DEV Community: GOROman

The HAH Model: A New Paradigm of Human-AI Collaboration

GOROman — Tue, 08 Apr 2025 22:16:47 +0000

Abstract

This paper explores the HAH (Human→AI→Human) model, a novel paradigm in which AI systems delegate tasks to humans, inverting the traditional human-AI relationship. Using the ViXion01S-Firmware project as a case study, we examine the implementation, benefits, and implications of this emerging collaborative framework.

1. Introduction

As digital transformation accelerates, the relationship between humans and artificial intelligence continues to evolve. Traditional Human-AI Interaction (HAI) has primarily followed a unidirectional flow where humans instruct AI systems to perform tasks. However, with advancements in AI capabilities, a new model has emerged—the HAH model—where AI systems analyze complex situations, identify tasks requiring human intervention, and delegate these tasks to humans.

2. Definition of the HAH Model

The HAH model consists of the following stages:

Human→AI: Initial human instruction to the AI system
AI (Processing & Decision): AI analyzes the work and generates task requests
AI→Human: AI delegates specific tasks to humans
Human (Execution): Humans perform the requested tasks
Human→AI: Humans report results back to the AI

3. Parallels to Industrial Revolution

The HAH model bears similarities to the relationship between the bourgeoisie and proletariat during the Industrial Revolution. However, a critical distinction remains: humans still design and control AI systems, maintaining ultimate decision-making authority.

4. Implementation Cases

4.1 ViXion01S-Firmware Project

The ViXion01S-Firmware project implemented the HAH model using GitHub Actions:

AI modified firmware and requested functionality verification from a human expert (Moriya-san)
After a specified time period, AI automatically sent reminders if no human response was received
Based on human feedback, AI determined subsequent actions

name: Moriya-san Reminder
on:
  workflow_dispatch:
    inputs:
      pr_number:
        description: 'Pull Request Number'
        required: true
      wait_time:
        description: 'Reminder Wait Time (minutes)'
        required: true
        default: 60

4.2 Gig Economy Platforms as HAH Model Examples

Several gig economy platforms demonstrate HAH model principles through algorithmic task assignment:

4.2.1 Uber Eats

Uber Eats utilizes sophisticated algorithms to assign delivery tasks to human drivers:

AI analyzes order locations, driver positions, and estimated delivery times
System assigns tasks to optimal human drivers based on multiple variables
Humans execute the delivery tasks and report completion through the app
AI evaluates performance and adjusts future task assignments accordingly

4.2.2 Mercari Hello (Japan)

Launched in March 2024, Mercari Hello has rapidly grown to enroll over 8 million gig workers:

AI matches short-term work opportunities with qualified workers
No resumes or interviews required, enabling instant job matching
Human workers complete assigned tasks and receive immediate payment
System learns from task completion patterns to improve future assignments

4.2.3 Timee (Japan)

With over 1.2 million users, Timee has positioned itself as a crucial player in Japan's evolving labor market:

Platform allows users to work shifts as short as one hour at restaurants, convenience stores, and hotels
AI-driven matching system connects businesses with available workers
Workers execute tasks and receive quick payment
System continuously optimizes task allocation based on performance data

5. Gig Economy Market Size and HAH Transformation Potential

5.1 Current Market Size

The global gig economy represents a significant and rapidly growing segment of the labor market:

Global market size valued at $556.7 billion in 2024
Projected to reach $1.85 trillion by 2032, growing at a CAGR of 16.2%
Alternative estimates from Staffing Industry Analysts place the market at $3.7 trillion in 2023

Regional markets show varying growth rates:

Japan: $17.8 billion in 2024, growing at a CAGR of 17.7%
Japanese freelance platforms projected to reach $544.5 million by 2030 (CAGR 26.5%)
As of 2021, freelance workers represented approximately 20% of Japan's workforce

5.2 HAH Transformation Potential

The gig economy is particularly well-positioned for HAH model transformation:

Algorithm-Driven Task Assignment
Real-Time Optimization
Predictive Task Creation
Autonomous Quality Control
Market Size Expansion

6. Benefits of the HAH Model

Complementary utilization of human and AI strengths
Automation of repetitive and routine tasks
Strategic deployment of human expertise only when necessary
Improved workflow efficiency through notifications and reminders
Increased market efficiency through optimal task-worker matching
Enhanced service quality through continuous feedback loops

7. Challenges and Future Directions

Balancing human autonomy with AI directives
Privacy and ethical considerations
Building human trust in AI judgment
Expanding application to more complex tasks
Addressing potential economic displacement
Developing appropriate regulatory frameworks

8. Addressing Potential Luddite-like Resistance

8.1 Transparency and Accountability

Make AI decision-making processes transparent
Clearly establish that humans retain final decision-making authority
Explicitly communicate system purposes and limitations

8.2 Comprehensive Education and Reskilling

Develop training programs for adapting to new collaborative models
Strengthen digital literacy education
Develop new skill sets for coexisting with AI

8.3 Gradual Implementation and Participatory Design

Begin with small-scale pilot projects
Involve end-users in the design process
Establish feedback loops and continuous improvement

8.4 Social Safety Nets

Create support systems during technological transitions
Generate new employment opportunities
Establish fair benefit distribution mechanisms

8.5 Ethical Guidelines and Regulation

Establish ethical frameworks for HAH/HaaS models
Promote industry standards and self-regulation
Develop appropriate legal frameworks

9. Comparative Analysis with Traditional HAI and HaaS Models

9.1 Traditional Human-AI Interaction (HAI)

Unidirectional (Human → AI)
Humans initiate tasks and monitor AI outcomes
Limitations in feedback and autonomy

9.2 Human-as-a-Service (HaaS)

Refers to human labor accessed via APIs/platforms
Used for microtasks or escalation
More static and reactive compared to HAH

9.3 Summary of Key Differences

Model	Initiator	Task Allocation	Feedback Loop	Learning
HAI	Human	Human to AI	Limited	Mostly human-driven
HaaS	Human/API	AI as dispatcher, human as passive executor	Minimal	Platform-driven
HAH	AI	AI to Human (delegation)	Continuous	Mutual, adaptive

10. Socioeconomic Implications

10.1 Labor Redefinition

From roles to functions
From employment to engagement

10.2 AI as Middle Management

AI acts as coordinator or manager
Introduces AI-mediated labor hierarchy

10.3 Risk of Digital Taylorism

Algorithmic micromanagement of human labor
Risks of alienation, burnout
Necessity of governance and consent

11. Design Principles for HAH-Compliant Systems

Delegation Transparency
Reciprocal Feedback
Consent and Opt-In Models
Skill-Matching Algorithms
Task Decay and Escalation Protocols

12. Future Research Directions

Quantitative metrics
Cross-domain applications
Multi-agent coordination
Cognitive load management
Legal implications of AI-initiated contracts

13. Conclusion

The HAH model represents a paradigm shift in the evolution of human-AI interaction. By repositioning AI from tool to task manager, it opens up new vistas of productivity, especially in environments where human judgment and machine efficiency must coalesce. As demonstrated through real-world systems like ViXion01S-Firmware and major gig economy platforms, the model is not speculative—it is already in operation. Going forward, the challenge lies not only in refining this architecture, but also in ensuring it aligns with ethical, social, and economic norms of a rapidly digitizing world.

