John Hu, cofounder of the Stan creator platform, explained how he and his cofounder built the AI tool Stanley in 14 days using vibe coding. He also described the development process, customer validation methods, and reported revenue milestones that followed the product’s launch.
Key Takeaways
- John Hu said Stanley was developed in 14 days through vibe coding.
- The founders relied on direct customer knowledge before building features.
- Early product testing included manual validation before automation.
- Hu said sharing development updates publicly helped attract initial users.
- Stanley later reached reported annual recurring revenue milestones.
John Hu vibe coding became the focus of a recent discussion after the Stan cofounder explained how he and his cofounder created the AI content tool Stanley in 14 days. Hu described the product development process, the methods used to validate customer demand before expanding features, and the early business results reported after the product launched.
Stan is a platform that provides tools for creators to sell digital products, memberships, courses, and other online offerings. Stanley is the company’s AI product designed to assist users with creating and scaling content for platforms including LinkedIn and Instagram.
Hu stated that the founders completed the initial version of Stanley within two weeks by relying on AI-assisted software development. He also shared four principles that guided the product’s launch, ranging from customer research to public product updates and AI-assisted outreach.
What Did John Hu Reveal About Building Stanley?
Stanley’s 14-Day Development Timeline
Hu said the initial version of Stanley was built in 14 days through what is commonly described as vibe coding, a development approach that combines natural language instructions with AI-assisted code generation.
According to Hu, the speed of development reflected the company’s decision to launch quickly and improve the product through continued iteration rather than extending the initial build process.
The first version of Stanley for LinkedIn launched in June 2025. Hu later said the Instagram version was introduced in March 2026 as the product expanded beyond its original audience.
Hu explained that both founders already had experience creating online content, which influenced many of the product decisions made during development.
Rather than treating the development schedule as a technical milestone by itself, Hu described it as part of a broader effort to move from product concept to customer testing without unnecessary delays.
The approach also reflects practices seen at other AI-focused startups that have emphasized lean development and automation while building commercial products, including AI startup revenue growth, where founders similarly described using artificial intelligence to accelerate development and operations.
What Strategies Did Hu Credit for Stanley’s Launch?
Hu said understanding customer needs represented the most important factor behind Stanley’s development.
According to Hu, AI-assisted coding can accelerate software creation, but founders still need to determine whether the product addresses a genuine customer problem. He said that distinction separates useful products from applications that fail to deliver practical value.
Hu explained that his team approached development from the perspective of active content creators. That familiarity, he said, allowed them to identify features that would solve specific challenges experienced by their intended users.
Another strategy involved publicly documenting the product’s development process. Hu said sharing updates about Stanley helped introduce the product to potential users while also creating opportunities to receive feedback during its early stages.
He stated that public posts about the product generated interest from prospective customers, some of whom later joined as early users after following the development process online.
Hu also described the use of AI-generated outreach during the product’s launch period. Rather than manually preparing every message, the company used Stanley itself to draft outreach emails for selected creators as part of its beta testing efforts.
Customer Research and Manual Testing
Before relying entirely on automation, Hu said the founders manually tested many of Stanley’s core functions.
One example involved validating content ideas before the AI system generated them independently. Hu explained that he personally created sample content and shared it with potential users to determine whether the suggestions matched customer expectations.
This approach allowed the founders to gather direct responses before expanding automated capabilities.
Hu compared the process to operating behind the scenes before the product became fully functional. By manually performing work that Stanley would later automate, the team collected feedback that informed future product decisions.
Hu said product development required continual prioritization rather than attempting to build every possible feature at once.
According to his explanation, the founders focused their development resources on improving the user experience while delaying work that was not immediately necessary for the initial release.
He also described customer interviews as an important source of product validation during Stanley’s early development.
What Revenue Milestones Did Stanley Report?
Hu shared several reported business metrics describing Stanley’s early commercial performance.
According to Hu, the LinkedIn version of Stanley reached approximately $200,000 in annual recurring revenue within six weeks of launch.
He also stated that the LinkedIn version later exceeded $1 million in annual recurring revenue during 2025.
Hu reported that the broader Stan business generates nearly $41 million in annual recurring revenue. He said approximately $38 million comes from Stan Store, while roughly $3 million is attributed to Stanley across its LinkedIn and Instagram products.
Those figures were presented by Hu as reported company performance metrics while explaining Stanley’s growth following its launch.
Hu connected those results to rapid product deployment, customer validation, and continuous iteration after release rather than delaying the launch until every planned feature had been completed.
Why Is Stanley’s Development Approach Drawing Attention?
Hu’s description of Stanley’s development has attracted attention because it provides a practical example of how AI-assisted software development was applied to launch a commercial product within a short timeframe.
His account focuses on combining AI tools with customer research rather than relying solely on automated code generation.
The process he described also emphasizes validating customer demand before expanding product functionality, using manual testing where appropriate and incorporating user feedback into future development.
Hu additionally presented public product updates as part of the company’s launch strategy, explaining that sharing development progress helped introduce Stanley to prospective customers while collecting feedback from early users.
The emphasis on AI-assisted development also aligns with wider coverage of AI software development platforms, where companies are applying artificial intelligence to accelerate software creation while maintaining oversight of development workflows.
Frequently Asked Questions
Who is John Hu?
John Hu is the cofounder of Stan, a platform that provides creators with tools to sell digital products and operate online businesses. He also helped develop the company’s AI product, Stanley.
What is Stanley by Stan?
Stanley is an AI content tool developed by Stan. It is designed to help creators develop and scale content for platforms including LinkedIn and Instagram.
What is vibe coding?
Vibe coding generally refers to an AI-assisted software development approach in which developers use natural language prompts alongside AI tools to generate and refine code.
What strategies did John Hu say helped Stanley launch successfully?
Hu said the company’s approach centered on understanding customer needs, manually validating product ideas before automation, sharing development updates publicly, and using AI-assisted outreach to engage potential early users.
