Evaluating LLM Systems in Production: From Implicit Signals to Safe Experiments
Show a practical way to move from guessing about LLM quality to measuring it using logs, labels, and simple experiments.
Blog Posts
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Structured Outputs with LLMs: How to Get Reliable JSON Every Time
Show how to turn a chatty LLM into a safe JSON-producing service that other systems can trust.
Designing Backpressure-First Systems: Surviving Traffic Spikes Without Falling Over
How to design web services that slow down gracefully instead of crashing when traffic or downstream latency spikes.
Designing Multi-Tenant SaaS with Strong Isolation by Default
How to design SaaS systems where many tenants share the platform but don't share failure modes, noisy neighbors, or data.
Drift-Safe AIoT: How to Monitor, Update, and Roll Back Edge Models in the Field
How to keep AI models on devices useful and safe after you ship them. Practical patterns for detecting drift, retraining, and safely rolling out updates to edge AIoT fleets.
Privacy-by-Design AIoT: Practical Patterns for Tiny Federated Learning on Devices
How to train useful AI models across many devices without pulling all the raw sensor data into the cloud.
Resilient OTA Updates for IoT: Rollout, Rollback, and Safety Checks That Actually Work
How to push firmware safely to thousands of devices without bricking them or waking the on-call team at 3 a.m.
Zero-Touch Secure Provisioning for Large IoT Fleets: A Practical Blueprint
How to get thousands of devices securely online without manual setup, stickers, or spreadsheets. A complete guide to zero-touch provisioning for IoT fleets.
Practical Zero-Trust Patterns for AIoT Edge Devices
How to design AI-powered devices that assume nothing is trusted: not the network, not the model, not even the local firmware.
Safe OTA Model Updates for AIoT: A/B Slots, Canary Rollouts, and Real-World Constraints
How to ship new AI models to thousands of flaky, low-power devices without bricking them or breaking behavior.