Moltbot (formerly known as Clawdbot) has become one of the most talked-about technologies in the AI enthusiast world in early 2026. This open-source project promises not just to answer queries but to perform tasks for you—managing email, calendars, files, and applications.

But what is Moltbot really, is it worth running yourself, and what risks are associated with it? All this is covered in the detailed breakdown below.

What is Moltbot?

Moltbot is an open-source personal AI assistant that runs on your own computer or server and is capable of performing actions on behalf of the user, not just generating text. It operates 24/7, receives commands via messengers, and performs a variety of tasks: from managing messages to automating routine processes.

Moltbot is not just a chatbot; it's an action-oriented agent: it perceives messages, plans steps to achieve a goal, and activates relevant tools or functions on the user's device.

Project History and Its Creator

Behind Moltbot is an unusual developer—Peter Steinberger, a figure well-known in the Apple ecosystem. His journey is the story of a developer who first created a successful commercial product and then completely reoriented his vision of technology towards personal AI.

From PDF Libraries to Artificial Intelligence

Peter started his career in the early iPhone era, was actively involved in the Apple community CocoaHeads, and taught iOS development at Vienna Technical University. His main project for a long time was PSPDFKit—a powerful SDK for working with PDFs, sold not directly to users but to companies as a software component. It helped integrate PDF functionality into other products and applications.

In 2021, Peter sold his share in PSPDFKit—reportedly as part of a deal with the investment company Insight Partners. But, contrary to stereotypes about success, this deal became an emotional blow: Peter lost not just a project, but part of his identity. He candidly wrote in his blog about burnout, emptiness, loss of purpose, and unsuccessful attempts to reboot through parties, rest, or even therapy. Nothing helped. He was left without an idea he wanted to return to every morning.

AI as a Second Life

Everything changed in 2024-2025—when the boom of large language models reached a critical mass. Peter again felt the urge to create something new: now he was inspired by the idea of a personal AI that would live not in the cloud, but in your home, on your computer, with access to tasks, files, and habits.

Thus, Clawdbot was born—a home AI agent with a claw for a head and an emoji lobster as a mascot. It was conceived as a helper that actually does something useful, not just a talking head with an API. The name "Clawdbot" was a play on words: claw + Claude (the name of the beloved language model from Anthropic).

The project quickly gained popularity on microblogs, Reddit, and Hacker News: people began to massively share use cases, run the agent on Mac minis, and experiment with extending its capabilities.

Transition to Moltbot

In January 2026, Anthropic (creator of Claude) requested a change to the project's name to avoid confusion with their trademark. Peter took this calmly and renamed Clawdbot to Moltbot. The name became even more interesting in meaning: molt is "molting," the renewal process that real-life lobsters go through. Thus, Moltbot symbolized growth, renewal, evolution—of both the project and Peter himself.

Now the default chatbot is named Molty, and the entire project officially resides at: github.com/moltbot/moltbot.

The Personal Becomes Technical

From a technical perspective, Moltbot is a reflection of Peter's internal state: he has always been a developer who thinks in terms of infrastructure, platforms, and "for growth." Instead of making just another chatbot, he created a structure that can be developed, adapted, and extended for any task. It's not just an assistant—it's an entire ecosystem into which anyone can integrate their own logic, skills, and workflow.

And now, as he admits in interviews, Moltbot is not just a project, but a new form of presence, a new form of life he found after an emotional crisis and leaving big business.

Moltbot's Technical Architecture: How It Works

At first glance, Moltbot might seem like just a "smart chatbot," but in reality, it's a full-fledged architectural platform consisting of several layers. Everything is built to be simultaneously flexible, extensible, and autonomous. Below is an explanation of the system's internal structure.

Core Concept

Moltbot is an AI agent that runs on a local machine, processes messages, performs actions, and interacts with external language models (Claude, OpenAI, Mistral, etc.).

At the same time, it:

• maintains internal memory (in the form of text files),
• connects to chats and applications via gateways,
• can run OS commands, read and change files,
• and all this—in continuous operation mode, as a service.

Core Components

1. Clawd (Agent Core)

This is the "brain" of the system—the agent that lives on your machine (Mac, Linux, Raspberry Pi, or WSL), monitors conversations, context, commands, and tasks, organizes "memory," and launches "skills," communicates with the model via API, and crafts prompts. It's written in TypeScript and runs on Node.js (or Bun).

