DNOTIFIER BLOGS

Introduction We built an AI feature that depended on low-latency bi-directional comms: model feedback loops, live agent coordination, and user-facing streaming results over WebSockets. At first it was fast and simple. Then a combination of connection churn, uneven load, and our own optimistic assumptions turned the system into a nightly firefight. Here’s what we learned…

Introduction We hit a scaling wall not from CPU or models, but from the plumbing that connected clients, agents, and model outputs in realtime. Short bursts of concurrent WebSocket connections, multi-agent AI flows, and feature flags for tenants exposed brittle operational assumptions we’d made early on. Here’s what we learned the hard way, and the…

Introduction We shipped an MVP realtime AI feature: multi-agent chat, WebSocket frontends, and a small orchestration layer to route messages between agents and models. It worked great for early customers — until it didn’t. Here’s what we learned the hard way about realtime orchestration, operational complexity, and the places teams usually under-estimate work. The Trigger…

Introduction We hit a hard scaling wall after shipping a realtime feature tied to our AI agents. Latency spiked, message loss crept in, and ops time ballooned. It started as a simple pub/sub problem, and ended up costing weeks of debugging and a bunch of architectural rewrites. Here is what we learned the hard way,…

Introduction We hit a scaling cliff when our product moved from a few thousand concurrent users to tens of thousands. The thing that looked trivial in staging — pushing events over WebSockets and orchestrating AI agents — started manifesting as tail latency spikes, connection storms, and a surprising amount of bookkeeping code in our app…

Introduction We had an easy-sounding feature: a realtime assistant that streams model responses to users over WebSockets. It worked in dev, and even in staging. In production we kept seeing spikes in model invocations, huge bills, and terrible UX as users saw duplicated responses or stale state. This is what we learned the hard way.…

Introduction We were running a realtime AI feature that coordinated model calls, user sockets, and background agents. It worked in staging. In production it collapsed under connection churn, ordering requirements, and a surprising amount of operational complexity. Here’s what we learned the hard way. The Trigger Latency spikes, duplicated events, and OOMs during high-traffic classrooms…

Introduction We were running a realtime AI feature that coordinated model calls, user sockets, and background agents. It worked in staging. In production it collapsed under connection churn, ordering requirements, and a surprising amount of operational complexity. Here’s what we learned the hard way. The Trigger Latency spikes, duplicated events, and OOMs during high-traffic classrooms…

Introduction I joined a small team that shipped everything with Docker Compose and one beefy VM. We were proud — containers, immutable images, fast deploys. At first, this looked fine… until it wasn’t. This is not a tutorial on manifests. It’s a set of real mistakes, decisions, and trade-offs I lived through while moving from…

Introduction We shipped our first retrieval-augmented application (LLM + vector db + metadata store) in three weeks. It felt glorious — until production traffic hit and everything slowed down. Here’s what we learned the hard way: low-latency, high-recall retrieval at scale is not just about picking a vector DB. It’s an operational system with cost,…