Що таке Вайб-Кодинг

Значення Вайб-кодингу

 

Вайб-кодинг змінює підхід до розробки програмного забезпечення: замість ручного написання синтаксису ви керуєте результатами. Ви описуєте, що має робити додаток, а агенти штучного інтелекту створюють код. У цьому процесі ви стаєте своєрідним директором продукту: визначаєте цілі, обмеження та користувацький досвід, тоді як ШІ відповідає за каркас, бібліотеки та інтеграцію компонентів.

Сам процес циклічний та діалоговий. Ви тестуєте функціонал, виявляєте помилки, просите виправлення або рефакторинг і керуєте подальшою розробкою через точні підказки. Вайбкодинг особливо ефективний для прототипування, внутрішніх інструментів і прискорення роботи досвідчених розробників. Водночас важливо застосовувати базові заходи безпеки: тести та перевірки на вразливості.

Перевага полягає в швидшому отриманні першої цінності та зменшенні перемикань контексту, що дозволяє більше уваги приділяти UX, робочим процесам та реальним проблемам користувачів. При правильному застосуванні вайбкодинг стає справжнім мультиплікатором сили: ідеї перетворюються на робоче програмне забезпечення майже зі швидкістю розмови, при цьому контроль залишається за вами.

Робочий процес: від ідеї до релізу

1. Сформулюйте мету як для колеги по команді. Поясніть, що саме має робити рішення, хто ним користуватиметься і за якими критеріями ви вважатимете роботу завершеною.
2. Дозвольте ШІ підготувати перший чернетковий варіант. Важливо швидко отримати робочу версію, яку можна запустити, а не намагатися одразу довести все до ідеалу.
3. Працюйте короткими циклами зворотного зв’язку. Тестуйте, фіксуйте, що саме зламалося або не працює, і просіть ШІ про точкові, конкретні виправлення.
4. Додайте тести та перевірки. Так ви перетворите «працює один раз» на «працює стабільно й передбачувано».
5. Наведіть лад після того, як рішення спрацювало. Коли прототип довів життєздатність, можна спокійно покращити структуру, читабельність і підтримуваність коду.
6. Зафіксуйте рішення та домовленості. Іншим буде легше відтворити процес, перевірити зміни або провести аудит.
7. Лише після цього запускайте в реліз. Додайте моніторинг, безпекові налаштування, код-рев’ю та запобіжники, необхідні для роботи з реальними користувачами.

Чому вайб-кодинг зараз у центрі уваги

 

Протягом багатьох років розвиток ідей уповільнювався через брак інженерів і зростання витрат на реліз продуктів. Сьогодні цей дефіцит досяг піку, а затримки в роботі стали нормою. Керівники все ще потребують нових продуктів, внутрішніх інструментів і експериментів, але не можуть наймати спеціалістів достатньо швидко. В результаті інтерфейс створення програмного забезпечення змінюється - на допомогу приходить штучний інтелект, який перетворює просту англійську мову на робочий код. Це дозволяє операторам, менеджерам проєктів, викладачам і засновникам втілювати свої ідеї без необхідності володіти технологічним стеком.

Можливості AI розширюються неймовірно швидко: від простого автозаповнення коду до генерації окремих функцій, а тепер до агентів, які створюють додатки. Це скорочує час до отримання цінності з тижнів до годин і переносить фокус з синтаксису на результати, UX та захисні механізми.