2. Gateway (External Communication)

This is the "gateway" that receives incoming messages from messengers and forwards them to the agent. It:

• provides a management web interface (Control UI),
• exposes an API for messages and WebSocket connections,
• can work with bots in Telegram, WhatsApp, Discord, etc.,
• can proxy connections (e.g., through a reverse proxy). 💡 By default, it listens on port 127.0.0.1:18789. For remote access, you need to change gateway.bind to 0.0.0.0 and ensure security (VPN, password, authorization).

3. Control UI (Local Interface)

A simple web interface based on Vite and Lit. Through it you can:

• manage Moltbot's configuration,
• view conversation logs,
• control active channels and skills,
• and even manually issue commands.

4. Skills

Each skill is an extension of the agent's functionality. It consists of a description (in Markdown or JSON format), code (in JavaScript, TypeScript, or Shell), arguments, and launch conditions.

Examples of skills:

• Spotify control,
• sending email,
• working with Google Docs or Notion,
• generating images via Stable Diffusion,
• screenshots, audio transcription, script execution.

Skills can be written yourself or downloaded from ClawdHub / MoltHub.

Memory Structure

Moltbot's memory is simple yet powerful. It is implemented as regular text files:

• memory/notes/YYYY-MM-DD.md – temporary notes,
• memory/facts.md – stable information about the user (name, habits, contexts),
• memory/history/ – log of communication and decisions made.

This allows for manual memory editing, control over what the bot "remembers," and copying or transferring data between devices.

Working with the Language Model

Moltbot does not contain its own model but connects to external APIs:

• Anthropic Claude (recommended: Claude 3 or 4.5 Opus),
• OpenAI GPT‑4 / GPT‑3.5,
• Mistral, Gemini, Perplexity – via OpenRouter or other proxies.

All requests to the model go through Clawd and are accompanied by system prompts, memory and notes, situation descriptions, and user preferences.

Results from the model can immediately trigger commands, skills, or provide answers.

Installation and Configuration

During installation, Moltbot:

• creates the ~/.moltbot/ directory,
• saves the configuration file moltbot.json,
• generates directories for skills, memory, and logs,
• installs a system daemon (systemd or launchctl on Mac),
• can automatically start the gateway and UI.

Security

This is a critically important component:

1. By default, Moltbot is only accessible from the local machine.
2. UI authorization is via token (gateway.auth.token).
3. It is not recommended to expose the port directly to the internet.
4. All API keys and tokens should be stored in secure environment variables.

Additionally, it is recommended to run it in an isolated system (e.g., a separate Mac mini), use VPN or SSH tunnels for external access, and periodically update and check the gateway configuration.

Architectural Features

• Cross-platform: Works on Mac, Linux, Windows (via WSL), Raspberry Pi.
• Modularity: You can change the core, model, channels, and skills independently.
• Fault tolerance: Support for fallback models (in case the main provider is unavailable).
• Fully transparent structure: Everything is stored in open files—no black boxes.

Capabilities and Integrations

Moltbot supports connections to numerous services and applications via "skills":

• Managing messages via Telegram, WhatsApp, Discord, Slack, Signal, iMessage, and others.
• Executing terminal commands and interacting with the local file system.
• Integrations with calendars, email, reminders, Telegram bots, and more complex task flows.
• Creating custom skills that can be exported to MoltHub—the community shares ready-made extensions.

Moltbot's key feature is that it is not limited to just answering but can perform actions at the system level.

Why Running on a Dedicated Device is Common Practice

Moltbot must run continuously—saving state, listening for events, and processing commands quickly. Running it on a laptop that frequently sleeps, disconnects from the network, or switches between networks disrupts its operation. Therefore, many enthusiasts prefer to set up a dedicated computer: often a Mac mini, but other devices (even a Raspberry Pi) will work.

The Mac mini became a popular choice due to its compactness, low power consumption, and integration with iMessage and other Apple services, which are harder to use on Linux.

Security Concerns – What You Need to Know

Moltbot's extended permissions are not only powerful but also a risk. Why?

