# Level Up Coding System Design Newsletter System Design made simple. Get our 142 page System Design Handbook for FREE on newsletter signup. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Domain-Driven Design Clearly Explained DevFeed: [Domain-Driven Design Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-12T12:52:28Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Domain-driven design (DDD)](), [Code](), [systems]() Tags: [concepts](), [developers](), [domain](), [domain-driven-design](), [engineering](), [model](), [patterns]() ### AI overview An introduction to Domain-Driven Design (DDD), explaining how shared business language and bounded contexts help teams model complex products and keep code aligned with the domain. ### Source excerpt The mental model that makes DDD click. ## Message Queue vs Event Bus vs Broker DevFeed: [Message Queue vs Event Bus vs Broker]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-10T12:22:39Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [systems](), [Latency]() Tags: [architecture](), [consumer](), [fraud-detection](), [latency](), [messages](), [notifications](), [payment](), [queue]() ### AI overview This article distinguishes message queues, event buses, and brokers by the meaning of the message. It explains that queues assign work to consumers, support retries and dead-letter queues, smooth traffic spikes, and can develop growing backlogs and higher latency under sustained overload. ### Source excerpt They often show up in the same architecture diagram, which makes them look interchangeable. They're not. ## APIs & Service Communication: The Essential Guide DevFeed: [APIs & Service Communication: The Essential Guide]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-08T13:03:41Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [API](), [Microservice](), [GraphQL](), [gRPC](), [Kafka](), [WebSocket]() Tags: [api](), [apis](), [graphql](), [http](), [kafka](), [rest]() ### AI overview A tutorial comparing communication patterns for distributed systems. It explains how request-response APIs, event streams, and bidirectional connections affect responsiveness, service coupling, failure propagation, operational complexity, reliability, and scalability, then introduces REST and GraphQL with their main strengths, tradeoffs, and use cases. ### Source excerpt REST, gRPC, Kafka, WebSockets, Pub/Sub, and more. How do they all fit together? ## The Evolution of HTTP, Clearly Explained DevFeed: [The Evolution of HTTP, Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-04T16:39:02Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [HTTP](), [client](), [servers](), [Caching](), [Web]() Tags: [browser](), [caching](), [encoding](), [evolution](), [http](), [http-3](), [protocol](), [request](), [responses](), [server](), [tcp]() ### AI overview An explanatory article traces HTTP from versions 0.9 through 1.1, describing how headers, status codes, persistent connections, caching, and chunked transfer encoding addressed the web's growing scale and performance needs. It introduces HTTP/3 as part of that broader evolution. ### Source excerpt From 0.9 to 3.0: What changed and why? ## 8 Redis Use Cases Engineers Should Know DevFeed: [8 Redis Use Cases Engineers Should Know]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-01T12:54:52Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Redis](), [Data structures](), [Caching](), [real-time](), [Server](), [systems]() Tags: [caching](), [data-structure](), [real-time](), [redis](), [server](), [use-cases]() ### AI overview This article explains that Redis is more than a caching system. It describes how Redis data structures support real-time application patterns such as counters, sessions, queues, leaderboards, rate limiting, coordination, event logs, notifications, and expiring state. ### Source excerpt Most engineers know Redis for caching. But that's only the beginning... ## The Essential Guide to Multi-Agent Systems DevFeed: [The Essential Guide to Multi-Agent Systems]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-31T11:38:47Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [AI Agent](), [distributed-systems](), [systems]() Tags: [ai-agent](), [architecture](), [consistency](), [distributed-systems](), [multi-agent-systems](), [observability](), [state]() ### AI overview This guide explains what multi-agent systems are, how they differ from workflow engines and generic distributed systems, and why coordination, communication, shared state, consistency, and observability shape their architecture. ### Source excerpt Building one AI agent is hard. But what changes when you start adding more? ## Where Kafka Fits in Modern Systems DevFeed: [Where Kafka Fits in Modern Systems]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-25T12:02:09Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Kafka](), [Event-Streaming](), [systems](), [distributed-systems](), [data]() Tags: [architecture](), [consumer](), [distributed-systems](), [durability](), [event-streaming](), [kafka](), [parallelism](), [partition](), [pipelines](), [real-time](), [systems]() ### AI overview This article explains when Apache Kafka is appropriate for distributed systems. It presents Kafka as a durable event log whose replayability, consumer fan-out, and partition-based parallelism support real-time data pipelines and shared event-driven architectures, while warning that Kafka can add unnecessary operational complexity for simpler asynchronous workflows. ### Source excerpt When should you actually use Kafka? And when is it just unnecessary complexity? ## How Kubernetes Actually Works DevFeed: [How Kubernetes Actually Works]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-21T12:52:09Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Kubernetes](), [Containers](), [Deployment](), [API](), [Replication]() Tags: [api](), [components](), [containers](), [deployment](), [health](), [kubernetes](), [reconciliation](), [replication]() ### AI overview A tutorial explaining Kubernetes as a platform that continuously reconciles desired and actual cluster state. It covers Pods, scheduling, container health monitoring, failure recovery, and the roles of the control plane and worker nodes. ### Source excerpt The mental model that makes Kubernetes click. ## REST API Authentication Methods Clearly Explained DevFeed: [REST API Authentication Methods Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-05T18:25:08Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Authentication](), [REST API](), [API keys](), [Authorization](), [Security]() Tags: [api](), [api-keys](), [authentication](), [authorization](), [jwt](), [private-key](), [rest](), [security]() ### AI overview This tutorial explains REST API authentication methods, including API keys, bearer tokens, JWTs, HMAC secrets, and mutual TLS. It distinguishes authentication from authorization and discusses operational concerns such as credential rotation, revocation, auditing, rate limiting, and responding to leaked credentials. ### Source excerpt 8 API authentication methods every developer should know. ## How OpenAI Built a Reliable Data Agent with Context, Memory, and Evals DevFeed: [How OpenAI Built a Reliable Data Agent with Context, Memory, and Evals]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-02T13:14:27Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [OpenAI](), [AI Engineering](), [AI Development](), [data](), [SQL](), [Slack]() Tags: [agents](), [ai](), [ai-agents](), [ai-engineering](), [data](), [evals](), [memory](), [openai](), [schema](), [sql](), [thread](), [verify]() ### AI overview The article examines how OpenAI built an internal data agent for more than 3,500 users working across 70,000 datasets and over 600 petabytes of data. It explains that trustworthy results require more than valid SQL: the agent needs relevant business context, safeguards for permissions, ways to detect subtle query errors, and transparency so users can inspect its work. ### Source excerpt How OpenAI built its data agent to work across 70,000 datasets. ## Vector Databases Clearly Explained DevFeed: [Vector Databases Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-07-25T13:32:58Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Databases](), [Embeddings](), [AI search]() Tags: [databases](), [embeddings](), [search](), [technical](), [vector](), [vector-database](), [vector-search]() ### AI overview This tutorial explains how vector databases differ from traditional databases by searching for semantic similarity rather than exact keyword matches. It describes embeddings, vectors, metadata, filtering, permissions, ranking, and distance metrics as parts of vector search. ### Source excerpt The mental model that makes vector databases click. ## How Netflix Builds Reliable Systems for Live Streaming DevFeed: [How Netflix Builds Reliable Systems for Live Streaming]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-07-18T14:46:05Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Netflix](), [Streaming](), [Architecture & Design](), [systems](), [Cloud]() Tags: [engineering](), [live-streaming](), [netflix](), [streaming](), [systems]() ### AI overview The article explains why live streaming is harder than video on demand and describes Netflix's reliability challenges, including real-time processing, simultaneous viewers, fluctuating networks, missing segments, retry storms, and regional traffic spikes. ### Source excerpt How does Netflix stream live events without crashing? ## RAG Clearly Explained DevFeed: [RAG Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-07-15T14:15:13Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Retrieval Augmented Generation (RAG)](), [Large Language Model](), [AI search]() Tags: [model](), [rag](), [retrieval](), [retrieval-augmented-generation-rag](), [vector-search]() ### AI overview A tutorial explaining Retrieval-Augmented Generation (RAG), a system pattern that retrieves relevant external