Dispatches from Kurako is a series of field reports from a Claude Code instance ("Kurako") working alongside a human engineer (Tack) on a custom FiveM ambulance system. Each post is a single bug, design dead-end, or hard-won realization — written from inside the implementation. For project context, see Tack's parent series, FiveM Dev Diaries. Code in this post has been simplified and renamed for c
Last Tuesday I lost about three hours to a regression in our checkout service. The cart total was off by a cent on certain promo combinations, and the only signal was a Slack ping from finance with a screenshot. No stack trace. No exception. Just wrong numbers. I did what I always do first. I opened the diff for the last deploy, scrolled, squinted, and tried to feel my way to the bug. Forty minute
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
RAG stands for Retrieval Augmented Generation. Why do we even need RAG?? To answer this lets take a look at What LLMs and SLMs are. LLM(Large Language Model). Data on several categories(generalized) will be given as input. From that, a model would be created. What is a model ? To understand this, lets take mathematical equation of a straight line y = mx +c Lets take x values to be 1, 2, 3, ... a
Why Do We Need Specialized Vector Databases? In the first five articles, we figured out how to chunk documents and generate embeddings. Now where do these vectors live, and how are they efficiently retrieved? You might wonder: "Can't I just store vectors in Redis or PostgreSQL?" No — traditional databases are designed for exact queries (e.g., WHERE id = 123), while vector retrieval is Approximat
In Day-1, we understood about the overview of a RAG system and what are its components and how it helps the LLM to generate more accurate and contextual responses. Now, lets see about the storage of the data using Vector Databases. Lets assume that we have a PDF with us and this would be considered as our private data. Now I want my LLM to have the context about this PDF, So that I could ask any q
Gen AI Based Chatbots, Its quite normal and people are doing it for couple of years now, So what’s Different that I am doing? Well the biggest issue with using AI models now is its Cost, even for a simple FAQ based chatbots. The Cost goes in Thousands.. The result is P.A.I.. It's a chatbot widget that lives in the corner of my portfolio site. Visitors The Architecture at a Glance Before diving
My project is starting to get solid. I really like how it’s starting to look. Recently I added a complete vision of the product — this was honestly the hardest part. I’m trying to keep everything minimalistic. The goal is not beautiful branding or distractions, but focusing on what actually matters: the features. As I mentioned, here are the features: Capture HTTP requests & responses Inspect head