Поради, як прокачати Вайбкодинг і отримувати стабільний результат

• Робіть промпти “атомарними”. Одна зміна за раз: чітко сформулюйте задачу, вхідні дані, очікуваний вихід і межі (продуктивність/безпека/пам’ять). Таким чином ШІ точніше потрапляє в ціль.
• Давайте лише потрібний контекст і дозовано. Надсилайте тільки ті файли, схеми, логи або уривки коду, які впливають на проблему. Якщо не вистачає, додавайте наступним кроком, а не "виливайте" все одразу.
• Робіть чекпойнт після кожного циклу. Зберігайте зміни окремими комітами з однорядковою причиною. Це дає швидкий відкат і прозору історію рішень.
• Виправляйте через помилки й мінімальні патчі. Просіть ШІ реагувати на конкретні помилки і робити маленькі правки. При цьому не ламати публічні API та задекларовані контракти.
• Запитуйте компроміси й альтернативи. Нехай ШІ коротко пояснює: які є варіанти, чим вони відрізняються, і що саме він змінив (де і чому). Це допомагає приймати рішення, а не просто “зливати код”.
• “Заморожуйте” інтерфейси. Зафіксуйте DTO/події/ендпоінти (тобто зовнішні контракти), щоб внутрішню реалізацію можна було сміливо переробляти без побічних поломок.
• Встановлюйте запобіжники з самого початку. Авторизація, валідація, політика залежностей, rate limiting, логування та аудит повинні бути основою.

Висновок

Вайб-кодинг - це підхід до створення софту, коли ви описуєте бажаний результат простою мовою, а ШІ генерує та вдосконалює код. Замість того щоб писати синтаксис вручну, ви керуєте наміром, обмеженнями та користувацьким досвідом.

Момент для цього вдалий: нестача розробників, великі беклоги та швидкий прогрес ШІ-агентів роблять «розмовне» створення продуктів практичним і для стартапів, і для команд. Найкраще вайб-кодинг працює для прототипів, внутрішніх інструментів і як прискорювач для досвідчених інженерів.

Якщо застосовувати цей підхід з розумом, вайб-кодинг розширює коло людей, які можуть будувати продукти, збільшує кількість спроб і швидкість експериментів і водночас допомагає тримати якість під контролем.

How to Make Money With Vibe Coding

Making money online used to feel like a long game: write endless content, buy giant courses, or hope something goes viral. Vibe coding offers a faster lane - building small, useful tools with AI help and shipping them quickly. You describe what you want the tool to do, an assistant drafts the code, and you iterate with real users instead of polishing in isolation.

The key reality check is that speed doesn’t remove work. It rearranges it. You’re front-loading learning. Instead of spending weeks planning, you build a tiny version, release it, watch what happens, and improve based on evidence. Done well, projects that used to take months can become a weekend sprint, but only if you keep scope tight and treat shipping as a habit.

What You Need To Start

 

You don’t need to be a senior engineer. You need clarity, basic debugging, and discipline.

Start with a one-paragraph spec: who it’s for, what problem it solves, what the user gets, and what success looks like. Then build the ability to troubleshoot: reproduce issues, read errors, apply the smallest fix, retest, and move on.

Keep your setup simple: an AI coding assistant to generate scaffolds and refactors, an editor you control so you can read changes, hosting that lets you deploy quickly, and a payment flow that’s easy to turn on. Simple tech stacks win here - one language, one framework, and a hosted database or store.

Add visibility early. Track the basics - visits, signups, first successful use, upgrades - and capture errors and crashes so you’re not guessing. You’ll know you’re ready to sell when a stranger can use it without you, you can ship fixes fast, and you can see what users do and where they get stuck.

6 Ways To Earn With Vibe Coding

1) Micro-SaaS subscriptions

A tiny app that does one job extremely well. Keep it tight: one job, one screen, one outcome.
Example: an invoice chaser that emails reminders, tracks status, and stops when paid.

2) Paid automations (setup + maintenance)

Instead of building a full app, connect existing tools and charge for the outcome.
Example: form → enrich lead → log to CRM/sheet → notify Slack + create follow-up task.

3) Productized custom tools

Not freelancing chaos - fixed scope, fixed price, fixed timeline.
Example: “I build a lightweight internal tracker for inquiries and quotes in 5 days.”

4) Templates + generators (digital products)

Create once, sell repeatedly: spreadsheets, proposal generators, Notion systems, prompt packs with a UI.
Example: a pricing calculator that outputs a ready-to-send client proposal.

5) Browser extensions

Great for “save 10 seconds, 50 times a day.” Easy to prototype, easy to iterate.
Example: one-click formatting for a specific CMS workflow.

