A Full Breakdown of gstack: How YC's President Uses AI to Write 10,000 Lines of Code Daily
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 executingrm -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:
1git clone https://github.com/garrytan/gstack.git2~/.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-hoursand 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
/qahttps://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
/browsefailed? Runcd ~/.claude/skills/gstack && bun install && bun run build
- Outdated version? Run
/gstack-upgrade, or setauto_upgrade: truein~/.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.
References
[1] Why Garry Tan's Claude Code setup has gotten so much love—and hate
[3] Reddit user's in-depth review of gstack
[5] Reddit user adapts gstack for C++ development
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A: No. Huang answered based on the narrow definition proposed by Lex Fridman (AI being able to start a $1 billion company), whereas the AGI definition in OpenAI's charter is "highly autonomous systems that outperform humans at most economically valuable work." There is a massive gap between the two standards, with the latter requiring a scope of capability far beyond the former. Q: Can current AI really operate a company independently? A: Not currently. Huang himself admitted that while an AI Agent might create a short-lived viral app, "the odds of building NVIDIA is zero." Current AI excels at structured task execution but still relies heavily on human guidance in scenarios requiring long-term strategic judgment, cross-domain coordination, and handling unknown situations. Q: What impact will the achievement of AGI have on everyday jobs? A: Even by the most optimistic definitions, the impact of current AI is primarily seen in improving the efficiency of specific tasks rather than fully replacing human work. Sam Altman also admitted in late 2025 that AGI's "social impact is much smaller than expected." In the short term, AI is more likely to change the way we work as a powerful assistant tool rather than directly replacing roles. Q: Why are tech CEOs so eager to declare that AGI has been achieved? A: The reasons are multifaceted. NVIDIA's core business is selling AI compute chips; the AGI narrative maintains market enthusiasm for investment in AI infrastructure. OpenAI's contract with Microsoft includes AGI trigger clauses, where the definition of AGI directly affects the distribution of tens of billions of dollars. Furthermore, in capital markets, the "AGI is coming" narrative is a major pillar supporting the high valuations of AI companies. Q: How far is China's AI development from AGI? 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From OpenAI to NVIDIA, every "we achieved AGI" claim is accompanied by a quiet lowering of the standard. As information consumers, what we need is not to chase headlines but to build our own framework for judgment. AI technology is undoubtedly progressing rapidly. The new chips, Agent platforms, and inference optimization technologies released at GTC 2026 are real engineering breakthroughs. But packaging these advancements as "AGI achieved" is more of a market narrative strategy than a scientific conclusion. Staying curious, remaining critical, and continuously tracking primary sources is the best strategy to avoid being overwhelmed by the flood of information in this era of AI acceleration. Want to systematically track AI industry trends? Try to save key sources to your personal knowledge base and let AI help you organize, query, and cross-verify. [1] [2] [3] [4] [5] [6]
The Rise of AI Influencers: Essential Trends and Opportunities for Creators
TL; DR Key Takeaways On March 21, 2026, Elon Musk posted a tweet on X with only eight words: "AI bots will be more human than human." This tweet garnered over 62 million views and 580,000 likes within 72 hours. He wrote this in response to an AI-generated image of a "perfect influencer face." This isn't a sci-fi prophecy. If you are a content creator, blogger, or social media manager, you have likely already scrolled past those "too perfect" faces in your feed, unable to tell if they are human or AI. This article will take you through the reality of AI virtual influencers, the income data of top cases, and how you, as a human creator, should respond to this transformation. This article is suitable for content creators, social media operators, brand marketers, and anyone interested in AI trends. First, let's look at a set of numbers that will make you sit up. The global virtual influencer market size reached $6.06 billion in 2024 and is expected to grow to $8.3 billion in 2025, with an annual growth rate exceeding 37%. According to Straits Research, this figure is projected to soar to $111.78 billion by 2033. Meanwhile, the entire influencer marketing industry reached $32.55 billion in 2025 and is expected to break the $40 billion mark by 2026. Looking at specific individuals, two