DEV Community: Walkman42

The role of artificial intelligence in the learning process of teenagers

Walkman42 — Thu, 07 Dec 2023 18:15:43 +0000

Henry Adams

Abstract:
With the rapid development of Artificial Intelligence (AI) technology, its application in the field of education is becoming increasingly widespread, especially in the learning process of adolescents. This paper aims to explore the multifaceted role of AI in adolescent learning, including promoting educational personalization, enhancing critical thinking and innovation capabilities, providing interactive learning experiences, assessing learning outcomes, assisting students with special needs, supporting social and emotional learning, and improving the quality and equity of education. This paper makes extensive use of AIGC tools, fully demonstrating the application of AI in the academic field.

Keywords: Artificial Intelligence, Youth Education, Personalized Learning, Critical Thinking, Innovation Skills, Interactive Learning, Learning Assessment, Special Education, Social Skills, Emotional Learning, Educational Equity

I. Introduction
In the field of education in the 21st century, artificial intelligence technology has become a key force in driving teaching innovation and improving the learning experience. Especially in youth education, the application of AI technology not only improves the accessibility of educational resources but also provides students with more personalized and efficient ways of learning.

II. Promoting Personalized Education
The application of artificial intelligence technology has made personalized education more possible. Based on big data analysis, AI systems can deeply understand each student's learning patterns, interests, and ability levels, achieving precise grasping of students' learning needs. On this basis, AI can tailor the best learning plan for each student, providing personalized learning resources and paths.
For example, based on the results of cognitive diagnostics, the system can accurately locate the student's knowledge construction situation, identify cognitive gaps, and provide targeted compensation; according to the learning literacy model, it can predict student learning outcomes, and then adjust teaching objectives, content, and methods, achieving optimal adaptation to individual differences. It is foreseeable that under the boost of AI technology, teaching content, learning progress, homework design, etc., will increasingly become "customized" for each individual, and students will truly realize their educational dreams of obtaining education as needed and developing their own potential.

Personalized learning not only improves learning efficiency but also brings education back to its human-centered origins, helping each student achieve their own excellent results. Compared to the uniform mass teaching model, this personalized learning strategy that addresses each student's differences more effectively stimulates their interest, initiative, and creativity in learning, truly achieving "teaching according to their aptitude." Artificial intelligence is an important technical support for building the future personalized education system and achieving educational equity and quality.

III. Promoting Critical Thinking and Innovation
Artificial intelligence provides students with a new platform for thinking and practical training, effectively cultivating their critical thinking and innovation abilities. Through complex situational simulations constructed by AI, students can immerse themselves, analyze problems, weigh options, and make plans, honing their judgment and decision-making skills in this virtual trial-and-error space. At the same time, the accompanying project management tools make team collaboration easy and efficient. Students can freely combine virtual teams and experience various positions in project development, learning to manage and coordinate large projects.
This learning model based on real scenarios and practical projects gives students the opportunity to face and solve real problems, cultivating their independent insights and creative thinking.
Artificial intelligence provides students with a relatively safe and open learning environment, allowing them to learn, think, innovate, cooperate, and grow in various simulated scenarios and project practices. This also opens up new possibilities for cultivating talents with key abilities in the future.

IV. Enhancing Interactive Learning Experiences
Artificial intelligence has brought a revolutionary interactive learning experience to education. Virtual simulation supported by AI transforms the internalization of originally boring knowledge into a vivid exploration, greatly enhancing the interactivity of learning. At the same time, the gamified learning approach sparks students' curiosity and enthusiasm for participation. They are no longer passive recipients of knowledge but active and autonomous learners.
In an interactive environment, complex concepts become intuitive and vivid, allowing students to operate and make mistakes without restrictions, deepening their understanding and developing the ability to solve practical problems. Artificial intelligence has injected vitality into education, making learning no longer dull and students no longer passive. This immersive, active, and interactive learning experience is an important hallmark of AI-enabled education and presents the future classroom in more diverse ways.

V. Assessment and Feedback on Learning Outcomes
As an effective supplement to the teaching process, artificial intelligence has realized real-time and personalized learning assessment and feedback, greatly optimizing the learning effect. AI systems can continuously track each student's learning situation, identify their knowledge blind spots, and provide targeted supplementation; at the same time, it also reflects the overall state of teaching and learning in a data-driven way, helping teachers adjust strategies in time. In this closed-loop system, students receive timely guidance from AI assistants and adjust their learning methods and progress accordingly, while teachers can improve the design of teaching activities based on AI-generated learning diagnostic results. This timely, personalized, and goal-oriented assessment feedback greatly promotes the internalization and absorption of

knowledge, improving learning effectiveness. Artificial intelligence makes learning assessment intelligent and normalized, realizing continuous tracking and care for each student, and opening a new horizon for precision teaching.

