Software
Modern backend systems rarely fail loudly. They fail quietly—through duplicated requests, partially completed workflows, retried executions, and replayed messages that look successful but slowly erode correctness. On AWS, these behaviors are not edge cases or misconfigurations. They are the default operating conditions of a resilient, distributed platform. API Gateway
AI
Interactive AI experiences are different from traditional RAG pipelines. They exist inside tight user-facing latency boundaries where even a 300–500 ms delay can change how users feel about the system. A chat interface that responds instantly feels intelligent; one that hesitates feels broken. When retrieval sits on the hot
Flutter
Client applications don’t usually fail because of one big bug. They fail because a dozen tiny unbounded behaviors accidentally combine: a user tapping too fast, a scroll listener firing dozens of times per second, a reconnect event triggering a sync job, or a background worker replaying offline writes all
Software
A timeline is often the most deceptively complex screen in any modern mobile experience. It looks simple: a neat vertical feed of moments, updates, and events. But behind the scenes, it is a battlefield of asynchronous data, inconsistent timestamps, unmerged identities, and late-arriving corrections. In a connected pet-tech ecosystem like
AI
Retrieval-Augmented Generation (RAG) systems rarely fail loudly. They fail in subtler, stranger ways: a support answer sounds different today than yesterday; a troubleshooting step the assistant always got right suddenly drifts; or the bot becomes strangely confident about something no one remembers teaching it. When you dig deeper, the underlying
Software
If you’ve ever shipped a RAG system into production, you already know the uncomfortable truth: these systems don’t fail dramatically. They fail quietly. No alarms. No error stacks. No catastrophic crashes. One day the answers simply aren’t as sharp as they used to be. The assistant feels
Software
AI assistants are becoming deeply embedded across apps, devices, and internal tools. They read context, recall previous turns, guide users through troubleshooting, analyze data, and invoke tools on behalf of the user. Yet the systems beneath them—models, prompts, routing layers, retrieval indexes, formatting logic, safety filters—are in constant
Software
At small scale, an internal event bus feels magical. One team adds “user.session.started”, another listens and fires analytics, another triggers notifications. No coordination meeting, no new REST endpoints, just vibes. Fast-forward a year: You’ve got dozens of topics, half a dozen “user.*” events, multiple schemas for “pet.
AI
The first LLM integration is usually simple: you pick one provider, one model, one API key, and ship. The second and third are where things start to hurt. Suddenly you have a “cheap” model for bulk tasks, a “smart” model for critical flows, maybe a vision model, maybe a provider
AI
When an AI feature behaves well, it feels effortless: the user asks a question, the model replies, and everything “just works.” But under that smooth surface, LLM calls are some of the most complex operations in your stack: huge prompts, variable latency, opaque provider behavior, and costs that can drift