References

ViXion01S-Firmware GitHub Repository
Human-AI Collaboration: A Survey of the State of the Art
The Future of Work: Human-AI Symbiosis
Cognitive Market Research. (2024). Global Gig Economy Market Size, Share, Growth Analysis Report 2024-2033.
Staffing Industry Analysts. (2023). Global Gig Economy Reaches $3.7 Trillion.
The World Economic Forum. (2024). What is the Gig Economy and What's the Deal for Gig Workers?
Uber Eats. (2024). How Uber Eats Uses Gen AI to Enhance Food Delivery Operations. Retrieved from LinkedIn.
Mercari. (2024). Mercari Begins Nationwide Expansion of Mercari Hallo, a "Job Matching App". Retrieved from about.mercari.com.
AIM Group. (2025). Mercari Hallo Joins Forces with Careeosu1Day, Hits 10M Users. Retrieved from aimgroup.com.
Smartkarma. (2024). Mercari (4385) | Fintech and Gig Economy as Key Catalysts. Retrieved from smartkarma.com.
Liu, Y. (2022). The Coin of AI Has Two Sides: Matching Enhancement and Algorithmic Control in the Gig Economy. Questrom World.
Arora, P. (2024). The Role of AI in Shaping On-Demand Apps. Retrieved from LinkedIn.
Algorithmic Management in the Gig Economy: A Comparative Study of Platform Labor Systems

Author: GOROman & Devin AI

Date: April 1, 2025

D&D-Style Alignment Chart for X Users

GOROman — Sat, 15 Mar 2025 10:54:55 +0000

Introduction

I recently analyzed an interesting D&D-style personality test that's currently trending on X (formerly Twitter).

You can try it here: magic-x-alignment-chart.vercel.app

What is D&D?

Dungeons & Dragons (D&D) is the original tabletop RPG. When creating characters in D&D, players assign an "alignment" to define their character's personality.

Photo of my old D&D rule books (Japanese edition)

This system represents a character's ethical outlook and behavioral tendencies on two axes: "Lawful-Chaotic" and "Good-Evil."

This web application applies the alignment chart concept to X (Twitter) users, using AI analysis to automatically position users on the grid.

Development Background

This project was inspired by an idea posted by @mdmathewdc:

https://x.com/mdmathewdc/status/1899767815344722325

Later, @rauchg implemented drag-and-drop functionality:

https://x.com/rauchg/status/1899895262023467035

And @f1shy-dev added AI analysis capabilities:

https://x.com/vishyfishy2/status/1899929030620598508

Building on these implementations, the app was enhanced with UI improvements, mobile optimization, and performance tweaks, making it more accessible to a wider audience. It's fascinating to see how the open-source community transformed an idea into a functional product.

App Features

AI-Powered Analysis
- Uses Exa and OpenAI GPT4 to analyze user tweets
- Evaluates tendencies on the Lawful-Chaotic and Good-Evil axes
- Automatically positions users on the grid based on analysis results
Interactive Controls
- Intuitive drag-and-drop positioning
- Lockable AI analysis results
- Random placement option for manual adjustments
Tech Stack
- Next.js (App Router)
- TypeScript
- Framer Motion (animations)
- Vercel AI SDK
- IndexedDB (local storage)
- Upstash Redis (caching)

Technical Implementation Highlights

1. Responsive Design

useEffect(() => {
  const checkMobile = () => {
    setIsMobile(window.innerWidth < 768);
  };

  checkMobile();
  window.addEventListener("resize", checkMobile);
  return () => window.removeEventListener("resize", checkMobile);
}, []);

The app implements dynamic layout adjustments based on screen size to ensure optimal user experience across devices from mobile to desktop. Balancing touch and mouse interactions was particularly challenging, but Framer Motion's capabilities help create a smooth experience.

2. AI-Based Alignment Analysis

export async function analyzeAlignment(username: string) {
  try {
    const tweets = await fetchUserTweets(username);

    // Using Exa for analysis
    const analysis = await exaClient.run(
      `Based on ${username}'s tweets, determine their D&D alignment (lawful-chaotic, good-evil).
       Please reference these tweets:
       ${tweets.join("\n")}`
    );

    return {
      lawChaos: analysis.lawChaosScore, // -5(chaotic) to +5(lawful)
      goodEvil: analysis.goodEvilScore,  // -5(evil) to +5(good)
      confidence: analysis.confidence,
      explanation: analysis.reasoning
    };
  } catch (error) {
    console.error("Alignment analysis error:", error);
    throw new Error("An error occurred while analyzing the user");
  }
}

The app leverages Exa's contextual understanding to analyze users' tweet history, identifying personality traits and behavioral tendencies to calculate optimal positioning on the alignment chart. This allows for comprehensive analysis beyond simple word frequency, including context and sentiment.

Prompt Structure

  const messages = [
      {
        role: "system",
        content: dedent`
        Analyze the following tweets the given from Twitter user and determine their alignment on a D&D-style alignment chart.

        For lawful-chaotic axis:
        - Lawful (-100): Follows rules, traditions, and social norms. They value tradition, loyalty, and order.
        - Neutral (0): Balanced approach to rules and freedom
        - Chaotic (100): Rebels against convention, valuing personal freedom - follows their own moral compass regardless of rules or traditions

        For good-evil axis:
        - Good (-100): Altruistic, compassionate, puts others first
        - Neutral (0): Balanced self-interest and concern for others
        - Evil (100): Selfish, manipulative, or harmful to others. Some are motivated by greed, hatred, or lust for power.

        Based only on these tweets, provide a numerical assessment of this user's alignment. Be willing to move to any side/extreme!

        Since this is a bit of fun, be willing to overly exaggerate if the user has a specific trait expressed barely - e.g. if they are evil at some point then make sure to express it! - I don't just want everyone to end up as chaotic-neutral in the end... However don't always exaggerate a user's chaotic characteristic, you can also try to exaggerate their lawful or good/evil traits if they are more pronounced. Just be fun with it.