VI. Assisting Students with Special Needs
Artificial intelligence provides valuable support for students with neurological developmental disorders such as intellectual disabilities, hearing impairments, autism, as well as groups with special learning needs like attention deficit disorders, language disorders, and reading disorders. Facing their various difficulties in knowledge acquisition and processing, AI can design customized teaching plans, conveying information through voice, text, graphics, and other multimodal methods, helping overcome learning obstacles. Rich online auxiliary resources also provide these students with more abundant self-learning materials.
At the same time, AI-assisted technology has bridged the gap between special education and general education, allowing them to integrate into regular classrooms for education. Based on a precise grasp of individual needs differences, artificial intelligence provides meticulous learning support for special groups, helping them overcome learning difficulties and enjoy equal and quality education.

VII. Supporting Social and Emotional Learning
Currently, social and emotional learning in adolescents is a priority in quality education. Artificial intelligence offers new possibilities for this. By constructing and participating in various virtual interactive scenarios, students can safely practice social skills, experience the consequences of different choices, and grow in a low-risk environment. This simulation not only teaches necessary communication methods but also makes students realize the impact of their words and actions on others, thereby cultivating better emotional intelligence. It can be said that AI teaching based on situational experience will effectively improve students' social adaptability and empathy, helping them establish and maintain high-quality interpersonal relationships. This is especially crucial for the healthy growth of adolescents. Compared to passively learning rules, actively participating in situational simulations greatly benefits them in social interactions.

VIII. Improving the Quality and Equity of Education
The introduction of artificial intelligence technology has powerfully promoted the improvement of educational quality and the fair sharing of educational resources. Take an online AI teaching assistant as an example: it can analyze each student's learning situation in real time, identify gaps in their knowledge construction, and then recommend corresponding learning tasks based on individual differences, providing personalized knowledge compensation, thus helping each student significantly improve learning effectiveness.
Moreover, with the ubiquity of remote networks, this kind of AI teaching assistant has actually broken through geographical barriers. It can synchronize the latest educational content quality to remote areas, allowing students everywhere to share quality educational resources.
Artificial intelligence is building a two-way bridge to elevate educational effectiveness and expand coverage, driving education towards higher quality and fairness.

IX. Conclusion
The application of artificial intelligence technology in the field of youth education fully demonstrates its tremendous potential to enhance teaching and learning efficiency. On the one hand, teaching optimization strategies based on data and algorithms have significantly improved the personalization, interactivity, and goal-orientation of the learning experience. Students can learn, think, explore, and practice in AI-assisted simulated situations and teaching projects. This not only greatly enhances learning initiative but also comprehensively cultivates core abilities such as innovation, critical thinking, social skills, and more. On the other hand, the ubiquitous network of remote teaching platforms built by artificial intelligence greatly expands the coverage of quality educational resources, allowing more students with special needs and those in remote areas to have fair and efficient learning opportunities. Artificial intelligence is profoundly reshaping the teaching and learning ecology of the new era, driving the transformation of youth education. Looking ahead, as related technologies and applications continue to mature and enrich, artificial intelligence will bring more innovative opportunities and developments to the field of education.

# Day 2: Tech Stack Decision and TDD

Walkman42 — Tue, 31 Oct 2023 15:45:31 +0000

Day 2: Tech Stack Decision and Technical Design Documentation

Upon receiving the complete PRD, it's time to analyze the project and decide on the required technology.

2.1 Socratic Questioning

The core logic revolves around fulfilling product feature requirements.

Instead of jumping to answers when encountering problems, it's crucial to keep asking questions. The best way to do this is through Socratic Questioning.

Socratic Questioning is an enlightening method of questioning that's typically used to lead someone deeper into their thoughts and help us discover answers.

Put yourself into the two identities of questioner and answerer, you can do:

Guided thinking through open-ended questions. The focus of Socratic questioning is not on giving answers but on asking questions. Through a series of these, the questioner helps guide the respondent to delve deeper into the essence of the issue.
Stimulate critical thinking. This method encourages respondents to voice their opinions and critically analyze answers.
Help in discovering answers. Instead of providing answers, the questioner allows the respondent to reach their own conclusions, leading to deeper understanding and internalization.
Facilitate deeper conversations. Socratic questioning can push discussions deeper, uncovering underlying assumptions and fostering layered logical reasoning.
Enhance learning capabilities. This method can cultivate independent thinking and learning skills, enabling one to actively ask and solve problems.

