Every AI Model Explained for Beginners (Simple Guide)
Introduction: In simple terms, AI models are like digital brains trained on massive amounts of data to understand language, images, code, and human instructions. Different companies build different models because each one is trained differently, designed for different goals, and better at certain tasks, such as writing, coding, reasoning, image, or video generation. What is an AI Model in a simple analogy An AI model is like a student that has read billions of books, websites, articles, and conversations from the Internet. Instead of memorizing exact answers, it learns patterns in language, images, code, and reasoning so it can predict what comes next, similar to how your phone predicts the next word when typing, but on a much more advanced level. In general, larger models with better training data and more advanced training methods tend to give smarter, more accurate, and more human-like responses. Limitations of AI Models Despite their impressive capabilities, AI models still have important limitations. They can sometimes “hallucinate” by confidently generating incorrect or completely made-up information, and their answers are not always factually accurate. AI models may also reflect biases from their training data, struggle with complex reasoning in certain situations, and some models may rely on outdated knowledge if they are not connected to real-time information sources. Large Language Model (LLM) A Large Language Model (LLM) is a type of AI designed to understand and generate human language. “Large” means it is trained on massive amounts of data from books, websites, articles, and conversations, “Language” means it works mainly with words and text, and “Model” refers to the AI system itself. Popular LLMs like ChatGPT, Claude, and Gemini can answer questions, write content, summarize information, translate languages, and even generate code. Main Types of AI Models: Generally, AI models are categorized into several major types, including flagship large language models (LLMs), open-source models, image generation models, video generation models, coding models, and audio or speech models. Let’s get started with flagship LLMs Part I: Closed-source Flagship LLMs 1. GPT — OpenAI’s Flagship LLM Series ChatGPT is powered by the GPT model family, which evolved from GPT-1, GPT-3, GPT-3.5 (arguably the most iconic version that triggered the global AI boom after ChatGPT launched in November 2022), GPT-4 (released in March 2023), GPT-4o, GPT-5, and newer variants like GPT-5.2. ChatGPT offers several pricing tiers, including Free, Go, Plus, and Pro plans. As an all-round AI assistant, ChatGPT performs well in writing, coding, web search, question answering, image generation, PDF analysis, and voice interactions, although some users feel its responses can occasionally be too long or overly cautious. Quick note: ChatGPT itself is not the AI model — GPT is the model. ChatGPT is the application or interface (similar to Microsoft Copilot, Gemini, or Claude), while GPT is the “brain” operating behind the scenes. 2. Gemini — Best Integration with the Google Ecosystem Gemini is Google’s flagship AI assistant and one of the strongest competitors to GPT models. Gemini is deeply integrated into the Google ecosystem, including Search, Gmail, Docs, Sheets, YouTube, and Android devices, making it especially convenient for everyday productivity and web-based tasks. Gemini offers several plans, including Gemini Free, AI Plus, AI Pro, and AI Ultra. Its strengths include fast responses, strong web search capabilities, video understanding, image generation, and seamless integration with Google services. Features like AI Mode and AI Overviews help users quickly summarize search results, while newer multimodal capabilities allow Gemini to understand text, images, videos, and documents together. 3. Claude — Built for Reasoning, Coding, and Work Tasks Claude is an AI assistant developed by Anthropic and is widely known for its strong reasoning, coding, and writing abilities. Many users prefer Claude for professional work because it handles long documents well, produces natural writing, and performs strongly in coding, research, spreadsheets, reports, and other office-related tasks. Claude currently offers Free, Pro, and Max plans (starting from around $100/month), with advanced models such as Claude Opus 4.6 powering higher-tier experiences. Unlike some competitors, Claude focuses more on productivity and reasoning rather than image generation. Its strengths include coding assistance, document analysis, integrations, tools, workflow automation, and “skills” that help users complete complex work tasks more efficiently. 