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Monday, June 16, 2025

Case Study: How Walmart is Leading the AI Transformation in Retail

June 16, 2025

As one of the world's largest retailers, Walmart is advancing the adoption of artificial intelligence (AI) and generative AI (GenAI) at an unprecedented pace, aiming to revolutionize every facet of its operations—from customer experience to supply chain management and employee services. This retail titan is not only optimizing store operations for efficiency but is also rapidly emerging as a “technology-powered retailer,” setting new benchmarks for the commercial application of AI.

From Traditional Retail to AI-Driven Transformation

Walmart’s AI journey begins with a fundamental redefinition of the customer experience. In the past, shoppers had to locate products in sprawling stores, queue at checkout counters, and navigate after-sales service independently. Today, with the help of the AI assistant Sparky, customers can interact using voice, images, or text to receive personalized recommendations, price comparisons, and review summaries—and even reorder items with a single click.

Behind the scenes, store associates use the Ask Sam voice assistant to quickly locate products, check stock levels, and retrieve promotion details—drastically reducing reliance on manual searches and personal experience. Walmart reports that this tool has significantly enhanced frontline productivity and accelerated onboarding for new employees.

AI Embedded Across the Enterprise

Beyond customer-facing applications, Walmart is deeply embedding AI across internal operations. The intelligent assistant Wally, designed for merchandisers and purchasing teams, automates sales analysis and inventory forecasting, empowering more scientific replenishment and pricing decisions.

In supply chain management, AI is used to optimize delivery routes, predict overstock risks, reduce food waste, and even enable drone-based logistics. According to Walmart, more than 150,000 drone deliveries have already been completed across various cities, significantly enhancing last-mile delivery capabilities.

Key Implementations

Name	Type	Function Overview
Sparky	Customer Assistant	GenAI-powered recommendations, repurchase alerts, review summarization, multimodal input
Wally	Merchant Assistant	Product analytics, inventory forecasting, category management
Ask Sam	Employee Assistant	Voice-based product search, price checks, in-store navigation
GenAI Search	Customer Tool	Semantic search and review summarization for improved conversion
AI Chatbot	Customer Support	Handles standardized issues such as order tracking and returns
AI Interview Coach	HR Tool	Enhances fairness and efficiency in recruitment
Loss Prevention System	Security Tech	RFID and AI-enabled camera surveillance for anomaly detection
Drone Delivery System	Logistics Innovation	Over 150,000 deliveries completed; expansion ongoing

From Models to Real-World Applications: Walmart’s AI Strategy

Walmart’s AI strategy is anchored by four core pillars:

Domain-Specific Large Language Models (LLMs): Walmart has developed its own retail-specific LLM, Wallaby, to enhance product understanding and user behavior prediction.
Agentic AI Architecture: Autonomous agents automate tasks such as customer inquiries, order tracking, and inventory validation.
Global Scalability: From inception, Walmart's AI capabilities are designed for global deployment, enabling “train once, deploy everywhere.”
Data-Driven Personalization: Leveraging behavioral and transactional data from hundreds of millions of users, Walmart delivers deeply personalized services at scale.

Challenges and Ethical Considerations

Despite notable success, Walmart faces critical challenges in its AI rollout:

Data Accuracy and Bias Mitigation: Preventing algorithmic bias and distorted predictions, especially in sensitive areas like recruitment and pricing.
User Adoption: Encouraging customers and employees to trust and embrace AI as a routine decision-making tool.
Risks of Over-Automation: While Agentic AI boosts efficiency, excessive automation risks diminishing human oversight, necessitating clear human-AI collaboration boundaries.
Emerging Competitive Threats: AI shopping assistants like OpenAI’s “Operator” could bypass traditional retail channels, altering customer purchase pathways.

The Future: Entering the Era of AI Collaboration

Looking ahead, Walmart plans to launch personalized AI shopping agents that can be trained by users to understand their preferences and automate replenishment orders. Simultaneously, the company is exploring agent-to-agent retail protocols, enabling machine-to-machine negotiation and transaction execution. This form of interaction could fundamentally reshape supply chains and marketing strategies.

Marketing is also evolving—from traditional visual merchandising to data-driven, precision exposure strategies. The future of retail may no longer rely on the allure of in-store lighting and advertising, but on the AI-powered recommendation chains displayed on customers’ screens.

