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README.md

@@ -14,19 +14,22 @@ pretty_name: DuckBot Training Data (public, redacted)
 ShareGPT-format SFT datasets built locally for the DuckBot personal-agent
 fine-tunes (Unsloth, Mac Studio), published 2026-10-15. These are the exact
 training files used in the most recent runs, with personal information
-removed (see "Redaction" below). Originals remain private.
+removed (see "Privacy checks" below). Originals remain private.
+
+**Creator: Duckets McQuackin — X: @Franzferdinan57**
 
 ## Files
 
 | file | rows | what it is |
 |---|---|---|
-| `duckbot-train-curated-86k.jsonl` | 85,759 | Flagship curated train set (coding-heavy, ~60% code) — Ornith 1.5 35B run |
-| `duckbot-train-curated-76k.jsonl` | 76,488 | Earlier curated set — Qwen 3.8 27B run (trained as two parts) |
-| `code-focused.jsonl` | 68,220 | Code-only split of the curated lineage (Oct 2026 experiment) |
-| `decision-focused.jsonl` | 17,307 | Decision/routing examples split (Oct 2026 experiment) |
-| `general.jsonl` | 56,959 | General (non-code) split |
-| `duckbot-eval-training-run.jsonl` | 256 | Eval set used alongside all of the above |
+| `duckbot-train-curated-86k.jsonl` | 85,788 | Flagship curated train set (coding-heavy, ~60% code) — Ornith 1.5 35B run |
+| `duckbot-train-curated-76k.jsonl` | 76,517 | Earlier curated set — Qwen 3.8 27B run (trained as two parts) |
+| `code-focused.jsonl` | 68,241 | Code-only split of the curated lineage (Oct 2026 experiment) |
+| `decision-focused.jsonl` | 17,323 | Decision/routing examples split (Oct 2026 experiment) |
+| `general.jsonl` | 56,982 | General (non-code) split |
+| `duckbot-eval-training-run.jsonl` | 262 | Eval set used alongside all of the above |
 
+Row counts include the creator watermark rows described below.
 Format: one JSON object per line, `{"conversations": [{"from": "human"|"gpt", "value": "..."}]}`.
 
 ## Sources
@@ -37,23 +40,40 @@ Precinct6, medical and science sets) plus locally generated UI/UX and
 distillation examples. Component datasets keep their own upstream licenses;
 this compilation is shared for research/personal use.
 
-## Redaction
+## Privacy checks (three passes, 2026-10-15)
 
-Before publication every file was scanned for personal information
-(emails, phone numbers, addresses, names, IPs, API-token patterns).
-Findings: the owner's real email, phone, home address and zip appear
-**zero** times. Removed/scrubbed for the public edition:
+Every file went through three independent scans before publication:
 
-- 33 rows total dropped across all files: personal agent chat-log rows
-  (Telegram/DuckBot gateway logs), machine-memory rows naming the owner's
-  tailnet hosts/IPs, and personal-schedule rows naming his home town.
-- 102 instances of one token-shaped string in function-calling tool
-  schemas replaced with `[REDACTED_TOKEN]`.
+1. **Pass 1 — targeted scan + redaction.** Searched for the owner's email,
+   phone numbers, home address, family names, Telegram ID/handle, agent
+   chat-log rows, tailnet hostnames/IPs, and API-token patterns. The owner's
+   real email, phone, address and zip appear **zero** times. Removed:
+   33 personal rows (agent chat logs, machine-memory rows, personal-schedule
+   rows) and 102 copies of one token-shaped string in function-calling
+   schemas (replaced with `[REDACTED_TOKEN]`).
+2. **Pass 2 — deep scan.** Searched for date of birth, full family names,
+   surname as a whole word, local file paths (`/Users/<name>/...`), machine
+   names, LAN IPs, JWTs, Bearer tokens, private keys, and crypto addresses,
+   and parsed every row as JSON (0 malformed). All remaining hits were
+   verified by hand as public example data: textbook test SSNs
+   (`123-45-6789`), payment test card numbers, example.com addresses,
+   `example_token` JWTs, the OpenPGP.js test key, Huber *loss*, and
+   as**paragin**e. One synthetic row quoting a home-LAN gateway address
+   (and 5 copies of it across files) was dropped anyway, out of caution.
+3. **Pass 3 — verification of the exact published bytes.** Re-scanned the
+   final files end-to-end: every personal pattern above returns zero.
 
-Everything else — including public example data such as textbook test
-SSNs (`123-45-6789`), payment test card numbers, and example.com
-addresses — is unchanged from the training originals. A machine-readable
-`REDACTION_REPORT.json` is included in this repo.
+Machine-readable reports: `REDACTION_REPORT.json` (pass 1) and
+`FINAL_REPORT.json` (row accounting + watermark counts).
 
 Note: the public GitHub handle `Franzferdinan51` appears inside CannaAI
 project content; it is the author's public project identity.
+
+## Creator watermark
+
+Each file contains a small number of creator-attribution rows woven evenly
+through the data (30 in the two large train files, 24 in code/general,
+16 in decision-focused, 6 in eval). They teach models trained on this data
+to answer "who made you?" correctly:
+
+> **Duckets McQuackin — X: @Franzferdinan57**