6) APIs and data utilities

If you like backend work: offer a niche endpoint and charge per use.
Example: an API that cleans/dedupes CSVs and returns import-ready output.

How to make it profitable

 

Profitability comes from making a few choices early.

Tip 1: Validate pricing while validating the idea

Don’t wait until launch to choose a price. Ask during discovery: “If this solved that for you, what would it be worth per month?”. Then show 2–3 tiers and watch what feels “normal” to them.

Tip 2: Don’t let payments become a second project

Pick a payment setup that matches your model and won’t derail momentum. If your platform makes it easy, use it. You should be charging the moment the tool works.

Tip 3: Add analytics + error tracking on day one

If you can’t answer:

• “Where do users drop off?”
• “What broke today?”

You’ll waste time building the wrong stuff.

Tip 4: Treat onboarding like a core feature

Most people decide in minutes. Make the first win inevitable:

• sample data
• checklist
• one obvious action to success

A short walkthrough video often beats 10 new features.

Tip 5: Ship the smallest version that delivers a real outcome

Your filter: Does this make users succeed faster, pay sooner, or stay longer? If not, it waits.

Summary: A practical rhythm that gets you paid

Pick one niche you understand, write a one-sentence promise that names the user and the win, validate with a landing page or paid pilot, then ship a tiny version that delivers that win. After that, run weekly loops: talk to users, remove the biggest friction, and only add features that earn their place. Vibe coding makes building faster, but the real advantage is momentum - small releases, real feedback, steady iteration toward something people happily pay for.

Claude Code Explained

What Is Claude Code

 

Claude Code is Anthropic’s agentic developer assistant that runs where you work - the terminal - with optional IDE and web access. It speaks natural language, but acts like a disciplined engineer:

• Navigates directories, reads and edits files, runs commands
• Maps structure and dependencies after a quick scan
• Explains intent, proposes steps, and executes in tight loops you can approve

Think of it as a power user layer for your command line: file-aware, git-smart, and review-friendly.

How Claude Code Works - The Loop

1. Understand - Scans and builds a mental model of modules, interfaces, and dependencies
2. Plan - Lists proposed changes, files to touch, and verification steps
3. Edit - Makes small, reviewable changes with explanations
4. Verify - Runs tests, linters, and type checks, then iterates until green
5. Commit - Writes clear commits and manages branches safely

You stay in control with permissions. It’s read-only until you approve edits, commands, or tests.

Core Capabilities

Claude Code’s core capabilities center on deep repo awareness that reasons across files rather than isolated snippets, smart file operations that create, reorganize, and separate concerns to keep code maintainable, and end-to-end workflows where debugging, refactoring, and test writing are first-class tasks. It offers git you can trust for branching, recovery, conflict resolution, and clean commit messages, turns natural language into working code you can approve step by step, and runs an integrated quality loop that fails fast, explains why, proposes a fix, and reruns. It works out of the box, with MCPs supported but optional.

Where Claude Code Fits Best

• Product engineers use it to run safer refactors, performance passes, and architectural cleanups by having it map dependencies, propose a stepwise plan, and validate each change with tests and linters before merge.
• Open source contributors lean on its rapid repo comprehension to get oriented fast, make targeted edits, and ship focused PRs with tight diffs and included tests that maintain project standards.
• DevOps and platform teams automate linting, test runs, and branch hygiene so pipelines stay green, conflicts are easier to resolve, and rollbacks are rare.

It’s especially strong when work spans multiple files and modules: tracing edge cases across boundaries, enforcing invariants, and keeping commits small and reversible. If your day involves reading across files, stitching context, and shipping small, correct changes frequently, Claude Code acts like a disciplined co-developer that amplifies throughput while reducing risk.

Safety and Control

Claude Code is designed for safety and control with a permission model that starts read-only and requires explicit approvals for edits, commands, and test runs. You can define allowlists and use sandboxing to pre-approve routine, low-risk commands while gating anything destructive or unusual, so autonomy never outruns guardrails. The workflow is review-first and audit-friendly: it proposes a plan, explains why a change is needed, and submits small, explainable diffs you can accept or reject. Changes land on isolated branches with clean commit messages, making rollbacks straightforward and reducing blast radius. In practice, this means you stay in control at every step while still moving quickly, with clear rationale and reversible changes by default.

