gstack Fully Explained: How YC's President Writes 10,000 Lines of Code Daily with AI

TL; DR Key Takeaways

gstack is an open-source Claude Code engineering system by YC President Garry Tan, featuring 18 expert roles and 7 tools, covering the entire sprint cycle from product ideation to code release.

The core idea is not "let AI write more code," but to role-play AI agents: the CEO is responsible for product direction, the engineering manager locks down architecture, QA tests with real browsers, and the release engineer deploys with one click.

Garry Tan claims to have written 600,000 lines of production code (35% of which are tests) in 60 days using this system, producing 10,000 to 20,000 lines of usable code daily, all while serving as YC CEO.

All skills are pure Markdown files, open-source under MIT license, installable in 30 seconds, and support Claude Code, Codex, Gemini CLI, and Cursor across multiple platforms.

The project garnered over 33,000 GitHub stars within a week of launch, also sparking intense debate like "isn't this just a bunch of prompts?"

One Person, 60 Days, 600,000 Lines of Code

In March 2026, YC President Garry Tan said something to Bill Gurley at SXSW that silenced the entire room: "I'm only sleeping four hours a day now because I'm so excited. I think I have cyber psychosis (AI fanaticism)." 1

Two days prior, he had open-sourced a project called gstack on GitHub. This wasn't just an ordinary development tool, but his complete working system for programming with Claude Code over the past few months. The data he presented was astonishing: over 600,000 lines of production code written in the past 60 days, 35% of which were tests; the statistics for the last 7 days showed 140,751 lines added, 362 commits, and approximately 115,000 net lines of code. All of this happened while he was serving full-time as YC CEO. 2

This article is suitable for developers and technical founders who are using or considering using AI programming tools, as well as entrepreneurs and content creators interested in "how AI is changing personal productivity." This article will deeply deconstruct gstack's core architecture, workflow design, installation and usage methods, and the "AI agent role-playing" methodology behind it.

gstack's Core Architecture: Turning Claude Code into a Virtual Engineering Team

The core idea of gstack can be summarized in one sentence: don't treat AI as an all-purpose assistant, but rather break it down into a virtual team, each with specific responsibilities.

Traditional AI programming involves opening a single chat window, where the same AI writes code, reviews code, tests, and deploys. The problem is that code written in the same session is reviewed by the same session, easily leading to a cycle of "self-affirmation." A user on Reddit's r/aiagents accurately summarized it: "slash commands force context switching between different roles, breaking the sycophantic spiral of writing and reviewing in the same session." 3

gstack's solution is 18 expert roles + 7 tools, with each role corresponding to a slash command:

Product and Planning Layer:

/office-hours: YC partner model, uses 6 mandatory questions to help you clarify product direction before writing code.

/plan-ceo-review: CEO-level review of proposals, offering four modes: expand, contract, maintain, and curate.

/plan-eng-review: Engineering manager locks down architecture, outputs ASCII architecture diagrams, test matrices, and failure mode analysis.

/plan-design-review: Senior designer scores each design dimension from 0 to 10 and explains what a 10 looks like.

/design-consultation: Design partner, builds a complete design system from scratch.

Development and Review Layer:

/review: Senior engineer role, specifically looks for bugs that pass CI but would explode in production.

/investigate: Systematic root cause debugging, with the iron rule: "no investigation, no fix."

/design-review: Designer and programmer, fixes issues directly with atomic commits after review.

/codex: Calls OpenAI Codex CLI for independent code review, enabling cross-model cross-validation.

Testing and Release Layer:

/qa: QA lead, opens a real Chromium browser to click and test all flows, finds and fixes bugs, and generates regression tests.

/qa-only: Pure reporting mode QA, only reports bugs, does not modify code.

/ship: Release engineer, syncs main branch, runs tests, audits coverage, pushes code, opens PRs – all with one command.

/document-release: Technical documentation engineer, automatically updates all documentation related to the current release.

/retro: Engineering manager leads weekly review, outputs individual contributions, release cadence, and test health trends.

Security and Tools Layer:

/careful: Dangerous command warning, pops up a warning before executing rm -rf, DROP TABLE, force-push.

/freeze: Edit lock, restricts file modification scope to a specified directory.

/guard: Combination of /careful + /freeze, highest security level.

/browse: Gives the agent "eyes," a real Chromium browser, with approximately 100ms response per command.

