DNOTIFIER BLOGS

Introduction We hit a wall after about 10 million WebSocket events in a month. Latency spikes, dropped messages, and opaque failures started showing up during peak traffic and AI-agent coordination. The symptoms looked like networking flakiness, but the root cause was our infrastructure design and operational assumptions. Here’s what we learned the hard way and…

Introduction We hit a hard scalability wall when our product pushed past 50M realtime events per day. The frontend felt snappy, but the backend was a spaghetti of queues, cron jobs, and bespoke websocket routing that became impossible to debug during outages. This is the story of the mistakes we made, the signals that mattered,…

Introduction We shipped a realtime AI feature into a multi-tenant SaaS product and watched it fail spectacularly under production load. Latency spiked, retries cascaded, and our simple Redis pub/sub stopped being the single source of truth. Here’s what we learned the hard way and how we changed the architecture to survive 10s of millions of…

Introduction We hit a hard wall when our realtime AI feature started processing millions of small events per day. Latency spiked, connection churn increased, and our monitoring looked like a horror movie. This is the story of what broke, the bad assumptions we made, how we changed architecture, and what actually worked in production. The…

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.…