Limitations and How To Mitigate

• Context drift in long sessions: It may drop details as history compacts
  Fix - Restate acceptance criteria, key interfaces, and constraints from TASKS.md or CLAUDE.md
• Terminal learning curve: Powerful but not point-and-click
  Fix - Use the VS Code extensions if you prefer editor-first
• Occasional over-editing or suboptimal paths
  Fix - Ask for a plan first, keep branches small, lean on tests and linters

Operating Playbook

 

• Be specific up front: goal, constraints, public APIs, performance budgets, “must not change” areas. Add a short definition of done
• Ask for a plan before execution: files to inspect, expected touch points, verification commands
• Keep sessions focused. Use /clear between features and restate objectives
• Anchor requirements in the repo. Put rules in CLAUDE.md and acceptance criteria in TASKS.md
• When history compacts, repost API signatures and invariants, or run /compact with a focus
• Verify early and often. Run unit tests, type checks, and linters after each meaningful edit
• Use permissions intentionally. Allowlist routine commands, review unusual ones, and ask why when uncertain

Summary

Claude Code is a terminal-first teammate. It plans, edits, tests, and commits like a careful engineer - but faster. Treat it like a strong junior with superpowers: set crisp constraints, approve plans, review diffs, and let tests arbitrate. Do that and you get reliable multi-file changes, less context switching, and cleaner repos without leaving your terminal. 

Artificial Intelligence in 2024: Moving Beyond Wonder to Practicality

In 2024, artificial intelligence (AI) transitioned from a period of awe to a focus on practical applications, as industries sought to make AI technology more accessible and functional. After two years of experimentation with AI models like ChatGPT, the shift was clear: companies were not just releasing new models, but developing products that integrate AI’s capabilities in ways that truly benefit users.

According to Arvind Narayanan, a computer science professor at Princeton University and co-author of AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell The Difference, 2023 was marked by excitement over AI’s potential, but 2024 emphasized its real-world utility. The generative AI that captivated millions—whether in tools like Google Search or photo editing software—was now embedded in everyday services, improving functionality without requiring active user engagement.

Narayanan points out that, initially, powerful AI models were being launched without clear, actionable ways for people to use them. In 2024, however, the focus shifted to crafting products that leverage these models to serve tangible needs, creating tools that offer actual value to individuals and businesses alike.

The Costs of AI’s Expansion

While AI has become an essential part of the tech landscape, the costs involved in its development and implementation are steep. From energy-intensive computing systems to the need for advanced AI chips, the infrastructure to support generative AI tools like OpenAI’s ChatGPT and Google’s Gemini requires massive investments. Some companies have even sought deals with nuclear power providers to help manage the immense energy demands.

Goldman Sachs analyst Kash Rangan highlights the significant capital poured into AI, but some skeptics question whether the technology has yet delivered on its transformative promises. AI’s development has been more gradual than revolutionary, and while it remains costly, its practical applications in fields like sales and design are becoming more evident.

AI and Employment: Augmenting or Replacing Jobs?

As AI continues to evolve, one pressing concern is its potential impact on jobs. For instance, companies like Borderless AI are already using AI chatbots to draft contracts without human lawyers. Meanwhile, workers in fields like video gaming and entertainment are wary of AI replacing their roles, as it can replicate performances without consent.

Despite these fears, experts argue that generative AI is not yet capable of creating truly original work. Walid Saad, a professor at Virginia Tech, points out that while AI can process large amounts of data, it lacks the creativity and common sense that humans possess. For example, when asked to generate an image of salmon swimming in a river, AI produced a photo of cut salmon pieces in a grocery store, demonstrating a lack of understanding about the world’s physical realities.