Admin-level access to the system can lead to hacking if interfaces are exposed externally or misconfigured. Also, unprotected Control UIs can expose API keys, messenger tokens, and other secrets. Atomic attacks via prompt injection are possible, where malicious input can force Moltbot to perform unintended actions.

Due to its popularity, the project has already become a target for fake tokens and fraudulent schemes related to old names and meme coins. Therefore, developers and experts strongly recommend running Moltbot in an isolated environment, carefully configuring authorization, and avoiding exposing ports to the internet.

Practical Use Case Examples

Moltbot is capable of performing real tasks, but most stories are still experimental:

• Automatic checking of email, calendars, and reminders.
• Sending daily audio reports on user tasks and activity.
• Managing notifications and integrating with cloud services.

However, stories about Moltbot buying a car by itself or fully organizing complex processes without user involvement remain rare and still require step-by-step human guidance.

In conclusion, Moltbot is one of the most impressive experiments with autonomous AI agents to date. It demonstrates how large language models can transition from chat to action, performing tasks, integrating with messengers and system tools.

But along with this, it requires technical expertise and careful security configuration, carries increased risk if deployed incorrectly, and for now remains a product for enthusiasts, not mainstream users.

If you want to try Moltbot—do so cautiously, on dedicated hardware, considering all risks. And for those seeking stability and security, it might be better to wait until the architecture of such agents matures further.

Contents

• What is a Local LLM?
• Can You Really Run an LLM on a Home Computer?
• Specialized & Advanced Models
• Step-by-Step: How to Run a Local LLM (Ollama + Open WebUI)
• Integrating Local LLMs with Automation (n8n Workflow)
• Local LLM vs. Cloud: Key Differences
• What is a Local LLM?
• Conclusion & Next Steps

Tired of recurring ChatGPT bills for work tasks? Or perhaps you work in a data-sensitive industry where using cloud AI services is simply not an option due to compliance and privacy?

If this sounds familiar, then running Large Language Models (LLMs) locally might be the powerful, self-hosted solution you've been looking for.

Local LLMs are a practical and secure alternative to cloud services. When a model runs on your own computer or server, you eliminate ongoing API costs and keep all your data within your private infrastructure. This is critical for sectors like healthcare, finance, and legal, where data confidentiality is paramount.

Furthermore, working with local LLMs is an excellent way to gain a deeper, hands-on understanding of how modern AI works. Experimenting with parameters, fine-tuning, and testing different models provides invaluable insight into their true capabilities and limitations.

What is a Local LLM?

A local LLM is a Large Language Model that runs directly on your hardware, without sending your prompts or data to the cloud. This approach unlocks the powerful capabilities of AI while giving you complete control over security, privacy, and customization.

Running an LLM locally means freedom. You can experiment with settings, adapt the model for specific tasks, choose from dozens of architectures, and optimize performance—all without dependency on external providers. Yes, there's an initial investment in suitable hardware, but it often leads to significant long-term savings for active users, freeing you from per-token API fees.

Can You Really Run an LLM on a Home Computer?

The short answer is: yes, absolutely. A relatively modern laptop or desktop can handle it. However, your hardware specs directly impact speed and usability. Let's break down the three core components you'll need.

Hardware Requirements

While not strictly mandatory, a dedicated GPU (Graphics Processing Unit) is highly recommended. GPUs accelerate the complex computations of LLMs dramatically. Without one, larger models may be too slow for practical use.

The key spec is VRAM (Video RAM). This determines the size of the models you can run efficiently. More VRAM allows the model to fit entirely in the GPU's memory, providing a massive speed boost compared to using system RAM.

Minimum Recommended Specs for 2026

• GPU: A dedicated card with at least 8GB VRAM (e.g., NVIDIA RTX 4060 Ti, AMD RX 7700 XT). 12GB+ is ideal for larger models.
• RAM: 16 GB of system memory (32 GB recommended for smoother operation).
• Storage: Sufficient SSD space for model files (50-100 GB free is a safe starting point).

Software & Tools

You'll need software to manage and interact with your models. These tools generally fall into three categories:

• Inference Servers: The backbone that loads the model and processes requests (e.g., Ollama, Llamafile, vLLM).
• Frontend Interfaces: Visual chat interfaces for a user-friendly experience (e.g., Open WebUI, Continue.dev, Lobe Chat).
• All-in-One Suites: Comprehensive tools that bundle everything together, perfect for beginners (e.g., GPT4All, Jan, LM Studio).

