[Engineering Lab]
Inside the systems.
How the pieces connect: code that shows the approach, the architecture behind it and the path a single event takes through a production system.
Thinking in code.
Illustrative Engineering Examples — written for this page to show the approach. Not verbatim production code.
from dataclasses import dataclass
@dataclass
class TradingSignal:
symbol: str
direction: str
entry: float
stop_loss: float
def validate_signal(signal: TradingSignal) -> bool:
if not signal.symbol:
return False
if signal.direction not in {"LONG", "SHORT"}:
return False
if signal.entry <= 0 or signal.stop_loss <= 0:
return False
if signal.direction == "LONG":
return signal.stop_loss < signal.entry
return signal.stop_loss > signal.entryIllustrative Illustrative Engineering Example
CREATE TABLE processed_events (
event_id TEXT PRIMARY KEY,
source TEXT NOT NULL,
event_type TEXT NOT NULL,
processed_at TEXT NOT NULL
);Illustrative Illustrative Engineering Example
async function loadSystemOverview() {
const response = await fetch("/api/overview");
if (!response.ok) {
throw new Error("Failed to load system overview");
}
const data = await response.json();
return {
status: data.status,
updatedAt: data.updated_at,
metrics: data.metrics
};
}Illustrative Illustrative Engineering Example
Why the examples look the way they do
- Validation before side effects. Signals, events and AI output are checked before anything is stored or executed.
- Idempotency at the storage layer. A unique key makes duplicate deliveries harmless instead of relying on “it shouldn’t happen”.
- Thin interfaces, explicit errors. The UI asks for state and fails loudly when it can’t get it — no silent stale data.
- Complete systems need more. Transactions, retries with limits, state recovery and monitoring turn these snippets into production behaviour.
Pick a layer. See what it touches.
Select a part of the system to see its connections and the case studies where it carries the most weight.
Language models for interpretation and drafting, always behind validation.
Connected to
Backend · Database · User Interface
Where it matters most
Async Python services that coordinate everything else.
Connected to
AI · Database · API · Monitoring · User Interface
Where it matters most
SQL state, event history and reporting data.
Connected to
Backend · Monitoring
Where it matters most
Exchanges, Telegram, AI providers, webhooks.
Connected to
Backend
Where it matters most
Logs, diagnostics and post-deployment checks.
Connected to
Backend · Database
Where it matters most
Mini Apps and dashboards on top of backend state.
Connected to
Backend · AI
Where it matters most
One event, five steps.
Not a list of technologies — the sequence of engineering actions every event goes through.
- 01
Input
An event arrives: a webhook, a scheduled tick, a user action.
- 02
Validation
Shape, required fields and duplicates are checked before any work.
- 03
Processing
Business logic runs against current state.
- 04
Persistence
State and event history are written in one transaction.
- 05
Notification
Interfaces and users are informed of the result.