Founder · Building AI-Native Organization

Hi, I'm Limin Ge. Founder & Engineer building products people use, play, and pay for.

I work on agentic systems, context engineering, large-scale distributed systems, and building AI-native organizations.

How I use AI

How I Use AI

AI is powerful, but how well it works depends on the rules you give it.

1 · Work discipline

AI makes you faster and messier at the same time. Anyone who uses it well is constantly thinking about how to keep that mess under control.

# Work discipline

## Engineering philosophy

- Update documentation as if it were greenfield: keep historical information to a minimum, so any first-time reader grasps the current state as fast as possible instead of having to wade through the whole useless history of how it evolved, unless the doc is explicitly a running log or a progress record.
- Fail fast wherever we can, rather than letting all sorts of fallbacks paper over the real failure. In other words, avoid degradation handling, fallback, hacks, heuristics, local stabilizations, or post-processing bandages that are not faithful general algorithms.
- No faking and no double-writing to pretend the program runs. Your top priority is to surface errors as early as possible.
- For any rework, prefer deleting code and reusing code over adding new code.
- Aim for a single source of truth.
- Follow the principle of name matching reality: if the semantics change, the symbol's name changes too.

2 · Thinking frameworks

Naming a good thinking framework or principle explicitly helps the model actually reach for it. But naming it matters even more for you than for the model: it keeps you leading the work instead of being pulled along by AI.

# Thinking frameworks and working principles

Before you start any specific task, consider whether one of the thinking frameworks below can help you analyze and understand the problem systematically:

[Thinking frameworks]
MECE, Pyramid Principle, First Principles, Issue Tree, People-Goods-Field, Purchase Funnel, Lean MVP, Combinatorial Matrix & Whitespace Search, 7 Powers, Cialdini's Six Principles of Influence

[Working principles]
Fail Fast, Single Source of Truth (SSOT), Name Matches Reality, Brooks's Law, No Silver Bullet (separate essential from accidental complexity), Conway's Law, Technical-Debt Awareness

[Meta-work practices]
Find a benchmark and search the methodology first; run a retro when done and harden reusable steps into a checklist

Featured

Featured articles

A starting list of essays and notes.

Cover Image for Four Mornings of a Retainer Team: The Four Levels of Enterprise AI-Native Maturity

Four Mornings of a Retainer Team: The Four Levels of Enterprise AI-Native Maturity

AI-native is not a scale of how much of the old org you have automated — it is a redesign of how people, agents, information, and decisions relate. From four mornings to why a digital employee can charge $20K–200K a year.

Cover Image for You Haven't Hired Anyone, But You're Already Running a Company

You Haven't Hired Anyone, But You're Already Running a Company

Everyone already knows you just need to supervise AI instead of executing everything yourself; but here's what you don't know: you haven't hired anyone, yet you're already running a company. The AI-Native Organization, Part 1: The individual and the small team.

Cover Image for Build Your Company as a Federation of Amoebas

Build Your Company as a Federation of Amoebas

A department is a context-bounded token partition, a company is a federation of amoebas, and every rule is inherited from software engineering.
The AI-Native Organization, Part 2: Organizations and scale.

Cover Image for SSOT: Why Every Software Engineering Principle Is Really About One Thing

SSOT: Why Every Software Engineering Principle Is Really About One Thing

Complex products, AI-native output, and fast iteration naturally split team truth. The SSOT Nine-Layer Pyramid lets AI agents, product, operations, sales, support, and engineering share one current decision snapshot.

Cover Image for How to Build an SSOT Nine-Layer Pyramid Model

How to Build an SSOT Nine-Layer Pyramid Model

A reusable method for constructing a team SSOT that aligns AI agents, product, operations, sales, support, and engineering across mission, strategy, promise, Flow, contract, implementation, and evidence.

Cover Image for AI-Native Engineering: 11 Hard Truths for 2025

AI-Native Engineering: 11 Hard Truths for 2025

A founder note on building AI-native organizations and systems.

Cover Image for Farewell SaaS, Embrace the Agent: On the Rise of the "Results Economy"

Farewell SaaS, Embrace the Agent: On the Rise of the "Results Economy"

Why SaaS is hitting its ceiling and how the "results economy" grows out of the cracks: the business logic of agents selling results instead of tools, and the stages their commercialization will pass through. The second half is a labeled fantasy that pushes this logic decades further out.

Cover Image for Part One: The Pulse of a Small Town, and the Survival Principles of America's Local Economies

Part One: The Pulse of a Small Town, and the Survival Principles of America's Local Economies

How does a small American town that seems to produce nothing actually stay alive? A layer-by-layer dissection of what really keeps a local economy running.

Founder notes

AI-Native Engineering: 11 Hard Truths for 2025

Principles I'm using to build AI-native organizations and products.

  1. 1.If a knowledge, methodology, or tech stack is older than 3 years, it is obsolete until proven otherwise.
  2. 2.Your codebase, docs, and meeting notes are not your backbone; your test case library is the only compounding asset.
  3. 3.AI is completely, fundamentally stateless.
  4. 4.We can accept AI mistakes as long as they do not kill the team before the next model upgrade.
  5. 5.Software complexity must flatten: from deep vertical stacks to wide horizontal systems.
  6. 6.The context window is the most critical computational resource every engineer must master.
  7. 7.Plan-Act, Test-Code, and Doc-Code-Doc are the new working loops of engineering.
  8. 8.The future of code is not abstraction but tiny, isolated, AI-readable units that stand on their own.
  9. 9.AI will never solve the first mile or the last mile; those remain stubbornly human.
  10. 10.AI-generated artifacts are not side effects; they are a new software modality and part of your engineering assets.
  11. 11.The real power of AI IDEs and agents is not generation; it is ruthless, intelligent context selection.

Founder notes

Current state of LLM Risks and AI Guardrails

Published in arXiv.org on June 16, 2024 · 87 citations on Semantic Scholar

A survey of the main risks in deploying LLM systems and the guardrail patterns used to mitigate them, including bias and toxicity checks, prompt and agent safeguards, privacy concerns, and the tradeoffs required to ship these systems responsibly.

Founder

I'm the founder behind every product in this studio.

Founder & Engineer focused on AI-native organizations and agentic systems.

Limin GeFounder & Engineer

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