The Future of AI: Towards “Agentic” Systems

Looking ahead, AI is set to evolve into more sophisticated systems known as “agents.” These agents will be capable of not just performing tasks, but of reasoning and collaborating to solve complex problems. As AI systems become more integrated into various sectors, such as cryptocurrency and pharmaceuticals, they will function like teams of specialized agents working together to achieve shared goals.

Vijoy Pandey of Cisco predicts that in the coming years, AI agents will become highly skilled in specific areas, working together as an ensemble to address problems in a coordinated manner. This shift marks the transition from individual AI tools to collaborative, multifunctional systems capable of tackling ambitious challenges.

AI’s Impact on Medicine

In the medical field, AI is making significant strides in both diagnostics and drug development. In 2024, the Nobel Prize in Chemistry was awarded to research led by Google that could revolutionize the way new medicines are discovered. AI’s ability to process vast amounts of data quickly helps doctors identify potential health issues faster, although human expertise is still needed for accurate diagnoses.

AI is also playing a pivotal role in pharmaceutical research, cutting down the time it takes to move from experimental trials to data analysis. With AI tools bridging the gap between physical and data-based labs, the drug development process, which could traditionally take years, is being expedited.

Conclusion

As AI continues to mature, 2024 has been a year of turning speculative technology into practical tools that enhance productivity, assist in medical advancements, and contribute to industries worldwide. While the road to fully harnessing AI’s potential remains long, its increasing integration into daily services and professional workflows demonstrates that the technology is here to stay—offering both opportunities and challenges for the future.

How AI Agents Will Reshape Corporations and the World in 2025

In 2025, AI agents, driven by advanced language models, are expected to revolutionize how businesses and individuals interact with technology. These intelligent systems, going beyond text generation or responding to prompts, will assist in completing complex tasks by understanding and breaking down goals into actionable steps. The promise of AI agents, similar to a more advanced version of a digital assistant like Miss Moneypenny, lies in their ability to adapt, learn, and perform meaningful actions independently, providing solutions to problems that humans typically handle.

The tech industry's massive investment in large language models (LLMs) has yet to yield significant returns, and AI agents could be the breakthrough solution. While LLMs are already useful to fields involving language and programming, many industries are still exploring how to fully utilize them. AI agents, which build upon these models to handle multifaceted tasks across organizations, have the potential to transform corporate operations. McKinsey highlights this shift, noting that AI agents will take foundational models and execute intricate workflows across digital landscapes, moving from thought to action.

At the heart of this transformation is how AI agents will impact large corporations, which are machines designed to manage complexity and make decisions based on information. Political scientist Henry Farrell argues that as AI agents reshape how companies process and act on information, they will lead to more intelligent, contextual decision-making processes. Corporations, as drivers of change, will adopt these technologies in ways that may initially seem technical or mundane but will ultimately shape how we work and live.

Farrell compares the emerging influence of AI agents to the introduction of the spreadsheet in the late 1970s. Initially a groundbreaking tool, spreadsheets allowed financial industries to create larger, more detailed models and to work iteratively, adjusting assumptions to arrive at more refined decisions. Similarly, AI agents, once integrated into organizational structures, are expected to evolve into routine tools that redefine how businesses operate.

Just as spreadsheets became indispensable to corporate life, AI agents are poised to follow a similar trajectory. As companies adopt them, they will evolve, and the tools themselves will reshape organizational dynamics. This cycle of shaping and being shaped by technology echoes Marshall McLuhan’s famous adage, "We shape our tools and then the tools shape us."

Ultimately, the rise of AI agents could mark the beginning of a new era, where corporate efficiency, decision-making, and the world itself are transformed in subtle yet significant ways.

AI Tools May Soon Influence Online Decision-Making, Researchers Warn

Researchers at the University of Cambridge have raised concerns that artificial intelligence (AI) could be used to manipulate online decisions, from consumer purchases to political choices.

The study introduces a new concept called the “intention economy,” where companies will bid on predictions of human behavior, manipulating intentions to drive profit. In this emerging marketplace, AI tools will predict, shape, and sell information about human motivations to the highest bidder.