representative cases are worth a closer look. Lil Miquela is widely recognized as the "first-generation AI influencer." This virtual character, born in 2016, has over 2.4 million followers on Instagram and has collaborated with brands like Prada, Calvin Klein, and Samsung. Her team (part of Dapper Labs) charges tens of thousands of dollars per branded post. Her subscription income on the Fanvue platform alone reaches $40,000 per month, and combined with brand partnerships, her monthly income can exceed $100,000. It is estimated that her average annual income since 2016 is approximately $2 million. Aitana López represents the possibility that "individual entrepreneurs can also create AI influencers." This pink-haired virtual model, created by the Spanish creative agency The Clueless, has over 370,000 followers on Instagram and earns between €3,000 and €10,000 per month. The reason for her creation was practical: founder Rubén Cruz was tired of the uncontrollable factors of human models (being late, cancellations, schedule conflicts), so he decided to "create an influencer who would never flake." A prediction by PR giant Ogilvy in 2024 sent shockwaves through the industry: by 2026, AI virtual influencers will occupy 30% of influencer marketing budgets. A survey of 1,000 senior marketers in the UK and US showed that 79% of respondents said they are increasing investment in AI-generated content creators. To see the underlying drivers of this change, you must understand the logic of brands. Zero risk, total control. The biggest risk with human influencers is "scandal." A single inappropriate comment or a personal scandal can flush millions of brand investment down the drain. Virtual influencers don't have this problem. They don't get tired, they don't age, and they won't post a tweet at 3 AM that makes the PR team collapse. As Rubén Cruz, founder of The Clueless, said: "Many projects were put on hold or canceled due to issues with the influencers themselves; it wasn't a design flaw, but human unpredictability." 24/7 content output. Virtual influencers can post daily, follow trends in real-time, and "appear" in any setting at a cost far lower than a human shoot. According to estimates by BeyondGames, if Lil Miquela posts once a day on Instagram, her potential income in 2026 could reach £4.7 million. This level of output efficiency is unmatched by any human creator. Precise brand consistency. Prada's collaboration with Lil Miquela resulted in an engagement rate 30% higher than regular marketing campaigns. Every expression, every outfit, and every caption of a virtual influencer can be precisely designed to ensure a perfect fit with the brand's tone. However, there are two sides to every coin. A report by Business Insider in March 2026 pointed out that consumer backlash against AI accounts is rising, and some brands have already begun to retreat from AI influencer strategies. A YouGov survey showed that more than one-third of respondents expressed concern about AI technology. This means virtual influencers are not a panacea; authenticity remains an important factor for consumers. In the face of the impact of AI virtual influencers, panic is useless; action is valuable. Here are four proven strategies for responding. Strategy 1: Deepen authentic experiences; do what AI cannot. AI can generate a perfect face, but it cannot truly taste a cup of coffee or feel the exhaustion and satisfaction of a hike. In a discussion on Reddit's r/Futurology, a user's comment received high praise: "AI influencers can sell products, but people still crave real connections." Turn your real-life experiences, unique perspectives, and imperfect moments into a content moat. Strategy 2: Arm yourself with AI tools rather than fighting AI. Smart creators are already using AI to boost efficiency. Creators on Reddit have shared complete workflows: using ChatGPT for scripts, ElevenLabs for voiceovers, and HeyGen for video production. You don't need to become an AI influencer, but you need to make AI your creative assistant. Strategy 3: Systematically track industry trends to build an information advantage. The AI influencer field moves incredibly fast, with new tools, cases, and data appearing every week. Randomly scrolling through Twitter and Reddit is far from enough. You can use to systematically manage industry information scattered everywhere: save key articles, tweets, and research reports into a Board, use AI to automatically organize and retrieve them, and ask your asset library questions at any time, such as "What were the three largest funding rounds in the virtual influencer space in 2026?". When you need to write an industry analysis or film a video, the materials are already in place instead of starting from scratch. Strategy 4: Explore human-AI collaborative content models. The future is not a zero-sum game of "Human vs. AI," but a collaborative symbiosis of "Human + AI." You can use AI to generate visual materials but give them a soul with a human