ByteHeavy
ByteHeavy
ByteHeavy
Our work
We're still early in publishing client portfolios, so these are examples of the kind of engineering we do — not specific client-project claims.
TypeScript, Postgres, Redis
Built for a mid-size Discord community that needed an in-server currency system without the exploits and race conditions that plague most 'quick' economy bots. Every balance change is wrapped in a serializable database transaction, so two simultaneous trades can't double-spend the same coins — a class of bug that has quietly bankrupted more Discord economies than any external attacker ever has. Sensitive admin actions (balance edits, item grants, bans) are logged to an immutable audit trail so disputes have a paper trail instead of a 'he-said-she-said.' The whole system was load-tested to hold up under peak concurrent load — thousands of interactions in the same few minutes during a community event — without ever hitting Discord's own API rate limits, because the request queue is throttled and batched on our side before it ever reaches Discord's servers.
Java, Paper API — portable pattern
A behavior-based detection layer originally built for a Minecraft (Paper API) server, designed to catch statistically anomalous movement, aim, and combat patterns without requiring players to install any client-side mod — the entire engine runs server-side, so there's nothing for a cheat developer to reverse-engineer or bypass on the client. The hard constraint was performance: detection logic runs inside the server's own tick loop, so every single check was profiled and optimized down to the microsecond to avoid adding any perceptible lag for the thousands of legitimate players around the one being watched. The underlying pattern — statistical anomaly detection running inside a tight real-time loop — isn't unique to Minecraft; the same architecture applies to fraud detection in any live system where 'something looks off' has to be caught in milliseconds, not reviewed a day later in a log file.
Go, PostgreSQL RLS
A backend serving multiple separate client organizations from one shared deployment, with PostgreSQL Row-Level Security enforcing data isolation at the database layer itself — not just in application code that a future bug could bypass. Even a fully compromised or badly misconfigured application server can't leak one tenant's data to another, because the database itself refuses the query before it ever runs, regardless of what the application code asked for. Built in Go specifically because its lightweight goroutines let one deployment comfortably hold open thousands of concurrent per-tenant connections without the memory overhead a thread-per-request model would carry at the same scale.
Go, Redis
A gateway service sitting in front of a set of backend APIs, built to absorb bursty, high-volume traffic without ever letting a spike take down the services behind it. Distributed rate limiting and response caching live in Redis, shared across every gateway instance, so the limit is enforced consistently no matter which instance a request happens to land on. It was explicitly designed around graceful degradation: a sudden 10x traffic spike gets throttled and queued at the edge, so the backend sees a smooth, absorbable curve instead of a wall — the difference between a slow response and a total outage.
// Client work previews are demoed live, not shipped as
// downloadable source — ask us for a walkthrough.