4. Perplexity Sonar — AI Search and Research Assistant Perplexity is an AI-powered search and research assistant designed to give fast, accurate answers with real-time web sources and citations. Unlike traditional chatbots that mainly rely on pre-trained knowledge, Perplexity focuses heavily on live web search, making it especially useful for research, fact-checking, news, and discovering up-to-date information. Perplexity uses its Sonar model family alongside other AI models to improve search accuracy and reasoning. Its strengths include fast web browsing, source citations, research summaries, and answering complex questions with references — making it popular among students, researchers, marketers, and professionals who need reliable information quickly. Perplexity offers both Free and Pro plans, with Pro users gaining access to more advanced AI models, deeper research features, and higher usage limits. 5. Grok — AI Integrated with the X Platform Grok is an AI assistant developed by xAI and is closely integrated with the X (formerly Twitter) platform. Grok is designed to provide real-time information, social media insights, and conversational AI experiences, making it useful for trending topics, live discussions, and internet culture. Its strengths include real-time X search, image generation, voice interaction, and fast access to trending content directly from the X ecosystem. Compared to some other AI assistants, Grok is known for having a more casual and humorous personality style. Grok offers both free access and premium tiers such as SuperGrok and SuperGrok Heavy, which provide higher usage limits and access to more advanced AI features. DeepSeek — Rising Open-Source AI Challenger DeepSeek is a Chinese AI model family developed by DeepSeek, a company backed by the quantitative hedge fund High-Flyer. DeepSeek quickly gained global attention for delivering strong reasoning, mathematics, and coding performance while offering many of its models as open-weight models that developers can freely access and run themselves. DeepSeek is especially popular among developers
Understanding Vibe Coding: Tools, Workflow & Basics
Disclaimer: This article is written to reinforce my understanding of Vibe Coding concepts learned from a local guru and AI Vibe Coding training provider. It is not meant to be an in-depth guide, but rather a beginner-friendly refresher to document and revisit the key ideas. What is Vibe Coding? Vibe Coding is a modern approach to software development where a person describes what they want to build in natural language, and AI generates the code needed to create it. The concept became widely discussed after AI researcher Andrej Karpathy used the term “vibe coding” to describe a style of programming where developers guide AI tools with intent rather than manually writing every line of code. In traditional programming, developers must write detailed code instructions such as functions, loops, and syntax.With vibe coding, the workflow changes: You describe the idea AI generates the code You test and refine the result In other words: Vibe Coding = Programming by describing the intent and letting AI handle much of the implementation. This method shifts the role of the developer from code writer to AI collaborator or director. Basic Components of an Application To understand vibe coding, it is helpful first to understand the three fundamental elements of most applications: the front end, back end, and database. The front end refers to the part of the application that users interact with directly. It is typically built using HTML, CSS, and JavaScript. HTML defines the structure of the page, CSS controls the visual design and layout, and JavaScript handles the logic and interactivity, such as buttons, animations, or dynamic content. The back end is the system that runs behind the scenes. It processes requests, handles business logic, communicates with the database, and ensures that the application functions properly. The database is where the application stores and manages its data, such as user information, settings, or content. During development, programmers often use tools such as terminals, network connections, and integrated development environments to build, test, and integrate these components. In vibe coding, AI tools help generate and integrate these elements quickly, allowing developers to focus more on the idea and functionality of the application rather than writing every line of code manually. Use Cases: Popular Use Cases of Vibe Coding Vibe coding allows people to build software by describing ideas in natural language. It is commonly used for rapid prototyping, creative experiments, and small functional tools. Below are several popular examples. 1. Landing Pages One of the most common uses of vibe coding is creating marketing landing pages. With a simple prompt, AI can generate: HTML page structure CSS styling and layout JavaScript for interaction Lead capture forms This is useful for: product launches event registration pages marketing campaigns lead generation pages Marketers can now use vibe coding to quickly test different campaign pages without hiring a full developer. 2. Simple Web Applications Vibe coding is also widely used to build small web apps or tools. Examples include: productivity tools habit trackers calculators AI content generators dashboards These tools often combine front-end, back-end logic, and database storage, but AI can generate most of the structure automatically. 