⭐ 𝗣𝗔𝗥𝗧 𝟮 — 𝐓𝐲𝐩𝐞 𝐒𝐚𝐟𝐞𝐭𝐲, 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 & 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐬𝐚𝐭𝐢𝐨𝐧 (𝐰𝐢𝐭𝐡 𝐀𝐈 𝐚𝐬 𝐚 𝐉𝐮𝐧𝐢𝐨𝐫 𝐏𝐚𝐫𝐭𝐧𝐞𝐫)

Real‑time systems fall apart without strict typing, solid tests, and predictable performance.
Here’s how I used AI to accelerate the “boring but critical” parts of engineering.

1. Type safety: AI generates TypeScript definitions

WebSocket streams demand strict typing for reliability and safe refactoring.

Prompt:
“Create TypeScript types for Bitfinex WebSocket API v2:

• subscription messages
• channel types
• trade data
• candle data
• order book data.”

AI generated clean enums and interfaces for:

• TRADES, TICKER, CANDLES, BOOK
• subscription messages
• raw trade tuples vs normalized objects

I adjusted a few fields to match the real API.

Time saved: ~2–3 hours.

1. Testing: AI writes unit tests

Memory‑bounded reducers are easy to get wrong under load.

Prompt:
“Write Vitest tests for memory‑bounded trades reducer:

• enforce MAX_TRADES
• remove oldest trades
• keep newest trades.”

AI generated a full test suite that:

pushed 1,500 trades

• asserted only the last 1,000 remained
• checked first and last IDs

I aligned it with my reducer signature.

Time saved: ~4–5 hours.

1. Performance — AI Helps Simplify, Not Over‑Engineer

In real‑time UIs, it’s tempting to over‑engineer performance. For CryptoApp, I kept it simple and used AI to validate the approach.

Prompt:
“Order book UI feels heavy under constant updates. Suggest lightweight performance strategies for React + AG Grid.”

AI suggested several options (batching, debouncing, transactional updates), but for this app I went with:

• a 100ms useThrottle on incoming order updates
• a memory‑bounded order book with MAX_BOOK_ORDERS = 100
• standard rowData updates into AG Grid

That was enough to keep the grid smooth and responsive without adding complexity I didn’t need.

⏱ Time saved: ~1 day of trial‑and‑error and premature optimisation

⭐ Real results

CryptoApp:

• 5,000+ LOC
• built in ~5 days (vs 3–4 weeks)
• 60 FPS stable
• ~60MB memory after 8 hours
• handler‑based, memory‑bounded, monitored, production‑ready

𝐍𝐨𝐭𝐞: These time‑savings are experience‑based estimates. AI sped up the repetitive work — the real engineering still came from me.

𝗪𝗿𝗶𝘁𝘁𝗲𝗻 𝗯𝘆 𝗥𝗶𝗰𝗮𝗿𝗱𝗼 𝗦𝗮𝘂𝗺𝗲𝘁𝗵
𝗦𝗲𝗻𝗶𝗼𝗿 𝗙𝗿𝗼𝗻𝘁‑𝗘𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 | 𝗥𝗲𝗮𝗹‑𝗧𝗶𝗺𝗲 𝗨𝗜 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