The Models Themselves

Finally, you need the AI model. The open-source ecosystem is thriving, with platforms like Hugging Face offering thousands of models for free download. The choice depends on your task: coding, creative writing, reasoning, etc.

Top Local LLMs to Run in 2026

The landscape evolves rapidly. Here are the leading open-source model families renowned for their performance across different hardware configurations.

Leading Universal Model Families

• Llama 4 / 3.2 (Meta AI): The benchmark for reasoning and instruction following. Available in sizes from 1B to 70B+ parameters. (Note: While Llama 4 exists, its larger variants may exceed standard home system capabilities).
• Qwen 3 (Alibaba): Excellent multilingual and coding capabilities, known for high efficiency. The Qwen2.5 and Qwen3 series offer strong performance-per-parameter.
• DeepSeek (DeepSeek AI): A top contender, especially the DeepSeek-R1 line, renowned for strong reasoning and programming skills. A powerful open-source alternative.
• Gemma 3 (Google): Lightweight, state-of-the-art models built from Gemini technology. Optimized for single-GPU deployment and great for limited resources.
• Mistral & Mixtral (Mistral AI): Famous for their efficiency. The Mixtral series uses a Mixture of Experts (MoE) architecture, offering high-quality output with lower active parameter counts.
• Phi-4 (Microsoft): The "small language model" champion. Designed to achieve impressive performance with a compact footprint, ideal for less powerful hardware.

Specialized & Advanced Models

• Reasoning Models: Optimized for step-by-step logic (e.g., DeepSeek-R1, QwQ).
• Coding Models: Fine-tuned for programming (e.g., DeepSeek-Coder, Qwen2.5-Coder, CodeGemma).
• Multimodal Models (VLM): Can understand both images and text (e.g., Llava-NeXT, Qwen-VL).
• Tool-Use/Agent Models: Can call functions and APIs, forming the basis for AI agents (often used with frameworks like LangChain).

Step-by-Step: How to Run a Local LLM (Ollama + OpenWebUI)

One of the easiest pathways for beginners and experts alike.

1. Install Ollama: Download and install from ollama.com. It works on Windows, macOS, and Linux.

2. Pull a Model: Open your terminal and run ollama pull llama3.2:3b (or mistral, qwen2.5:0.5b, etc.).

3. Run it: Test it in the terminal with ollama run llama3.2:3b.

4. Add a GUI (Optional but Recommended): Deploy Open WebUI (formerly Ollama WebUI) via Docker or pip. It gives you a ChatGPT-like interface accessible in your browser, connecting seamlessly to your local Ollama server.

Integrating Local LLMs with Automation (n8n Workflow)

The real power unlocks when you integrate your local LLM into automated workflows. Using a low-code platform like n8n, you can create intelligent automations.

Simple Chatbot Workflow in n8n:

1. Set up Ollama as described above.
2. In n8n, use the Chat Trigger node to start a conversation.
3. Connect it to the Ollama node. Configure it to point to http://localhost:11434 and select your model (e.g., llama3.2).
4. Execute the workflow. You now have a private, automated AI chat within your n8n canvas, ready to be extended with databases, APIs, and logic.

Local LLM vs. Cloud: Key Differences

Aspect Local LLM Cloud LLM (e.g., ChatGPT, Claude)

Infrastructure Your computer/server Provider's servers (OpenAI, Google, etc.)

Data Privacy Maximum. Data never leaves your system. Data is sent to the provider for processing.

Cost Model Upfront hardware cost + electricity. No per-use fees. Recurring subscription or pay-per-token (ongoing cost).

Customization Full control. Fine-tune, modify, experiment. Limited to provider's API settings.

Performance Depends on your hardware. High, consistent, and scalable.

Offline Use Yes. No. Requires an internet connection.

FAQ: Running LLMs Locally in 2026

Q: How do local LLMs compare to ChatGPT-4o?

A: The gap has narrowed significantly. For specific, well-defined tasks (coding, document analysis, roleplay), top local models like Llama 3.2 70B, Qwen 3 72B, or DeepSeek-R1 can provide comparable quality. The core advantages remain privacy, cost control, and customization. Cloud models still lead in broad knowledge, coherence, and ease of use for general conversation.