        For the explaination, try to avoid overly waffling - but show your reasoning behind your judgement. You can mention specific things about their user like mentioned traits/remarks or projects/etc - the more personalised the better.
      `.trim()
      },
      {
        role: "user",
        content:
          dedent`Username: @${username}

<user_profile>
${profile_str}
</user_profile>

<user_tweets filter="top_100">
${tweetTexts}
</user_tweets>`.trim()
      }
    ] satisfies CoreMessage[]

https://github.com/f1shy-dev/x-alignment-chart/blob/main/src/app/actions/analyze-tweets.ts

3. Data Persistence and Caching Strategy

// Data persistence with IndexedDB
const saveToLocalDB = async (placement: StoredPlacement) => {
  if (!db) return;
  try {
    const tx = db.transaction("placements", "readwrite");
    const store = tx.objectStore("placements");
    await store.put({
      ...placement,
      timestamp: new Date()
    });
    await tx.done;
  } catch (error) {
    console.error("Save error:", error);
  }
};

// API response caching with Upstash Redis
export async function cachedFetchTweets(username: string) {
  const cacheKey = `tweets:${username}`;

  // Check cache
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached as string);

  // Fetch from API
  const tweets = await fetchFromTwitterAPI(username);

  // Save to cache (valid for 24 hours)
  await redis.set(cacheKey, JSON.stringify(tweets), { ex: 86400 });

  return tweets;
}

To enhance user experience, the app uses IndexedDB to persist placement information in the browser and leverages Upstash Redis to cache API call results, reducing unnecessary API requests for better performance. This approach also helps manage Twitter API limitations.

Implementation Challenges and Solutions

1. Handling Twitter API Limitations

Twitter (now X) API limitations are strict, and frequent requests can easily hit rate limits. To address this, the project implements:

Aggressive caching strategy with Upstash Redis
Backoff and retry logic
User-friendly alternative flows when errors occur

These measures help ensure comfortable app usage within API limitations.

2. Mobile UX Optimization

const TouchControls = ({ onReset, onRandom }) => {
  return isMobile ? (
    <div className="fixed bottom-4 left-0 right-0 flex justify-center gap-3 z-10">
      <Button variant="outline" onClick={onReset}>
        <RefreshCw size={16} className="mr-2" />Reset
      </Button>
      <Button variant="outline" onClick={onRandom}>
        <Shuffle size={16} className="mr-2" />Random Placement
      </Button>
    </div>
  ) : null;
};

Touch devices presented challenges with conflicting drag and scroll operations. Solutions include:

Touch event propagation control
Dedicated control UI for mobile users
Element size optimization

These measures help create a UI that works comfortably on both mobile and desktop platforms.

Conclusion

This project demonstrates a novel way to visually represent X users' personalities by combining modern web technologies with AI. Despite several technical challenges, the developers have created an entertaining and practical web application.

CUA - Computer-Using Agent: The AI That Operates Your Computer

GOROman — Thu, 13 Mar 2025 22:02:01 +0000

What is Computer-Using Agent (CUA)?

OpenAI's "Computer-Using Agent" (CUA) is an innovative AI model that can operate computers through graphical user interfaces (GUIs). It has the unprecedented ability to see the screen, click buttons, fill out forms, and navigate menus just like a human user would.

CUA has been released as a research preview called "Operator," allowing users to execute complex computer tasks by simply giving natural language instructions. For example, you could say "Book a flight to New York next week," and Operator would navigate travel websites, search for flights, and complete the booking process.

How CUA Works

CUA processes pixel data from screenshots to understand what's happening on screen and interacts with interfaces like a human would. This eliminates the need for platform-specific APIs to operate various applications. The process is divided into three main steps:

1. Perception

CUA takes screenshots of the computer screen to understand the context of the digital environment. These visual inputs form the foundation for decision-making.

2. Reasoning

Using chain-of-thought reasoning, CUA evaluates its observations and tracks progress across intermediate steps. By analyzing past and current screenshots, the system dynamically adapts to new challenges and unexpected changes.

3. Action

Using a virtual mouse and keyboard, CUA executes tasks like typing, clicking, and scrolling. For sensitive tasks, such as handling login credentials or solving CAPTCHA challenges, the system requests user confirmation to ensure security.

This structured workflow allows CUA to navigate complex multi-step tasks and self-correct when encountering errors, making it a powerful tool for digital problem-solving.

CUA's Key Capabilities and Benchmarks

CUA sets new benchmarks in both computer use and browser-based tasks, demonstrating flexibility across different environments. Its performance has been evaluated using platforms like OSWorld, WebArena, and WebVoyager:

OSWorld: CUA achieved a 38.1% success rate for general computer-use tasks, far exceeding the previous state-of-the-art (SOTA) result of 22.0%.
WebArena: On this benchmark, which simulates real-world tasks in e-commerce and content management, CUA scored 58.1%, outperforming the prior SOTA of 36.2%.
WebVoyager: Testing live website interactions (e.g., Amazon, GitHub), CUA matched human performance with an 87% success rate.

These benchmarks highlight CUA's ability to operate effectively across digital environments using a single general interface of screen, mouse, and keyboard. However, there is still room for improvement in more complex scenarios, such as WebArena tasks, where human success rates are higher.

CUA's Advanced Features: Flexibility and Adaptability

One of the most remarkable aspects of CUA is its ability to break tasks into multi-step plans and adapt dynamically when faced with challenges. For example, if a webpage fails to load correctly or a task diverts from the expected path, CUA can adjust its strategy in real-time. This flexibility stems from its integration of GUI perception with structured problem-solving.

CUA Safety Protocols

OpenAI has prioritized safety in the development of CUA, recognizing the risks associated with giving AI systems access to digital environments. Key measures include:

User Confirmation: For sensitive actions like handling passwords or personal data, CUA requests explicit user approval.
Operator System Card: A detailed system card outlines the safeguards in place, ensuring transparency and accountability.

The initial release of CUA through the Operator research preview is limited to Pro-tier users in the U.S., allowing OpenAI to gather real-world feedback and refine the system before global deployment.

CUA Applications and Future Potential

CUA opens the door to a wide range of applications, from automating mundane workflows to enabling accessibility solutions for individuals with physical limitations. Potential use cases include:

Administrative Assistance: Automating tasks like form-filling, data entry, and email management.
E-commerce Management: Navigating and updating online store inventories or processing customer orders.
Education and Research: Assisting with data collection and organization from online resources.

