CodersBucket CEO has built 10 products, including GymCity and HelloVacancy, and shares hard-won lessons on product development. His key insight: bad code rarely kills a product, but the wrong problem does.
TL;DR
- CodersBucket CEO shares insights from building 10 products, emphasizing the importance of problem validation and MVP scoping.
- Key lessons include building with real users, solving one problem well, and designing for reliability.
- Common mistakes involve building before validating and ignoring onboarding, with the root cause often being a lack of user behavior observation.
What happened
CodersBucket CEO, with experience since 2006, has built and delivered over 180 client projects and owns 10 products, including GymCity, WebEquique, and HelloVacancy.
He shares product development practices learned from his ventures, emphasizing the importance of problem validation and MVP scoping.
His companies have delivered more than 180 client projects, and he has experience in building AI-ready CRM, AI sales agent, and other software products.
Why it matters
For developers and startups, the key takeaway is to validate the problem before writing code and to scope an MVP around one job, one user type, and one success metric.
Investors should look for startups that follow these practices, as they are more likely to succeed in the competitive AI market.
The article highlights the importance of building with real users, solving one problem well, and designing for reliability, which are critical for AI startups.
Key facts
- CodersBucket CEO has built 10 products, including GymCity, WebEquique, and HelloVacancy.
- He has delivered over 180 client projects since 2006.
- CB Insights found that 43% of venture-backed startup failures since 2023 were due to poor product-market fit.
- Pendo found that 80% of features in the average software product are rarely or never used.
- The CEO's companies have delivered more than 180 client projects.
- He emphasizes the importance of problem validation, MVP scoping, and building with real users.
- Common product development mistakes include building before validating, overloading the MVP, and ignoring onboarding.
Context
In the AI industry, product development is crucial for startups to succeed in a competitive market.
With the rise of AI coding assistants and LLM agents, the risk has shifted from building software to picking the right problem and learning from real usage faster than competitors.
The article provides valuable insights for developers, startups, and investors on how to approach product development in the AI industry.