SIS: The Visual Dashboard That Makes Stephanie's AI Understandable
🔍 The Invisible AI Problem
How do you debug a system that generates thousands of database entries, hundreds of prompts, and dozens of knowledge artifacts for a single query?
SIS is our answer a visual dashboard that transforms Stephanie’s complex internal processes into something developers can actually understand and improve.
📰 In This Post
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- 🔎 See how Stephanie pipelines really work – from Arxiv search to cartridges, step by step.
- 📜 View logs and pipeline steps clearly – no more digging through raw DB entries.
- 📝 Generate dynamic reports from pipeline runs – structured outputs you can actually use.
- 🤖 Use pipelines to train the system – showing how runs feed back into learning.
- 🧩 Turn raw data into functional knowledge – cartridges, scores, and reasoning traces.
- 🔄 Move from fixed pipelines toward self-learning – what it takes to make the system teach itself.
- 🖥️ SIS isn’t just a pretty GUI - it’s the layer that makes Stephanie’s knowledge visible and usable.
- 🈸️ Configuring Stephanie – We will show you how to get up and running with Stephanie.
- 💡 What we learned – the big takeaway: knowledge without direction is just documentation.
❓ Why We Built SIS
When you’re developing a self-improving AI like Stephanie, the real challenge isn’t just running pipelines it’s making sense of the flood of logs, evaluations, and scores the system generates.