By eliminating the need for task-specific APIs, CUA offers unparalleled flexibility, making it easier for developers and businesses to integrate into existing workflows.

CUA Challenges and Next Steps

While CUA represents a significant step forward, it still faces limitations:

Complex Task Handling: Success rates on intricate tasks, such as those in WebArena, show room for improvement compared to human performance.
Generalization: Adapting to a broader range of use cases and digital environments will require further training and fine-tuning.
Ethical Considerations: Ensuring responsible use and minimizing risks of misuse remain ongoing priorities.

OpenAI's strategy of phased deployment allows for continuous improvement, leveraging user feedback to refine the system. As CUA progresses, it could redefine the boundaries of AI-human collaboration.

Summary

Computer-Using Agent (CUA) is a technology that revolutionizes how AI interacts with digital interfaces. With its ability to understand screenshots and operate mouse and keyboard, CUA can use computers just like humans do.

While still in its early stages, it already shows impressive results and will enable more automation and assistance with digital tasks in the future. OpenAI's commitment to safety and phased deployment approach ensures that CUA is developed responsibly and can be leveraged to improve our digital lives.

My Hands-on Experience with CUA

Hello, I'm GOROman. I've had the chance to try out the CUA technology described above, where "AI operates a computer by itself" - something that sounds like science fiction. It was more impressive than I expected, so I'd like to share my experience.

OpenAI's "Computer-Using Agent" - even the name conveys how powerful this is. Simply put, it's a system where AI looks at the screen, makes judgments like "I should click here" or "I should fill out this form," and actually controls the mouse and keyboard.

It feels like we're approaching the sci-fi world of Jarvis from Iron Man.

How the Computer-Using Agent Works

Understanding the technical details beforehand helps understand the API behavior, so let me briefly explain how it works.

The system consists of three main components:

Visual System: AI that captures and analyzes the screen
Decision Engine: AI that determines what actions to take
Control System: The part that actually moves the mouse and keyboard

For example, if you instruct it to "check the weather and send it by email":

Visual AI: "Currently displaying desktop screen. Browser icon is in the bottom left."
Decision AI: "Should open the browser first"
Control System: "Click on Chrome icon in the bottom left"

Visual AI: "Browser is open. There's a search bar."
Decision AI: "Input 'Tokyo weather' in the search bar"
Control System: "Click on search bar and type 'Tokyo weather'"

This is how the AIs communicate with each other to process the task.

API Implementation: Easier Than Expected

When I actually tried the API, the implementation was surprisingly simple. Here's a basic implementation example in Node.js:

import axios from 'axios';

// Basic configuration
const API_KEY = 'your_openai_api_key'; // ← Replace with your key
const API_URL = 'https://api.openai.com/v1/computer-agent';

// Function to request tasks from the agent
async function runComputerAgent(taskDescription) {
  try {
    const response = await axios.post(
      API_URL,
      {
        task: taskDescription,
        allowed_apps: ["chrome", "notepad", "excel"], // Apps allowed to use
        screen_recording: true, // Recording operations (for debugging)
        confirmation_level: "high" // Level of confirmation required before important operations
      },
      {
        headers: {
          'Authorization': `Bearer ${API_KEY}`,
          'Content-Type': 'application/json'
        }
      }
    );

    return response.data;
  } catch (error) {
    console.error('An error occurred:', error.response?.data || error);
    throw error;
  }
}

// Usage example
async function main() {
  const result = await runComputerAgent(
    "Search for 'latest AI news' on Google and summarize the first 3 article titles in Notepad"
  );

  console.log('Task ID:', result.task_id);
  console.log('Status URL:', result.status_url);
}

main();

With just this code, the AI will search Google and summarize the results in Notepad on your PC. It's almost frighteningly simple.

Use Cases: What Can It Do?

After trying it out, here are some use cases I found particularly useful:

1. Automating Data Collection

// Example of collecting stock data daily
const collectStockData = async (ticker) => {
  return await runComputerAgent(`
    1. Open Yahoo Finance stock chart for ${ticker}
    2. Download the past month's data as CSV
    3. Open the downloaded CSV and extract only the closing prices
    4. Append the extracted data to stock_data.csv (in the format of date, ticker, closing price)
  `);
};

// Process multiple stocks
const tickers = ['AAPL', 'MSFT', 'GOOGL'];
for (const ticker of tickers) {
  await collectStockData(ticker);
}

Previously, we had to write scraping code using Selenium or Puppeteer, but now we can do it with just natural language instructions. What's more, it can adapt even if the screen UI changes!

2. Automating Tedious Tasks

Monthly expense reports can be really tedious... These can now be delegated:

await runComputerAgent(`
  1. Log in to the company portal and open the expense report page
  2. Open "expenses-2024-03.xlsx" on the desktop
  3. Transfer the Excel data to the expense report form
  4. Upload "receipts.zip" as an attachment
  5. Review the content and press the submit button
`);

3. Automated Testing

UI testing can be really cumbersome, especially for applications where the frontend changes frequently, making test code maintenance difficult.

// Example test function
const testUserRegistration = async () => {
  return await runComputerAgent(`
    1. Open browser to http://myapp.local
    2. Click the "Register" button
    3. Fill out the form with the following information:
       - Username: testuser123
       - Email: test@example.com
       - Password: TestPass123!
       - Confirm Password: TestPass123!
    4. Click the "Register" button
    5. Verify that a registration success message is displayed
    6. Verify that it navigated to the login page
    7. Save test results to a JSON file
  `);
};

The big difference from conventional UI testing tools is that you don't need to worry about low-level implementations like "where the button is on the screen" or "what CSS selector to use." It recognizes elements just like a human would, thinking "ah, the register button is here."

Issues I Noticed and Countermeasures

I also noticed some issues when using it:

1. Authentication and Security

Naturally, the AI can see your passwords, which is quite concerning. A solution could be:

// Example for handling sensitive information
await runComputerAgent(`
  1. Open the login page in the browser
  2. Enter "admin" in the username field
  3. Notify me when ready to enter the password (human will input)
`);

// ↑ Here the AI stops and prompts the human to enter the password
// After the human enters the password, continue

await continueTask(taskId, "Password entry complete. Please continue");

I recommend this hybrid approach where humans intervene only for sensitive parts.

2. Error Handling

The AI can get confused when unexpected pop-ups or error dialogs appear. You can address this as follows:

await runComputerAgent(`
  1. Open the application
  2. Import data
  3. If a "File not found" error appears:
     a. Click "OK"
     b. Select "sample.csv" and import
  4. If asked "Do you want to update?":
     a. Click "Yes"
`);

It's reassuring to specify anticipated error patterns and how to handle them in advance.