3. Interactive SVG Animations Designers can use vibe coding to create interactive animations using SVG and JavaScript. Examples include: animated logos interactive diagrams marketing visuals scrolling animations AI can quickly generate SVG code and animation logic, reducing manual coding time. 4. Mini Games Another fun use case is building small browser-based games. Examples: clicker games lucky draw generators simple puzzle games pixel-style mini games For example, a 财神 (God of Wealth) lucky number generator in which users click a character to reveal a 4D number can be built using Vibe-Coding. 5. Automation Tools Vibe coding can also create automation scripts. Examples include: data scraping tools automated email workflows report generators social media automation AI can generate scripts that interact with APIs or perform repetitive tasks. 6. Internal Business Tools Companies can also use vibe coding to build internal tools quickly. Examples: CRM dashboards internal reporting systems task management tools data visualization panels These tools may not need perfect production-level code, making vibe coding ideal for fast internal development. 7. Educational Experiments Vibe coding is also widely used for learning and experimentation. Beginners can quickly build projects like: quiz apps learning flashcards language practice tools small simulations This makes coding more accessible to people without traditional programming backgrounds. In practice, vibe coding is most commonly used for landing pages, small web apps, interactive animations, mini games, automation tools, and experimental prototypes. Tools / Software / Platforms Vibe Coding Tools & Platforms Below are several vibe coding tools and platforms. Some were taught and demonstrated during the 2-day training course, while others were introduced by more advanced course mates that I have come across but have not yet personally tested. Antigravity (Local Tool) Antigravity is a local vibe coding environment that allows users to generate applications using AI prompts and instantly preview the results. It helps creators quickly build front-end interfaces, animations, or small apps before pushing the project to version control platforms like GitHub. This makes it useful for rapid experimentation and prototype development. Google AI Studio Google AI Studio is a platform for building applications using Google’s AI models. Developers can test prompts, generate code, and integrate AI capabilities into their applications. In vibe coding, it is often used to generate logic, build AI-powered features, or experiment with prompt-based coding workflows. ChatGPT Codex ChatGPT Codex is an AI coding assistant that can generate, explain, and debug code across multiple programming languages. In vibe coding, it allows users to describe features or applications in natural language and receive working code snippets or full components. It is widely used for rapid prototyping and problem solving during development. Claude Code Claude Code is an AI-powered coding assistant designed to help developers write, refactor, and understand code efficiently. It is particularly strong at analyzing large codebases and providing structured code improvements. In vibe coding workflows, it is often used to refine AI-generated code and
Top 10 AI Skills You May Need to Learn in 2026
What are AI Skills? AI skills refer to the abilities required to utilize, comprehend, and operate with artificial intelligence tools and systems. They don’t only apply to engineers or data scientists. Today, AI skills encompass a range of areas, including using AI tools effectively, understanding how AI makes decisions, working with data, and applying AI to real-world business or work problems. As AI becomes part of everyday software, from search engines and chatbots to marketing tools and automation platforms, having basic AI skills is quickly becoming as important as knowing how to use spreadsheets or email. Skill #1: Prompt Engineering Prompt engineering is the skill of writing clear, structured instructions (prompts) to get better, more accurate results from AI tools like chatbots, image generators, and AI assistants. Instead of asking vague questions, prompt engineering focuses on telling the AI exactly what you want, in what format, and for what purpose. Example usage: For text-to-image, a simple prompt like “Create a marketing image” may return an unfocused visual. A prompt-engineered version such as “Create a 1:1 marketing image of a modern coffee shop interior, warm lighting, minimal design, suitable for Instagram ads” produces a far more usable result. For text-to-video, instead of “Make a product video”, a clearer prompt like “Generate a 15-second product demo video showing a smartphone app interface, with smooth transitions and captions explaining key features” helps the AI create content that matches real business needs. Real-world business application: Companies could use prompt engineering to create social media visuals, ad creatives, explainer videos, product mockups, and promotional content