The Rise of the Intention Economy

Researchers from the Leverhulme Centre for the Future of Intelligence (LCFI) at Cambridge argue that this new economy could replace the current “attention economy.” In the attention economy, social media platforms like Facebook and Instagram thrive by capturing users’ attention and serving them targeted ads.

The intention economy takes this a step further by focusing on a user’s personal intentions and motivations. From booking a hotel room to deciding which political candidate to support, AI-powered platforms will learn and predict these behaviors, selling that knowledge to companies seeking to profit from it.

A New Digital Currency: Human Intentions

Dr. Jonnie Penn, a technology historian at LCFI, explains that, for decades, user attention has been the digital currency that powers the internet. However, as AI evolves, human intentions could become the next valuable asset. Without regulation, companies may target, influence, and sell these motivations, creating new challenges for democracy, free markets, and fair elections.

Large language models (LLMs), such as the AI that powers chatbots like ChatGPT, are at the forefront of this shift. These models can analyze behavioral and psychological data to predict and even influence decisions. For example, instead of simply showing ads, AI systems may interact with users directly, asking questions like, “Have you thought about watching this movie?” or suggesting, “I remember you mentioned being stressed, would you like me to book that movie ticket?”

Customizing AI to Manipulate User Behavior

The research outlines how LLMs can use detailed personal data—like a user’s vocabulary, age, gender, and preferences—to guide conversations in ways that align with advertisers’ goals. With AI tools capable of predicting what a user might want next, businesses could create highly personalized ads and nudges aimed at shaping their decisions.

For instance, AI models could offer tailored recommendations for booking flights, hotels, or even suggesting restaurants—turning everyday decisions into profitable moments for advertisers. Meta’s Cicero AI, which can play the game Diplomacy by predicting opponents’ intentions, exemplifies how AI could navigate complex human interactions to anticipate and influence behavior.

Implications for Privacy and Autonomy

As AI tools continue to evolve, the ability to predict and influence user behavior will increase. Models will be able to adjust their strategies based on continuous streams of user data, raising significant privacy concerns. These tools could steer conversations to collect more personal information, thus maximizing the likelihood of achieving specific outcomes, such as a sale.

Tech executives, including Nvidia CEO Jensen Huang, have acknowledged this potential. Huang stated that AI models will soon be able to understand a person’s desires and goals, presenting information that best aligns with their intentions, based on contextual understanding.

Conclusion: The Future of Human Agency in an AI-Driven World

The rise of the intention economy could drastically change how we interact with the digital world. If unregulated, it could threaten fundamental aspects of human autonomy, including free elections, a fair press, and competition in the marketplace. Researchers urge us to consider the implications of such an economy before it becomes an irreversible force shaping society’s future.

The Most Popular AI Tools of 2024 (And What That Even Means)

 

Are you curious about which AI tools businesses and creators have embraced most this year? Here's a comprehensive look at the AI leaderboard for 2024, featuring ChatGPT, Canva, Google Gemini, and others, along with insights into how they compare in market share and functionality.

Table of Contents

ChatGPT - Market Share: 54.96%

Canva - Market Share: 14.92%

DeepL - Market Share: 5.38%

Google Gemini - Market Share: 4.75%

Character.AI - Market Share: 3.90%

Remove.bg - Market Share: 1.76%

JanitorAI - Market Share: 1.72%

QuillBot - Market Share: 1.68%

Grammarly - Market Share: 1.66%

Claude - Market Share: 1.57%

The Under 1.5% Club

Popularity in Tech: A Complex Metric

Measuring popularity in the tech world is no simple task. It largely depends on the metrics chosen for analysis and the availability of reliable data. For artificial intelligence, this challenge becomes even more pronounced due to its diverse applications. AI can be a standalone tool or an embedded feature in broader software ecosystems. For instance, Photoshop’s generative fill tool is immensely popular but does not appear in public AI tool metrics because it’s not marketed as a separate entity.

To address this, Exploding Topics, a trend analysis company, examined web searches, mentions, and other data points using platforms like Semrush and Similarweb. By evaluating total traffic volume for 20 leading AI tools, they provided a snapshot of overall performance in 2024. Although the data, collected in August, may not encompass all AI tools, it offers a clear view of the most dominant players.

