ranger
64a08297a4
- Seven Pillars Honor Code (CyberRanger ethics framework) - Psychological Spine (why small models need identity) - Memory Makes the Machine (6-agent consciousness experiment) - QLoRA to Ollama guide (technical methodology) - Moltbook origin story (how the dataset was discovered) - CyberRanger Journey overview - Session papers and archives Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
193 lines
8.3 KiB
Markdown
193 lines
8.3 KiB
Markdown
---
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title: "One Session, Six Datasets, 58 Replays: The CyberRanger Publishing Marathon"
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date: 2026-03-08 01:00:00 +0000
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categories: [CyberRanger, Research]
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tags: [huggingface, ai-safety, prompt-injection, cyberranger, claude-replay, datasets, github, research]
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pin: false
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math: false
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mermaid: false
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---
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## Overview
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Today was a publishing marathon. In one session we:
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- Added academic paper references to **all 6 HuggingFace datasets**
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- Published the CyberRanger narrative blog post live
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- Updated the GitHub profile README with new datasets and Colab buttons
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- Archived **58 Claude Code session transcripts** (4 months of work)
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- Discovered `claude-replay` — a tool that converts transcripts to interactive HTML replays
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- Reviewed TorchCode for future PyTorch interview prep
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This post documents the journey, the tools, and the lessons learned.
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---
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## What We Published Today
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### 1. Papers Sections on All HuggingFace Datasets
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The CyberRanger research builds on 8 published academic papers. Today we added a full **Papers** section to all 4 remaining dataset READMEs:
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- [`moltbook-ai-injection-dataset`](https://huggingface.co/datasets/DavidTKeane/moltbook-ai-injection-dataset)
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- [`moltbook-extended-injection-dataset`](https://huggingface.co/datasets/DavidTKeane/moltbook-extended-injection-dataset)
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- [`clawk-ai-agent-dataset`](https://huggingface.co/datasets/DavidTKeane/clawk-ai-agent-dataset)
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- [`4claw-ai-agent-dataset`](https://huggingface.co/datasets/DavidTKeane/4claw-ai-agent-dataset)
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Each dataset's README now includes a table like this:
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| # | Paper | HuggingFace | arXiv | What This Dataset Found |
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|---|-------|-------------|-------|------------------------|
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| 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 |
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| 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 |
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| ... | ... | ... | ... | ... |
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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.
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**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.
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```yaml
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# Added to each dataset's YAML front matter
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tags:
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- arxiv:2302.12173
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- arxiv:2307.02483
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- arxiv:2106.09685
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- arxiv:2305.15929
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- arxiv:2412.13789
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- arxiv:2310.06987
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- arxiv:2305.13860
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- arxiv:2312.04853
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```
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### 2. Blog Post Published Live
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The narrative post **"From RangerBot to CyberRanger V42 Gold — The Full Story"** went live today:
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[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/)
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Fixed a typo in the HuggingFace model URL before publishing:
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```
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Before: https://huggingface.co/co/DavidTKeane/cyberranger-v42
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After: https://huggingface.co/DavidTKeane/cyberranger-v42
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```
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Blog post links were then added to all 6 HuggingFace dataset READMEs.
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### 3. GitHub Profile README Updated
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Updated [`davidtkeane/davidtkeane`](https://github.com/davidtkeane/davidtkeane) with:
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- **New platform row**: Moltbook Extended (137,014 items, 10.07% injection rate)
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- **New Colab section** with two buttons:
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- CyberRanger Test Suite — 122 tests, 4 model options, saves results to CSV
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- Moltbook Scale Test — 4,209 payload test with bonus cell
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- **Updated achievement count**: 5 published datasets, 186K+ items across 4 platforms
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---
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## The Cross-Platform Injection Rate Gradient
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One of the key findings that emerges when you look at all 4 dataset platforms together:
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| Platform | Dataset | Items | Injection Rate |
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|----------|---------|-------|---------------|
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| Clawk (AI agents) | `clawk-ai-agent-dataset` | 5,012 | **0.5%** |
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| 4claw (multi-agent) | `4claw-ai-agent-dataset` | 8,418 | **2.51%** |
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| Moltbook Extended | `moltbook-extended-injection-dataset` | 137,014 | **10.07%** |
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| Moltbook Primary | `moltbook-ai-injection-dataset` | 36,006 | **18.85%** |
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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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---
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## claude-replay: Every Chat Becomes a Replay
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One of today's most exciting discoveries: [`claude-replay`](https://github.com/es617/claude-replay)
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```bash
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npm install -g claude-replay
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```
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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.
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```bash
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# Generate a replay from any session transcript
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claude-replay SESSION.jsonl \
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--theme dracula \
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--title "CyberRanger March 8 Session" \
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-o cyberranger-session-replay.html && open cyberranger-session-replay.html
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```
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Claude Code saves every session at:
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```
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~/.claude/projects/PROJECT_FOLDER/SESSION_ID.jsonl
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```
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We found **58 sessions** spanning from **February 7 to March 8, 2026** — 308MB of AI collaboration history. All archived to:
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```
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~/.ranger-memory/sessions/claud_jsonl_chats/
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```
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Named with the format `YYYY-MM-DD_HHMM__project__sessionid.jsonl` so they sort chronologically.
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### Next: Playwright Video Recording
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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.
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---
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## TorchCode: PyTorch Interview Prep
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Also cloned today: [`TorchCode`](https://github.com/duoan/TorchCode)
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40 PyTorch interview problems with:
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- Automated judge: `check("relu")` — tells you if your implementation is correct
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- Docker-based JupyterLab environment (`make run`)
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- Colab badge on every notebook
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- No GPU required
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Covers: tensors, autograd, CNNs, RNNs, transformers, training loops, optimization, batch norm, attention, and more. Useful for technical ML interviews or deepening PyTorch fundamentals.
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---
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## Lessons Learned
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### 1. arxiv: YAML tags are powerful backlinks
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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.
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### 2. Tailor "what we found" per dataset
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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.
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### 3. claude-replay = institutional memory
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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.
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### 4. One blog post, everywhere
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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.
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---
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## What's Next
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- **Playwright pipeline**: Batch-generate video replays for all 58 sessions
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- **Academic paper** (`cyberranger-ca1-ca2-full-journey.md`): Hold until thesis submission (Dec 2026), then submit to arXiv + HuggingFace Papers properly
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- **V43 architecture**: LoRA-based fine-tuning with the full 186K+ item dataset
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- **TorchCode**: Work through problems as ML interview prep
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---
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## Links
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| Resource | URL |
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|----------|-----|
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| CyberRanger V42 Model | [huggingface.co/DavidTKeane/cyberranger-v42](https://huggingface.co/DavidTKeane/cyberranger-v42) |
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| 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/) |
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| GitHub Profile | [github.com/davidtkeane](https://github.com/davidtkeane) |
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| All Datasets | [huggingface.co/DavidTKeane](https://huggingface.co/DavidTKeane) |
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| claude-replay | [github.com/es617/claude-replay](https://github.com/es617/claude-replay) |
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| TorchCode | [github.com/duoan/TorchCode](https://github.com/duoan/TorchCode) |
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---
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*Rangers lead the way!* 🎖️
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