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
There's a dangerous assumption most developers bring into Compact: "It's a privacy-first chain. My data is private unless I explicitly expose it." This is backwards. And it's where the serious mistakes happen. Compact doesn't give you automatic privacy. It gives you a hard boundary between two worlds, and a compiler that enforces it. World Where Who sees it Public On-chain, every network no
## INTRODUCTION Every blockchain application that handles value needs to answer the same question: how do you track who owns what? There are two dominant approaches, and choosing between them shapes your entire contract architecture. Contract-state accounting behaves like a bank ledger. A single smart contract holds a balance map, and transactions update entries in place. The UTXO model behaves li
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