GTPlanner
The open-source planning engine behind The Agent Builder — turns natural language into structured PRDs for "vibe coding".
Open-source work, developer tools, and research papers — from AI agents and knowledge extraction to small utilities I actually use.
The open-source planning engine behind The Agent Builder — turns natural language into structured PRDs for "vibe coding".
A full AI-agent development platform that shifts AI from "code generation" to "requirement clarification & planning" — concept to shipped product.
Knowledge triple extraction from semi-structured web using a pre-trained markup language model (HTMLBERT).
Revisiting the negative data of distantly supervised relation extraction — tackling noisy negatives.
Research implementation of collective loss functions for machine-learning optimization.
Diff LaTeX files against previous versions with visual previews — track changes in academic papers easily.
A Python framework for Learning-to-Rank (LTR) algorithms with multiple ranking models and evaluation metrics.
A minimal production-ready template integrating FastAPI, PocketFlow, and pocketflow-tracing.
Tracing and debugging for PocketFlow apps — workflow execution insight and performance monitoring.
A structured template for Large Language Model requirement specifications (大模型需求规格说明书模板).
Deployment tools and configurations for Python applications.
Interactive slides introducing Multi-Agent Systems (MAS), built with modern web tech.
Presentation slides for the GTPlanner project — planning algorithms and methodology.
Generate QR codes for WiFi sharing — clean, privacy-focused, no data stored.
A modern AI chatbot with multi-model support and conversation management.
A FastAPI server for ebook conversion — EPUB to text with a clean API.
A tool for creating WeChat text-based emoticons (制作微信文字表情).
A curated collection of important LLM benchmark papers and evaluation resources.
A curated list around the Manus stack: multimodal models, orchestration, multi-agent systems, tooling.
A comprehensive glossary of machine-learning terms and concepts for students and practitioners.
Looking for the full write-up? Read the complete projects archive.
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