at scale. This allows marketing, design, and sales teams to produce visual assets quickly, without relying heavily on designers or video editors. Skill #2: AI Automation AI automation is the skill of connecting AI tools (e.g, ChatGPT) with workflows to automatically handle repetitive tasks, without manual effort. With no-code AI workflow automation, users can build logic-based processes that trigger actions, analyze data, and generate outputs using AI, all without writing code. Example usage: A no-code workflow can automatically receive a form submission, summarize the content using AI, classify the lead type, and send a personalized follow-up email, all triggered in real time using tools like n8n or Zapier. Real-world business application: Businesses use AI automation to streamline customer support, lead qualification, content generation, reporting, and internal operations. This reduces human workload, minimizes errors, and allows teams to scale processes efficiently without hiring additional staff or developers. Skill #3: AI Literacy (Understanding How AI Works) AI literacy is the ability to understand how different AI tools work, what they are good at, and when to use each one. It’s not about building AI models, but about knowing their capabilities, limitations, strengths, and weaknesses so you can choose the right tool for the right task. Example usage: An AI-literate user knows that ChatGPT is strong at reasoning and structured writing, Gemini works well with Google ecosystem data, Claude excels at handling long documents, while tools like DeepSeek, Qwen, or Copilot may perform better in coding, enterprise workflows, or specific regional use cases. Real-world business application: In business, AI literacy helps teams select the right AI tool for tasks like writing, coding, data analysis, customer support, or research, instead of relying on one tool for everything. This leads to better output quality, lower costs, fewer mistakes, and smarter AI adoption across departments. Skill #4: AI Image Generation & Design AI image generation and design is the skill of using AI tools to create, edit, enhance, and design visuals from text prompts or existing images. This includes text-to-image generation, AI photo retouching, background removal, image enhancement, and AI-assisted web or UI design, without traditional design software or skills. Example usage: A user can generate creative visuals from text prompts, enhance low-quality product photos, remove or replace backgrounds, or create website layout mockups using AI design tools, all within minutes. Real-world business application: Businesses use AI image generation to produce ad creatives, social media posts, product images, landing page designs, and branding assets at scale. This could reduce design costs, speed up content production, and allow non-designers to create professional-looking visuals quickly. Advertisement: 🙏 Skill #5: AI Video Generation & Video Editing AI video generation and editing is the skill of using AI to create, edit, and enhance video content faster and more efficiently. This includes automatic video clipping, text-to-video (generative video), AI-assisted editing, and intelligent b-roll search, allowing creators to produce videos without advanced editing skills. Example usage: AI tools can clip long videos into short-form content using platforms like FireCut or Opus Clip, generate videos directly from text prompts, and automatically find relevant b-roll footage to match a script or voiceover. Real-world business application: Businesses could use AI video tools to produce social media videos, ads, tutorials, product demos, and training content at scale. This helps marketing and content teams reduce editing time, maintain consistent output, and repurpose long-form content across multiple platforms efficiently. Skill #6: AI-Assisted Software Development & No-Code AI Web Development AI-assisted software development is the skill of using AI tools to help write, review, debug, and improve code, while no-code AI web development focuses on building applications and internal tools without traditional programming. These tools allow users to turn ideas into working software much faster. Example usage: Developers can use AI-powered editors like Cursor or Replit to generate code, fix bugs, and explain unfamiliar logic. Non-developers can use platforms like Retool to create dashboards, admin panels, and workflows by connecting databases and APIs with minimal coding. Real-world business application: Businesses use AI-assisted development to build internal tools, prototypes, dashboards, and customer-facing apps faster and at lower cost. This reduces reliance on large engineering teams, speeds up product development, and enables non-technical teams to create useful software solutions independently. Skill #7: API Integration for AI Workflows API integration for AI workflows is the skill of connecting AI services to other software systems using APIs so data can flow automatically between tools. Instead of using AI manually through a chat interface, APIs allow AI