3 posts tagged with "Artificial Intelligence"

Having spent over a decade building, scaling, and contributing to open-source business application ecosystems—including key leadership and community building around ERPNext—we have witnessed the immense power of open source to democratize business software. Open-source frameworks allow teams to rapidly prototype applications, automate complex workflows, and retain complete control over their digital infrastructure.

Yet, as open-source frameworks expand into large enterprises, regulated industries, and government organizations, severe friction points emerge. Traditional framework designs often force a choice between rapid initial development velocity and long-term architectural stability.

We built Framework M to eliminate that compromise. This is our story, our architectural manifesto, and our vision for enterprise-grade open source.

As AI coding assistants and LLM pair programmers become central to daily software development, engineering teams face a new challenge: context fragmentation. Developers prompt AI agents with conflicting assumptions about framework APIs, missing model schemas, or outdated coding conventions.

In July 2026, we introduced the m skills command group in the Framework M Studio CLI, providing a standardized workflow to discover, pull, version-control, and manage LLM Skill Definitions across multiple repositories and custom namespaces directly within your workspace.

Generative AI agents and LLM pair programmers are rapidly changing how business applications are built, maintained, and operated. However, most traditional web frameworks treat AI as a superficial gimmick—adding simple chat widgets while forcing agents to guess database schemas, API parameters, and form layouts.

Framework M is engineered AI-first. Studio CLI (m docs export) provides five specialized machine schema exporters under ./docs/machine/: corpus.jsonl, core.jsonl, doctypes.jsonl, ui.jsonl, and openapi.jsonl.