Open Design as a four-plane design harness for coding agents

Open Design 0.16.1: A Design Harness for Coding Agents

Open Design is easy to misread. The name also belongs to the older open-design movement around shared product blueprints, but this article is about nexu-io/open-design : an open-source workspace that turns coding agents into a design production system. It is not a model, and it is not simply an image generator. It is a harness. The harness gives an agent a controlled vocabulary, reusable workflows, visual constraints, an artifact loop, and a place to inspect the result. That distinction matters because the quality ceiling still comes from the model and the operator; Open Design improves the path between intention and output. ...

July 22, 2026 · 9 min · 1791 words · Xinwei Xiong, Me
A quiet archive of linked memory cards illustrating Mem0 OSS v3 retrieval

Mem0 OSS v3 in Practice: Memory Architecture, Retrieval, and Trade-offs

This project note is part of my attempt to understand open-source AI systems by building with them, reading their migrations, and writing down where the abstraction holds—and where it leaks. Project learning list The problem is not remembering more An LLM can carry a conversation while the relevant messages still fit inside its context window. That is short-term continuity, not memory in the human sense and not durable application state. When the session ends, the model does not naturally retain that a user prefers terse answers, changed jobs last month, or abandoned an earlier plan. ...

May 9, 2025 · 11 min · 2303 words · Xinwei Xiong, Me
Blank cards passing through a wooden ranking staircase toward a reader

LLM Recommendation Systems: Retrieval, Ranking, RAG, and Evaluation

Recommendation systems create a tempting illusion: the newer the model, the more advanced the system. Anyone who has operated one knows that the model is only the part above water. Below it sit exposure bias, latency budgets, feature freshness, inventory constraints, exploration risk, and counterfactual evaluation. Large language models add a useful semantic layer, but they do not make those older problems disappear. They also introduce new ones: higher cost, variable output, and explanations that can sound persuasive without reflecting the reasons an item was ranked. ...

April 23, 2025 · 18 min · 3653 words · Xinwei Xiong, Me
Documents flowing through a measured conversion pipeline into structured Markdown

Microsoft MarkItDown 0.1.6: A Practical Document-to-Markdown Guide

A document converter is a bridge, not a source of truth. The important question is not whether the output looks clean at first glance, but whether the bridge preserves the evidence your next system needs. MarkItDown is easy to demonstrate: install a package, pass it a file, receive Markdown. The difficult work begins one minute later. A PDF may contain selectable text, scanned pages, diagrams, tables, or all four. A slide deck may hide essential numbers inside screenshots. A spreadsheet may be understandable only through formulas, merged cells, and spatial relationships. No single conversion mode handles every case equally well. ...

April 21, 2025 · 15 min · 3073 words · Xinwei Xiong, Me
A checkpointed LangGraph StateGraph with recovery paths

LangGraph Architecture in 2026: StateGraph, Persistence, and Recovery

This project is an ongoing journey — learning AI open source projects with steady, daily progress. Through hands-on work with real projects and AI tooling, the goal is to develop the ability to solve complex problems and document the process. Notion List Basic Information: Project Name: LangGraph GitHub URL: langchain-ai/langgraph Main Tech Stack: Python, JavaScript/TypeScript, LangChain, LangSmith, checkpoint stores, LLM providers 1. What LangGraph Actually Solves LangGraph is the low-level orchestration framework and runtime in the LangChain ecosystem. It is useful when an agent must retain typed state, branch or loop, pause for a person, survive a process failure, or expose each transition to tracing and tests. You can use LangGraph without using LangChain’s model abstractions. ...

April 19, 2025 · 7 min · 1348 words · Xinwei Xiong, Me
GPT Researcher pipeline from query planning and retrieval to a cited report

GPT Researcher Guide: Python, Docker, MCP, Costs & Limits

A long report can look like certainty while merely arranging uncertainty more elegantly. The useful question is not how many pages an agent writes, but how a claim entered the report and whether a reader can walk back to its source. The short verdict GPT Researcher is a good fit when a team needs a programmable research pipeline, source traceability, and deployment control. It is not a truth machine. It automates planning, retrieval, context assembly, and report writing; it does not make weak pages authoritative or make every citation support the sentence beside it. ...

April 14, 2025 · 10 min · 2024 words · Xinwei Xiong, Me
A layered search system connecting multimodal data, embeddings, reranking, and grounded answers

Jina AI in 2026: Embeddings, Reranking, Reader, and Jina Serve

Jina used to be easiest to explain as a cloud-native neural-search framework. That description is still true, but it is no longer sufficient. In 2026, the name covers two related products: Jina Search Foundation: hosted APIs and model families for embeddings, reranking, web reading, search, and research. Jina Serve: the open-source framework for turning Python AI components into services and composing them into distributed flows. The distinction matters. Search Foundation gives an application retrieval intelligence; Jina Serve gives a team control over how its own services run. One is a set of capabilities, the other an orchestration layer. Treating them as interchangeable usually leads either to unnecessary infrastructure or to a hosted dependency that was never consciously chosen. ...

April 12, 2025 · 12 min · 2381 words · Xinwei Xiong, Me

Combining GitHub and Google Workspace for Effective Project Management

Project Management - Google Edition I wrote an article before Project management from theory to practice . It introduces Github’s project management method, Github Projects, and cooperates with Github to realize the integration of the entire development and project management. However, some problems may arise. Today, let’s talk about the problems that may arise and give the corresponding solutions. Let’s summarize by the way. At this stage, I think it is the best practice method for project management of open source projects. ...

February 22, 2024 · 21 min · 4333 words · 熊鑫伟 (Xinwei Xiong)