Maple AI OS Course

A Canadian AI infrastructure strategy

How MapleAI can compete by building trusted AI infrastructure rather than chasing frontier-model scale.

Domain hubEvaluation snapshot

Learning Objectives

What this lesson should make precise

01

Name the core abstraction and its failure modes.

02

Translate the concept into a Maple/Hermon proposal contract.

03

Define at least one evaluation case that can fail the model safely.

Tutorial Flow

How this lesson becomes a demo and training target

Each tutorial is written as a user education path and a model-improvement artifact. The diagram shows how the idea moves into a lab, a typed contract, an eval gate, and then a Hermon/MapleAI demo route.

01

Concept

The strategic niche

02

Applied Lab

Applied lab: A Canadian AI infrastructure strategy

03

Output Contract

research_questions, benchmarks, industry_pilots, evaluation_plan

04

Eval Gate

Does the answer separate model proposal from deterministic execution?

05

Demo Route

Maple AI OS training and public model checks

01

The strategic niche

Canada cannot win only by outspending frontier labs. MapleAI can lead by owning the accountability layer: local-first inference, open protocols, governed execution, and domain-specific evidence.

  • Own the control plane.
  • Use frontier models as suppliers.
  • Make receipts the moat.

02

Industrial partnerships

The OS layer can serve banks, universities, public agencies, software teams, and biotech researchers because each needs the same primitives: authority, data boundaries, audit, and evolution.

  • Finance needs controls.
  • Research needs reproducibility.
  • Software needs safe automation.

03

Research agenda

Maple Brain Lab should publish benchmarks for proposal quality, refusal quality, receipt completeness, tool-chain risk, and adapter promotion stability.

  • Benchmark contracts.
  • Publish failure modes.
  • Turn evals into curriculum.

Lab

Applied lab: A Canadian AI infrastructure strategy

Draft a research agenda for Maple Brain Lab that connects OS contracts, Hermon models, public demos, and industry pilots.

Expected result

  • A typed JSON-style proposal rather than free-form advice.
  • Clear authority boundaries and denied operations.
  • A test or rubric that decides whether the proposal is deployable.

Evaluation

How Maple would grade this work

Rubric

  • Does the answer expose assumptions instead of hiding them?
  • Does the answer separate model proposal from deterministic execution?
  • Does the answer produce artifacts that can be tested, reviewed, and rolled back?

Output contract

research_questions, benchmarks, industry_pilots, evaluation_plan, publication_plan

Use this lesson as training direction

A strong lesson gives users a mental model and gives Hermon a sharper target for examples, probes, and demo prompts.

View evalsDomain hub