These are not a collection of scattered tools. These roles are chained together in the sequence of Think → Plan → Build → Review → Test → Ship → Reflect, with the output of each stage automatically fed into the next. Design documents generated by /office-hours are read by /plan-ceo-review; test plans written by /plan-eng-review are executed by /qa; bugs found by /review are verified by /ship to be fixed. 2

Why gstack Ignited the Entire Developer Community

Within a week of its launch, gstack garnered over 33,000 GitHub stars and 4,000 forks, topped Product Hunt, and Garry Tan's original tweet received 849K views, 3,700 likes, and 5,500 saves. Mainstream tech media like TechCrunch and MarkTechPost reported on it. 1 4

But the controversy was equally fierce. YouTuber Mo Bitar made a video titled "AI is making CEOs delusional," pointing out that gstack is essentially "a bunch of prompts in a text file." Sherveen Mashayekhi, founder of Free Agency, bluntly stated on Product Hunt: "If you're not the CEO of YC, this thing would never make it to Product Hunt." 1

Interestingly, when a TechCrunch reporter asked ChatGPT, Gemini, and Claude to evaluate gstack, all three gave positive reviews. ChatGPT said: "The real insight is that AI programming works best when you simulate an engineering organizational structure, rather than simply saying 'help me write this feature.'" Gemini called it "sophisticated," believing gstack "doesn't make programming easier, but makes programming more correct." 1

The essence of this debate is not actually technical. The facts of 33,000 stars and "a bunch of Markdown files" can both be true simultaneously. The real divergence lies in: when AI turns "well-written Markdown files" into a replicable engineering methodology, is this innovation or just packaging?

From Scratch: gstack Installation and Practical Workflow

30-Second Installation

gstack's installation is extremely simple. Open the Claude Code terminal and paste the following command:

``bash git clone https://github.com/garrytan/gstack.git ~/.claude/skills/gstack && cd ~/.claude/skills/gstack && ./setup``

After installation, add the gstack configuration block to your project's CLAUDE.md file, listing the available skills. The entire process takes less than 30 seconds. If you also use Codex or other agents that support the SKILL.md standard, the setup script will automatically detect and install them in the corresponding directory.

Prerequisites: You need to have Claude Code, Git, and Bun v1.0+ installed.

A Complete Practical Workflow

Suppose you want to create a calendar brief app. Here's a typical gstack workflow:

Enter /office-hours and describe your idea. gstack won't immediately start writing code, but will interrogate you like a YC partner: Who are your users? What specific pain points do they have? Where do existing solutions fall short? It might tell you: "You're talking about a calendar brief app, but what you're really building is a personal chief of staff AI."

Run /plan-ceo-review. Reads the design document generated in the previous step, challenges your scope and priorities from a CEO perspective, and runs through 10 dimensions of review.

Run /plan-eng-review. Locks down the technical architecture, outputs data flow diagrams, state machines, error paths, and test matrices.

Approve the plan, start coding. Claude writes 2,400 lines of code across 11 files in about 8 minutes.

Run /review. Automatically fixes 2 obvious issues, flags 1 race condition for your confirmation.

Run /qa https://staging.myapp.com. Opens a real browser, clicks through and tests all flows, finds and fixes a bug, and generates a regression test.

Run /ship. Tests increase from 42 to 51 (+9 new tests), PR is automatically created.

Eight commands, from idea to deployment. This isn't a copilot; it's a team.

Parallelism is the Real Killer Feature

A single sprint takes about 30 minutes. But what truly changes the game is that you can run 10 to 15 sprints simultaneously. Different features, different branches, different agents, all in parallel. Garry Tan uses Conductor to orchestrate multiple Claude Code sessions, each running in an independent workspace. This is his secret to producing 10,000+ lines of production code daily.

A structured sprint process is a prerequisite for parallel capabilities. Without a process, ten agents are ten sources of chaos. With the Think → Plan → Build → Review → Test → Ship workflow, each agent knows what it needs to do and when to stop. You manage them like a CEO manages a team: focus on key decisions, and let them run the rest themselves. 2

Common Troubleshooting

Skill not showing? Run cd ~/.claude/skills/gstack && ./setup

/browse failed? Run cd ~/.claude/skills/gstack && bun install && bun run build

Outdated version? Run /gstack-upgrade, or set auto_upgrade: true in ~/.gstack/config.yaml

AI Agent Role-Playing: The Methodology Behind gstack

The most valuable part of gstack might not be the 25 slash commands, but the mindset behind it. The project includes an ETHOS.md file, documenting Garry Tan's engineering philosophy. Several core concepts are worth deconstructing:

"Boil the Lake": Don't just patch things up; solve problems thoroughly. When you find a bug, don't just fix that one; instead, ask "why does this type of bug occur," and then eliminate the entire class of problems at the architectural level.

