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
It started at midnight I had 24 hours, a free Replit subscription, and an idea: what if I could build something like Miro — but actually understand every line of code in it? The core problem I had to solve first Multiplayer sync sounds simple until you actually build it. The hard part isn't sending a canvas update — it's figuring out what to send. canvas.on('object:modified', (e) => { socket.emi
FutureMe has 15 million letters in its database. They've been there since 2002. Some of them will be there in 2050. Evengood will have zero. This week I shipped The Quiet Letter — a feature where you write to your future self today, we email it on a date you pick, and we hard-delete the row from our database within 24 hours of sending it. The email is the only artifact. We don't keep a copy. Every
It was around 1am and I had three feeds open. X on my phone, Reddit on one monitor, Hacker News on the other. I was reading about a plane crash, a new AI model, and a meme war about whether oat milk counts as milk. And I realised I had no idea what the internet was actually feeling about any of it. The feeds told me what was happening. They didn't tell me how it felt. That's when the idea hit me.
I write a lot of READMEs. I ship faster than I document. I work with AI agents that write code in seconds and READMEs in minutes, and somewhere between the first commit and the third refactor, the README I wrote on Tuesday stops matching the code I wrote on Friday. The install command says npm start. The package.json defines start:prod. Anyone copying that command would have failed instantly. I'd
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