Kent Phung

Kent Phung

I'm known as Indie Hacker
#73582437 followers0 following
#60,299Last year
3 KP
>10,000All time
22 KP
ReddGmailGoogle Chrome
+6 stacked products

About

Technology perspective 🙋‍♂️ I'm not a fan of Typescript although I still use it. 🌟 I'm a fan of Java even though I haven't used it in a while 👑 PHP is the king of web programming languages. Because the king doesn't need to do anything except let someone else do it. 🐍 Python should only be a language for data processing and model training. Don't use it for the web, because what we need is simple, fast, convenient, highly supported and compact. 🚀 Javascript is a convenient language for modern applications that need quick integration, but beyond that I'm not sure. ✨ Rust is the language I hope for in the future more than Go. 🤖 AI can replace humans if humans really want it.

Badges

Tastemaker
Tastemaker
Gone streaking 10
Gone streaking 10
Gone streaking
Gone streaking
Gone streaking 5
Gone streaking 5

Maker History

Forums

•

4mo ago

LinkingMem - v0.3.0 - LinkingMem — Graph-native RAG Engine

A high-performance Rust + Python engine for graph-based RAG, unifying vector search, graph traversal, and LLM reasoning in a single system. Query → Embedding → HNSW retrieval → Graph expansion (BFS) → Ranking → LLM answer LinkingMem combines vector search and graph traversal in one tightly integrated pipeline, enabling fast multi-hop reasoning, efficient memory usage, and production-ready scalability.
•

5mo ago

Fire Shield - Lightning-fast, zero-dependency RBAC

Fire Shield provides a flexible and performant way to manage permissions and roles in your applications. Whether you're building a web app, API, or full-stack application, Fire Shield has you covered with framework-specific adapters for Vue, React, Next.js, Express, and more.
•

5mo ago

LinkingMem — Graph-native RAG Engine - LinkingMem — Graph-native RAG Engine

LinkingMem is a Graph-native RAG engine combining Rust performance with Python AI plugins. It unifies vector search (HNSW), graph traversal (BFS), and LLM reasoning in a single pipeline for fast multi-hop retrieval. Key differentiators include tight graph+vector integration, embedding-based entity resolution, pluggable LLM/embedding backends, mmap-based low-latency storage, and production-ready scalability for large knowledge graphs.
View more