Architectural Constraints Alignment in AI-assisted, Platform-based Service Development

AI-assisted development tools enable rapid prototyping of services but often lack awareness of architectural constraints, infrastructure dependencies, and organizational standards required in production environments. Consequently, generated artifacts may exhibit brittle behavior and limited deployability. We propose a retrieval-augmented scaffolding approach that combines platform-based code generation with agentic clarification loops to expose and resolve architectural constraint ambiguities. By combining template retrieval with structured interaction, the method embeds production-relevant considerations during service scaffolding. Evaluation indicates improved architectural consistency and deployability compared to general-purpose AI code generation workflows, suggesting that constraint-aware retrieval is essential for aligning AI-assisted service development with production software engineering practices.

A new operation mode for depth-focused high-sensitivity ToF range finding

We introduce pulsed correlation time-of-flight (PC-ToF) sensing, a new operation mode for correlation time-of-flight range sensors that combines a sub-nanosecond laser pulse source with a rectangular demodulation at the sensor side. In contrast to previous work, our proposed measurement scheme attempts not to optimize depth accuracy over the full measurement: With PC-ToF we trade the global sensitivity of a standard C-ToF setup for measurements with strongly localized high sensitivity -- we greatly enhance the depth resolution for the acquisition of scene features around a desired depth of interest. Using real-world experiments, we show that our technique is capable of achieving depth resolutions down to 2mm using a modulation frequency as low as 10MHz and an optical power as low as 1mW. This makes PC-ToF especially viable for low-power applications.