Agile AI Development Lifecycle vs Nano

A framework and methodology for transforming pre-AI software development lifecycle into AI-native development at scale.

An AI-native security and observability platform for cloud, local, hybrid, and air-gapped teams. Sub-second search, detection engineering, OpenTelemetry, pivt AI, and...

About Agile AI Development Lifecycle

A framework and methodology for transforming pre-AI software development lifecycle into AI-native development at scale. At first it looks like an easy-to-adopt (just drop markdown files into your repository) framework that should take one from pre-AI software development lifecycle to the AI-native future. In its nature it is very similar to compound engineering. - all of them are a collection of skills in markdown prose that rely on human-in-the-loop review. Notably both of them were introduced to the public a few months before AWS released AI-DLC, but it's hard to compete with AWS in terms of visibility.

About Nano

An AI-native security and observability platform for cloud, local, hybrid, and air-gapped teams. Sub-second search, detection engineering, OpenTelemetry, pivt AI, and deployment control in one place. Run nano in the cloud, locally, hybrid, or air-gapped. nano was built from scratch, not refactored: a dedicated cluster, one query language over your security and OpenTelemetry data, agentic investigation, and deployment options from cloud to air-gapped. So when a service slows down, "is this an outage or a breach?" is one query, not two tools and a guess.