From the initial product analysis, we determined that the product will take the form of a website. So, starting with Socratic Questioning:

Why not a mobile app?
- Limited time – websites generally have shorter development cycles than mobile apps and broader applicability.
- So, if time permits, it should be a mobile app? Apparently, there's no reason to exclude it. Who says it can't be a mobile app?
- A computer screen is bigger, providing a better chat experience.
- Can't a phone optimize to offer the same experience? In theory, yes, so a mobile app could be considered.
- Using a computer is more efficient, especially for work purposes.
- Can't one work on a phone? Yes, but there's a noticeable efficiency gap that seems hard to bridge in the short term.
- Does this mean no need for a mobile version? Not at all. Different scenarios and user habits might exist, but given the current ROI, it's not a priority.

Through continuous questioning, we can clarify many details.

Both mobile and PC versions share foundational logic and much reusable code. So, when choosing technology, mobile app system architectures in the future should be considered.

Considerations from a performance and technical implementation perspective:

Potential challenges:
- The peak value caused when a certain prompt is popular may cause OpenAI interface call congestion.
- Given its UGC (User Generated Content) nature, the growth of prompts might be rapid. How to plan for database sharding in advance?
- With chat content being extensive text, will there be a search requirement? How can IO operations be minimized? How can database efficiency be maximized?
Possible technical issues:
- Compared to news websites, there's a more significant database strain.
User experience:
- Opt for technologies that offer superior user experiences, including loading speed, interactivity, and usability.
- Consider the support level of the chosen tech for mobile devices and different browsers.
Performance and scalability:
- Evaluate the performance of different technologies, such as response times, concurrent processing capability, and data handling prowess.
- Anticipate potential growth and expansions, ensuring the chosen tech offers scalability and maintainability.
Security:
- Assess the security of the tech, ensuring it meets project security and compliance needs.
- Consider the tech's defenses against common threats, like SQL injection and cross-site scripting.

Considerations from a cost perspective:

Development efficiency and cost:
- Assess how the tech choice impacts the development cycle, choosing those that enhance efficiency.
- Weigh the cost-effectiveness of the technology, including development, maintenance, and operational costs.
Tech ecosystem and community support:
- Opt for technologies with vibrant communities and abundant resources to quickly resolve issues.
- Consider the update and maintenance status of the tech, ensuring its continued growth and support.
Team skills and experience:
- Take into account the team's expertise and experience, choosing technologies they're familiar with or can quickly master.
- Decide if additional training or resources are required to support the tech's adoption and application.
Future tech trends:
- Stay informed about industry tech trends, avoiding those that might soon be obsolete.
- Gauge the support level of the tech for emerging technologies and standards, ensuring the project stays technologically ahead.
Integration with external systems:
- Think about the tech's support for integration with external systems and services, like API integration and data exchange.
Testability and maintainability:
- Go for technologies that support automated testing, continuous integration, and continuous deployment to boost project quality and maintainability.
- Think about the tech's documentation, debugging tools, and monitoring utilities to facilitate development and operations.

Appendix 1. PRD of GPT-Onion

Walkman42 — Tue, 31 Oct 2023 11:43:53 +0000

Product Requirements Document of GPT-Onion

Executive Summary

GPT-Onion is a community-centered platform that empowers users to learn, build, and showcase AI functionalities using AI prompts. These prompts serve as instructions for AI models like ChatGPT, facilitating applications like text generation, language translation, creative content creation, and information query resolution.

Vision

The vision of GPT-Onion is to offer a collaborative environment where individuals can discover, share, and create AI prompts, fostering a community passionate about AI and creativity.

Target Audience

Writers, designers, and developers seeking to boost productivity.
Individuals interested in utilizing AI for learning, research, and problem-solving.
AI enthusiasts keen on exploring, sharing, and crafting AI prompts.

User Roles

Visitor:
- Browse public AI prompt library.
- Use prompts in conversation mode as a visitor.
- Prompt for login during an attempted chat.
- View community-curated Collections.
- Access learning and tutorial resources.
Registered User:
- Create and manage personal accounts.
- Create, edit, and delete their AI prompts.
- Create, edit, and delete their collections.
- Interact with community members (e.g., commenting and rating).
Community Administrator:
- Manage community content and users.
- Provide user support and guidance.
- Analyze platform usage and feedback to optimize features.

Feature List

1. i18n

Switch and select the system language.

2. AI Prompt Library

Browse and Search:
- Filter and search prompts based on keywords, categories, and ratings.
- Offer multiple sorting options like newest, most popular, and top-rated.
Prompt Details:
- Display creator, description, ratings, comments, and usage instances of the prompt.
- Provide options to utilize the prompt, like copying to clipboard, sharing, and rating.

3. Create and Share AI Prompts

Prompt Creation:
- Offer form to input prompt title, description, and specific commands.
- Provide real-time preview to demonstrate how AI responds to the prompt.
Edit and Delete:
- Allow users to edit or delete prompts they've created.
- Provide an option to undo deletions.
Share and Embed:
- Generate shareable links and embed codes.
- Enable users to share prompts on social media and websites.