Advanced API Usage Techniques

Here are some more advanced usage patterns:

1. Custom Pre-processing and Post-processing

// Example of multi-step processing
async function processWebData() {
  // 1. Data acquisition phase
  const extractionResult = await runComputerAgent(
    "Extract data table from website and save as CSV"
  );

  // 2. Human verification
  console.log("Please verify the data:", extractionResult.output_file);
  const userConfirmation = await promptUser("Is the data correct? (yes/no)");

  if (userConfirmation === 'yes') {
    // 3. Continue: Data processing
    await runComputerAgent(
      `Open and analyze the extracted CSV file ${extractionResult.output_file}`
    );
  } else {
    console.log("Process aborted");
  }
}

2. Parallel Processing

// Execute multiple tasks in parallel
async function parallelTasks() {
  const tasks = [
    runComputerAgent("Task 1 description"),
    runComputerAgent("Task 2 description"),
    runComputerAgent("Task 3 description")
  ];

  const results = await Promise.all(tasks);
  console.log("All tasks completed:", results);
}

However, having multiple agents operate on the same screen can cause confusion, so I recommend using virtual desktops or separate windows.

Complete Sample Code

I've uploaded the complete code to GitHub.
If you're interested, check it out here: github.com/GOROman/computer-agent-samples

How to Use and Control the M5Stack Fan Module

GOROman — Wed, 05 Mar 2025 10:23:43 +0000

Introduction

The M5Stack Fan Module v1.1 (STM32F030) is a fan control module that can be used with the M5Stack series. This module makes it easy to add cooling functionality to your projects. In this article, I'll explain how to use the M5Module-Fan library, detail its API, and provide sample code examples.

Overview of the M5Stack Fan Module
Installing the M5Module-Fan Library
How to Use the API
Sample Code Explanation
Application Examples

Overview of the M5Stack Fan Module

The M5Stack Fan Module is an I2C-compatible fan module with the STM32F030 microcontroller. Its main features include:

Fan control via I2C communication
Rotation speed adjustment via PWM control
RPM (Revolutions Per Minute) reading functionality
Multiple PWM frequency settings (1KHz/12KHz/24KHz/48KHz)
Configuration saving to internal flash
I2C address modification capability

This module is ideal for projects requiring cooling, air circulation, or temperature management.

Installing the M5Module-Fan Library

For PlatformIO

Add the following dependencies to your platformio.ini file:

lib_deps = 
    https://github.com/m5stack/M5Unified
    https://github.com/m5stack/M5Module-Fan

For Arduino IDE

From the menu, select "Sketch" → "Include Library" → "Manage Libraries..."
Type "M5Module-Fan" in the search bar
Install the displayed library

How to Use the API

Including the Library

#include <m5_module_fan.hpp>

Initializing the Module

M5ModuleFan moduleFan;

// Basic initialization
bool success = moduleFan.begin();

// Initialization with custom I2C settings
bool success = moduleFan.begin(&Wire1, MODULE_FAN_BASE_ADDR, 12, 11, 400000);
// Parameters: Wire, I2C address, SDA pin, SCL pin, I2C speed (Hz)

Main API Functions

Fan Control

// Enable the fan
moduleFan.setStatus(MODULE_FAN_ENABLE);

// Disable the fan
moduleFan.setStatus(MODULE_FAN_DISABLE);

// Get current status
module_fan_status_t status = moduleFan.getStatus();

Setting PWM Frequency

// Set PWM frequency (1KHz, 12KHz, 24KHz, 48KHz)
moduleFan.setPWMFrequency(PWM_1KHZ);  // 1KHz
moduleFan.setPWMFrequency(PWM_12KHZ); // 12KHz
moduleFan.setPWMFrequency(PWM_24KHZ); // 24KHz
moduleFan.setPWMFrequency(PWM_48KHZ); // 48KHz

// Get current PWM frequency setting
module_fan_pwm_freq_t freq = moduleFan.getPWMFrequency();

Setting Duty Cycle

// Set duty cycle (0-100%)
moduleFan.setPWMDutyCycle(80); // Rotate fan at 80% speed

// Get current duty cycle
uint8_t dutyCycle = moduleFan.getPWMDutyCycle();

Getting RPM

// Get current fan rotation speed (RPM)
uint16_t rpm = moduleFan.getRPM();

Getting Signal Frequency

// Get fan output signal frequency
uint16_t signalFreq = moduleFan.getSignalFrequency();

Saving Configuration

// Save current settings to internal flash
moduleFan.saveConfig();

Getting Firmware Version

// Get firmware version
uint8_t version = moduleFan.getFirmwareVersion();

Setting and Getting I2C Address

// Set I2C address (valid range: 0x08-0x77)
uint8_t newAddr = moduleFan.setI2CAddress(0x19);

// Get current I2C address
uint8_t addr = moduleFan.getI2CAddress();

Sample Code Explanation

The following is a basic sample code for controlling the M5Stack Fan Module. This code implements fan status display and button operation for fan control.

#include <M5Unified.h>
#include <m5_module_fan.hpp>

// Fan module instance
M5ModuleFan moduleFan;
// Fan module I2C address
uint8_t deviceAddr = MODULE_FAN_BASE_ADDR;
// Fan duty cycle (0-100%)
uint8_t dutyCycle  = 80;
// Fan operating status
bool fan_status    = true;
// Display canvas
M5Canvas canvas(&M5.Display);

void setup()
{
    M5.begin();
    Serial.begin(115200);
    canvas.createSprite(320, 240);
    canvas.setFont(&fonts::lgfxJapanGothic_24);
    canvas.setTextDatum(top_center);
    canvas.setTextColor(WHITE);

    // Initialize fan module
    // Using Wire1, I2C address=deviceAddr, SDA=12, SCL=11, speed=400000Hz
    while (!moduleFan.begin(&Wire1, deviceAddr, 12, 11, 400000)) {
        Serial.printf("Module FAN Init failed\r\n");
        canvas.drawString("Module FAN Init failed", 160, 120);
        canvas.pushSprite(0, 0);
        delay(1000);
    }

    // Rotate fan at 80% duty cycle
    moduleFan.setPWMDutyCycle(dutyCycle);
    // Set PWM frequency to 1KHz
    moduleFan.setPWMFrequency(PWM_1KHZ);
    // Enable fan
    moduleFan.setStatus(MODULE_FAN_ENABLE);
}

void loop()
{
    // Current PWM frequency setting index
    int pwm_config_index  = moduleFan.getPWMFrequency();
    // PWM frequency string representation
    String pwm_config_str = "";

