Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
The problem: too many clients, too few discovery hooks We expose Supabase Edge Functions as MCP (Model Context Protocol) servers. The clients that hit them are heterogeneous — Claude Desktop, Codex CLI, Cursor, VS Code Continue, a couple of in-house browser extensions. None of them ship with a hard-coded "use WorkOS AuthKit, scope is tool:ai_chat, audience must contain urn:jibun:tool:<tool>" rec
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E
A Fully Native, Dependency‑Free Web5 Case Study TL;DR: This case study demonstrates how the Ascoos OS Kernel 1.0.0 performs OAuth2 authentication, event‑driven processing, torrent file creation, and secure P2P upload using raw sockets — all without frameworks, external libraries, or middleware. 🔗 Full source code: https://github.com/ascoos/oauth2-torrent-upload Modern decentralized systems