4. Collections

Create and Manage:
- Craft new collections with title, description, and cover images.
- Add, edit, and remove prompts from collections.
Browse and Search:
- Offer categories and tags to help users find specific collections.
- Display detailed information about the collection like its creator, description, and included prompts.
Share and Embed:
- Generate shareable links and embed codes.
- Allow users to share collections on social media and websites.

5. Community Interaction

Ratings and Comments:
- Offer star ratings and textual commenting capabilities.
- Display ratings and comments from other users.
User Communication:
- Provide a user forum or chat functionality.
- Allow users to direct message and respond to other users.
Voting and Tipping:
- Let users upvote prompts.
- Offer a tipping feature for users.

6. User Accounts and Profiles

Account Management:
- Modify account settings like username, password, and email.
- View account activity history and statistics.
Profile:
- Edit personal details like avatar, bio, and contact information.
- Showcase prompts and collections crafted by the user.

7. Learning Resources and Support

Tutorials and Guides:
- Offer tutorials on creating and sharing prompts.
- Provide guides on using the platform and community features.
Help Center and Support:
- Offer a Frequently Asked Questions (FAQ) section.
- Provide an option to contact the support team for assistance and problem resolution.

Non-Functional Requirements

Performance:
- Fast response times.
- Efficiently handle multiple concurrent user interactions.
Security:
- Protect user data.
- Ensure compliance with privacy and data protection regulations.
Usability:
- Intuitive UI/UX.
- Open to individuals with varying levels of AI expertise.

Future Enhancements

Integration with other AI models and platforms.
Further community-building features to boost collaboration and learning.

Conclusion

GPT-Onion aims to be the epicenter for AI enthusiasts and creators to explore, learn, and collaborate. By offering an interactive environment for sharing and crafting prompts and driving it through a community, it expands the potential of AI, making it an indispensable platform for anyone keen on leveraging AI for various applications.

Day 1: The Fool's Journeys (2)

Walkman42 — Tue, 31 Oct 2023 11:29:47 +0000

1.2.2 Internationalization (i18n)

If you ask me what tasks should be considered before starting coding to save time, i18n is definitely a significant one.

i18n is the abbreviation for "internationalization". It involves designing and developing products so they can be easily adapted for different languages, cultures, and regions. The significance of considering i18n at the beginning includes:

Code Structure: Internationalization requires specific code structures and organizations to separate language and culture-related content (like text, date, and number formats) from application codes. Modifying these structures might become complex if internationalization isn't considered from the start.
Resource Files: i18n often involves extracting text and other localization resources from code into external resource files. Considering this early on allows for clearer organization and easier updates for different language versions.
UI Design: Different languages have different length and format requirements. Thinking about i18n ensures that UI design has enough flexibility to accommodate different text lengths and layouts.
Functionality and Cultural Adaptability: Some features or content may not be suitable or relevant in certain cultures or regions. Thinking about this can help teams avoid unnecessary feature development or later adjustments.
Technology Choices: Some technologies and tools have built-in i18n support, while others might not. Considering internationalization early on can influence the choice of tech stack, ensuring the chosen tech supports internationalization.
Avoid Rework: Introducing i18n later in the project might require a significant amount of code refactoring and UI adjustments. Thinking about it early can prevent this rework, thus saving time and costs.
Testing: Keeping i18n in mind ensures that proper localization and internationalization tests are done throughout the project lifecycle, preventing localization-related issues found later on.
Market Release: Thinking about internationalization can speed up product launches in global markets, as no additional localization is needed post-launch.

Considering i18n at the start of a project can prevent many potential issues, thus saving time and resources. It ensures a smoother and more efficient process of internationalization and localization.

Whether it's a frontend framework or a backend one, either we choose a framework that supports i18n, or we prepare a solution in advance.

We will delve deeper into this issue in the subsequent technology selection phase.

1.2.3 Minimum Viable Product (MVP)

The concept of MVP (Minimum Viable Product) was first proposed by Eric Ries, published in the Harvard Business Review, with a subsequent publication "The Lean Startup".

Reading through the PRD, if we need to cut down on features, where can we start?

User Accounts and Profiles: While it enhances the user experience, its primary purpose is to facilitate user interactions. It also helps us better understand our customers for future operations and maintenance. It seems indispensable.
Learning Resources and Support: Important for user understanding, but intuitive interface design can reduce user reliance on help documents. Even without it, core users can grasp most functionalities through exploration.
Collections: It's all about convenience, letting users manage their keywords systematically. Still, its absence won't affect the core function.
Community Interaction: Engages users and triggers sharing. While important, its absence won't hinder core users.
Prompt Library: Without this, our project becomes meaningless. Clearly, it's a core function, the very foundation.
Chatting: Chatting with GPT, clicking prompts for instant use is convenient and simple in logic. If absent, the user experience will drop significantly.

