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This week’s system design refresher:
Monolithic vs Microservices vs Serverless
CLI vs MCP
Comparing 5 Major Coding Agents
Essential AWS Services Every Engineer Should Know
JWT Visualized
Monolithic vs Microservices vs Serverless
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A monolith is usually one codebase, one database, and one deployment. For a small team, that’s often the simplest way to build and ship quickly. The problem arises when the codebase grows. A tiny fix in the cart code requires redeploying the whole app, and one bad release can take down everything with it.
Microservices try to solve that by breaking the system into separate services. Product, Cart, and Order run on their own, scale separately, and often manage their own data. That means you can ship changes to Cart without affecting the rest of the system.
But now you are dealing with multiple moving parts. You generally need service discovery, distributed tracing, and request routing between services.
Serverless is a different model. Instead of managing servers, you write functions that run when something triggers them, and the cloud provider handles the scaling. In many cases, you only pay when those functions actually run.
However, in serverless, cold starts can add latency, debugging across lots of stateless functions can get messy, and the more you build around one cloud’s runtime, the harder it gets to switch later.
Most production systems don't use just one approach. There's usually a monolith at the core, and over time teams spin up a few services where they need independent scaling or faster deploys. Serverless tends to show up later for things like notifications or background jobs.
CLI vs MCP
AI agents need to talk to external tools, but should they use CLI or MCP?
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Both call the same APIs under the hood. The difference is how the agent invokes them.
Here's a side-by-side comparison across 6 dimensions:
Token Cost: MCP loads the full JSON schema (tool names, descriptions, field types) into the context window before any work begins. CLI needs no schema, so saves more context window.
Native Knowledge: LLMs were trained on billions of CLI examples. MCP schemas are custom JSON the model encounters for the first time at runtime.
Composability: CLI tools chain with Unix pipes. Something like gh | jq | grep runs in a single LLM call. MCP has no native chaining. The agent must orchestrate each tool call separately.
Multi-User Auth: CLI agents inherit a single shared token. You can't revoke one user without rotating everyone's key. MCP supports per-user OAuth.
Stateful Sessions: CLI spawns a new process and TCP connection per command. MCP keeps a persistent server with connection poo