🚀 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 and let us know what you think.

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

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?

System design fundamentals have become more important than ever in the age of AI. Let’s review some of the most popular Kafka use cases today. Top 5 Kafka Use Cases Kafka was built for log processing, but now it has been used in other distributed systems for moving events reliably from one system to another. - The log thing is still where most teams start, though. Systems use Kafka for collecting logs from different services and then route them to tools like Elasticsearch and Kibana. - Kafka is also used for streaming user clicks, product events, and ML features that require recent data into tools like Flink. - Another use case of Kafka is to stream performance metrics and system events into monitoring and alerting systems so the teams can catch the issues early on. - Kafka can be used to record database changes into search indexes, caches, analytics tools, or replicated systems. - Kafka helps services to publish and consume events independently, instead of direct service-to-service calls. Are there any other use cases you have used Kafka for?

Hiring: Part Time Instructor, Write Production Grade Code with AI We’re hiring a part-time instructor for “𝐖𝐫𝐢𝐭𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐆𝐫𝐚𝐝𝐞 𝐂𝐨𝐝𝐞 𝐰𝐢𝐭𝐡 𝐀𝐈”. This is a live, cohort-based course for software engineers. This course is about teaching engineers how to reliably ship production-grade software with coding agents. Students will learn how to delegate real work to coding agents, write specs and plans that agents can execute, and verify, review, and secure the code that comes back. You’ll help refine the curriculum, teach live sessions, answer student questions, and share practical lessons from your own experience building software with AI. The initial onboarding requires some preparation, but after that the commitment is approximately 2 to 10 hours every two weeks. This is a flexible opportunity that fits alongside a full time engineering role. You’re a great fit if you: - Are excited about helping engineering teams avoid AI slop - 5+ years of professional software engineering experience building production systems - Use AI coding agents such as Claude Code, Codex, Cursor, etc., daily. - Understand how coding agents work, including planning, tool use, context management, and common failure modes. - Have experience breaking complex engineering problems into clear specifications and implementation plans that AI can execute - Have worked extensively with large or legacy codebases and know how to make them AI friendly - Have reviewed significant amounts of AI generated code and know how to verify correctness, catch subtle bugs, identify security risks, and maintain long term code quality - Have strong software engineering fundamentals, including testing, CI/CD, debugging, code review, etc. - Enjoy teaching If this sounds like you, email jobs@bytebytego.com with your background, how you use AI in your engineering workflow, or any teaching, writing, speaking work that demonstrates your expertise.