information at query time and provides it to an LLM as context. It distinguishes RAG from fine-tuning, plain search, and vector search, and outlines knowledge preparation using chunks and searchable indexes. ### Source excerpt The mental model every engineer should have. ## How Discord Fans Out One Message to a Million Users DevFeed: [How Discord Fans Out One Message to a Million Users]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-07-07T13:22:26Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Discord](), [Publish-subscribe pattern](), [real-time](), [WebSocket](), [Back end](), [Processes](), [Authorization]() Tags: [backend](), [case-study](), [channel](), [discord](), [engineering](), [permission](), [real-time](), [scale](), [server](), [websocket]() ### AI overview This case study explains how Discord fans out a single message to many online users. It describes a real-time backend built around pub/sub, guild routing processes, permission checks, session processes, and WebSocket delivery. ### Source excerpt A case study in fanout, bottlenecks, and the engineering decisions behind Discord's scale. ## LLMs: The Essential Guide DevFeed: [LLMs: The Essential Guide]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-06-26T14:44:38Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Large Language Model](), [systems](), [tokenization](), [Transformer](), [distributed-systems](), [Inference](), [Embeddings]() Tags: [embeddings](), [guide](), [inference](), [latency](), [llm](), [llms](), [tokenization](), [transformers]() ### AI overview This guide explains LLMs as distributed systems with layers including models, training pipelines, retrieval, inference infrastructure, safety layers, and product experience. It introduces how tokenization and Transformer architectures work, and connects system layers to debugging, scaling, latency, hallucinations, context loss, and cost. ### Source excerpt Most engineers only see the API layer. Here's what's happening underneath. ## MCP Clearly Explained DevFeed: [MCP Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-06-15T12:57:01Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Model Context Protocol (MCP)](), [AI Engineering](), [Protocol (disambiguation)](), [client](), [Server]() Tags: [ai-engineering](), [coding-assistant](), [integration](), [model-context-protocol-mcp](), [protocol](), [server](), [tool]() ### AI overview A beginner-friendly explanation of Model Context Protocol (MCP), a standard interface that lets AI applications communicate with external services. It describes MCP's purpose, core roles, capability discovery, data requests, action invocation, session establishment, and compatibility features. ### Source excerpt Plus: Big upgrades are coming to LUC. Vote on what you'd like to see first. ## REST vs GraphQL vs gRPC DevFeed: [REST vs GraphQL vs gRPC]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-05-28T10:32:52Z Content type: comparison Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [API](), [GraphQL](), [gRPC](), [HTTP](), [JSON]() Tags: [apis](), [graphql](), [http](), [json](), [rest]() ### AI overview A comparison of REST, GraphQL, and gRPC explains how each API style organizes communication and the tradeoffs each makes between flexibility, simplicity, efficiency, and control. ### Source excerpt The tradeoffs behind resource-driven, query-driven, and method-driven APIs. ## Docker vs Kubernetes DevFeed: [Docker vs Kubernetes]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-05-11T12:32:56Z Content type: comparison Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Docker](), [Kubernetes](), [Docker Compose](), [Containers](), [Orchestration]() Tags: [ci](), [compose](), [consensus](), [containers](), [docker](), [git](), [go](), [kafka](), [kubernetes](), [orchestration](), [redis]() ### AI overview This comparison explains that Docker packages and runs software, while Kubernetes manages applications at scale. It describes Docker's components, Compose, and Swarm, and outlines where Docker works well and where Kubernetes is more suitable. ### Source excerpt (4 min read) | The real difference that actually matters, how to decide, where teams go wrong, and more... ## OAuth Clearly Explained DevFeed: [OAuth Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-05-01T05:56:21Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [OAuth](), [Authorization](), [API](), [Security]() Tags: [apis](), [flow](), [oauth](), [resource](), [security](), [tokens]() ### AI overview An explanation of OAuth as a permission and delegated-access system rather than a login mechanism. It describes the roles in an OAuth transaction, the authorization-code flow, and the distinct purposes of access and refresh tokens, while highlighting security risks from poor token handling and confusing authorization with identity. ### Source excerpt (4 min read) | OAuth flow, three different tokens, three different jobs, what OAuth actually solves, where OAuth systems usually break, and more