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
Firmware
Adaptive sampling turns your wearable into a system that feels alive—delivering the right data at the right moment while preserving battery for when it matters. Instead of locking sensor rates in firmware, we set behavior at the app layer with a small, transparent policy engine. The device advertises capabilities;
Software
Your sensors never sleep. An IMU chirps at 100–200 Hz, a load cell hums at 10–100 Hz, a microphone races at kilohertz. The instant you do real work—FIR/IIR filtering, debouncing, feature extraction—on the main isolate, your once-silky Flutter UI starts to stutter. The jank isn’
Firmware
Networks misbehave in the real world. BLE links jitter, Wi-Fi bursts collide, buffers overflow, and low-power sensors occasionally flip a bit. The worst part isn’t the packet loss—it’s the visible glitch your user sees: a frozen chart, a jumpy graph, or a dropped audio blip. This post
Software
Real‑time telemetry is the heartbeat of connected products. When the stream looks smooth, teams ship with confidence; when it stutters, trust erodes fast. The silent killers aren’t exotic algorithms—they’re everyday mistakes: rendering on every message, letting arrays grow forever, and forgetting to tear down listeners during