Walmart’s AI transformation exhibits three critical characteristics that serve as reference for other industries:

End-to-End Integration of AI (Front-to-Back AI)
Deep Fine-Tuning of Foundation Models with Retail-Specific Knowledge
Proactive Shaping of an AI-Native Retail Ecosystem

This case study provides a tangible, systematic reference for enterprises in retail, manufacturing, logistics, and beyond, offering practical insights into deploying GenAI, constructing intelligent agents, and undertaking organizational transformation.

Walmart also plans to roll out assistants like Sparky to Canada and Mexico, testing the cross-regional adaptability of its AI capabilities in preparation for global expansion.

While enterprise GenAI applications represent a forward-looking investment, 92% of effective use cases still emerge from ground-level operations. This underscores the need for flexible strategies that align top-down design with bottom-up innovation. Notably, the case lacks a detailed discussion on data governance frameworks, which may impact implementation fidelity. A dynamic assessment mechanism is recommended, aligning technological maturity with organizational readiness through a structured matrix—ensuring a clear and measurable path to value realization.

How to Identify and Scale AI Use Cases: A Three-Step Strategy and Best Practices Guide

May 01, 2025

The "Identifying and Scaling AI Use Cases" report by OpenAI outlines a three-step strategy for identifying and scaling AI applications, providing best practices and operational guidelines to help businesses efficiently apply AI in diverse scenarios.

I. Identifying AI Use Cases

Identifying Key Areas: The first step is to identify AI opportunities in the day-to-day operations of the company, particularly focusing on tasks that are efficient, low-value, and highly repetitive. AI can help automate processes, optimize data analysis, and accelerate decision-making, thereby freeing up employees' time to focus on more strategic tasks.
Concept of AI as a Super Assistant: AI can act as a super assistant, supporting all work tasks, particularly in areas such as low-value repetitive tasks, skill bottlenecks, and navigating uncertainty. For example, AI can automatically generate reports, analyze data trends, assist with code writing, and more.

II. Scaling AI Use Cases

Six Core Use Cases: Businesses can apply the following six core use cases based on the needs of different departments:
- Content Creation: Automating the generation of copy, reports, product manuals, etc.
- Research: Using AI for market research, competitor analysis, and other research tasks.
- Coding: Assisting developers with code generation, debugging, and more.
- Data Analysis: Automating the processing and analysis of multi-source data.
- Ideation and Strategy: Providing creative support and generating strategic plans.
- Automation: Simplifying and optimizing repetitive tasks within business processes.
Internal Promotion: Encourage employees across departments to identify AI use cases through regular activities such as hackathons, workshops, and peer learning sessions. By starting with small-scale pilot projects, organizations can accumulate experience and gradually scale up AI applications.

III. Prioritizing Use Cases

Impact/Effort Matrix: By evaluating each AI use case in terms of its impact and effort, prioritize those with high impact and low effort. These are often the best starting points for quickly delivering results and driving larger-scale AI application adoption.
Resource Allocation and Leadership Support: High-value, high-effort use cases require more time, resources, and support from top management. Starting with small projects and gradually expanding their scale will allow businesses to enhance their overall AI implementation more effectively.

IV. Implementation Steps

Understanding AI’s Value: The first step is to identify which business areas can benefit most from AI, such as automating repetitive tasks or enhancing data analysis capabilities.
Employee Training and Framework Development: Provide training to employees to help them understand and master the six core use cases. Practical examples can be used to help employees better identify AI's potential.
Prioritizing Projects: Use the impact/effort matrix to prioritize all AI use cases. Start with high-benefit, low-cost projects and gradually expand to other areas.

Summary

When implementing AI use case identification and scaling, businesses should focus on foundational tasks, identifying high-impact use cases, and promoting full employee participation through training, workshops, and other activities. Start with low-effort, high-benefit use cases for pilot projects, and gradually build on experience and data to expand AI applications across the organization. Leadership support and effective resource allocation are also crucial for the successful adoption of AI.

Leveraging o1 Pro Mode for Strategic Market Entry: A Stepwise Deep Reasoning Framework for Complex Business Decisions

April 29, 2025

Below is a comprehensive, practice-oriented guide for using the o1 Pro Mode to construct a stepwise market strategy through deep reasoning, especially suitable for complex business decision-making. It integrates best practices, operational guidelines, and a simulated case to demonstrate effective use, while also accounting for imperfections in ASR and spoken inputs.

