poleHansen

poleHansen / baibaiAIGC

Public
47
7
89% credibility
Found Mar 28, 2026 at 47 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A collection of scripts that refines long documents such as graduation theses by dividing them into chunks, applying sequential AI prompts over three rounds, and tracking the process while supporting conversion between Word files and plain text.

How It Works

1
👀 Discover the tool

You find a helpful kit for polishing long papers like graduation theses using smart AI assistance.

2
📂 Prepare your workspace

You create simple folders to hold your original document, working versions, and final polished results.

3
📄 Add your thesis

You drop your paper file, whether plain text or Word document, into the starting folder.

4
🔄 Start AI refinement

You kick off the three-round process where AI breaks the text into small pieces and improves each one thoughtfully.

5
📊 Track progress

After each round, you check the records to see improvements and move to the next refinement.

6
🔍 Review and finalize

You look over the updated text after all rounds, converting back to Word if needed.

🎓 Enjoy your polished thesis

Your document is now cleaner, more concise, and ready for submission or sharing.

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Star Growth

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AI-Generated Review

What is baibaiAIGC?

baibaiAIGC is a Python toolkit for running multi-round AI text reduction on long documents like theses or papers. Drop a DOCX or TXT file into an origin folder, and it extracts text, intelligently chunks it by paragraphs and sentences to fit LLM limits, then applies three sequential prompts via OpenAI-compatible APIs to condense content while preserving structure. Outputs trackable intermediate and final versions in a finish folder, with JSON records logging paths, scores, and timestamps.

Why is it gaining traction?

It stands out with dead-simple CLI commands for one-off rounds, dry runs for testing chunks without API costs, and automatic progress detection across rounds—no manual state management. Developers appreciate the flexible LLM client supporting custom endpoints and env vars, plus seamless DOCX import/export for non-coders. At 47 stars, it's niche but hooks users needing repeatable, chunk-aware processing without building from scratch.

Who should use this?

Academic researchers shortening lit reviews or full papers before submission. Students automating thesis abstracts through iterative AI polishing. Python scripters prototyping LLM pipelines for Chinese-language docs, where sentence-aware chunking shines.

Verdict

Worth forking for targeted doc condensation workflows—solid CLI and tracking make it immediately usable despite 47 stars signaling early maturity. Low 0.9% credibility score reflects sparse docs, but clean Python scripts and JSON outputs lower risks for quick experiments.

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