"Plan less. Achieve more." — northr.ai
A structured learning repository for northr.ai — an adaptive AI planning system that lets you define your direction once and handles the weekly details automatically.
northr.ai is an adaptive planning platform that acts as a thinking partner for individuals and teams who want to move from intention to execution — without drowning in planning overhead. Its core promise:
Define your direction once, let AI handle the weekly details.
Unlike traditional planning tools that require constant manual updates, northr.ai continuously adapts your schedule and priorities based on your goals, context, and progress — giving you focus without friction.
| Concept | Description |
|---|---|
| Direction | A clear north star — your goal or objective — that you define once |
| Adaptive Planning | AI-driven weekly scheduling that responds to your actual progress and constraints |
| Weekly Details | Auto-generated task breakdowns, time blocks, and check-ins managed by AI |
| Plan less | Reduce planning overhead by letting AI handle scheduling mechanics |
| Achieve more | Keep attention on outcomes, not administrative planning tasks |
This repository follows a numbered directory convention (100–1900 series) where each directory represents a distinct learning phase. All flows, stories, and tasks are placed at repository root level.
learning-northr/
├── README.md # This file — overview and navigation
├── CHANGELOG.md # Version history of this learning repository
│
├── 100/ # Getting Started
│ ├── README.md # Phase overview
│ └── 100-getting-started.md # Account setup, first look, UI orientation
│
├── 200/ # Core Concepts
│ ├── README.md # Phase overview
│ └── 200-core-concepts.md # Direction, adaptive planning, weekly cycle
│
├── 300/ # Defining Your Direction
│ ├── README.md # Phase overview
│ └── 300-defining-direction.md # Setting goals, north star, long-term objectives
│
├── 400/ # Adaptive Weekly Planning
│ ├── README.md # Phase overview
│ └── 400-adaptive-weekly-planning.md # How AI generates and manages weekly plans
│
├── 500/ # Working with AI Suggestions
│ ├── README.md # Phase overview
│ └── 500-ai-suggestions.md # Accepting, rejecting, tuning AI recommendations
│
├── 600/ # Progress Tracking & Check-ins
│ ├── README.md # Phase overview
│ └── 600-progress-tracking.md # Check-in rhythms, progress signals, retrospectives
│
├── 700/ # Team Collaboration
│ ├── README.md # Phase overview
│ └── 700-team-collaboration.md # Shared directions, team plans, alignment features
│
├── 800/ # Integrations
│ ├── README.md # Phase overview
│ └── 800-integrations.md # Calendar, task tools, and third-party connections
│
├── 900/ # Advanced Features
│ ├── README.md # Phase overview
│ └── 900-advanced-features.md # Custom rules, priority tuning, AI feedback loops
│
├── 1000/ # Practical Workflows
│ ├── README.md # Phase overview
│ └── 1000-practical-workflows.md # Real-world usage patterns and recipes
│
├── 1100/ # Troubleshooting & FAQs
│ ├── README.md # Phase overview
│ └── 1100-troubleshooting.md # Common issues and how to resolve them
│
├── 1200/ # Comparing northr.ai to Alternatives
│ ├── README.md # Phase overview
│ └── 1200-comparison.md # vs. Motion, Reclaim, OKR tools, manual planning
│
├── 1300/ # Patterns & Anti-patterns
│ ├── README.md # Phase overview
│ └── 1300-patterns.md # What works well, what to avoid
│
├── 1400/ # Extending northr.ai
│ ├── README.md # Phase overview
│ └── 1400-extending.md # API access, webhooks, automation possibilities
│
├── 1500/ # northr.ai for Cloud Engineers
│ ├── README.md # Phase overview
│ └── 1500-cloud-engineers.md # Using northr.ai in a DevOps / Platform Engineering context
│
├── 1600/ # Case Studies
│ ├── README.md # Phase overview
│ └── 1600-case-studies.md # Real-world applications and lessons learned
│
├── 1700/ # Reference
│ ├── README.md # Phase overview
│ └── 1700-reference.md # Glossary, keyboard shortcuts, settings reference
│
├── 1800/ # Resources & Further Reading
│ ├── README.md # Phase overview
│ └── 1800-resources.md # Links, articles, related tools, community
│
└── 1900/ # Archive
├── README.md # Phase overview
└── 1900-archive.md # Deprecated notes, superseded content
Follow the numbered directories in sequence for a guided learning experience, or jump to any section based on your current need:
100 → 200 → 300 → 400 # Foundation: understand and set up northr.ai
500 → 600 → 700 # Core usage: work with AI, track progress, collaborate
800 → 900 → 1000 # Power user: integrations, advanced features, workflows
1100 → 1200 → 1300 # Mastery: troubleshoot, compare, recognize patterns
1400 → 1500 # Advanced: extend and apply in your domain
1600 → 1700 → 1800 # Reference: case studies, glossary, resources
1900 # Archive: historical notes
| Repository | Topic |
|---|---|
| learning-crossplane-inner-workings | Crossplane internals |
| learning-audit-automation | CycloneDX-centred audit automation |
| learning-envoy-proxy | Envoy Proxy deep dive |
- Numbered directories (100–1900): Each represents a self-contained learning phase.
- README.md per directory: Provides a concise phase overview.
- Content files: Named
NNN-topic.mdto match their directory prefix. - Flows, stories, tasks: Placed at repository root level, not nested in subdirectories.
- Progressive depth: Earlier directories are introductory; later ones assume prior knowledge.
| Phase | Directory | Status |
|---|---|---|
| Getting Started | 100 | 🟡 In Progress |
| Core Concepts | 200 | 🟡 In Progress |
| Defining Direction | 300 | ⬜ Planned |
| Adaptive Weekly Planning | 400 | ⬜ Planned |
| AI Suggestions | 500 | ⬜ Planned |
| Progress Tracking | 600 | ⬜ Planned |
| Team Collaboration | 700 | ⬜ Planned |
| Integrations | 800 | ⬜ Planned |
| Advanced Features | 900 | ⬜ Planned |
| Practical Workflows | 1000 | ⬜ Planned |
| Troubleshooting | 1100 | ⬜ Planned |
| Comparison | 1200 | ⬜ Planned |
| Patterns | 1300 | ⬜ Planned |
| Extending | 1400 | ⬜ Planned |
| Cloud Engineers | 1500 | ⬜ Planned |
| Case Studies | 1600 | ⬜ Planned |
| Reference | 1700 | ⬜ Planned |
| Resources | 1800 | ⬜ Planned |
| Archive | 1900 | ⬜ Planned |
MIT — see LICENSE for details.
Maintained by vanHeemstraSystems