voice and perspective. Analysis from points out that AI influencers are suitable for experimental, boundary-pushing concepts, while human influencers remain irreplaceable in building deep audience connections and solidifying brand value. The biggest challenge in tracking AI virtual influencer trends is not too little information, but too much information that is too scattered. A typical scenario: You see a tweet from Musk on X, read a breakdown post on Reddit about an AI influencer earning $10,000 a month, find an in-depth report on Business Insider about brands retreating, and then scroll past a tutorial on YouTube. This information is scattered across four platforms and five browser tabs. Three days later, when you want to write an article, you can't find that key piece of data. This is exactly the problem solves. You can use the to clip any webpage, tweet, or YouTube video to your dedicated Board with one click. AI will automatically extract key information and build an index, allowing you to search and ask questions in natural language at any time. For example, create an "AI Virtual Influencer Research" Board to manage all relevant materials centrally. When you need to produce content, ask the Board directly: "What is Aitana López's business model?" or "Which brands have started to retreat from AI influencer strategies?", and the answers will be presented with links to the original sources. It should be noted that YouMind's strength lies in information integration and research assistance; it is not an AI influencer generation tool. If your need is to create virtual character images, you still need professional tools like Midjourney, Stable Diffusion, or HeyGen. However, in the core creator workflow of "Research Trends → Accumulate Materials → Produce Content," can significantly shorten the distance from inspiration to finished product. Q: Will AI virtual influencers completely replace human influencers? A: Not in the short term. Virtual influencers have advantages in brand controllability and content output efficiency, but the consumer demand for authenticity remains strong. Business Insider's 2026 report shows that some brands have begun to reduce AI influencer investment due to consumer backlash. The two are more likely to form a complementary relationship rather than a replacement one. Q: Can an average person create their own AI virtual influencer? A: Yes. Many creators on Reddit have shared their experiences of starting from scratch. Common tools include Midjourney or Stable Diffusion for generating consistent images, ChatGPT for writing copy, and ElevenLabs for generating voice. The initial investment can be very low, but it requires 3 to 6 months of consistent operation to see significant growth. Q: What are the income sources for AI virtual influencers? A: There are mainly three categories: brand-sponsored posts (top virtual influencers charge thousands to tens of thousands of dollars per post), subscription platform income (such as Fanvue), and derivatives and music royalties. Lil Miquela earns an average of $40,000 per month from subscription income alone, with brand collaboration income being even higher. Q: What is the current state of the AI virtual idol market in China? A: China is one of the most active markets for virtual idol development globally. According to industry forecasts, the Chinese virtual influencer market will reach 270 billion RMB by 2030. From Hatsune Miku and Luo Tianyi to hyper-realistic virtual idols, the Chinese market has gone through several development stages and is currently evolving toward AI-driven real-time interaction. Q: What should brands look for when choosing to collaborate with virtual influencers? A: It is crucial to evaluate three points: the target audience's acceptance of virtual personas, the platform's AI content disclosure policies (TikTok and Instagram are strengthening related requirements), and the fit between the virtual influencer and the brand's tone. It is recommended to test with a small budget first and then decide whether to increase investment based on data. The rise of AI virtual influencers is not a distant prophecy but a reality happening right now. Market data clearly shows that the commercial value of virtual influencers has been verified—from Lil Miquela's $2 million annual income to Aitana López's €10,000 monthly earnings, these numbers cannot be ignored. But for human creators, this is not a story of "being replaced," but an opportunity to "reposition." Your authentic experiences, unique perspectives, and emotional connection with your audience are core assets that AI cannot replicate. The key lies in using AI tools to improve efficiency, using systematic methods to track trends, and using authenticity to build an irreplaceable competitive moat. Want to systematically track AI influencer trends and accumulate creative materials? Try building your dedicated research space with and start for free. 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