才可信，持续在场 | 跟公司无关的 stock photo |
| 3 | Focus on one idea at a time | 一篇一个故事 / 一个指标，拿不准就删短 | 「Big things coming soon…」 |
| 4 | Make it visual | 照片/短视频完胜纯文字，手机就够 | 过度精修图、堆满字的图 |
Updates（每周）＝标题 → 发生了什么 → 为什么重要，铺三个渠道。Spotlights（高光）＝每周跟官方 prompt 讲「你是谁」，竖屏 9:16 / 1080p+ / 约 1 分钟。
① 新增「技术」整节（本节重点）。 你定的调：大多数 AI 落地翻车、追 fancy 科技；我们把最高端的软件 + 最高端 AI 算法（VLA + world model）跟最踏实实际的部署接成一条 flywheel——deployment → production → feedback → research，每天在真实商业运营里解决切实问题。放在「痛点」之后、「证据」之前：先讲问题 → 讲我们怎么解、为什么别人抄不了 → 再用数字证明它真在跑。
② 点明架构（Option B）：vision-language-action model + world model，但只说「前沿 AI 同源」（the same class of technology the leading labs are racing to put into robots），不写「world's most advanced」这种没有 benchmark 佐证的绝对话。
③ 「为什么是你」、使命、痛点数字都维持 v6，没动。
The bet.
"In the 21st century, we still use humans as machines. We're bringing robotics to food & beverage."
We build robots that make food and drinks — and we run them in our own cafés, today.
Three layers, one machine:
Here's what that looks like: a customer orders a matcha latte. The machine makes it. A person hands it over with a conversation. The machine does the repetitive making; the human does the connecting. And the machine learns from every cup, so the one running today is smarter than the one we opened with.
This isn't a render or a lab prototype. It's on the counter in three of our own stores — two in Champaign, one in Palo Alto — serving customers right now.
The restaurant industry is in a slow-burning crisis, and it gets worse every year.
That's not just a broken business model. It's a waste of people.
Most AI has a dirty secret: it works in a demo and breaks in the real world. Companies chase fancy technology for its own sake — then discover their model can't survive contact with a real customer, a real rush, a real mess.
We didn't build a model. We built a loop.
One connected system that runs from research to deployment to production and back — every day of real operation feeds the next iteration. That flywheel is the whole company: the more we run, the smarter the machine gets.
At the top of that loop is the frontier of AI: vision-language-action models and world models — the same class of technology the leading AI labs are racing to put into robots. At the bottom is the most grounded thing in our industry: running it in real stores, every day, solving real problems.
Three stores. 240,000+ cups. 1,007 trading days. This isn't a lab video — it's a robot doing a real job in front of real customers.
Most companies have the fancy model or the practical deployment. We have both, wired together — state-of-the-art software and AI, landed on the most practical work there is: daily commercial operation.
That's the point. Not technology for its own sake. The best technology, actually landing.
We didn't write a white paper. We built the machine and opened three stores to run it.
Palo Alto — opened October 2025 — has kept going straight through the summer and winter breaks that empty out a college town like Champaign.
A new drink is a recipe, not a hire.
Coffee today. Matcha tomorrow. Tea next month. Same hardware. A chain that wants to serve one more menu item hires another person; we push a new recipe to the machine.
That is the entire thesis: machines scale. Headcount doesn't.
$4.8M raised to date.
We're not here to fire everyone. We're here to change what the job is.
The machine takes the repetitive, back-breaking, rush-hour work. The person takes the part that actually matters — greeting, recommending, connecting. The barista becomes a host.
That's a better job, a better store, and the reason our customers post about us on Google, Yelp, and Instagram without being asked.
We are not building coffee robots. We are building the operating system for food service.
The long game is any store, any cuisine, running on our platform — the machine is the constant, the menu is the variable. We already hold 25+ letters of intent from operators who want in — demand, not revenue, today. Today our own three stores are the proof rig. Tomorrow the platform runs everywhere.
We didn't start with a business plan. We started with an obsession — a love of technology, and a belief that advanced machines could change how people live, for the better. We wanted to put that to work in the most human place we knew: food.
We started with a single food truck and one question: can a robot and a person together serve better than a person alone?
Three stores later, we believe the answer is yes — and we believe the restaurant of the future is neither all-people nor all-robot. It's both, doing what each does best.
We intend to bring that to the whole industry, and in time far beyond it: machines doing the repetitive work, people doing the human work, technology serving everyone.
痛点数字出处（已验）：
130% 离职率 → verified · Black Box Intelligence（TDn2K People Report）+ DailyPay（QSR 130–150%）。人力占营收 → verified · 30–35% · National Restaurant Association + BLS。技术声明（本节新增，标注状态）：
VLA + world model 架构 → 你拍板点名（Option B）。facts.yaml 尚无 tech 段，上页前建议和工程师落一句「机器确实跑 VLA + world model」的确认，写进 facts 备查。文案只说「前沿 AI 同源」，不写绝对化「world's most advanced」。需签字的数字（公开发布前落导出）：
$1,735,844 累计收入（缺 Sep–Dec 2023 + Jun–Jul 2026 POS 导出）。$4.8M 累计融资（缺 cap table 导出）。你本人给的初衷，我翻成英文。完整版做 Spotlight 引言 / 创始人自述，短版做 1 分钟竖屏视频开场。
We started this for one reason: we're obsessed with technology, and we believe in what it can do for people. The vision of advanced machines changing how human beings live — for the better — is what fires us up. We've always asked where robots and AI belong in everyday life, and we've always wanted the same answer: they take over the hard, repetitive work, and give people back the good parts. We want that to benefit everyone, not just a few. That's been our whole intention from the very first day.
We're obsessed with technology — with the idea that advanced machines can change human life for the better. That's the vision that's always driven us. Where do robots belong? Doing the hard, repetitive work, so people get back the human parts. We want it to serve everyone. That's why we started.