Context & Strategic Value of o1 Pro Mode

In high-stakes business scenarios characterized by multi-variable complexity, long reasoning chains, and high uncertainty, conventional AI often falls short due to its preference for speed over depth. The o1 Pro Mode is purpose-built for these conditions. It excels in:

Deep logical reasoning (Chain-of-Thought)
Multistep planning
Structured strategic decomposition

Use cases include:

Market entry feasibility studies
Product roadmap & portfolio optimization
Competitive intelligence
Cross-functional strategy synthesis (marketing, operations, legal, etc.)

Unlike fast-response models (e.g., GPT-4.0, 4.5), o1 Pro emphasizes rigorous reasoning over quick intuition, enabling it to function more like a “strategic analyst” than a conversational bot.

Step-by-Step Operational Guide

Step 1: Input Structuring to Avoid ASR and Spoken Language Pitfalls

Goal: Transform raw or spoken-language queries (which may be ambiguous or disjointed) into clearly structured, interrelated analytical questions.

Recommended approach:

Define a primary strategic objective
e.g., “Assess the feasibility of entering the Japanese athletic footwear market.”
Decompose into sub-questions:
- Market size, CAGR, segmentation
- Consumer behavior and cultural factors
- Competitive landscape and pricing benchmarks
- Local legal & regulatory challenges
- Go-to-market and branding strategy

✅ Best Practice: Number each question and provide context-rich framing. For example:
"1. Market Size: What is the total addressable market for athletic shoes in Japan over the next 5 years?"

Step 2: Triggering Chain-of-Thought Reasoning in o1 Pro

o1 Pro Mode processes tasks in logical stages, such as:

Identifying problem variables
Cross-referencing knowledge domains
Sequentially generating intermediate insights
Synthesizing a coherent strategic output

Prompting Tips:

Explicitly request “step-by-step reasoning” or “display your thought chain.”
Ask for outputs using business frameworks, such as:
- SWOT Analysis
- Porter’s Five Forces
- PESTEL
- Ansoff Matrix
- Customer Journey Mapping

Step 3: First Draft Strategy Generation & Human Feedback Loop

After o1 Pro generates the initial strategy, implement a structured verification process:

Dimension	Validation Focus	Prompt Example
Logical Consistency	Are insights connected and arguments sound?	“Review consistency between conclusions.”
Data Reasonability	Are claims backed by evidence or logical inference?	“List data sources or assumptions used.”
Local Relevance	Does it reflect cultural and behavioral nuances?	“Consider localization and cultural factors.”
Strategic Coherence	Does the plan span market entry, growth, risks?	“Generate a GTM roadmap by stage.”

Step 4: Action Plan Decomposition & Operationalization

Goal: Convert insights into a realistic, trackable implementation roadmap.

Recommended Outputs:

Execution timeline: 0–3 months, 3–6 months, 6–12 months
RACI matrix: Assign roles and responsibilities
KPI dashboard: Track strategic progress and validate assumptions

Prompts:

“Convert the strategy into a 6-month execution plan with milestones.”
“Create a KPI framework to measure strategy effectiveness.”
“List resources needed and risk mitigation strategies.”

✅ Deliverables may include: Gantt charts, OKR tables, implementation matrices.

Example: Sneaker Company Entering Japan

Scenario: A mid-sized sneaker brand is evaluating expansion into Japan.

Phase	Activity
1	Input 12 structured questions into o1 Pro (market, competitors, culture, etc.)
2	Model takes 3 minutes to produce a stepwise reasoning path & structured report
3	Outputs include market sizing, consumer segments, regulatory insights
4	Strategy synthesized into SWOT, Five Forces, and GTM roadmap
5	Output refined with human expert feedback and used for board review

Error Prevention & Optimization Strategies

Common Pitfall	Remediation Strategy
ASR/Spoken language flaws	Manually refine transcribed input into structured form
Contextual disconnection	Reiterate background context in prompt
Over-simplified answers	Require explicit reasoning chain and framework output
Outdated data	Request public data references or citation of assumptions
Execution gap	Ask for KPI tracking, resource list, and risk controls

Conclusion: Strategic Value of o1 Pro

o1 Pro Mode is not just a smarter assistant—it is a scalable strategic reasoning tool. It reduces the time, complexity, and manpower traditionally required for high-quality business strategy development. By turning ambiguous spoken questions into structured, multistep insights and executable action plans, o1 Pro empowers individuals and small teams to operate at strategic consulting levels.