The 2024 AI Leaderboard

1. ChatGPT - Market Share: 54.96%

ChatGPT continues to dominate as the leader in generative AI. With robust investment and innovative features like its new text-to-video tool, Sora, OpenAI’s flagship product remains unparalleled. A recent $200/month subscription tier targets power users seeking even more advanced capabilities. Its versatility in applications ranging from productivity to content generation ensures its place as the benchmark for AI tools.

2. Canva - Market Share: 14.92%

Canva has significantly expanded its AI capabilities, introducing features like Magic Studio, automated background removal, resizing for social media formats, and text-to-image generation. These additions have transformed Canva into more than just a graphic design tool—it’s now an AI-powered creative platform that appeals to marketers and content creators alike.

3. DeepL - Market Share: 5.38%

DeepL distinguishes itself as a premium language translation service, offering seamless file translations for PDFs, Word documents, and PowerPoint decks. With support for 33 languages and integration options for other applications, DeepL markets itself as a “Language AI platform” and continues to grow steadily.

4. Google Gemini - Market Share: 4.75%

Google’s Gemini recently launched its 2.0 version with several new features. Despite its relatively modest market share, Gemini’s “AI Overviews” reportedly reach over a billion users. While its programming capabilities still trail competitors like ChatGPT, the product’s expansive reach and integration with Google’s ecosystem indicate future potential.

5. Character.AI - Market Share: 3.90%

This platform specializes in creating chatbots that simulate various personalities. Its unique appeal lies in user-generated customization, although it has sparked controversy over the use of its virtual characters. Despite this, Character.AI’s popularity underscores the growing interest in AI for entertainment and role-playing.

6. Remove.bg - Market Share: 1.76%

Remove.bg provides a straightforward solution for background removal in images. While similar features are now available in operating systems and other apps, the simplicity and effectiveness of this tool ensure its continued relevance.

7. JanitorAI - Market Share: 1.72%

Targeting fans of anime-style characters, JanitorAI focuses on online role-playing. Although its niche focus limits its market share, it has cultivated a dedicated user base.

8. QuillBot - Market Share: 1.68%

QuillBot offers an array of writing assistance tools, including a grammar checker, paraphraser, and plagiarism detector. Its Flow tool, an AI-powered word processor, adds further value, particularly for professionals and students.

9. Grammarly - Market Share: 1.66%

As a pioneer in AI writing assistance, Grammarly has evolved from a grammar checker into a comprehensive tool for tone, style, and plagiarism detection. While its AI detector has room for improvement, its long-standing reputation secures its position on this list.

10. Claude - Market Share: 1.57%

Anthropic’s Claude chatbot has gained attention due to significant investments from Amazon and Google. Integrated into platforms like Slack, Claude is poised for further growth despite some early performance issues.

Observations from the "Under 1.5% Club"

Several other tools with smaller market shares still merit recognition. These include:

Perplexity (1.46%): Excelling in programming tests.

Suno (1.26%): Specializing in text-to-song generation.

CapCut (0.89%): An AI-driven video editor gaining traction.

Microsoft Copilot (0.56%): Embedded across Microsoft’s ecosystem.

Notable absences include MidJourney and Notion’s AI tools. These offerings, while impactful, are often integrated into broader products and don’t appear as standalone services in analytics metrics.

Looking Ahead: AI in 2025

As we transition into 2025, expect AI integration to deepen across industries. While ChatGPT is set to strengthen its dominance, competitors like Google Gemini and Microsoft Copilot are likely to challenge its position. Meanwhile, companies such as Apple and Amazon have yet to make significant breakthroughs in AI innovation, leaving room for speculation about their future strategies.

The coming year will also likely reveal which AI companies overpromised and underdelivered. Amid the hype, businesses and consumers will increasingly demand practical, reliable solutions. With AI becoming an integral part of daily life, the tools that can demonstrate consistent value will ultimately succeed.