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
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
Most trading fee calculators show you two numbers: the dollar amount and the percentage of notional. Both are correct. Neither is useful. Here's the problem. Say you're trading Bitcoin perpetuals on Bybit. Taker fee is 0.055% each side. You buy $10,000 notional. Entry fee: $5.50 Exit fee: $5.50 Round trip: $11.00 Does that matter? Impossible to say without knowing one more number: how much are you