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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 LAST CALL FOR ENROLLMENT: Become an AI Engineer - Cohort 7 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
EP209: 12 Claude Code Features Every Engineer Should Know
2026-04-04 · via ByteByteGo Newsletter

This guide from Datadog provides best practices on how to use Cloud SIEM to detect threats, investigate incidents, and reduce blind spots across cloud and Kubernetes environments.

You’ll learn how to:

  • Analyze CloudTrail, GCP audit, and Azure logs for suspicious activity

  • Detect authentication anomalies and common attack patterns

  • Monitor Kubernetes audit logs for lateral movement and misuse

  • Correlate signals across services to accelerate investigations

Get the ebook

This week’s system design refresher:

  • 12 Claude Code Features Every Engineer Should Know

  • How Agentic RAG Works?

  • How does REST API work?

  • 7 Key Load Balancer Use Cases

  • Our New Book on Behavioral Interviews Is Now Available on Amazon!

  1. CLAUDE. md: A project memory file to define custom rules and conventions. Claude reads at the start of every session.

  2. Permissions: Control which tools Claude can and can't use.

  3. Plan Mode: Claude plans before it acts. You can review them before any code changes.

  4. Checkpoints: Automatic snapshots of your project to revert to if something goes wrong.

  5. Skills: Reusable instruction files Claude follows automatically.

  6. Hooks: Run custom shell scripts on lifecycle events like PreToolUse or PostToolUse.

  7. MCP: Connect Claude to any external tools like databases and third-party services.

  8. Plugins: Extend Claude with third-party integrations containing skills, MCPs, and hooks.

  9. Context: Feed Claude what it needs and manage the current context window with /context.

  10. Slash Commands: Create shortcuts for tasks you run often. Type / and pick from your saved commands.

  11. Compaction: Compress long conversations to save tokens.

  12. Subagents: Spawn parallel agents for complex tasks. Divide large multi-step workflows and run them simultaneously.

Over to you: Which Claude Code feature do you use the most? Any features you wish were on this list?

Image

A traditional RAG has a simple retrieval, limited adaptability, and relies on static knowledge, making it less flexible for dynamic and real-time information.

Agentic RAG improves on this by introducing AI agents that can make decisions, select tools, and even refine queries for more accurate and flexible responses. Here’s how Agentic RAG works on a high level:

  1. The user query is directed to an AI Agent for processing.

  2. The agent uses short-term and long-term memory to track query context. It also formulates a retrieval strategy and selects appropriate tools for the job.

  3. The data fetching process can use tools such as vector search, multiple agents, and MCP servers to gather relevant data from the knowledge base.

  4. The agent then combines retrieved data with a query and system prompt. It passes this data to the LLM.

  5. LLM processes the optimized input to answer the user’s query.

Unblocked gives Cursor, Codex, Claude and Copilot the organizational knowledge to generate mergeable code without the back and forth. It pulls context from across your engineering stack, resolves conflicts, and cuts the rework cycle by delivering only what agents need for the task at hand.

Unblock your agents

What are its principles, methods, constraints, and best practices? I hope the diagram below gives you a quick overview.

diagram
Image
  1. Traffic Distribution: Load Balancers help evenly distribute traffic among multiple server instances.

  2. SSL Termination: Load Balancers can offload the responsibility of SSL termination from the backend servers, thereby reducing their workload.

  3. Session Persistence: Load Balancers ensure that all requests from a user hit the same instance to maintain session persistence.

  4. High Availability: Improves the system’s availability by rerouting traffic away from failed or unhealthy servers to healthy ones.

  5. Scalability: Load Balancers facilitate horizontal scaling when additional instances are added to the server pool to handle increased traffic.

  6. DDoS Mitigation: Load Balancers can help mitigate the impact of DDoS attacks by rate limiting requests or distributing them across a wider surface.

  7. Health Monitoring: Load Balancers also monitor the health and performance of server instances and remove failed or unhealthy servers from the pool.

Over to you: Which other load balancer use case will you add to the list?

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The book is written by Steve Huynh and published by ByteByteGo. Steve is a former principal engineer at Amazon. His ability to break down complex interview dynamics into clear, actionable advice made this book possible. Still, it took us two years to get it ready.

Here's what's inside:

  • 130+ interview questions, from the most common to the ones that catch candidates off guard

  • 72 example stories showing what strong answers look like, from entry level to principal

  • Clear guidance on what interviewers look for, including key signals and red flags

  • High-Signal Storytelling, a framework to build a story bank for any behavioral interview

  • A practical prep plan and interview-day techniques for follow-ups and unexpected questions

Order your copy on Amazon

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