Thus, as long as the prompt library function exists, the project is meaningful.

This brings us to the most important concept of this article series: the Minimum Viable Product (MVP).

The MVP is a product development concept that emphasizes building a product with core functionalities in the shortest time, using minimal resources, to test its market potential and gather user feedback as soon as possible. An MVP is not a fully matured or refined product but an initial version made to validate key assumptions and understand user requirements..

Main objectives of MVP:

Validate the core assumptions of the product: By building the core features of the product, the team can test its main value proposition and confirm its market demand.
Reduce development time: By focusing solely on key features, the team can quickly roll out the product instead of waiting for a complete version.
Save resources: Avoid investing significant time and money in functionalities or concepts that haven't been validated.
Gather user feedback: MVP allows the team to collect feedback from actual users, leading to necessary iterations for the product.
Adapt to market changes: By swiftly releasing and iterating the product, teams can flexibly adapt to market changes and demands.

MVP development process:

Market research: Understand the target users and market needs.
Define core assumptions: Determine the product's main value proposition and essential features.
Design and develop MVP: Focus resources on developing the core functionalities.
Launch and introduce to the market: Engage target users with the MVP.
Collect feedback: Gather data and feedback based on actual user experiences.
Iterate and improve: Adjust the product based on the feedback received.

Advantages of MVP:

Reduce risks: By validating core assumptions, teams can avoid developing products with no market.
Accelerate learning: Through interactions with real users, teams can learn and adjust their strategy faster.
Resource efficiency: MVP ensures the team's time and money are effectively allocated to the most valuable features.

However, it's essential to note that MVP is not an excuse to produce a low-quality or half-baked product. It should be a functional, value-bringing product for users, albeit more focused and limited in features and functionalities.

There's quite a bit of preparation work to be done on the first day, especially in terms of requirement organization. Tomorrow, once we've received the organized requirements, we'll start the technical selection phase.

Day 1: The Fool's Journey

Walkman42 — Tue, 31 Oct 2023 11:18:25 +0000

Just as the first card in the Tarot deck signifies, we embark on the Fool's Journey. Everyone hopes to swiftly reach the destination, leading many to search for shortcuts. Yet, the true shortcut lies in not taking detours.

1.1 The Impossible Triangle

In the role of project leaders, it's crucial to understand the "Impossible Triangle": Time, Features, and Manpower.

Time: A central element in project management. Every endeavor comes with a deadline or a strict timeline that must be met. These time constraints often arise from stakeholder expectations. Project managers must set a precise plan, determine the Work Breakdown Structure (WBS), and pinpoint milestones to ensure timely project completion.
Feature Complexity: This dimension covers the scope, features, and intricacies of a project. An increase in project functionality or complexity often results in more time and resources. This surge is due to the demand for additional development, testing, and support. Managers must align with stakeholders on the project's features and complexity to ensure the objectives are met, making necessary trade-offs based on feasibility and resource availability.
Manpower: Human resources are vital for the success of any project. This encompasses the team members, their skill set, expertise, and any additional external resources required. Adequate planning and allocation of manpower are essential to fulfill project needs, including determining team size, identifying skill necessities, and coordinating resources. Insufficient or improper allocation could lead to delays, quality issues, or budget overruns.

In the realm of project management, managers and teams must strike a balance among these three pillars to achieve optimal outcomes. This often involves constant tweaks and decisions to ensure the project meets the criteria of time, functionality, and resources. Effective communication with stakeholders is also essential to align their expectations and requirements and find the equilibrium within the "Impossible Triangle" for project success. Achieving this balance demands meticulous planning, efficient communication, and adaptable management tactics.

Moreover, none of these three factors is universally paramount, but within a specific project, priorities invariably emerge. Given our current project, our human resources are fixed, and we've also predetermined that completion within a week is non-negotiable. Thus, with two sides of the triangle set in stone, our only wiggle room is the complexity of the features.

In product development, the allure of countless features and innovations can be overwhelming. Faced with an endless feature list, tough choices are inevitable. The bridge between aspiration and reality is determined by feasibility and core value.

List all the features, then slash it in half, and half again. Develop what remains and launch.

Remember, done is better than perfect. When listing all features, first halve the list, retaining only those crucial to the product's core value. Then, scrutinize this condensed list, cutting it in half once more. What you're left with is the truly essential set of features that will allow you to launch your product quickly.

Time is a precious commodity. Attempting to implement every feature all at once not only drags out the project but might also divert focus from essential features.
Perfection is the enemy of the product. Striving for perfection can mire the team in endless tweaks, leaving completion ever out of reach.
Rapid feedback is key. A swift market entry means real-time user feedback, facilitating the necessary iterations and enhancements. This is far more practical than squandering time on chasing perfection.
The landscape and needs evolve. Over an extended development cycle, both market dynamics and user requirements can shift. Being bogged down with feature buildup might mean missing the market's prime window by the time the product launches.

