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ByteByteGo Newsletter

Why An LLM’s Memory Gets Expensive and How to Fix It LLM Security Basics: The Full Threat Model Hiring: Part Time Instructor, Write Production Grade Code with AI A Detailed Guide to Idempotency, Delivery Semantics, and Deduplication How ChatGPT Optimizes its Agent Loop: Harness, API, and Inference Why DoorDash, Instacart, and Uber Eats Integrated LLMs Into Search Three Different Ways How NVIDIA Builds Open Models for the Age of AI A Beginner’s Guide to Clocks, Causality, and Ordering in Distributed Systems Best Practices for Building AI Agents That Work in Production Inside Roblox’s Bet on World Models MCP vs A2A vs ACP: How AI Agents Actually Talk to Each Other A Guide to Multi-Tenancy: Benefits and Challenges AI Customer Support at Scale: The Travel Industry’s $Billion Bet How LLMs Learn to Be Helpful (RLHF vs DPO) How Microsoft Ships AI Agents at Enterprise Scale EP221: How Docker Works Under the Hood LAST CALL FOR ENROLLMENT: Become an AI Engineer - Cohort 7 Streaming vs Batch: Two Philosophies of Data Processing The Agent Loop: How AI Goes From Answering Questions to Doing Things ChatGPT vs Gemini vs Claude: How They Differ Proof of Human: How to Verify a Person Is Real and Unique Multi-Region Architecture: Going Global Without Going Broke How OpenAI Delivers Low-Latency Voice AI for 900M Users Inside Thinking Machines’ Interaction Models How AI Agents Manage Memory and Avoid Forgetfulness EP220: RAG vs Graph RAG vs Agentic RAG Top Anti-Patterns to Avoid in Service Architecture Large Language Models vs Small Language Models An Ex-Meta L8’s Agentic Engineering Setup AI-Native Leaders: The Organizational Playbook for Engineering Transformation at Scale
LAST CALL FOR ENROLLMENT: Become an AI Engineer - Cohort 7
ByteByteGo · 2026-07-06 · via ByteByteGo Newsletter

Our 7th cohort of Becoming an AI Engineer starts in less than a week. This is a live, cohort-based course created in collaboration with best-selling author Ali Aminian and published by ByteByteGo.

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Here’s what makes this cohort special:

  • Learn by doing: Build real world AI applications, not just by watching videos.

  • Structured, systematic learning path: Follow a carefully designed curriculum that takes you step by step, from fundamentals to advanced topics.

  • Live feedback and mentorship: Get direct feedback from instructors and peers.

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We are focused on skill building, not just theory or passive learning. Our goal is for every participant to walk away with a strong foundation for building AI systems.

If you want to start learning AI from scratch, this is the perfect platform for you to begin.

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