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README.md

@@ -299,7 +299,14 @@ Prompt: Build a more complex Python CLI tool with 8 commands, 4 question types (
 
 **Severity assessment of bugs:** All bugs found in Test 2 are **surface-level generation quality issues**, not architectural or logic errors. The scoring pipeline, knowledge tracker, and data persistence all work correctly. The MCQ header parsing and T/F duplicate issues would likely be fixed in a single follow-up prompt ("fix the question text including markdown headers" / "fix the T/F for/else logic"), requiring minimal effort. No bugs required architectural changes.
 
-**Note on the 80k generation:** The Quiz Engine was completed entirely autonomously — the model managed file creation, virtual environment setup, testing, and SSH transfer across ~80k tokens of output with zero human intervention. This demonstrates strong agentic capability for sustained, complex coding tasks.
+**Note on the 80k generation:** The Quiz Engine was completed entirely autonomously across ~80k tokens with zero human intervention. The model:
+1. Created the full project structure and wrote all code
+2. Set up a Python virtual environment
+3. Ran its own test suite, reviewed the results, and debugged failures
+4. When asked to deliver the project as a zip file — and with no `zip` utility installed on the system — the model independently found an alternative compression method (tar.gz) and used it
+5. Autonomously SSH'd into a separate machine and transferred the compressed project to a specific download folder
+
+This demonstrates strong agentic capability: problem-solving around missing tools, cross-machine file operations, and sustained multi-step task completion without guidance.
 
 **Note on alternative approaches:** During development, the author also attempted a more "stable" variant using clean base Qwen3.5-27B weights in the duplicated zone (instead of dnhkng's FP8-origin layers). Ironically, the variant with standard base model layers performed worse on coding tasks — exhibiting looping behavior and variable scoping errors on the same prompts that this Splice model handled cleanly. The dnhkng FP8-origin layers in the reasoning zone appear to contribute meaningfully to coding stability.