gps-denied-desktop/_ai/dev_tutorials/tutorial_kickstart.md

1 Research Phase

1.01 🧑‍💻 Developers: Problem statement

Discuss

Discuss the problem and create in the _docs/00_problem next files and folders:

• problem_description.md: Our problem to solve with the end result we want to achieve.
• input_data: Put to this folder all the necessary input data and expected results for the further tests. Analyze very thoroughly input data and form system's restrictions and acceptance criteria
• restrictions.md: Restrictions we have in real world in the -dashed list format.
• acceptance_criteria.md: Acceptance criteria for the solution in the -dashed list format. The most important part, determines how good the system should be.
• security_approach.md: Security requirements and constraints for the system.

Example:

• problem_description.md We have wing type UAV (airplane). It should fly autonomously to predetermined GPS destination. During the flight it is relying on the signal form GPS module.
  But when adversary jam or spoof GPS, then UAV either don't know where to fly, or fly to the wrong direction. So, we need to achieve that UAV can fly correctly to the destination without GPS or when GPS is spoofed. We can use the camera pointing downward and other sensor data like altitude, available form the flight controller. Airplane is running Ardupliot.
• input_data
  • orthophoto images from the UAV for the analysis
  • list of expected GPS for the centers for each picture in csv format: picture name, lat, lon
  • video from the UAV for the analysis
  • list of expected GPS for the centers of video per timeframe in csv format: timestamp, lat, lon for each 1-2 seconds
  • ...
• restrictions.md
  • We're limiting our solution to airplane type UAVs.
  • Additional weight it could take is under 1 kg.
  • The whole system should cost under $2000.
  • The flying range is restricted by eastern and southern part of Ukraine. And so on.
• acceptance_criteria.md
  • UAV should fly without GPS for at least 30 km in the sunshine weather.
  • UAV should fly with maximum mistake no more than 40 meters from the real GPS
  • UAV should fly correctly with little foggy weather with maximum mistake no more than 100 meters from the real GPS
  • UAV should fly for minimum of 500 meters with missing internal Satellite maps and the drifting error should be no more than 50 meters.
• security_approach.md
  • System runs on embedded platform (Jetson Orin Nano) with secure boot
  • Communication with ground station encrypted via AES-256
  • No remote API access during flight - fully autonomous
  • Firmware signing required for updates

1.05 🧑‍💻 Developers: Git Init

Initialize Repository

git init
git add .
git commit -m "Initial: problem statement and input data"

Branching Strategy

• main: Documentation and stable releases
• stage: Planning phase artifacts
• dev: Implementation code

After research phase completion, all docs stay on main. Before planning phase, create stage branch. Before implementation phase, create dev branch from stage. After integration tests pass, merge dev → stage → main.

1.10 ✨AI Research: Restrictions and Acceptance Criteria assessment

Execute /1.research/1.10_research_assesment_acceptance_criteria

In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context: - problem_description.md - restrictions.md - acceptance_criteria.md - security_approach.md - Samples of the input data

Revise

• Revise the result, discuss it
• Overwrite acceptance_criteria.md and restrictions.md

Commit

git add _docs/00_problem/
git commit -m "Research: acceptance criteria and restrictions assessed"

1.20 🤖✨AI Research: Research the problem in great detail

Execute /1.research/1.20_research_problem

In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context: - problem_description.md - restrictions.md - acceptance_criteria.md - security_approach.md - Samples of the input data

Revise

• Revise the result from AI.
• Research the problem as well
• Add/modify/remove some solution details in the draft. (Also with AI)
• Store it to the _docs/01_solution/solution_draft.md

1.30 🤖✨AI Research: Solution draft assessment

Execute /1.research/1.30_solution_draft_assessment

In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context: - problem_description.md - restrictions.md - acceptance_criteria.md - security_approach.md - Samples of the input data

Revise

• Research by yourself as well - how to solve additional problems which AI figured out, and add them to the result.

Iterate

• Rename previous solution_draft.md to {xx}_solution_draft.md. Start {xx} from 01
• Store the new revised result draft to the _docs/01_solution/solution_draft.md
• Repeat the process 1.30 from the beginning

When the next solution wouldn't differ much from the previous one, or become actually worse, store the last draft as _docs/01_solution/solution.md

1.35 🤖📋AI plan: Tech Stack Selection

Execute /1.research/1.35_tech_stack_selection

Revise

• Review technology choices against requirements
• Consider team expertise and learning curve
• Document trade-offs and alternatives considered

Store

• Save output to _docs/01_solution/tech_stack.md

1.40 🤖✨AI Research: Security Research

Execute /1.research/1.40_security_research

Revise

• Review security approach against solution architecture
• Update security_approach.md with specific requirements per component

Quality Gate: Research Complete

Review @_docs/00_templates/quality_gates.md - Gate 1

Commit

git add _docs/
git commit -m "Research: solution and security finalized"

2. Planning phase

Note

: If implementation reveals architectural issues, return to Planning phase to revise components.

