o3 summary of application of principles to a SaaS solution from this excellent post

Principle in the essay	What it means for your platform
Malleability over rigidity – software should be “clay” users reshape, not sealed appliances. (inkandswitch.com)	Treat every table viewer, chart, approval flow, and even SQL query as editable objects the AI (or a power user) can regenerate on demand. Don’t hard-wire business logic.
“Gentle slope” from user to creator – small tweaks demand little skill; deeper changes require only incremental learning. (inkandswitch.com)	Embed low-friction UI editors (drag-drop columns, natural-language “add a filter”) right in the live view. The AI scaffolds new code only when simpler tweaks won’t do.
Tools, not avocado-slicer apps – ship general, composable primitives that cooperate over shared data. (inkandswitch.com)	Expose a library of micro-tools (table grid, form generator, KPI card). All speak one JSON/SQL contract so the AI can mix and match them into bespoke dashboards or endpoints.
Shared data substrate – modern equivalent of a filesystem for collaboration. (inkandswitch.com)	Your dynamic-schema JSON tables are that substrate; every tool reads/writes through one introspection API, letting the AI compose queries without knowing each client’s schema up front.
Composable UI surface – like OpenDoc parts or Dynamicland objects. (inkandswitch.com)	Make each widget embeddable; the AI passes prop blocks to stitch them into pages (“place a pivot-table left of this chart”).
AI ≠ silver bullet – LLMs need stable contracts and small parts to compose. (inkandswitch.com)	Define a prompt/response DSL for: {data_def, query_sql, ui_props}. The AI plugs these parts in instead of emitting whole new monoliths.
Communal creation & governance – local groups remix and fork tools. (inkandswitch.com)	Provide versioned “tool bundles.” Admins can review, diff, and publish AI-generated reports across the org.
Security, business model, culture – malleability introduces new challenges. (inkandswitch.com)	Sandbox execution, add audit trails, and offer a marketplace/licensing model for reusable AI micro-tools. Encourage experimentation with reversible change history.

Other interesting notes:

• Canonical JSON schema + introspection API – the single source of truth every tool and AI prompt references.
• Live editor layer – direct-manipulation UI plus “Ask AI to tweak” buttons → smooth on-ramp from viewing to creating.
• Plugin registry – each AI-generated query + view pair is packaged as a shareable micro-tool; users compose them via UI or prompt.
• Sandbox & diff review – AI output runs in a contained VM; admins approve before promotion to prod.
• Prompt contract library – publish schemas for queries, mutations, view definitions so AI outputs remain composable across clients.