xbid.ai is open source. To help you navigate the stack, I am starting a walkthrough video series, each one covering a specific topic such as the data pipeline and strategies.
These videos are primarily aimed at developers. Extending strategies and running your own agent requires some technical background, and the best place to start is by forking the repo at github.com/xbid-ai/xbid-ai. If you hit specific technical questions, feel free to reach out.
But don’t worry if you are not a coder. The live xbid.ai platform will expose : ai signals derived from the same strategy layer that powers our production agent. Services will be available in free and pro tiers — connect your wallet, adjust outputs to your assets and risk profile, and benefit from the engine without touching code. The : ai lab will also open a way to participate in ongoing experiments and earn for free — more on this soon.
Walkthrough 1 — Multi-LLM AI Agent on Stellar: Data Pipeline Deep Dive (Reflector Oracles)
Chapters
- 0:00 Meet xbid.ai
- 0:40 Data Ingestion – Reflector Adapter
- 1:00 Configuring Adapter Plugins
- 1:30 Using Reflector Oracles
- 2:20 Pipeline Integration (fetch, normalize, distill)
- 4:10 Conclusion & Next Steps
Walkthrough 2 — Multi-LLM AI Agent on Stellar: Strategies (Spot Trading)
Chapters
- 0:00 Strategy Introduction
- 0:40 Configuration and Runtime Settings
- 1:05 Signals
- 2:01 Execution Layer
- 2:44 Live Agent Trading KALE
- 3:20 Conclusion & Next Steps
Let’s Hug
We are building xbid.ai in the open. Follow our experiments, and open-source work at huggingface.co/xbid-labs and github.com/xbid-ai. We publish code, data, and explore new approaches to reasoning systems. Contributions, feedback, and collaboration are always welcome.
Not financial advice. This is an experiment in onchain intelligence.