extra-steps.dev

A Kubernetes controller is a loop: read the desired state from a YAML file, observe the actual state of the cluster, compute the diff, take action to converge. Every controller in the system runs this same loop independently.

while True:
    desired = read_spec("deployment.yaml")   # what you asked for
    actual  = observe_cluster()               # what's running
    diff    = desired - actual                 # what's wrong
    for action in plan(diff):
        execute(action)                        # fix it
    sleep(interval)

A container is a process with resource limits (cgroups) and an isolated view of the system (namespaces). Docker didn't invent any of this — it made it usable. The underlying kernel features have existed since 2008.

# What Docker does, the hard way
unshare --mount --uts --ipc --net --pid --fork bash
mount -t overlay overlay -o lowerdir=/base,upperdir=/diff,workdir=/work /merged
echo $$ > /sys/fs/cgroup/memory/container1/cgroup.procs
echo "512m" > /sys/fs/cgroup/memory/container1/memory.limit_in_bytes

Watch a folder for changes, sync the diffs to a remote server. Dropbox's genius was making that invisible — but the primitives are file-watching, delta transfer, and a storage backend. BrandonM was right about the parts, wrong about the product.

# Watch for changes, sync the diffs
inotifywait -mr ~/Dropbox -e modify,create,delete |
  while read path action file; do
    rsync -avz ~/Dropbox/ remote:~/backup/
  done

An embedding is a function that maps text to a fixed-size array of numbers. Similar text maps to nearby points. 'Semantic search' is computing this hash, then finding the nearest neighbors. It's a hash function that preserves meaning instead of uniqueness.

// "Embedding" — text in, number array out
const vec = await embed("how do I reset my password");
// [0.021, -0.187, 0.441, ...] (1536 floats)

// "Semantic search" — nearest neighbor lookup
const results = await vectorDB.query(vec, { topK: 5 });

Check the input before processing it. Check the output before returning it. Reject or transform anything that doesn't pass. It's the same pattern as web form validation and HTML sanitization — applied to natural language instead of HTML.

function guardrail(input: string, output: string) {
  if (containsPII(input))    throw new Error("PII detected in input");
  if (isOffTopic(input))     throw new Error("Input out of scope");
  if (containsHarmful(output)) return FALLBACK_RESPONSE;
  if (!matchesSchema(output))  return retry(input);
  return output;
}

A system prompt is a README for the model. 'Few-shot examples' are worked examples. 'Chain of thought' is asking someone to show their work. Prompt engineering is technical writing — the skills transfer directly, the mystification doesn't.

You are a code reviewer. When reviewing, check for:
1. Security: hardcoded secrets, injection vulnerabilities
2. Performance: O(n²) loops, unnecessary allocations

Example:
Input: `eval(user_input)`
Output: "CRITICAL: arbitrary code execution via eval()"

Search your documents for relevant chunks. Concatenate them into the prompt. Call the LLM. That's RAG. The 'retrieval' is a search query, the 'augmentation' is string concatenation, and the 'generation' is the same LLM call you were already making.

const chunks = await searchIndex.query(userQuestion, { topK: 5 });
const context = chunks.map(c => c.text).join("\n\n");
const response = await llm.chat({
  system: `Answer using this context:\n${context}`,
  messages: [{ role: "user", content: userQuestion }],
});

There is no cloud, it's just someone else's computer. Serverless takes this one step further: it's someone else's process on someone else's computer. You upload a function, a trigger fires, your code runs in an isolated environment, you get billed by the millisecond.

# What "serverless" looks like from the platform's perspective
def handle_request(event):
    container = pool.get_or_create("user-123-fn-abc")
    result = container.invoke(user_function, event)
    bill(user="user-123", duration_ms=container.last_duration)
    return result

When something happens, send an HTTP POST to a URL someone registered. That's a webhook. 'Event-driven architecture' in SaaS is usually just one server POSTing JSON to another server's endpoint when state changes.

// The webhook sender — just a POST request
await fetch(registeredUrl, {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({ event: "payment.succeeded", data: payment }),
});

The LLM can't remember anything between conversations — it's stateless. 'Memory' is just your application writing facts to a file (or database) and reading them back into the system prompt next time. The model isn't remembering. You are.

// "Remembering" something
await fs.appendFile('memory.txt', `User prefers TypeScript\n`);

// "Recalling" it next session
const memories = await fs.readFile('memory.txt', 'utf-8');
const response = await llm.chat({
  system: `What you know about this user:\n${memories}`,
  messages: [userMessage],
});

The LLM outputs JSON describing which function to call and with what arguments. You parse it and call the function. The API wraps this in structured types, but that's the whole thing.

// LLM returns: { name: "get_weather", arguments: { location: "NYC" } }
const response = await llm.chat(messages, { tools });

if (response.tool_calls) {
  for (const call of response.tool_calls) {
    const fn = tools[call.name];          // look up the function
    const result = await fn(call.arguments); // call it
    messages.push(toolResult(call.id, result));
  }
}

MCP is JSON-RPC 2.0 over stdio. A tool call is a JSON-RPC request sent to a subprocess on stdin; the result comes back on stdout. Same pattern as LSP.

// Client → Server (stdin)
{"jsonrpc":"2.0","method":"tools/call","params":{"name":"read_file","arguments":{"path":"/foo"}},"id":1}

// Server → Client (stdout)
{"jsonrpc":"2.0","result":{"content":[{"type":"text","text":"file contents..."}]},"id":1}

An agent is a while loop. Each iteration: send messages to LLM, get back either a tool call or a final response. Execute the tool, append the result, repeat. Everything else is optimization.

messages = [system_prompt, user_message]

while True:
    response = llm.chat(messages)
    if response.has_tool_calls():
        for call in response.tool_calls:
            result = dispatch(call.name, call.arguments)
            messages.append(tool_result(call.id, result))
    else:
        print(response.text)
        break

A skill is a markdown file with YAML frontmatter that gets appended to the system prompt. The LLM reads it like any other instruction. There's no runtime magic — it's string concatenation.

---
name: code-reviewer
description: Reviews code for correctness and style
---

When reviewing code, check for:
1. Off-by-one errors
2. Unhandled edge cases
3. Missing error handling

Always explain *why* something is wrong, not just that it is.