1.2 Product Features and the PRD

PRD stands for Product Requirements Document. The primary aim of crafting a PRD is to accurately define product functionalities and user experience.

Given our tight seven-day window, many, once they have a rough idea, are chomping at the bit to pen down the initial lines of code. Yet, as the saying goes, "A well-prepared PRD can save countless hours down the line." Many mid-project quandaries arise from ill-defined initial requirements and rules. A well-drafted PRD can offer clear project expectations:

Reduce Iterations: Clear requirements from the outset can cut back on the number of iterations later.
Risk Management: With set requirements and constraints, teams can identify and handle potential risks more effectively.
Resource Allocation: A precise PRD enables better allocation of resources and time by project managers and their teams.
Clear Direction: A PRD ensures the team's alignment on the product's objectives and anticipated functionalities.
Guidance Provision: It offers a clear directive for development, design, and testing teams.
Enhanced Communication: The PRD serves as a tool for communication within the team, ensuring everyone's on the same page regarding product direction.
Evaluation & Feedback: The PRD provides a standard for product assessment, helping teams measure if the end product meets the initial expectations.

Clearly, adopting an iterative approach facilitates a stepwise project progression, and a PRD is the best tool to minimize non-productive iterations.

Here's a simplified PRD demonstration for this project: Appendix: GPT-onion Product PRD

Armed with a comprehensive feature list, we can then prioritize based on the next stages of practical implementation. While I'm currently uncertain about the completion percentage over the seven days, we can at least establish a hierarchy, focusing first on the most crucial aspects.

1.2.1 User Roles

To see what is often unseen.

The crafting of the "User Roles" section is an area frequently overlooked by rookie product managers who haven't systematically executed projects before.

Reasons why user roles tend to be overlooked:

Assumptions About the User: Teams might already have their own assumptions about the user, thinking they know what users want, hence bypassing the step to define user roles.
Overemphasis on Technology: Tech-driven teams might overly focus on technical implementation and features, sidelining the actual needs of users.
Time and Resource Constraints: In a rapid development setting, teams might feel they lack the time to delve into and define user roles.
Lack of User Research: Not conducting ample user research, or not viewing it as a priority, could lead to sidelining user roles.
Document Complexity: Writing out user roles could compound the complexity of the PRD. Some teams might opt to simplify the PRD to hasten the development process.

So, what are the user roles for GPT-Onion?

Visitor:
- Browse the public AI prompt library.
- Engage with the dialogue interface using prompts as a guest.
- Get prompted to log in when attempting to chat.
- View community-curated collections (Collections).
- Access educational and tutorial resources.
Registered User:
- Create and manage personal accounts.
- Create, edit, and delete their own AI prompts.
- Create, edit, and delete their own curated collections.
- Interact with community members (e.g., commenting and rating).
Community Admin:
- Manage community content and users.
- Provide user support and guidance.
- Analyze platform usage and feedback to enhance platform features.

Once we have the user roles clearly listed, some readers might have an epiphany, "Oh, how did I forget about the admin?"

This underscores the importance of determining roles before pondering over features. We are prone to subjectively putting ourselves in the shoes of a registered user when mulling over problems since that represents the majority's need status and core demands.

However, when the role of a visitor is taken into account, some intricate details of the feature process become explicitly articulated. This can minimize code revisions for developers later on. The tighter the timeframe, the more time this saves.

Development should not only account for the usual logic but also make allowances for atypical logic.

Recall our initial saying, "The real shortcut is not taking detours."

Thus, the necessity of penning user roles is evident:

Specify Target Users: Determining the product's target users lays the foundation for product design and development. This aids the team in concentrating efforts on delivering the most valuable features to specific user groups.
Amplify Resonance: Grasping user roles can assist the team in resonating better with users, understanding their pain points, and requirements.
Guide Design Choices: User roles proffer a framework for designers, aiding them in factoring user needs and expectations when designing interfaces and interactions.
Facilitate Efficient Feature Decision-making: Knowing the user roles and their tasks can guide the team in discerning which features are imperative and which are secondary.

Reinventing the Wheel in 7 Days:Guide to Project Management and Technical Practices

Walkman42 — Tue, 31 Oct 2023 08:31:14 +0000

🥳 Introduction

Chinese version(中文版):https://walkman42.gitbook.io/best-practice-in-7days

Prelude

Whether it's ChatGPT, Claude, Llma2, or any other, there's a surge of large language models appearing in the market.

What can these applications achieve? What are these models capable of? And what is the significance of multimodality?

