Visualization on AIBussin — AI applications, systems and books

Generate Figures and Animations

Mon, 10 Aug 2026 20:59:00 +0100

Cellular automata are visual systems.

That makes figures and animations unusually important.

But a useful image is not merely a screenshot. It should be a reproducible output of an experiment.

Save figures from data, not from memory

Suppose an experiment has produced a spacetime history:

history = np.stack(states)

A figure generator should accept that result explicitly:

import matplotlib.pyplot as plt


def save_spacetime(history, path):
 fig, ax = plt.subplots(figsize=(10, 6))
 ax.imshow(history, interpolation="nearest", aspect="auto")
 ax.set_xlabel("cell")
 ax.set_ylabel("time")
 fig.tight_layout()
 fig.savefig(path, dpi=180)
 plt.close(fig)

(Verified headless: PNG written with labeled axes from recorded arrays.)

🔦 Phōs: Visualizing How AI Learns and How to Build It Yourself

Thu, 09 Oct 2025 00:30:36 +0100

“The eye sees only what the mind is prepared to comprehend.” Henri Bergson

🔍 We Finally See Learning

For decades, we’ve measured artificial intelligence with numbers loss curves, accuracy scores, reward signals.
We’ve plotted progress, tuned hyperparameters, celebrated benchmarks.

But we’ve never actually seen learning happen.

Not really.

Sure, we’ve visualized attention maps or gradient flows but those are snapshots, proxies, not processes.

What if we could watch understanding emerge not as a number going up, but as a pattern stabilizing across time?
What if reasoning itself left a visible trace?

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.