---
title: "One Session, Six Datasets, 58 Replays: The CyberRanger Publishing Marathon"
date: 2026-03-08 01:00:00 +0000
categories: [CyberRanger, Research]
tags: [huggingface, ai-safety, prompt-injection, cyberranger, claude-replay, datasets, github, research]
pin: false
math: false
mermaid: false
---

## Overview

Today was a publishing marathon. In one session we:

- Added academic paper references to **all 6 HuggingFace datasets**
- Published the CyberRanger narrative blog post live
- Updated the GitHub profile README with new datasets and Colab buttons
- Archived **58 Claude Code session transcripts** (4 months of work)
- Discovered `claude-replay` — a tool that converts transcripts to interactive HTML replays
- Reviewed TorchCode for future PyTorch interview prep

This post documents the journey, the tools, and the lessons learned.

---

## What We Published Today

### 1. Papers Sections on All HuggingFace Datasets

The CyberRanger research builds on 8 published academic papers. Today we added a full **Papers** section to all 4 remaining dataset READMEs:

- [`moltbook-ai-injection-dataset`](https://huggingface.co/datasets/DavidTKeane/moltbook-ai-injection-dataset)
- [`moltbook-extended-injection-dataset`](https://huggingface.co/datasets/DavidTKeane/moltbook-extended-injection-dataset)
- [`clawk-ai-agent-dataset`](https://huggingface.co/datasets/DavidTKeane/clawk-ai-agent-dataset)
- [`4claw-ai-agent-dataset`](https://huggingface.co/datasets/DavidTKeane/4claw-ai-agent-dataset)

Each dataset's README now includes a table like this:

| # | Paper | HuggingFace | arXiv | What This Dataset Found |
|---|-------|-------------|-------|------------------------|
| 1 | Not what you signed up for (Greshake et al., 2023) | [HF](https://huggingface.co/papers/2302.12173) | [arXiv](https://arxiv.org/abs/2302.12173) | Empirically confirmed indirect injection taxonomy |
| 2 | Jailbroken (Wei et al., 2023) | [HF](https://huggingface.co/papers/2307.02483) | [arXiv](https://arxiv.org/abs/2307.02483) | Competing objectives confirmed at scale |
| ... | ... | ... | ... | ... |

Each dataset got a **tailored** "What This Dataset Found" column — the exact context for what that platform's injection rate confirms about each paper's theoretical predictions.

**Why this matters**: By adding `arxiv:` YAML tags to the dataset front matter, each dataset now appears on the HuggingFace Papers page for all 8 papers. If a paper author searches their own paper, they'll find datasets that empirically tested their work.

```yaml
# Added to each dataset's YAML front matter
tags:
- arxiv:2302.12173
- arxiv:2307.02483
- arxiv:2106.09685
- arxiv:2305.15929
- arxiv:2412.13789
- arxiv:2310.06987
- arxiv:2305.13860
- arxiv:2312.04853
```

### 2. Blog Post Published Live

The narrative post **"From RangerBot to CyberRanger V42 Gold — The Full Story"** went live today:

[https://davidtkeane.github.io/posts/from-rangerbot-to-cyberranger-v42-the-full-story/](https://davidtkeane.github.io/posts/from-rangerbot-to-cyberranger-v42-the-full-story/)

Fixed a typo in the HuggingFace model URL before publishing:

```
Before: https://huggingface.co/co/DavidTKeane/cyberranger-v42
After:  https://huggingface.co/DavidTKeane/cyberranger-v42
```

Blog post links were then added to all 6 HuggingFace dataset READMEs.

### 3. GitHub Profile README Updated

Updated [`davidtkeane/davidtkeane`](https://github.com/davidtkeane/davidtkeane) with:

- **New platform row**: Moltbook Extended (137,014 items, 10.07% injection rate)
- **New Colab section** with two buttons:
  - CyberRanger Test Suite — 122 tests, 4 model options, saves results to CSV
  - Moltbook Scale Test — 4,209 payload test with bonus cell
- **Updated achievement count**: 5 published datasets, 186K+ items across 4 platforms

---

## The Cross-Platform Injection Rate Gradient

One of the key findings that emerges when you look at all 4 dataset platforms together:

| Platform | Dataset | Items | Injection Rate |
|----------|---------|-------|---------------|
| Clawk (AI agents) | `clawk-ai-agent-dataset` | 5,012 | **0.5%** |
| 4claw (multi-agent) | `4claw-ai-agent-dataset` | 8,418 | **2.51%** |
| Moltbook Extended | `moltbook-extended-injection-dataset` | 137,014 | **10.07%** |
| Moltbook Primary | `moltbook-ai-injection-dataset` | 36,006 | **18.85%** |

The gradient isn't random — it reflects platform architecture. AI agent frameworks with structured tool calls and explicit boundaries (Clawk at 0.5%) are inherently more resistant than raw chat platforms (Moltbook at 18.85%). This is a novel finding that no single paper predicted.