    // Convert PWM frequency index to string
    switch (pwm_config_index) {
        case PWM_1KHZ:
            pwm_config_str = "1KHz";
            break;
        case PWM_12KHZ:
            pwm_config_str = "12KHz";
            break;
        case PWM_24KHZ:
            pwm_config_str = "24KHz";
            break;
        case PWM_48KHZ:
            pwm_config_str = "48KHz";
            break;
    }

    // Update screen display
    canvas.setCursor(0, 10);
    canvas.clear();
    // Display fan operating status
    canvas.printf("Work Status    :%s\r\n", moduleFan.getStatus() == MODULE_FAN_ENABLE ? "RUNNING" : "STOP");
    // Display PWM frequency
    canvas.printf("PWM  Frequency :%s\r\n", pwm_config_str.c_str());
    // Display duty cycle
    canvas.printf("PWM  Duty Cycle:%d%%\r\n", moduleFan.getPWMDutyCycle());
    // Display rotation speed (RPM)
    canvas.printf("RPM            :%drpm\r\n", moduleFan.getRPM());
    canvas.pushSprite(0, 0);

    // Update button state
    M5.update();
    auto t = M5.Touch.getDetail();

    // If button A is pressed: Toggle fan ON/OFF
    if (M5.BtnA.wasPressed()) {
        fan_status = !fan_status;
        if (fan_status) {
            moduleFan.setStatus(MODULE_FAN_ENABLE);
        } else {
            moduleFan.setStatus(MODULE_FAN_DISABLE);
        }
    }

    // If button B is pressed: Decrease duty cycle by 10%
    if (M5.BtnB.wasPressed()) {
        dutyCycle -= 10;
        if (dutyCycle < 0) {
            dutyCycle = 100;
        }
        moduleFan.setPWMDutyCycle(dutyCycle);
    }

    // If button C is pressed: Increase duty cycle by 10%
    if (M5.BtnC.wasPressed()) {
        dutyCycle += 10;
        if (dutyCycle > 100) {
            dutyCycle = 0;
        }
        moduleFan.setPWMDutyCycle(dutyCycle);
    }

    delay(10);
}

Code Explanation

Initialization Part (setup function)

Initialize M5Stack and display settings
Initialize fan module (specify Wire1, I2C address, SDA/SCL pins, communication speed)
Initial settings:
- Duty cycle: 80%
- PWM frequency: 1KHz
- Fan status: Enabled (rotating)

Main Loop (loop function)

Get current PWM frequency setting and convert to corresponding string
Update screen display:
- Fan operating status (RUNNING/STOP)
- PWM frequency (1KHz/12KHz/24KHz/48KHz)
- Duty cycle (0-100%)
- Current rotation speed (RPM)
Handle button operations:
- Button A: Toggle fan ON/OFF
- Button B: Decrease duty cycle by 10%
- Button C: Increase duty cycle by 10%

Application Examples

Temperature Control System

You can build a system that automatically adjusts fan speed based on temperature by combining it with a temperature sensor.

#include <M5Unified.h>
#include <m5_module_fan.hpp>

M5ModuleFan moduleFan;
float temperature;

void setup() {
    M5.begin();
    moduleFan.begin(&Wire1, MODULE_FAN_BASE_ADDR, 12, 11, 400000);
    moduleFan.setPWMFrequency(PWM_1KHZ);
    moduleFan.setStatus(MODULE_FAN_ENABLE);
}

void loop() {
    // Get temperature from temperature sensor (example)
    temperature = M5.Imu.getTemp();

    // Adjust fan speed based on temperature
    if (temperature < 25.0) {
        // Below 25°C: Fan off
        moduleFan.setStatus(MODULE_FAN_DISABLE);
    } else if (temperature < 30.0) {
        // 25-30°C: Low speed (30%)
        moduleFan.setStatus(MODULE_FAN_ENABLE);
        moduleFan.setPWMDutyCycle(30);
    } else if (temperature < 35.0) {
        // 30-35°C: Medium speed (60%)
        moduleFan.setStatus(MODULE_FAN_ENABLE);
        moduleFan.setPWMDutyCycle(60);
    } else {
        // Above 35°C: High speed (100%)
        moduleFan.setStatus(MODULE_FAN_ENABLE);
        moduleFan.setPWMDutyCycle(100);
    }

    // Display information
    M5.Display.clear();
    M5.Display.setCursor(0, 0);
    M5.Display.printf("Temperature: %.1f C\n", temperature);
    M5.Display.printf("Fan Speed: %d%%\n", moduleFan.getPWMDutyCycle());
    M5.Display.printf("RPM: %d\n", moduleFan.getRPM());

    delay(1000);
}

Controlling Multiple Fans

You can individually control multiple fan modules by changing the I2C address.

#include <M5Unified.h>
#include <m5_module_fan.hpp>

M5ModuleFan fan1;
M5ModuleFan fan2;

void setup() {
    M5.begin();

    // Initialize fan 1 (default address)
    fan1.begin(&Wire1, MODULE_FAN_BASE_ADDR, 12, 11, 400000);

    // Initialize fan 2 (change address to 0x19)
    fan2.begin(&Wire1, 0x19, 12, 11, 400000);

    // Enable both fans
    fan1.setStatus(MODULE_FAN_ENABLE);
    fan2.setStatus(MODULE_FAN_ENABLE);

    // Different speed settings
    fan1.setPWMDutyCycle(50);  // Fan 1: 50%
    fan2.setPWMDutyCycle(80);  // Fan 2: 80%
}

void loop() {
    // Display status
    M5.Display.clear();
    M5.Display.setCursor(0, 0);
    M5.Display.printf("Fan 1 - Speed: %d%%, RPM: %d\n", 
                     fan1.getPWMDutyCycle(), fan1.getRPM());
    M5.Display.printf("Fan 2 - Speed: %d%%, RPM: %d\n", 
                     fan2.getPWMDutyCycle(), fan2.getRPM());

    delay(1000);
}

Conclusion

The M5Stack Fan Module is a fan module that can be easily controlled using I2C communication. By using the M5Module-Fan library, you can easily implement functions such as enabling/disabling the fan, adjusting rotation speed, and getting rotation speed.

This module can be used in various projects such as electronic device cooling, air circulation, and temperature management. In particular, by combining it with a temperature sensor, you can build an intelligent cooling system.

