I recently participated in the DigitalOcean Gradient™ AI Hackathon with a project I’m pretty excited about — Repo Promoter Agent. The idea came from a simple but recurring problem: promoting open-source projects on GitHub is often harder than building them.
💡 Inspiration
As someone who maintains several open source repositories, I noticed that my projects often struggle to reach the right audience. Writing promotional content — tweets, summaries, posts — takes time that could otherwise go into building features. I wanted a tool that could take a repository URL and instantly generate reusable marketing copy for it.
🚀 What It Does
Repo Promoter Agent does exactly that. Paste any public GitHub repo URL into the app, and it generates a complete promo pack:
- A catchy headline
- A short summary and key benefits
- Tweet-sized snippets
- A LinkedIn-style post
- A call to action
All generated content is stored, indexed, and searchable — so you can quickly reuse good material across different projects.
🧩 How I Built It
The app combines several components:
- Frontend: A simple web UI with “Generate” and “Search” screens
- Backend: Written in Go, it fetches repo data and structures input for the AI agent
- AI Layer: A Gradient AI agent processes the input and returns well‑structured JSON output
- Search Engine: Apache Solr 10 indexes the generated content for fast retrieval
Once the agent returns the JSON promo pack, it’s stored and instantly searchable in Solr.
🧠 What I Learned
Using DigitalOcean’s Gradient AI platform was surprisingly smooth. The key insight was to think of prompt design as a contract — if the backend can trust the JSON format, the rest of the pipeline becomes predictable. It’s a great exercise in bridging LLM creativity with backend reliability.
⚙️ Challenges
Getting Solr 10 configured correctly took significant tuning, especially for schema and query logic. Another tricky aspect was finding the right balance between AI creativity and structure — ensuring the agent’s output stayed valid JSON without sounding robotic.
💰 Costs
Here’s what the first day of testing looked like:
- App (29 hours): $1.07
- Droplet (34 hours): $1.62
- Database (58 hours): $1.69
- Agents: $0.15
Total: $4.53 for a live day of experimenting. The cost will increase as I test more agents and models.
🛠️ Built With
- GitHub
- Go
- Solr
- Gradient AI
If you want to explore the project or see it in action, check out the Devpost page here.