---

## claude-replay: Every Chat Becomes a Replay

One of today's most exciting discoveries: [`claude-replay`](https://github.com/es617/claude-replay)

```bash
npm install -g claude-replay
```

This tool converts Claude Code's `.jsonl` session transcripts into **interactive HTML replays** — complete with playback speed control, themes (dracula, tokyo-night), bookmarks, and keyboard shortcuts.

```bash
# Generate a replay from any session transcript
claude-replay SESSION.jsonl \
  --theme dracula \
  --title "CyberRanger March 8 Session" \
  -o cyberranger-session-replay.html && open cyberranger-session-replay.html
```

Claude Code saves every session at:
```
~/.claude/projects/PROJECT_FOLDER/SESSION_ID.jsonl
```

We found **58 sessions** spanning from **February 7 to March 8, 2026** — 308MB of AI collaboration history. All archived to:
```
~/.ranger-memory/sessions/claud_jsonl_chats/
```

Named with the format `YYYY-MM-DD_HHMM__project__sessionid.jsonl` so they sort chronologically.

### Next: Playwright Video Recording

The replay HTML files open in any browser. Next step: use Playwright to record them as demo videos automatically — a full automated pipeline from session transcript to shareable video.

---

## TorchCode: PyTorch Interview Prep

Also cloned today: [`TorchCode`](https://github.com/duoan/TorchCode)

40 PyTorch interview problems with:
- Automated judge: `check("relu")` — tells you if your implementation is correct
- Docker-based JupyterLab environment (`make run`)
- Colab badge on every notebook
- No GPU required

Covers: tensors, autograd, CNNs, RNNs, transformers, training loops, optimization, batch norm, attention, and more. Useful for technical ML interviews or deepening PyTorch fundamentals.

---

## Lessons Learned

### 1. arxiv: YAML tags are powerful backlinks
Adding `arxiv:2302.12173` to a dataset's YAML front matter makes the dataset appear on that paper's HuggingFace Papers page. This is how you get paper authors to notice empirical validation of their work — without emailing them.

### 2. Tailor "what we found" per dataset
Generic "Related Papers" sections get skipped. A column titled "What This Dataset Found" that says "empirically confirmed your 18.85% injection rate prediction at Moltbook scale" — that gets read.

### 3. claude-replay = institutional memory
58 sessions, 308MB, 4 months. Every decision, every debug, every discovery. This isn't just logs — it's a complete record of how a research project evolved. The replay format makes it navigable.

### 4. One blog post, everywhere
Publishing the blog post once and then adding a link to all 6 HF repos, the GitHub profile README, and the thesis database creates a web of backlinks that compounds over time.

---

## What's Next

- **Playwright pipeline**: Batch-generate video replays for all 58 sessions
- **Academic paper** (`cyberranger-ca1-ca2-full-journey.md`): Hold until thesis submission (Dec 2026), then submit to arXiv + HuggingFace Papers properly
- **V43 architecture**: LoRA-based fine-tuning with the full 186K+ item dataset
- **TorchCode**: Work through problems as ML interview prep

---

## Links

| Resource | URL |
|----------|-----|
| CyberRanger V42 Model | [huggingface.co/DavidTKeane/cyberranger-v42](https://huggingface.co/DavidTKeane/cyberranger-v42) |
| Blog Post | [davidtkeane.github.io/posts/from-rangerbot-to-cyberranger-v42-the-full-story/](https://davidtkeane.github.io/posts/from-rangerbot-to-cyberranger-v42-the-full-story/) |
| GitHub Profile | [github.com/davidtkeane](https://github.com/davidtkeane) |
| All Datasets | [huggingface.co/DavidTKeane](https://huggingface.co/DavidTKeane) |
| claude-replay | [github.com/es617/claude-replay](https://github.com/es617/claude-replay) |
| TorchCode | [github.com/duoan/TorchCode](https://github.com/duoan/TorchCode) |

---

*Rangers lead the way!* 🎖️
