Hi HN! I'm Hyojun Kang, CEO & Co-founder of Hops, and we've built an AI-powered tool that lets you build backoffice applications through natural language conversation.
# Why we built this:
As former startup engineers, we faced a constant struggle: balancing time between core product development and building backoffice. While these tools were essential for operations, their development and maintenance consumed significant engineering hours that could have been spent on our main product.
We needed a system that would let us build these tools quickly while maintaining full control over the development process. That's where we saw an opportunity: by leveraging AI, we could not only generate the tools we need but also eliminate the learning curve associated with traditional low-code platforms.
Now, instead of studying platform-specific concepts, engineers can simply describe what they need in natural language and get working solutions immediately.
# What makes Hops different:
Instead of traditional low-code platforms that require learning new interfaces and concepts, Hops uses two familiar paradigms:
- Natural language - describe what you need, and our AI generates complete workflows
- Notion-like interface - just type "/" to add components, similar to writing in Notion
# Technical details:
- Connects to existing databases (MySQL, PostgreSQL, MongoDB) and APIs
- Generate both frontend components and backend logic
- No new infrastructure needed - works with your existing stack
- AI understands database schema and generates appropriate queries
- Built-in workflow system for data transformation and task automation
# Try it out:
You can test Hops at https://app.hops.pub . We've set up a demo environment with sample databases so you can experience how the AI understands and implements your requirements.
Example conversation:
"Create a page that displays a list of customers as a searchable table and displays the order history of the selected customer as a relational table"
-> Hops generates a complete page with the requested table, appropriate database queries, and filtering logic.
We'd love to hear your thoughts and feedback, especially from others who've dealt with internal tool development challenges.
Hi HN! I'm Hyojun Kang, CEO & Co-founder of Hops, and we've built an AI-powered tool that lets you build backoffice applications through natural language conversation.
# Why we built this:
As former startup engineers, we faced a constant struggle: balancing time between core product development and building backoffice. While these tools were essential for operations, their development and maintenance consumed significant engineering hours that could have been spent on our main product.
We needed a system that would let us build these tools quickly while maintaining full control over the development process. That's where we saw an opportunity: by leveraging AI, we could not only generate the tools we need but also eliminate the learning curve associated with traditional low-code platforms.
Now, instead of studying platform-specific concepts, engineers can simply describe what they need in natural language and get working solutions immediately.
# What makes Hops different:
Instead of traditional low-code platforms that require learning new interfaces and concepts, Hops uses two familiar paradigms:
- Natural language - describe what you need, and our AI generates complete workflows
- Notion-like interface - just type "/" to add components, similar to writing in Notion
# Technical details:
- Connects to existing databases (MySQL, PostgreSQL, MongoDB) and APIs
- Generate both frontend components and backend logic
- No new infrastructure needed - works with your existing stack
- AI understands database schema and generates appropriate queries
- Built-in workflow system for data transformation and task automation
# Try it out:
You can test Hops at https://app.hops.pub . We've set up a demo environment with sample databases so you can experience how the AI understands and implements your requirements.
Example conversation:
"Create a page that displays a list of customers as a searchable table and displays the order history of the selected customer as a relational table"
-> Hops generates a complete page with the requested table, appropriate database queries, and filtering logic.
We'd love to hear your thoughts and feedback, especially from others who've dealt with internal tool development challenges.