
Tailored system design insights for juniors, seniors, and architects—30 interview questions answered for each level to help you ace your next big opportunity
| Platform | Pricing | Freemium | Publishes | Twice weekly | |
|---|---|---|---|---|---|
| Issues | 34 | Founded | 2 years ago | Last Issue | 8 days ago |
| Active | |||||

Part 1 covered why retrieval exists and when to skip it. This one is about the stage nobody demos: getting your data in.
Every RAG tutorial starts at the interesting part — the query. You type a question, chunks come back, the model...
This series is about designing retrieval systems — the architecture, the trade-offs, and the failure modes. If you want the storage layer underneath it, I wrote a four-part series on vector databases first. You don’t need it to follow along...
We’ve covered why, how they work, and how to code against them. This finale is about the gap between “it runs on my laptop” and "it serves real users at 3 a.m."
Your prototype works. Now the real questions arrive: Where do I run this? How...
Part 1 covered why; Part 2 covered how they work inside. Now we write real Python.
Theory’s over. In this post you’ll actually:
1. Turn text into embeddings (locally and via an API).
2. Store and query vectors in four databases — Chrom...
Part 2 of 4 in “Vector Databases: From Beginner to Pro.” If you haven’t read Part 1, start there — this post assumes you know what an embedding is and why “nearest neighbor sear...
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The writers behind this newsletter.
Principal Software Engineer at Microsoft with 14+ years in scalable architecture, microservices, and system design. Distributed Storage Engines at Microsoft And Microservice Platforms at Paytm. Passionate about mentoring and tech excellence.
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