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
AI
LLM features usually start life in a happy place: a handful of users, low traffic, and plenty of quota. Latency looks fine, tokens are cheap, logs are quiet. Then a product launch, a marketing campaign, or a new “Ask AI” button lands — and suddenly your once-stable service is drowning in
AI
Modern AI products feel personal only when they remember: that bug you were debugging yesterday, the pet profile you set up last month, the long-running coaching thread you keep coming back to. Under the hood, though, LLM inference APIs don’t remember anything. Every call is a clean slate: you
AI
Most teams add AI the same way they add any new feature: ship a model, wire an endpoint, move on. A few months later, you’re juggling three versions of the “scoring service”, five subtly different summarizers, and a bundle of feature flags nobody fully trusts. Changing one model feels
Software
When a backend is young, everything feels fast: one service, a few routes, everyone knows where things live. A year later, the same service is handling alerts, audits, preferences, billing hooks, experiment flags, and “just one more feature” after every sprint. Regression risk climbs, onboarding slows, and even small refactors
Software
When a streaming backend is designed well, it feels effortless: memory stays flat, latency is predictable, and reconnect storms are just another Tuesday. Even as traffic grows and features pile up, the pipeline keeps behaving like a well-tuned instrument instead of a fragile chain of patches. A pattern that consistently
Software
Real-time apps are at their best when they feel calm. Numbers update, charts glide, tiles pulse with new information—but nothing spikes, freezes, or drifts. The UI feels more like a live instrument panel than a slideshow. To get there, though, you need more than a WebSocket and a couple