Don't read about my work. Break it yourself.
Seven working simulations of the patterns behind my production payment systems — money that can't double-spend, webhooks that heal themselves, transactions that survive an app kill, sockets that reconnect without missing a cent. Everything on this page runs in your browser, hand-coded, zero libraries. Every demo maps to a system that actually shipped.
Pay on a dying network.
Get charged exactly once.
The classic double-charge: the request lands, the response gets lost, the client retries, the server executes again. The fix is an idempotency key minted with the intent and reused on every retry — the pattern behind all 15+ money-moving flows I shipped.
Client mints the key
A unique key is attached to the payment intent before the first byte leaves the device.
Retries reuse it
Timeouts, taps, crash-recovery — every resend carries the same key, never a fresh one.
The server replays, never re-executes
A key it has seen returns the original receipt. The ledger takes exactly one debit, no matter how many requests arrive.
The network here loses the response, not the request — every retry really does hit the server. Flip the switch off and run it again to watch the same storm triple-charge the account.
The payment that survives
a dead webhook.
Stripe says the customer paid — but your endpoint is down. Amateur billing marks the order paid on the client and hopes. Mine runs state machines: the order stays pending until the webhook truly lands, and the failed delivery opens a first-class incident that retries itself with backoff until it recovers.
State machines, not booleans
draft → pending → paid. Every transition recorded, none skippable, refunds included.
Incidents are first-class rows
received → open → retrying → recovered — with exponential backoff and an operator deep-link on every attempt.
The ledger never guesses
Until the signed event verifies, money is pending — never assumed. Persistent failure triggers dunning, not silence.
The good run is boring — that's the point. For the real show: flip the endpoint DOWN, checkout, watch the incident open and the backoff clock tick — then bring the endpoint back and watch it recover on its own. No human, no lost money.
Four ticks.
Zero lies.
WhatsApp-style ticks look trivial until you build them honestly: every tick must be driven by a real acknowledgment — server ack, device ack, read receipt — never a timer. And when the socket drops, "offline" is a state the queue understands, not an error dialog.
Every tick is an ack
◷ queued → ✓ server ack → ✓✓ device ack → ✓✓ read receipt. Four states, four proofs.
Offline is a state
Messages sent with the socket down wait in a durable queue — the UI shows ◷ honestly instead of pretending.
Order survives the outage
On reconnect the queue flushes FIFO — the recipient can never see message 3 before message 2.
Kill the socket, fire off three messages, bring it back — the queue flushes in order with each tick earned by its own ack. This is the exact contract behind the 4-state ticks in Bubbles.
Approve with a key that
never leaves the phone.
Passkey-style approval for sensitive operations, the way WebAuthn does it: the server issues a random challenge, the device signs it with a private key locked in secure hardware, and the server verifies with the public key. Nothing secret ever crosses the network — and a captured signature is worthless twice.
The server issues the challenge
Random bytes + a single-use nonce + a short expiry. There is nothing secret in it — it's a question, not a password.
Biometrics gate the key
The private key sits in the secure enclave; Face ID unlocks a signature, never the key itself.
Replays die twice
The nonce burns on first use and the challenge expires in seconds. Yesterday's signature answers a question nobody is asking.
Try the attacks: verify once, then REPLAY the captured signature — the burned nonce kills it. Or request a challenge, LET IT EXPIRE, and watch the signature window close.
One pipeline. The scariest
event goes first.
A wallet's device-security signals — startup snapshots, heartbeats, blocked operations, tamper detections — all flow through one prioritized queue to one backend. No side doors. A debugger detection never waits behind routine heartbeats, and the server decides the punishment, not the client.
Four sources, one queue
Startup, 60-second heartbeats, blocked sensitive ops, native threat detections — every signal takes the same path.
Priority, not FIFO
Critical detections ship at priority 100; heartbeats at 20. Offline events persist locally and retry — nothing is dropped.
The server enforces
The client only reports. The backend answers with allow, cooldown or force-logout — and the app obeys.
Queue a few heartbeats with the uplink OFF, inject a threat, and watch it cut the line the moment the uplink returns — then watch the server's cooldown verdict lock sensitive operations. Cooldown is demo-scaled to 20s.
Kill the app mid-transfer.
Nothing breaks.
Phones die at the worst moments — mid-payment, mid-submit. So every transaction writes a journal entry before every step. On relaunch, the app finds the in-flight transaction, asks the server what it actually saw, and resumes or safely retries — with the same idempotency key, so the answer can never be "charged twice".
Journal first, act second
created → authorized → submitted → confirmed. Each transition is durable before it happens — write-ahead, like a database.
Relaunch replays the journal
An in-flight transaction is found with its last durable step. The app never guesses what happened next.
Reconcile, then resume
"Did you see tx_9f21?" If yes — adopt the server's result. If no — resubmit with the same key. Both paths end with exactly one charge.
Kill it early (before submit) and recovery resubmits with the same key. Kill it late (after submit) and recovery discovers the charge already executed and adopts it. Either way: exactly one charge.
The socket dies. The ledger
doesn't miss a beat.
Live balances over WebSockets are easy — until a tunnel, an elevator, a dead spot. Reconnecting is not enough: you have to answer "what did I miss?" Every event carries a sequence number; the client resumes with a cursor and the server replays exactly the gap. No gaps, no duplicates, no refresh button.
Backoff with jitter
Reconnect at 1s, 2s, 4s… plus jitter — so ten thousand clients don't stampede the gateway the second it recovers.
Cursor, not hope
The client reconnects with last_seq; the server replays everything after it, in order.
Exactly-once painting
Duplicates are dropped by sequence number. The balance is correct, not approximately correct.
Kill the network and let a few fills happen without you — the server keeps trading. Reconnect and watch the cursor resume replay exactly the missed window (plus one deliberate duplicate, dropped on arrival). The two ledgers must agree to the cent.
Seven patterns. Real production systems.
One engineer.
Everything you just clicked is hand-written vanilla JS — no frameworks, no libraries — because the patterns matter more than the tools. I can walk you through the production versions of any of them, live.