Scale problems are usually a handful of endpoints, one database pattern and a deploy process that makes every fix slow. Reviewers here have run high-traffic Kotlin and Java services, absorbed large DDoS attacks and moved managed databases in-house, and they start by measuring where your system actually spends its time.
Best for: seed startup · scale-up · corporate innovation team (especially teams where non-engineers ship with AI tools).
Selected cases from these experts’ profiles.
A public API serving thousands of requests per second was hit by massive distributed attacks.
One of our experts led the backend response: hardening the API layer and the surrounding cloud setup on Kubernetes so attack traffic could be absorbed and filtered.
The attacks were mitigated and the platform stayed online for its players.
Interactive-video services had to serve heavy traffic without an ever-growing fleet. One of our experts, as CTO, led optimization of the backend's high-load endpoints. Throughput per node went from tens of thousands to hundreds of thousands of requests per second, more than an order of magnitude.
The managed cloud database was a growing line on the cloud bill and queries were slower than they needed to be.
One of our experts moved the database from the managed service into self-hosted Postgres running in the team's Kubernetes cluster, and took over monitoring of the whole cloud setup.
Cloud spend went down and queries got roughly a third faster.
Each release took hours of manual deployment work.
One of our experts, working as the DevOps engineer, automated the cloud setup with infrastructure-as-code and custom monitoring metrics.
Release deployment went from hours to minutes.
A small team writes much of its Kotlin/Spring code with AI assistants and nobody senior has time to read every PR.
Per-PR review focused on transaction boundaries, payment idempotency, Kafka consumer behaviour, jOOQ/JPA query patterns and migration safety, with comments that explain the why.
Reviewed PRs, plus a short recurring note on patterns the AI keeps getting wrong in this codebase.
— Invental · software studio · Montevideo, UY
Describe the system and the question. We match a lead expert from this page, or a better fit from the network, and confirm scope before anything starts.
A link or short description of the code or system, what you want checked, your stack, and when you need the answer. No repository access is needed until scope is agreed.