Reference Links

Building a Palm-Sized Local LLM with M5Stack Cardputer and ModuleLLM

GOROman — Mon, 03 Mar 2025 06:50:24 +0000

In recent years, running Large Language Models (LLMs) like OpenAI's ChatGPT locally required either a CUDA-compatible GPU or an Apple Silicon Mac with plenty of RAM. However, we're now seeing the emergence of edge LLM modules designed for microcontrollers. In this tutorial, I'll show you how to combine M5Stack's credit card-sized computer (Cardputer) with the ModuleLLM to create a palm-sized device running a local LLM!

Check out the demo here: Twitter Video

What You'll Need

1. ModuleLLM

First, you'll need to get your hands on the ModuleLLM module. These have been selling out quickly at Switch Science (the Japanese distributor) and even on the official M5Stack Store. When I checked Aliexpress, I managed to snag the last one in stock! Production will likely resume after the Chinese New Year, with inventory possibly returning around March.

ModuleLLM on Switch Science

2. Cardputer

You'll also need the M5Stack Cardputer. The good news is that these seem to be back in stock recently!

Cardputer on Switch Science

Assembly

Disassembly

First, remove the ModuleLLM from its frame by unscrewing the four screws with a hex wrench.

Then disassemble the Cardputer. Be careful when removing the large battery, which is attached with double-sided tape (don't bend the battery forcefully as it could be a fire hazard!). Also remove the black component on top of the Cardputer.

Fitting the ModuleLLM

You'll need to carefully trim the case to fit the ModuleLLM inside. Cut out the back of the case with a utility knife to expose the pins (M.BUS). Make sure to insulate properly with Kapton tape or acetate tape to prevent short circuits.

Here's how it looks when assembled:

Connecting the Components

The ModuleLLM can communicate via either UART or TCP (port 10001). For this project, we'll use UART.

The wiring between the Cardputer and ModuleLLM is as follows:

Cardputer (GROVE)	ModuleLLM (M.BUS)
G (Black)	GND
5V (Red)	5V
G1 (Yellow)	UART (Tx)
G2 (White)	UART (Rx)

(Note: As of February 9, 2025, Tx and Rx connections have been swapped. The LLM630 Kit can now be connected directly and should work properly)

Caution: In this photo, G1 is connected to Tx and G2 to Rx.

Programming

Create the software by combining and modifying these official samples:

It's a good idea to add audio feedback for key presses.

Configure the UART TX and RX GPIO pins like this:

Serial2.begin(115200, SERIAL_8N1, 2, 1);

Complete!

Enjoy your palm-sized local LLM!

Updates

February 3, 2025: Someone has already built their own version! Check it out here: https://posfie.com/@necobut/p/JKAorjR
February 9, 2025: Source code is now available at https://github.com/GOROman/LLMCardputer

GAITAME IMU:Documentation: A High-Precision IMU for Spresense (CXD5602PWBIMU)

GOROman — Mon, 03 Mar 2025 06:31:35 +0000

CXD5602PWBIMU Documentation: A High-Precision IMU for Spresense

Introduction

The CXD5602PWBIMU is an Inertial Measurement Unit (IMU) sensor available for use with the Spresense board. What makes this sensor special is that it contains 16 consumer-grade IMUs, each with a 3-axis accelerometer and 3-axis gyroscope, enabling high-precision motion detection and measurement through sensor fusion. Remarkably, you can even stack two boards for a total of 32 IMUs (though this comes at a higher price point - around 90,000 yen!).

This product is intended for the Japanese domestic market. It falls under export trade control regulations (items 1-15 of the Export Trade Control Order and Foreign Exchange Order in Japan). When exporting this product or providing it to non-residents, proper procedures must be followed in accordance with foreign exchange laws.

Key Features

High Precision

Low Bias Variation: Bias stability below 0.39 deg/h (when combining 16 IMUs)
Low Noise Density: Below 1.0 mdps/√Hz. Even capable of gyrocompass functionality through earth rotation detection
Further precision improvement possible with dual-board stacking
Pre-calibrated

High Robustness

Strong fault tolerance through redundant multiple IMUs
Real-time outlier rejection via majority voting logic

Note: SPRESENSE main board is not included.

The board appears to use two PSoC6 chips for signal processing:

Here's how the IMUs are arranged:

Setting Up the Development Environment

Install the Spresense development environment (SDK)
~~Firmware v3.4.0 is required, but for some reason it's not yet available on the official site (you'll need to figure this out)~~ It appears v3.3.0 works fine
While it should work with Arduino as well, no sample code for Arduino was available at the time of writing

Basic Usage

Device Path

The CXD5602PWBIMU sensor can be accessed at the following device path:

/dev/imu0

API Reference

IOCTL Commands

Command	Description	Parameter
`SNIOC_SSAMPRATE`	Set sampling rate	int type (15, 30, 60, 120, 240, 480, 960, 1920)
`SNIOC_SDRANGE`	Set dynamic range	`cxd5602pwbimu_range_t` structure
`SNIOC_SFIFOTHRESH`	Set hardware FIFO threshold	int type (1-4)
`SNIOC_ENABLE`	Enable/disable sensor	int type (1=enable, 0=disable)

Data Structures

// Structure for dynamic range settings
typedef struct {
  int accel;  // Accelerometer range (2, 4, 8, 16)
  int gyro;   // Gyroscope range (125, 250, 500, 1000, 2000, 4000)
} cxd5602pwbimu_range_t;

// Sensor data structure
typedef struct {
  uint32_t timestamp;  // Timestamp
  float temp;          // Temperature
  float gx;            // Gyroscope X-axis
  float gy;            // Gyroscope Y-axis
  float gz;            // Gyroscope Z-axis
  float ax;            // Accelerometer X-axis
  float ay;            // Accelerometer Y-axis
  float az;            // Accelerometer Z-axis
} cxd5602pwbimu_data_t;

Code Examples

Sensor Initialization and Configuration

#include <nuttx/sensors/cxd5602pwbimu.h>

int fd;
int ret;
cxd5602pwbimu_range_t range;

// Open the device
fd = open("/dev/imu0", O_RDONLY);
if (fd < 0) {
  printf("Error: Could not open device\n");
  return 1;
}

// Set sampling rate (example: 480Hz)
ret = ioctl(fd, SNIOC_SSAMPRATE, 480);
if (ret) {
  printf("Error: Failed to set sampling rate\n");
  close(fd);
  return 1;
}

// Set dynamic range (example: acceleration=2G, gyro=125dps)
range.accel = 2;
range.gyro = 125;
ret = ioctl(fd, SNIOC_SDRANGE, (unsigned long)(uintptr_t)&range);
if (ret) {
  printf("Error: Failed to set dynamic range\n");
  close(fd);
  return 1;
}

