The Big Archive for System Design - 2023 Edition (PDF) is available now. And it's completely FREE. The PDF contains 𝐚𝐥𝐥 𝐦𝐲 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐩𝐨𝐬𝐭𝐬 published in 2023. What’s included in the PDF? 🔹 Netflix's Tech Stack 🔹 Top 5 common ways to improve API performance 🔹 Linux boot Process Explained 🔹 CAP, BASE, SOLID, KISS, What do these acronyms mean? 🔹 Explaining JSON Web Token (JWT) to a 10 year old Kid 🔹 Explaining 8 Popular Network Protocols in 1 Diagram 🔹 Top 5 Software Architectural Patterns 🔹 OAuth 2.0 Flows 🔹 What does API gateway do? 🔹 Linux file system explained 🔹 18 Key Design Patterns Every Developer Should Know 🔹 Best ways to test system functionality 🔹 Top 6 Load Balancing Algorithms 🔹 Top 12 Tips for API Security 🔹 𝐀𝐧𝐝 100+ 𝐦𝐨𝐫𝐞 – Like, follow and subscribe to our newsletter to receive the PDF download link: bit.ly/3KCnWXq

If you want to redo it yourself, here's the GitHub repo with step-by-step instructions you can follow: github.com/mikhailsychevbyte…

Watch an ex-YouTube engineer rebuild YouTube in 45 minutes. By the end it's running: - Home feed - Video upload to the cloud - Video playback from the cloud - Data in a real database - Search, using multimodal embeddings - Deployed to a real URL All of those explained in under an hour.

Jev is TypeSafe AI's first System One Model. It is 100x faster and cheaper than frontier LLMs. That opens up a lot of use cases people usually skip because the big models are too slow or too expensive for them. Here are 9 use cases where we think Jev could be used instead of an LLM.

@zahanm and I sat down with the ByteByteGo team for a deep dive on the GPT-Live system. It's nicely illustrated and gets into a few details that we weren't able to cover in our previous GPT-Live blog post. Check it out!

GPT-Live is the most natural, conversational voice model we've tried. Under the hood, it pairs a new generation of voice model architecture with a serving system built for low latency. To understand how it all works end to end, we sat down with two engineers on the GPT Voice team: Zahan Malkani and Justin Uberti (the creator of WebRTC). In our deep dive, you'll learn: - The three generations of voice systems: cascaded pipelines, turn-based end-to-end models, and full-duplex models - Why separating thinking from talking is the core idea behind GPT-Live - The engineering behind the serving system, including the live and async paths - How evaluation differs for full-duplex voice systems - Lessons for building realtime systems, and what's next for voice

LLM, RAG, AI Agent & Agentic AI

🚀 Announcing ByteByteGo Live: learn AI live from real builders. I’ve spent the past 8 years on education: thousands of hours writing books, deep dive newsletters, YouTube, etc. Two issues I keep seeing are: 1) it’s very different to learn from real builders. 2) most online courses never get finished (~4% completion). Over the past 12 months, we’ve been working on what we can do to change that. Today, I’m excited to announce ByteByteGo Live: live cohort courses taught by real builders. - Taught by real builders. Our instructors include very senior engineers from Google, Meta, etc., as well as @KentBeck , the creator of TDD. They teach what they use in their own work. - Live cohorts. Every course runs live with a group of people, and instructors answer your questions in real time. The current lineup: - AI Engineering Fundamentals - Build with Claude Code - Build Production Grade AI Systems - Rebuild YouTube with AI - AI Evals in Practice - AI Cost Optimization - Trust-optimized AI Development We have big plans for the year ahead: more courses, more instructors, and deeper hands on projects. Check out go.bytebytego.com/live-launc… and let us know what you think.

9 Distributed Systems Patterns You Should Know

How the JVM Actually Works

MCP vs RAG vs AI Agents

What keeps the Internet running. We probably forget they exist until the day something breaks.

What Is Harness Engineering, Explained Visually.

Visualizing a SQL query in 5 mins SQL statements are executed by the database system in several steps, including: - Parsing the SQL statement and checking its validity - Transforming the SQL into an internal representation, such as relational algebra - Optimizing the internal representation and creating an execution plan that utilizes index information - Executing the plan and returning the results

Latency Numbers Every Developer Should Know, Explained in 6 Minutes

12 most-starred skill repos on GitHub as of August 2026. What's your favorite?

How does Claude's text watermark work? Anthropic recently shared their intent to watermark text so they can identify AI-generated text. This post is based on my understanding of how it works. LLMs produce text word by word. At each step, they generate probabilities for the next likely word. Instead of sampling randomly from those words, the watermarking trick changes which words are allowed to be picked. How to watermark a response? Step 1: The model produces probabilities for the next word. Step 2: Normally a random number generator picks one of the good candidates. With watermarking, a keyed function takes a secret key plus the previous few words and decides which candidates are valid to pick from. Step 3: This repeats for the whole response. Places where there are multiple plausible choices carry the watermark signal. How to detect a watermarked text? Step 1: For any candidate word in the text, we check whether it is a valid choice based on the secret key and the few preceding words. If the word is valid, that is counted as a match. Step 2: Run this across the entire text. Watermarked text matches far more often. The overall match rate can be treated as an AI-generated score. I’m personally getting quite annoyed by the false negatives from all these AI text detection techniques, especially for technical writing. What's your thoughts on AI text detection? Do you think AI text detection is useful, or will it create more problems?

What is MCP Really About clearly explained in 5 minutes

Ollama vs. vLLM vs. SGLang: Which one do you prefer?

HTTPS clearly explained in 7 minutes

9 Types of API Testing