EverPrompt - AI Prompt Management
Design and built an AI prompt management platform with AI capabilities to solve my own prompt organization challenges.
Project Overview
I was struggling to keep track of my AI prompts and wanted to get better at working in the AI space, so I decided to build something that could help both me and others. This became part of my broader effort to establish thought leadership in AI. I’ll admit the product-market fit isn’t perfect yet, but the real value has been in the learning and building process.
Tools & Technologies
Framework
AI & Backend
CMS
Services
The Challenge
Personal Pain Point
Losing valuable prompts in endless chat histories and having to recreate them constantly.
Learning Opportunity
Wanted to understand AI integration and prompt management better while building in public.
Market Gap
Existing solutions felt clunky or incomplete for power users who work with AI daily.
Technical Challenge
Building a responsive app with a range of integrations that work together.
Role & Responsibilities
I handled full-stack development and design, managing AI integration with Claude API, database design and backend architecture, user interface and experience design, and content management system setup. Everything from concept to deployment was solo work.
Key Features
I built the entire platform using modern tools while learning new technologies. The prompt library lets you organize and categorize AI prompts with AI auto-tagging and search functionality. AI-powered features use Claude API for auto-tagging and organizing prompts intelligently. The template builder creates reusable prompt templates with variable placeholders for different use cases. Content management happens through Strapi CMS integration for easy updates. I built it with Astro and React islands for optimal performance and developer experience. Supabase handles storing and managing prompt data along with authentication.
Results & Impact
Personal Learning
Used Astro framework, React islands architecture, and Chrome extension development in one project.
Technical Growth
Successfully integrated multiple APIs (Claude, Supabase, Resend, Strapi) into a cohesive platform.
User Feedback
Currently gathering feedback from early users to refine product-market fit and feature priorities.
AI Leadership
Established credibility in AI tooling space through building and sharing the development process.
Key Learnings
Building while learning accelerates skill development more than theoretical study. I learned more in three weeks of building than I would have in months of tutorials. Chrome extensions require careful consideration of permissions and user privacy. This was new territory and the security model is more complex than standard web apps. AI integrations work best when they enhance rather than replace user workflows. The auto-tagging feature helps but doesn’t try to do everything automatically. Product-market fit takes time. Building is just the first step. The hard part is finding the people who actually need what you built. Modern development tools enable rapid prototyping without sacrificing quality. Astro and React islands let me move fast without creating technical debt. Branding and product design skills are extremely valuable when building with AI. Technical capability alone isn’t enough. The product needs to feel good to use.