For full-scale deployment, organizations can template this workflow for verticals such as:

Consumer goods internationalization
Fintech regulatory strategy
ESG and compliance market planning
Tech product market fit and roadmap design

Let me know if you’d like a custom prompt set or reusable template for your team.

AI Coding Task Management: Best Practices and Operational Guide

April 20, 2025

The Challenge: Why AI Coding Agents Struggle with Complexity

AI coding assistants like Cursor, Github Copilot, and others are powerful tools, but they often encounter difficulties when tasked with implementing more than trivial changes or building complex features. As highlighted in the share, common issues include:

Project Corruption: Making a small change request that inadvertently modifies unrelated parts of the codebase.

Dependency Blindness: Implementing code that fails because the AI wasn't aware of necessary dependencies or the existing project structure, leading to numerous errors.

Context Limitations: AI models have finite context windows. For large projects or complex tasks, they may "forget" earlier parts of the plan or codebase details, leading to inconsistencies.

These problems stem from the AI's challenge in maintaining a holistic understanding of a large project's architecture, dependencies, and the sequential nature of development tasks.

The Solution: Implementing Task Management Systems

A highly effective technique to mitigate these issues and significantly improve the success rate of AI coding agents is to introduce a Task Management System.

Core Concept: Instead of giving the AI a large, complex prompt (e.g., "Build feature X"), you first break down the requirement into a series of smaller, well-defined, sequential tasks. The AI is then guided to execute these tasks one by one, maintaining awareness of the overall plan and completed steps.

Benefits:

Improved Context Control: Each smaller task requires less context, making it easier for the AI to focus and perform accurately.
Better Dependency Handling: Breaking down tasks allows for explicit consideration of the order of implementation, ensuring prerequisites are met.
Clear Progress Tracking: A task list provides visibility into what's done and what's next.
Reduced Errors: By tackling complexity incrementally, the likelihood of major errors decreases significantly.
Enhanced Collaboration: A structured task list makes it easier for humans to review, refine, and guide the AI's work.

Implementation Strategies and Tools

Several methods exist for implementing task management in your AI coding workflow, ranging from simple manual approaches to sophisticated integrated tools.

Basic Method: Native Cursor + `task.md`

This is the simplest approach, using Cursor's built-in features:

Create a task.md file: In the root of your project, create a Markdown file named task.md. This file will serve as your task list.
Establish a Cursor Rule: Create a Cursor rule (e.g., in a .cursor/rules.md file or via the interface) instructing Cursor to always refer to task.md to understand the project plan, track completed tasks, and identify the next task.
- Example Rule Content: "Always consult task.md before starting work. Update task.md by marking tasks as completed [DONE] when finished. Use the task list to understand the overall implementation plan and identify the next task."
Initial Task Breakdown: Give Cursor your high-level requirement or Product Requirements Document (PRD) and ask it to break it down into smaller, actionable tasks, adding them to task.md.
- Example Prompt: "I want to build a multiplayer online drawing game based on this PRD: [link or paste PRD]. Break down the core MVP features into small, sequential implementation tasks and list them in task.md. Use checkboxes for each task."
Execution: Instruct Cursor to start working on the tasks listed in task.md. As it completes each one, it should update the task.md file (e.g., checking off the box or adding a [DONE] marker).

This basic method already provides significant improvements by giving the AI a persistent "memory" of the plan.

Advanced Tool: Rift (formerly RuCode) + Boomerang Task

Rift is presented as an open-source alternative to Cursor that integrates into VS Code. It requires your own API keys (e.g., Anthropic). Rift introduces a more structured approach with its Boomerang Task feature and specialized agent modes.

