CyberRanger/paper/2026-03-08-cyberranger-session-papers-replays-archives.md

title, date, categories, tags, pin, math, mermaid

title	date	categories	tags	pin	math	mermaid
One Session, Six Datasets, 58 Replays: The CyberRanger Publishing Marathon	2026-03-08 01:00:00 +0000	CyberRanger Research	huggingface ai-safety prompt-injection cyberranger claude-replay datasets github research	false	false	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
• moltbook-extended-injection-dataset
• clawk-ai-agent-dataset
• 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	arXiv	Empirically confirmed indirect injection taxonomy
2	Jailbroken (Wei et al., 2023)	HF	arXiv	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.

# 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/

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 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

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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.

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claude-replay: Every Chat Becomes a Replay

One of today's most exciting discoveries: claude-replay

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.

# 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

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
Blog Post	davidtkeane.github.io/posts/from-rangerbot-to-cyberranger-v42-the-full-story/
GitHub Profile	github.com/davidtkeane
All Datasets	huggingface.co/DavidTKeane
claude-replay	github.com/es617/claude-replay
TorchCode	github.com/duoan/TorchCode

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Rangers lead the way! 🎖️