Markleft vs Nano

AI output seldom meets the intent on the first prompt, so iteration is necessary. Today, iteration usually means providing feedback in a follow-up prompt, which leads to a...

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 Markleft

AI output seldom meets the intent on the first prompt, so iteration is necessary. Today, iteration usually means providing feedback in a follow-up prompt, which leads to a rewrite of the whole document. The prompt that explains the intent behind the revision remains buried in the chat. That is absurd, this workflow has been solved for decades by comments and tracked changes in Word, or by Suggesting mode in Google Docs. Let's play through the process of creating a document, and iterate once.

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.