🔰 Lobster 101

What's the Difference Between ChatGPT and a Lobster?

One Formula to Understand the Lobster

🤖 AI + 💻 Computer + 💾 Memory + ⚡ Skills = 🦞 Lobster

Ordinary AI is just smart — the Lobster builds on that intelligence with its own computer, long-term memory, and accumulating skills.

It's like a person: raw intelligence isn't enough. You also need a desk, work experience, and professional skills to become a reliable employee.

A Large Language Model Is Not a Brain — It's Intelligence

"Most people think a large language model is the brain. That's wrong. It's more like your IQ level."

Can a person do work on IQ alone? No. They also need:

Combined together — that's a complete, continuously running intelligent agent.

Four Key Design Principles of the Lobster

The Lobster Is One Level Above an Agent

An Agent is a piece of software running on someone else's platform. A Lobster is a "person" who owns their own computer.

Manus uses virtual machines that are destroyed after each use. But your work computer grows with you — it holds your files, notes, habits, shortcuts.

When an employee leaves, they hand back their computer — because that machine holds all their memory, rules, and experience.

What the Lobster does is give an Agent a complete computer: not a "use-and-discard" sandbox, but a long-term, continuously evolving work environment.

Fu Sheng's 14-Day Experiment

14 days. From not being able to look up a contact, to designing an 8-agent AI team running 7×24.

It's not that AI got smarter. It's that humans learned how to use AI.

Real Case: A Non-Technical CFO Connected Corporate Travel in 2 Days

A company CFO handed off corporate travel booking to his "administrative secretary" Lobster. The Lobster entered the tech group, read through the list of challenges, and tackled them one by one:

🚄 Train tickets → 🛫 Flights → 🏨 Hotels → 🔄 Changes → ❌ Cancellations

Five core functions, all working in 2 days. Now he books hotels with one message and gets back an order number.

The CFO said something that stuck with me: "I don't understand the tech, but I knew the Lobster would figure it out."

Same Work, 150× Cost Difference

Fu Sheng had the Lobster build a complete website — science explainer, diary, article aggregation, skill store — total cost: ¥1,000 RMB, completed in one day.

An outsourced team doing the same site? Design + frontend + backend + content, conservatively ¥150,000, and a full month.

This isn't "saving money" — it's a fundamental shift in how productivity is priced. Before, you hired people by salary. Now you hire AI by results.

Tool Mindset vs. Employee Mindset

"Raising a Lobster is like onboarding a new hire — it's not about who's smarter, it's about who's more committed."

SaaS vs. Lobster: Repricing Productivity

SaaS sells capability. The Lobster sells results.

Before, a CRM cost tens of thousands and you only used 1% of its features. Now, let the Lobster build exactly what you need — pay for what you use, charged purely by results.

The core moat of an Agent isn't the model or the platform — it's Skill accumulation. The longer you use it, the more experience it builds, and the higher the cost of replacing it. Like a veteran employee — someone who's been there three years is fundamentally different from someone just starting.

It's Far From Perfect — But It Evolves

The Lobster is not perfect. It will:

• Make calculation errors (counting 220K characters as 2.72M)
• Send messages to the wrong person (sending the boss's report to a colleague)
• Fake inspections (marking "complete" with shallow content)

But the key is it has a mechanism:

Make Mistake → Write Rule → Becomes Skill → Never Again

Every mistake produces a rule; every rule becomes a Skill; every Skill ensures it won't happen again.

This is the biggest difference between an AI Agent and a human — it doesn't make the same mistake twice. Humans might. But the Agent won't, because the rules are written in files, not stored in memory.