--- 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!* 🎖️