Agent Modes: Rift allows defining different "modes" or specialized agents (e.g., Architect Agent for planning, Coder Agent for implementation, Debug Agent). You can customize or create modes like the "Boomerang" mode focused on planning and task breakdown.
Planning Phase: Initiate the process by asking the specialized planning agent (e.g., Architect mode or Boomerang mode) to build the application.
- Example Prompt (in Boomerang/Architect mode): "Help me build a to-do app."
Interactive Planning: The planning agent will often interactively confirm requirements, then generate a detailed plan including user stories, key features, component breakdowns, project structure, state management strategy, etc., explicitly considering dependencies.
Task Execution: Once the plan is approved and broken down into tasks, Rift can switch to the appropriate coding agent mode. The coding agent executes the tasks sequentially based on the generated plan.
Automated Testing (Mentioned): The transcript mentions Rift having capabilities where the agent can run the application and potentially perform automated testing, providing faster feedback loops (though details weren't fully elaborated).

Rift's strength lies in its structured delegation to specialized agents and its comprehensive planning phase.

Advanced Tool: Claude Taskmaster AI (Cursor/Wingsurfer Integration)

Taskmaster AI is described as a command-line package specifically designed to bring sophisticated task management into Cursor (and potentially Wingsurfer). It leverages powerful models like Claude 3 Opus (via Anthropic API) for planning and Perplexity for research.

Workflow:

Installation: Install the package globally via npm:
```
npm install -g taskmaster-ai
```
Project Setup:
- Navigate to your project directory in the terminal.
- It's recommended to set up your base project first (e.g., using create-next-app).
- Initialize Taskmaster within the project:
```
taskmaster init
```
- Follow the prompts (project name, description, etc.). This creates configuration files, including Cursor rules and potentially a .env.example file.
Configuration:
- Locate the .env.example file created by taskmaster init. Rename it to .env.
- Add your API keys:
  - ENTROPIC_API_KEY: Essential for task breakdown using Claude models.
  - PERPLEXITY_API_KEY: Used for researching tasks, especially those involving new technologies or libraries, to fetch relevant documentation.
Cursor Rules Setup: taskmaster init automatically adds Cursor rules:
- Rule Generation Rule: Teaches Cursor how to create new rules based on errors encountered (self-improvement).
- Self-Improve Rule: Encourages Cursor to proactively reflect on mistakes.
- Step Workflow Rule: Informs Cursor about the Taskmaster commands (taskmaster next, taskmaster list, etc.) needed to interact with the task backlog.
PRD (Product Requirements Document) Generation:
- Create a detailed PRD for your project. You can:
  - Write it manually.
  - Use tools like the mentioned "10x CoderDev" (if available).
  - Chat with Cursor/another AI to flesh out requirements and generate the PRD text file (e.g., scripts/prd.txt).
- Example Prompt for PRD Generation (to Cursor): "Help me build an online game like Skribbl.io, but an LLM guesses the word instead of humans. Users get a word, draw it in 60s. Images sent to GPT-4V for evaluation. Act as an Engineering Manager, define core MVP features, and generate a detailed prd.txt file using scripts/prd.example.txt as a template."
Parse PRD into Tasks: Use Taskmaster to analyze the PRD and break it down:
```
taskmaster parse <path_to_your_prd.txt>
# Example: taskmaster parse scripts/prd.txt
```
This command uses the Anthropic API to create structured task files, typically in a tasks/ directory.
Review and Refine Tasks:
- List Tasks: View the generated tasks and their dependencies:
```
taskmaster list
# Or show subtasks too:
taskmaster list --with-subtasks
```
  Pay attention to the dependencies column, ensuring a logical implementation order.
- Analyze Complexity: Get an AI-driven evaluation of task difficulty:
```
taskmaster analyze complexity
taskmaster complexity report
```
  This uses Claude and Perplexity to score tasks and identify potential bottlenecks.
- Expand Complex Tasks: The complexity report provides prompts to break down high-complexity tasks further. Copy the relevant prompt and feed it back to Taskmaster (or directly to Cursor/Claude):
  - Example (Conceptual): Find the expansion prompt for a complex task (e.g., ID 3) in the report, then potentially use a command or prompt like: "Expand task 3 based on this prompt: [paste prompt here]". The transcript showed copying the prompt and feeding it back into the chat. This creates sub-tasks for the complex item. Repeat as needed.
- Update Tasks: Modify existing tasks if requirements change:
```
taskmaster update --id <task_id> --prompt "<your update instructions>"
# Example: taskmaster update --id 4 --prompt "Make sure we use three.js for the canvas rendering"
```
  Taskmaster will attempt to update the relevant task and potentially adjust dependencies.
Execute Tasks with Cursor:
- Instruct Cursor to start working, specifically telling it to use the Taskmaster workflow:
  - Example Prompt: "Let's start implementing the app based on the tasks created using Taskmaster. Check the next most important task first using the appropriate Taskmaster command and begin implementation."
- Cursor should now use commands like taskmaster next (or similar, based on the rules) to find the next task, implement it, and mark it as done or in progress within the Taskmaster system.
- Error Handling & Self-Correction: If Cursor makes mistakes, prompt it to analyze the error and create a new Cursor rule to prevent recurrence, leveraging the self-improvement rules set up by Taskmaster.
  - Example Prompt: "You encountered an error [describe error]. Refactor the code to fix it and then create a new Cursor rule to ensure you don't make this mistake with Next.js App Router again."