This series aims to discuss the magic that can be woven when we approach novel innovations, combining societal and professional experiences. Let's set an ambitious target: to replicate flowgpt.com, achieving 80% of its core functionalities, all within a week.

The essence of this series isn't merely about achieving the core functionalities. It delves deeper into sharing best practices experienced during the project's implementation. As an architect and manager with nearly 20 years of R&D experience, I will guide you through the journey of receiving a project, dissecting its requirements, making technical choices, and finally executing it.

In this week, we'll traverse the path from project analysis and planning to coding, all done by a single individual armed with just a MacBook.

The journey ahead is long, filled with challenges.
Yet, as we embark on this adventure, let's motivate each other and tread ahead.

Chapter 1· Computer Vision (2) Image Text Recognition (OCR)

Walkman42 — Sun, 17 Sep 2023 01:30:45 +0000

2. Recognizing Text Information on Cards (Chinese and English)

Now that we have successfully identified each card, the next step is to recognize the information on the cards.

Solution Comparison

Here are several candidate solutions:

Tesseract OCR: Tesseract is an open-source OCR engine developed by Google. It can recognize multiple languages and excels in text recognition accuracy. Tesseract's Python binding library is called pytesseract, making it easy to integrate with Python.
EasyOCR: EasyOCR is a user-friendly OCR library with support for multiple languages and pretrained models. It is based on deep learning technology and can be used for text detection and recognition in images.
PyOCR: PyOCR is a Python wrapper for OCR that can integrate with various OCR engines, including Tesseract and CuneiForm, providing flexibility to choose the appropriate OCR engine based on requirements.
Google Cloud Vision API: Google offers a cloud-based text recognition API that can be used via the Python SDK. It can recognize multiple languages, handwritten text, and printed text, with powerful image analysis capabilities.
Microsoft Azure Computer Vision API: Microsoft Azure provides a set of computer vision APIs, including text recognition. You can access these APIs using Azure's Python SDK for tasks like text recognition and other image analysis tasks.
Amazon Textract: Amazon Textract is Amazon's OCR service for extracting text and structured data from scanned documents. Amazon provides a Python SDK, making it easy to integrate into Python applications.[HA]

Among these options, options 4, 5, and 6 are cloud services and may incur costs, but they offer enhanced efficiency and stability compared to the others.

EasyOCR supports over 80 languages and performs better than Tesseract OCR in Chinese text recognition. Additionally, CnOCR, which uses Baidu PaddlePaddle models, offers good efficiency and accuracy and can complement EasyOCR.

Special Handling for Macbook Air M1/M2 Chip

I use a Macbook Air with an M1 chip, but the installation of EasyOCR on this machine can be quite cumbersome.

When installing it using the default method, you may encounter the error message "Could not initialize NNPACK! Reason: Unsupported hardware," and EasyOCR won't work. Therefore, if you are using the same development environment as mine, you can follow the steps below to resolve this issue.

If circumstances allow, we can create a virtual environment locally using conda (or other virtual environment software):

conda create --name openmmlab
conda activate openmmlab

Installation of non-Mac M1 chip machines

conda install pytorch torchvision cpuonly -c pytorch
pip install easyocr

That's right, if your hardware is not Apple's M1/M2 chip, congratulations on saving one and a half hours of compilation and installation time.

Installation of Mac M1/M2 chip machine

Continuing with the installation process, proceed to install PyTorch. Please note that you should use "USE_NNPACK=0" to disable NNPACK. This step is necessary because PyTorch's optimal configuration is typically designed for Nvidia GPUs. However, Macbook machines are not equipped with Nvidia GPUs, so you must enable the "cpuonly" option.

git clone https://github.com/pytorch/pytorch.git
cd pytorch
export USE_NNPACK=0 
# This step will take longer
python setup.py install

Next, proceed with the installation of mmdetection, which is an open-source deep learning project for Object Detection and Instance Segmentation. It is built on the PyTorch framework and serves as a powerful tool for researchers and engineers to train and deploy models for object detection and instance segmentation.

Please note that it is essential to download and install the source code to avoid the errors mentioned earlier.

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -r requirements/build.txt
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.

Installing the star of the show: the EasyOCR library.

pip install easyocr

At this point, the preparations have been completed.

Chapter 1· Computer Vision(1) Object Detection

Walkman42 — Sat, 16 Sep 2023 13:13:28 +0000

0. Getting Started with Computer Vision Through Gaming

最近开始将我的教程翻译成英文，由于翻译过程中涉及示例代码的i18n修改，进度可能要稍微落后于我的中文博客。如果您有中文阅读能力，可以移步这里。
Recently, I started translating my tutorials into English.
Due to internationalization modifications in the example code during the translation process, and the progress may be slightly behind my Chinese blog. If you have the ability to read Chinese, you can move HERE.
本文中文地址|Chinese source text link

Recently, I've become fascinated with a game called "Night of the Full Moon", which is a card-based auto-battler game similar to Teamfight Tactics and Hearthstone.
Due to the lack of comprehensive official resources, it's quite challenging for a tech enthusiast like me to get started.
Okay, we are going to create a game database together.

