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
If you've tried to follow any AI coding discussion in the last six months, you've probably felt like everyone suddenly started speaking a dialect you never signed up to learn. "Vibe coding." "Agentic workflows." "Context windows." "Prompt engineering." The jargon is multiplying faster than JavaScript frameworks, and that's saying something. Matt Pocock — who you might know from his TypeScript educ
GitHub Copilot just got a lot more complicated — and not in a good way. If you tried to sign up for Copilot Pro recently and hit a wall, that's not a bug. GitHub quietly paused new sign-ups for Copilot Pro, Pro+, and Student plans starting in late April 2026. No end date announced. No workaround offered. Just a message and a door that won't open. That alone would be worth covering. But they made t
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
Anthropic now ships at least three different memory models inside the Claude product family, and they don't behave the same way. Claude.ai has a chat memory feature for Pro, Max, Team, and Enterprise users that summarizes prior conversations and injects that summary into new chats. Claude Code has CLAUDE.md files plus a separate "auto memory" directory the model writes to itself, both loaded at se