2.05 🧑‍💻 Developers: Create stage branch

git checkout -b stage

2.10 🤖📋AI plan: Generate components

Execute /2.planning/2.10_plan_components

Revise

  - Revise the plan, answer questions, put detailed descriptions
  - Make sure stored components are coherent and make sense

Store plan

  - Save plan to `_docs/02_components/00_decomposition_plan.md`

2.15 🤖📋AI plan: Components assessment

Execute /2.planning/2.15_plan_asses_components

Revise

  - Clarify the proposals and ask to fix found issues

2.17 🤖📋AI plan: Security Check

Execute /2.planning/2.17_plan_security_check

Revise

  - Review security considerations for each component
  - Ensure security requirements from 1.40 are addressed

2.20 🤖AI agent: Generate Jira Epics

Jira MCP

Add Jira MCP to the list in IDE:

"Jira-MCP-Server": {
   "url": "https://mcp.atlassian.com/v1/sse"
}

Execute /2.planning/2.20_plan_jira_epics

Revise

  - Revise the epics, answer questions, put detailed descriptions
  - Make sure epics are coherent and make sense

2.22 🤖📋AI plan: Data Model Design

Execute /2.planning/2.22_plan_data_model

Revise

  - Review entity relationships
  - Verify data access patterns
  - Check migration strategy

Store

  - Save output to `_docs/02_components/data_model.md`

2.25 🤖📋AI plan: API Contracts Design

Execute /2.planning/2.25_plan_api_contracts

Revise

  - Review interface definitions
  - Verify error handling standards
  - Check versioning strategy

Store

  - Save output to `_docs/02_components/api_contracts.md`
  - Save OpenAPI spec to `_docs/02_components/openapi.yaml` (if applicable)

2.30 🤖📋AI plan: Generate tests

Execute /2.planning/2.30_plan_tests

Revise

  - Revise the tests, answer questions, put detailed descriptions
  - Make sure stored tests are coherent and make sense

2.35 🤖📋AI plan: Risk Assessment

Execute /2.planning/2.37_plan_risk_assessment

Revise

  - Review identified risks
  - Verify mitigation strategies
  - Set up risk monitoring

Store

  - Save output to `_docs/02_components/risk_assessment.md`

2.40 🤖📋AI plan: Component Decomposition To Features

Execute

For each component in _docs/02_components run /2.planning/2.40_plan_features_decompose --component @xx__spec_[component_name].md

Revise

  - Revise the features, answer questions, put detailed descriptions
  - Make sure features are coherent and make sense

Quality Gate: Planning Complete

Review @_docs/00_templates/quality_gates.md - Gate 2

Commit

git add _docs/
git commit -m "Planning: components, tests, and features defined"

3. Implementation phase

3.05 🤖📋AI plan: Initial structure

Create dev branch

git checkout -b dev

Context7 MCP

Add context7 MCP to the list in IDE:

"context7": {
   "command": "npx",
   "args": [
     "-y",
     "@upstash/context7-mcp"
   ]
 }

Execute: /3.implementation/3.05_implement_initial_structure

This will create:

• Project structure with CI/CD pipeline
• Environment configurations (see @_docs/00_templates/environment_strategy.md)
• Database migrations setup
• Test infrastructure

Review Plan

  - Analyze the proposals, answer questions
  - Improve plan as much as possible so it would be clear what exactly to do

Save Plan

• when plan is final and ready, save it as _docs/02_components/structure_plan.md

Execute Plan

  - Press build and let AI generate the structure

Revise Code

- Read the code and check that everything is ok

3.10 🤖📋AI plan: Feature implementation

Execute

For each component in _docs/02_components run /3.implementation/3.10_implement_component @component_folder

Revise Plan

  - Analyze the proposed development plan in a great detail, provide all necessary information
  - Possibly reorganize plan if needed, think and add more input constraints if needed
  - Improve plan as much as possible so it would be clear what exactly to do

Save Plan

• when plan is final and ready, save it as [##]._plan_[component_name] to component's folder

Execute Plan

  - Press build and let AI generate the code

Revise Code

  - Read the code and check that everything is ok

3.20 🤖📋AI plan: Code Review

Execute /3.implementation/3.20_implement_code_review

Can also use Cursor's built-in review feature.

Revise

  - Address all found issues
  - Ensure code quality standards are met

3.30 🤖📋AI plan: CI/CD Validation

Execute /3.implementation/3.30_implement_cicd

Revise

• Review pipeline configuration
• Verify all quality gates are enforced
• Ensure all stages are properly configured

3.35 🤖📋AI plan: Deployment Strategy

Execute /3.implementation/3.35_plan_deployment

Revise

• Review deployment procedures per environment
• Verify rollback procedures documented
• Ensure health checks configured

Store

• Save output to _docs/02_components/deployment_strategy.md

3.40 🤖📋AI plan: Integration tests and solution checks

Execute /3.implementation/3.40_implement_tests

Revise

• Revise the plan, answer questions, put detailed descriptions
• Make sure tests are coherent and make sense

3.42 🤖📋AI plan: Observability Setup

Execute /3.implementation/3.42_plan_observability

Revise

• Review logging strategy
• Verify metrics and alerting
• Check dashboard configuration

Store

• Save output to _docs/02_components/observability_plan.md

3.45 🧑‍💻 Developer: Final Quality Gate

Quality Gate: Implementation Complete

Review @_docs/00_templates/quality_gates.md - Gate 3

Checklist

• All components implemented
• Code coverage >= 75%
• All tests pass
• Code review approved
• CI/CD pipeline green
• Deployment tested on staging
• Observability configured

Merge after tests pass

git checkout stage
git merge dev
git checkout main
git merge stage
git push origin main

3.50 🧑‍💻 Developer: Post-Implementation

Documentation

• Update README with final setup instructions
• Create/update runbooks using @_docs/00_templates/incident_playbook.md
• Document rollback procedures using @_docs/00_templates/rollback_strategy.md

Handoff

• Stakeholders notified of completion
• Operations team briefed on monitoring
• Support documentation complete