The cards in the game look like this:

English Example	Chinese Example

Objective of the Machine Vision Section: To record the information of minion cards in the game into the database through the identification of game screenshots.

Given the numerous minions in the game (202 in total), in order to analyze the compatibility between minions at a glance, we need a more intuitive and convenient method for data filtering and presentation.

To achieve this goal, let's first organize the general approach and steps:

Identify the cards in the image.
Recognize the text information on the cards.
Enter into the database after removing duplicates. We will gradually discuss the technical means and open-source libraries that will be used in this process later on.

To enhance the reading experience, the code snippets in this document are provided as essential feature examples, omitting relatively complex logic and conditionals that might be present in your actual project.
The code is intended for reference and learning purposes; you may need to make adjustments according to your specific business requirements in your project.

1.Identify the cards in the image

For humans, what a playing card is may be quite clear, but how can we make a computer understand what a playing card is?

Of course, we start by providing a clear definition:

A playing card is a rectangular flat object with patterns, numbers, text, or symbols on it.

Next, we need to instruct the computer to identify these playing cards from images. Here's the general approach:

Load the image.
Convert the image to grayscale.
Perform Canny edge detection.

Here, we will primarily make use of the OpenCV library for image processing.

OpenCV (Open Source Computer Vision Library) is an open-source computer vision library that provides a vast array of tools, algorithms, and functions for image and video processing. Originally developed by Intel, it is released under a BSD license and is free to use in both commercial and research applications. This library aims to make computer vision tasks more accessible and finds wide applications in various fields, including machine learning, image processing, computer vision research, industrial automation, and embedded systems.

import cv2

# Load the image
img = cv2.imread('images/01.jpg')
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Apply object detection
edges = cv2.Canny(img_gray, threshold1=30, threshold2=100)

# Find contours
contours, hierarchy = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

From the image above, it's apparent that we have identified some contours. However, there is an issue where information at the top, such as the race, card type, and card level, has also been detected by the edge detection. This clearly does not meet our expectations. Therefore, we are redefining the concept of a card and improving this by excluding contours that are too small.

A playing card is a rectangular flat object with patterns, numbers, text, or symbols on it.
Minimum area of 36,000 square pixels (>=150x240).

import cv2

# Load the image
img_path = 'images/01.jpg'
img = cv2.imread(img_path)
if img is None:
    print("Image load failed!")
    exit(0)

# Convert the image to RGB before displaying
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Find contours
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(img_gray, threshold1=30, threshold2=100)
contours, hierarcy = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Filter out small contours based on the area[HA]
min_contour_area = 36000
large_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > min_contour_area]

# Draw contours and label them
img_contours = img_rgb.copy()
for i, contour in enumerate(large_contours):
    # Get the bounding box of the contour
    x, y, w, h = cv2.boundingRect(contour)

    # Draw the contour and label it
    cv2.rectangle(img_contours, (x, y), (x + w, y + h), (0, 255, 0), 2)
    cv2.putText(img_contours, str(i + 1), (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

# Display the image with contours and labels
cv2.imshow('Contours', img_contours)
cv2.waitKey(0)

Let's take a look at the results[HA]:

What we see	What the computer sees

In the code, we performed color conversion with the goal of reducing information interference when detecting edges. Here is a brief overview of the purposes of different color conversions:

Grayscale Image:
- Reduced Computational Complexity: When your task involves image processing or analysis without the need for color information, converting the image to grayscale can reduce computational complexity. Grayscale images contain only brightness information, omitting color.
- Feature Extraction: In some computer vision tasks like face detection or object recognition, grayscale images are often sufficient for extracting essential features because color information may not be critical.
RGB Image:
- Color-Related Tasks: If your task involves color information, such as color being an important feature in image classification, you should retain the color information in RGB (Red, Green, Blue) images.
- Segmentation Tasks: RGB images are generally more useful in image segmentation tasks because color can help distinguish different objects or regions.
Other Color Spaces:
- HSV (Hue, Saturation, Value) Space: The HSV color space is commonly used for color analysis and processing because it more intuitively represents color attributes. It may be more useful for certain color-related tasks.
- Lab (L*, a*, b*) Space: Lab color space is often used for color-related tasks and image analysis because it separates brightness information (L channel) from color information (a and b channels), making color processing easier.

These color conversions are chosen based on the specific requirements of your computer vision task and whether color information is essential for achieving your goals.