Bytebytego

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

Which open model are you most excited about?

MCP vs A2A vs ACP

Microsoft Foundry runs AI agents for 80,000+ enterprises. We wanted to understand what it takes to build AI agents at this scale, so we spoke with @amrcn_werewolf , VP of Product for Microsoft Core AI. He explained the two high level engineering ideas behind the platform, summarized in the diagram below. 1. Retrieval as a Subagent Classic RAG is a one-shot lookup. When the first retrieval fails, the whole agent fails. Foundry wraps retrieval in an agentic loop, following these steps: Step 1: The retrieval subagent plans which sources to query. Step 2: Queries the knowledge sources: docs, wikis, and blob storage. Step 3: Evaluates the results. If bad, then triggers another iteration. Step 4 - Returns a grounded answer with citations. When iteration runs out, it returns a structured "I don't know" instead of hallucinating. 2. Eval and Optimizer Loop The second big idea in Foundry is an automated loop that optimizes the agent: Step 1: Rubrics check the agent's specific behaviors. All pass? The agent ships. A rubric fails? The Agent Optimizer kicks in. Step 2: It generates candidate fixes in parallel. Step 3: It scores each candidate against the rubrics. Step 4: The best one becomes the new agent version. The biggest lesson from Microsoft's team is that the harness matters as much as the model. Full breakdown: blog.bytebytego.com/p/how-microsof…

git merge vs git rebase Which one do you prefer?

12 popular vector databases help you get the right context to the model

Design Patterns Cheat Sheet The cheat sheet briefly explains each pattern and how to use it. What's included? - Factory - Builder - Prototype - Singleton - Chain of Responsibility

RT @alexxubyte: API Security Best Practices Most API breaches happen because of broken authorization, leaked secrets, or missing rate limi…

RAG vs Graph RAG vs Agentic RAG

An Ex-Meta L8’s Agentic Engineering Setup In this guest article, @kunchenguid shares the agentic engineering workflow he uses on a day-to-day basis. Read the full article here: blog.bytebytego.com/p/an-ex-meta-l…

Redis Data Structures Every Engineer Should Know

Single Agent vs. Multi-Agent Architecture Some tasks need a single agent. Others need a whole team. Knowing the difference is the skill. Single-agent system: One reasoning LLM that plans, picks a tool, and loops on its own until the task is done. Use a single agent when: - the task is a clear, linear sequence - one agent can hold the whole problem in its head - you want something simple to build and easy to debug Multi-agent system: An orchestrator that splits a task into subtasks and routes each one to a specialized agent. Use multi-agent when: - subtasks can run in parallel - one agent writes and another independently verifies the work - the problem is too big for one agent to coordinate alone Single agents are cheaper and easier to build, but they hit a ceiling on complex work. Multi-agent systems are more capable and more reliable, but they add coordination cost. Start with a single agent. Move to multi-agent only when context or reliability become the bottleneck. Over to you: Are you running single-agent or multi-agent systems in production?

Twelve models worth knowing in 2026, each with one standout strength.

SLMs vs. LLMs, Clearly Explained

The Typical AI Agent Stack, Explained

How to Run LLMs Locally

Salesforce deployed 20,000 enterprise AI agents. The biggest lesson? The work is inverted! Traditional software → 90% of the effort comes before launch AI agents → 90% comes after We sat down with John Kucera, CPO of Agentforce, to learn what separates agents that deliver real value from those that stall after a good demo. Teams that treat launch as the finish line stay stuck in pilot mode. Teams that treat it as the starting line scale. The full playbook covers: - Why most enterprise agents fail - Pre-launch foundations (scope, KPIs, guardrails) - The feedback loop that gates scaling - 3 anti-patterns from 20,000 deployments - Where agent architecture is heading next Full article linked in the tweet below 👇

Token Spend Out of Control? The Case for Smarter Routing Token spend has quietly become one of the biggest costs of running AI agents. An agent loops, resends its full context every step, and burns millions of tokens on a single task. To see how teams keep this under control in production, we sat down with @s_breitenother and @sytses, co-founders of @kilocode, an open-source coding agent that runs through these loops every day. Their answer: a smart router that sends each request to the cheapest model that can actually handle it, so you only pay frontier prices when the task truly needs it. Full article linked in the tweet below 👇

We’re looking for multiple part-time instructors to teach AI and engineering cohort-based live courses. This is a great fit if you love teaching, enjoy sharing what you know, and want a meaningful side thing alongside your main work. The role has some upfront time investment to get familiar with the curriculum and prepare, but after that, it’s designed to be a limited commitment (2-5 hours bi-weekly). It offers stable income, good upside, and a chance to share your knowledge while working with ambitious learners. We’re especially looking for instructors in: - Building Production-Grade AI Systems - System Design - AI Security & LLM Red-Teaming - AI Evals Intensive - AI Cost Optimization - Agentic AI Coding - Build with Codex - AI for Engineering Leaders - AI Automation - Others, please suggest Ideal instructors are hands-on, clear communicators, and excited to teach. If this sounds like you, email us at jobs@bytebytego.com with your background, the topics you’d be excited to teach, and any teaching, writing, or speaking samples.

How OpenAI Built Its Data Agent Most teams building data agents stack routers, fine-tunes, and complex retrieval pipelines on top of multiple LLMs. OpenAI didn't. Their data agent runs on a single model and only 13 tools, across 1.5 exabytes and 90,000 tables. It's "pretty vanilla" by design. We spoke with Emma Tang, Head of Data Platform Engineering at OpenAI, to better understand the architecture and the engineering decisions behind it. The article covers: - The architecture behind the data agent - The six layers of context that make a single LLM reliable across 90,000 tables - How OpenAI Uses Codex Internally: 3 Use Cases - Five practical lessons for any team building a domain agent - Where OpenAI's data platform is headed next