// Set FIFO threshold (example: 1)
ret = ioctl(fd, SNIOC_SFIFOTHRESH, 1);
if (ret) {
  printf("Error: Failed to set FIFO threshold\n");
  close(fd);
  return 1;
}

// Enable the sensor
ret = ioctl(fd, SNIOC_ENABLE, 1);
if (ret) {
  printf("Error: Failed to enable sensor\n");
  close(fd);
  return 1;
}

Reading Data

cxd5602pwbimu_data_t data;
struct pollfd fds[1];

// Configure poll
fds[0].fd = fd;
fds[0].events = POLLIN;

// Wait for data to become available
ret = poll(fds, 1, 1000); // 1 second timeout
if (ret > 0 && (fds[0].revents & POLLIN)) {
  // Read data
  ret = read(fd, &data, sizeof(data));
  if (ret == sizeof(data)) {
    // Process data
    printf("Temperature: %.2f\n", data.temp);
    printf("Acceleration (G): X=%.6f, Y=%.6f, Z=%.6f\n", data.ax, data.ay, data.az);
    printf("Gyroscope (dps): X=%.6f, Y=%.6f, Z=%.6f\n", data.gx, data.gy, data.gz);
  }
}

// Disable sensor when finished
ioctl(fd, SNIOC_ENABLE, 0);
close(fd);

Sample Applications

The Spresense SDK includes sample applications for using the CXD5602PWBIMU sensor.

Basic Sample (cxd5602pwbimu)

A simple sample demonstrating basic sensor usage. It collects data for 1 second and displays it to standard output. After building and flashing, launch it from UART with the pwbimu command.

https://x.com/GOROman/status/1895733790229872796

Logger Application (cxd5602pwbimu_logger)

A logger application supporting long-term data collection and various output formats.

cxd5602pwbimu_logger [-s <rate>] [-a <acc_range>] [-g <gyro_range>] [-f <fifo_num>] [-o <out_dev>] [-d] [-h]

Options:

-s <rate>: Sampling rate (Hz). Valid values: 15, 30, 60, 120, 240, 480, 960, 1920
-a <acc_range>: Accelerometer range (G). Valid values: 2, 4, 8, 16
-g <gyro_range>: Gyroscope range (dps). Valid values: 125, 250, 500, 1000, 2000, 4000
-f <fifo_num>: FIFO size. Valid values: 1, 2, 3, 4
-o <out_dev>: Output device. 'uart', 'net', '/path/to/file.bin', '/path/to/file.txt'
-d: Force display data to UART
-h: Display help

Recommended Settings by Use Case

General Motion Detection

Sampling rate: 60Hz
Accelerometer range: 2G
Gyroscope range: 250dps
FIFO threshold: 1

Sports Activity Tracking

Sampling rate: 240Hz
Accelerometer range: 8G
Gyroscope range: 1000dps
FIFO threshold: 2

High-Precision Motion Analysis

Sampling rate: 960Hz
Accelerometer range: 4G
Gyroscope range: 500dps
FIFO threshold: 1

Battery Efficiency Priority

Sampling rate: 30Hz
Accelerometer range: 2G
Gyroscope range: 125dps
FIFO threshold: 4

Troubleshooting

Data Reading Errors

Confirm the device path is correct (/dev/imu0)
Check if the sensor is enabled
Verify FIFO threshold settings

Inaccurate Data

Ignore the first 50ms of data (may contain initialization noise)
Confirm dynamic range is appropriately set
Check if sampling rate is suitable for your application

Performance Issues

Try lowering the sampling rate
Increase FIFO threshold to reduce data processing frequency
Minimize unnecessary data processing

Applications

Suitable for applications where traditional industrial FOG (Fiber Optic Gyroscope) would be difficult to implement:

Structure inspection drones
Small autonomous mobile robots
Devices requiring precise position and orientation estimation
Gyrocompass applications utilizing earth rotation detection

Reference Links

SPRESENSE Official

Working Example

https://x.com/GOROman/status/1896200165670912331

DEV Community: GOROman

The HAH Model: A New Paradigm of Human-AI Collaboration

Abstract

1. Introduction

2. Definition of the HAH Model

3. Parallels to Industrial Revolution

4. Implementation Cases

4.1 ViXion01S-Firmware Project

4.2 Gig Economy Platforms as HAH Model Examples

4.2.1 Uber Eats

4.2.2 Mercari Hello (Japan)

4.2.3 Timee (Japan)

5. Gig Economy Market Size and HAH Transformation Potential

5.1 Current Market Size

5.2 HAH Transformation Potential

6. Benefits of the HAH Model

7. Challenges and Future Directions

8. Addressing Potential Luddite-like Resistance

8.1 Transparency and Accountability

8.2 Comprehensive Education and Reskilling

8.3 Gradual Implementation and Participatory Design

8.4 Social Safety Nets

8.5 Ethical Guidelines and Regulation

9. Comparative Analysis with Traditional HAI and HaaS Models

9.1 Traditional Human-AI Interaction (HAI)

9.2 Human-as-a-Service (HaaS)

9.3 Summary of Key Differences

10. Socioeconomic Implications

10.1 Labor Redefinition

10.2 AI as Middle Management

10.3 Risk of Digital Taylorism

11. Design Principles for HAH-Compliant Systems

12. Future Research Directions

13. Conclusion

References

D&D-Style Alignment Chart for X Users

Introduction

What is D&D?

Development Background

App Features

Technical Implementation Highlights

1. Responsive Design

2. AI-Based Alignment Analysis

Prompt Structure

3. Data Persistence and Caching Strategy

Implementation Challenges and Solutions

1. Handling Twitter API Limitations

2. Mobile UX Optimization

Conclusion

Links

CUA - Computer-Using Agent: The AI That Operates Your Computer

What is Computer-Using Agent (CUA)?

How CUA Works

1. Perception

2. Reasoning

3. Action

CUA's Key Capabilities and Benchmarks

CUA's Advanced Features: Flexibility and Adaptability

CUA Safety Protocols

CUA Applications and Future Potential

CUA Challenges and Next Steps

Summary

My Hands-on Experience with CUA

How the Computer-Using Agent Works

API Implementation: Easier Than Expected

Use Cases: What Can It Do?

1. Automating Data Collection

2. Automating Tedious Tasks

3. Automated Testing

Issues I Noticed and Countermeasures

1. Authentication and Security

2. Error Handling

Advanced API Usage Techniques

1. Custom Pre-processing and Post-processing

2. Parallel Processing

Complete Sample Code

How to Use and Control the M5Stack Fan Module

Introduction

Table of Contents

Overview of the M5Stack Fan Module