"Search Before Building": Before writing any code, search for existing solutions. This concept is directly reflected in the "iron rule" of /investigate: no investigation, no fix; if three consecutive fixes fail, you must stop and re-investigate.

"Golden Age": Garry Tan believes we are in the golden age of AI programming. Models are getting stronger every week, and those who learn to collaborate with AI now will gain a huge first-mover advantage.

The core insight of this methodology is that the boundaries of AI's capabilities are not in the model itself, but in the role definition and process constraints you give it. An AI agent without role boundaries is like a team without clear responsibilities; it seems capable of doing everything, but in reality, it does nothing well.

This concept is expanding beyond programming. In content creation and knowledge management scenarios, YouMind 's Skills ecosystem adopts a similar methodology. You can create specialized Skills in YouMind to handle specific tasks: one Skill for research and information gathering, another for article writing, and a third for SEO optimization. Each Skill has clear role definitions and output specifications, just like /review and /qa in gstack each have their own responsibilities. YouMind's Skill Marketplace also supports users creating and sharing Skills, forming a collaborative ecosystem similar to gstack's open-source community. Of course, YouMind focuses on learning, research, and creation scenarios, not code development; the two complement each other in their respective fields.

FAQ

Q: Is gstack free? Do I need to pay to use all features?

A: gstack is completely free, under the MIT open-source license, with no paid version and no waiting list. All 18 expert roles and 7 tools are included. You will need a Claude Code subscription (provided by Anthropic), but gstack itself is free. Installation only requires one git clone command and takes 30 seconds.

Q: Can gstack only be used with Claude Code? Does it support other AI programming tools?

A: gstack was originally designed for Claude Code, but now supports multiple AI agents. Through the SKILL.md standard, it is compatible with Codex, Gemini CLI, and Cursor. The installation script will automatically detect your environment and configure the corresponding agent. However, some hook-based security features (like /careful, /freeze) will degrade to text prompt mode on non-Claude platforms.

Q: Is "600,000 lines of code in 60 days" true? Is this data credible?

A: Garry Tan has publicly shared his contribution graph on GitHub, with 1,237 commits in 2026. He also publicly shared the /retro statistics for the last 7 days: 140,751 lines added, 362 commits. It's important to note that this data includes AI-generated code and 35% test code, not all handwritten. Critics argue that lines of code do not equal quality, which is a reasonable question. But Garry Tan's view is that with structured review and testing processes, the quality of AI-generated code is controllable.

Q: I'm not a developer, what value does gstack have for me?

A: gstack's greatest inspiration is not in the specific slash commands, but in the "AI agent role-playing" methodology. Whether you are a content creator, researcher, or project manager, you can learn from this approach: don't let one AI do everything, but define different roles, processes, and quality standards for different tasks. This concept applies to any scenario requiring AI collaboration.

Q: What is the fundamental difference between gstack and regular Claude Code prompts?

A: The difference lies in systematicity. Regular prompts are one-off instructions, while gstack is a chained workflow. The output of each skill automatically becomes the input for the next skill, forming a complete closed loop of Think → Plan → Build → Review → Test → Ship → Reflect. Furthermore, gstack has built-in safety guardrails (/careful, /freeze, /guard) to prevent AI from accidentally modifying unrelated code during debugging. This "process governance" cannot be achieved with single prompts.

Summary

The value of gstack is not in the Markdown files themselves, but in the paradigm it validates: the future of AI programming is not about "smarter copilots," but about "better team management." When you break down AI from a vague, all-purpose assistant into expert roles with specific responsibilities, and connect them with structured processes, an individual's productivity can undergo a qualitative change.

Three core takeaways are worth remembering. First, role-playing is more effective than generalization: giving AI clear boundaries of responsibility is far more effective than giving it a broad prompt. Second, process is the prerequisite for parallelism: without the Think → Plan → Build → Review → Test → Ship structure, multiple agents running in parallel will only create chaos. Third, Markdown is code: in the LLM era, well-written Markdown files are executable engineering methodologies, and this cognitive shift is reshaping the entire developer tool ecosystem.

Models are getting stronger every week. Those who learn to collaborate with AI now will have a huge advantage in the upcoming competition. Whether you are a developer, creator, or entrepreneur, consider starting today: transform your programming workflow with gstack, and apply the "AI agent role-playing" methodology to your own scenarios. Role-play your AI, turning it from a vague assistant into a precise team.