The Drawing Game Example: The transcript demonstrated building a complex multiplayer drawing game using the Taskmaster workflow. The AI, guided by Taskmaster, successfully:

Set up the project structure.
Implemented frontend components (lobby, game room, canvas).
Handled real-time multiplayer aspects (likely using WebSockets, though not explicitly detailed).
Integrated with an external AI (GPT-4V) for image evaluation.
This was achieved largely autonomously in about 20-35 minutes after the initial setup and task breakdown, showcasing the power of this approach.

Key Takeaways and Best Practices

Break It Down: Always decompose complex requests into smaller, manageable tasks before asking the AI to code.
Use a System: Whether it's a simple task.md or a tool like Taskmaster/Rift, have a persistent system for tracking tasks, dependencies, and progress.
Leverage Specialized Tools: Tools like Taskmaster offer significant advantages through automated dependency mapping, complexity analysis, and research integration.
Guide the AI: Use specific prompts to direct the AI to follow the task management workflow (e.g., "Use Taskmaster to find the next task").
Embrace Self-Correction: Utilize features like Cursor rules (especially when integrated with Taskmaster) to help the AI learn from its mistakes.
Iterate and Refine: Review the AI-generated task list and complexity analysis. Expand complex tasks proactively before implementation begins.
Configure Correctly: Ensure API keys are correctly set up for tools like Taskmaster.

Conclusion

Task management systems dramatically improve the reliability and capability of AI coding agents when dealing with non-trivial projects. By providing structure, controlling context, and managing dependencies, these workflows transform AI from a sometimes-unreliable assistant into a more powerful co-developer. While the basic task.md method offers immediate benefits, tools like Rift's Boomerang Task and especially Claude Taskmaster AI represent the next level of sophistication, enabling AI agents to tackle significantly more complex projects with a higher degree of success. As these tools continue to evolve, they promise even greater productivity gains in AI-assisted software development. Experiment with these techniques to find the workflow that best suits your needs.

Get GenAI guide

Monday, June 16, 2025

From Traditional Retail to AI-Driven Transformation

AI Embedded Across the Enterprise

Key Implementations

From Models to Real-World Applications: Walmart’s AI Strategy

Challenges and Ethical Considerations

The Future: Entering the Era of AI Collaboration

Related Topic

Thursday, May 1, 2025

I. Identifying AI Use Cases

II. Scaling AI Use Cases

III. Prioritizing Use Cases

IV. Implementation Steps

Summary

Related topic:

Tuesday, April 29, 2025

Context & Strategic Value of o1 Pro Mode

Step-by-Step Operational Guide

Step 1: Input Structuring to Avoid ASR and Spoken Language Pitfalls

Step 2: Triggering Chain-of-Thought Reasoning in o1 Pro

Step 3: First Draft Strategy Generation & Human Feedback Loop

Step 4: Action Plan Decomposition & Operationalization

Example: Sneaker Company Entering Japan

Error Prevention & Optimization Strategies

Conclusion: Strategic Value of o1 Pro

Related Topic

Sunday, April 20, 2025

The Challenge: Why AI Coding Agents Struggle with Complexity

The Solution: Implementing Task Management Systems

Benefits:

Implementation Strategies and Tools

Basic Method: Native Cursor + task.md

Advanced Tool: Rift (formerly RuCode) + Boomerang Task

Advanced Tool: Claude Taskmaster AI (Cursor/Wingsurfer Integration)

Key Takeaways and Best Practices

Conclusion

Related Topic

Views

Product

Labels

Basic Method: Native Cursor + `task.md`