Stephanie on AIBussin — AI applications, systems and books

The Nexus Blossom: How AI Thoughts Turn into Habits

Wed, 19 Nov 2025 23:23:16 +0100

We turn any text into scorables, grow them into blossoms, and weave them into a living graph. Then we A/B test the graph’s thinking quality.

From Photo Albums to Movies: Teaching AI to See Its Own Progress

Sun, 07 Sep 2025 08:50:43 +0100

🥱 TLDR

This post details the implementation of:

PACS: Implicit Actor–Critic Coupling via a Supervised Learning Framework for RLVR
NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings within our self-improving AI, Stephanie.

The core idea is to move beyond static, single-point feedback to a richer, more dynamic form of learning:

SIS: The Visual Dashboard That Makes Stephanie's AI Understandable

Mon, 25 Aug 2025 15:30:43 +0100

🔍 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

🔎 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.

A Novel Approach to Autonomous Research: Implementing NOVELSEEK with Modular AI Agents

Tue, 27 May 2025 22:40:50 +0100

Summary

AI research tools today are often narrow: one generates summaries, another ranks models, a third suggests ideas. But real scientific discovery isn’t a single step—it’s a pipeline. It’s iterative, structured, and full of feedback loops.

In this post, I show how to build a modular AI system that mirrors this full research lifecycle. From initial idea generation to method planning, each phase is handled by a specialized agent working in concert.