This is a submission for the , a Reddit community known for "brutal but loving" literary critique. The concept is exactly what I wanted: direct, specific feedback that doesn't sugarcoat problems but always offers solutions. The reality was different. The community requires extensive karma-building before you can receive a critique — other authors report spending days earning enough credit. And after all that effort, the critiques I read varied wildly in quality.
So I built my own.
Destructive Reader LLM is a Python CLI tool that takes a fiction chapter and delivers structured literary critique in the r/DestructiveReaders style. It uses NVidia Nemotron Nano 30B via
Running the tool against a chapter from my published novel:
python destructive-reader-llm.py Markdown/01-AWAKENING.md
Step 2: Build the main tool
I gave Copilot CLI a clear spec: read INSTRUCTIONS.md, accept a chapter filename as argument, build a combined prompt, send to Ollama, save the critique as <chapter-name>-critique-<timestamp>.md. Copilot CLI read my instructions file to understand the context, then generated the complete destructive-reader-llm.py — 145 lines covering argument parsing, file loading, prompt construction, API calls, and output saving. It worked on first run.
Overall impression
The free tier Haiku 4.5 model in Copilot CLI was more than capable for this kind of structured code generation. Copilot handled the boilerplate and let me focus on what actually matters — the critique prompt and the workflow design. From first prompt to working tool: one session, no debugging required beyond correcting a model name.
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