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We're running our first hackathon: Build with VectorAI DB, win Claude subscriptions

#hackathon #vectordatabase #database #ai

The Actian VectorAI DB Build Challenge is our first community hackathon, and we want to see what you build. Solo or team, beginner or experienced, local or cloud. If you've been looking for a reason to actually ship something with a vector database, this is it.

April 13-18, 2026 | Virtual | Register on DoraHacks

What you're building

An AI application that solves a real, tangible problem using Actian VectorAI DB. It can run on your laptop, on a server, in the cloud, wherever. The only rule: VectorAI DB has to be a core part of your stack, not something you bolted on at the end.

Your project also needs to go beyond basic similarity search. Pick at least one of these:

Hybrid Fusion - combine multiple search signals into one ranked result. Not just meaning, not just keywords. Both, fused together.

What that looks like in practice: A job board that ranks candidates by semantic fit ("backend engineer who gets distributed systems") AND keyword match ("Golang, Kubernetes") merged into one list using RRF or DBSF.

Filtered Search - pair vector search with structured filters on your data so results are actually useful, not just semantically close.

What that looks like in practice: A campus event finder that understands what you're looking for but also filters by date, location, and student org. So you're finding events you can go to, not just events that sound similar.

Named Vectors / Multimodal - store and search across different data types in the same collection. Text, images, audio, whatever fits your idea.

What that looks like in practice: A study tool where you search your notes by typing a question or uploading a diagram. Both hit the same knowledge base, just through different vector spaces.

Bonus points for running locally, on ARM, or offline. No fixed weight, judges' call.

Not sure what to build?

Some starting points, but don't let these limit you:

  • A RAG app over any dataset you actually care about (research papers, course notes, documentation, news)
  • A semantic search tool with smart filters (campus events, job listings, study materials)
  • A recommendation engine that combines meaning and metadata
  • An anomaly detection or monitoring system
  • An AI agent with vector-powered memory
  • A multimodal search tool across text and images

Getting started

The database runs in Docker and works natively on Mac (including Apple Silicon), Linux, and Windows. No Rosetta, no platform flags needed.

# Clone the repo and start the database
docker compose up

# Install the Python client
pip install actian-vectorai
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Not sure where to begin? Start with the featured RAG example:

pip install -r examples/rag/requirements.txt
python examples/rag/rag_example.py
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It walks you through building a complete retrieval-augmented generation app from scratch. You'll have something running in under 10 minutes.

VectorAI DB handles storage and search. You bring your own embedding model. A good default to start with is sentence-transformers/all-MiniLM-L6-v2, fast, lightweight, and works well for most text use cases.

pip install sentence-transformers
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For the full API docs and more examples, check the repo README linked in Discord.

Prizes

🥇 1st place team: Claude Max 5x, 3 months per person

🥈 2nd place team: Claude Max 5x, 1 month per person

🥉 3rd place team: Claude Pro, 1 month per person

Teams of up to 4. Solo submissions welcome.

How we judge

  • Use of Actian VectorAI DB (30%): Is VectorAI DB doing real work in this app? Does the team know why they used it the way they did?
  • Real-world impact (25%): Does it solve something people actually care about? Would someone use this?
  • Technical execution (25%): Does it work? Is the code coherent and the architecture thought through?
  • Demo and presentation (20%): Can you explain what you built and why it matters?

How to submit

All submissions go through DoraHacks. You'll need a public GitHub or GitLab repo with a README, a working demo (video, Loom, or live link), and a short write-up covering what you built, why, and which technical requirement you used.

Results announced April 20 on Discord.

Join us

Register: dorahacks.io/hackathon/2097/detail

Discord for support, team formation, and progress sharing: discord.gg/432A2M63Py

Drop a comment if you're in. See you April 13.

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