Q: What's the cheapest way to run a local LLM?

A: For zero software cost, start with Ollama and a small, efficient model like Phi-4-mini, Qwen2.5:0.5B, or Gemma 3 2B. These can run on CPUs or integrated graphics. The "cost" is then just your existing hardware and electricity.

Q: Which LLM is the most cost-effective?

A: "Cost-effective" balances performance and resource needs. For most users in 2026, models in the 7B to 14B parameter range (like Mistral 7B, Llama 3.2 7B, DeepSeek-R1 7B) offer the best trade-off, running well on a mid-range GPU (e.g., RTX 4060 Ti 16GB).

Q: Are there good open-source LLMs?

A: Yes, the ecosystem is richer than ever. Major open-source families include Llama (Meta), Mistral/Mixtral, Qwen (Alibaba), DeepSeek, Gemma (Google), and Phi (Microsoft). There are also countless specialized models for coding, math, medicine, and law.

Conclusion & Next Steps

Running an LLM locally in 2026 is a powerful, practical choice for developers, privacy-conscious professionals, and AI enthusiasts. It demystifies AI, puts you in control, and can be more economical in the long run.

Ready to start?

1. Assess your hardware.
2. Install Ollama and pull a small model.
3. Experiment with different models and frontends like Open WebUI.
4. Automate by integrating with n8n or similar tools to build private AI agents.

The journey to powerful, private, and personalized AI begins on your own machine.

Working with neural networks is no longer a privilege for IT specialists. Today, generative AI helps create content, solve work tasks, write code, and even earn money. This doesn't require deep technical knowledge or programming skills. It's enough to choose the right tool, understand the basic principles, and make your first query. Most beginners face the same problem: where to start, which platform to choose, how to write a prompt to get a good answer. This article breaks down specific steps, examples, and common mistakes.

Contents

• Why Everyone Should Know How to Work with Neural Networks Today: Numbers and Opportunities
• Step-by-Step Plan: How to Start Working with a Neural Network in 30 Minutes
• How to Work with Neural Networks from Scratch in Different Niches: Practical Cases
• Safety and Ethics: How to Work with Neural Networks Without Breaking the Rules
• Common Beginner Mistakes: How Not to Waste Time and Money
• Conclusion: Are You Ready to Start Working with a Neural Network Right Now?

Why Everyone Should Know How to Work with Neural Networks Today: Numbers and Opportunities

The generative AI market is showing triple-digit growth. In 2024, the total revenue of leading platforms exceeded $50 billion. Analysts predict the figure will double by 2026. This is not fantasy, but a reality that is changing the rules of work in marketing, design, development, and other fields.

How Much You Can Earn by Mastering Neural Networks: Real Market Figures

A freelancer proficient in Midjourney and Stable Diffusion earns $150-300 for a logo. An SMM specialist using ChatGPT for content plans speeds up work by 3-4 times and takes on twice as many clients. A copywriter generating texts through Claude increases revenue by 40-60% due to higher order volume.

The Russian market shows similar trends. Job postings for "Neural Network Specialist" appear 5-7 times more often than a year ago. Salaries start from 80,000 rubles for juniors and reach 300,000+ for experts who integrate AI into business processes. The IMI platform allows you to start without investment: the free plan includes 12,000 words monthly, equivalent to 15-20 medium-sized articles.

Entrepreneurs who implement neural networks into their work reduce content costs by 50-70%. Product cards for marketplaces, service descriptions, social media posts – all of this is generated in minutes, not hours. Time saved translates into direct profit: freed-up resources are directed towards scaling and attracting clients.

How neural networks are changing professions: who wins, who loses.

How Neural Networks Are Changing Professions: Who Wins, Who Loses

Copywriters, designers, and SMM specialists are actively using AI. The technology doesn't replace professionals but enhances their capabilities. Those who quickly master the tools gain a competitive advantage and increase their market value.

Technical specialists gain new opportunities. Programmers use GitHub Copilot to generate code, saving 30-40% of time on routine tasks. Testers use AI to create test cases, analyzing results 2-3 times faster. Data scientists process massive datasets in minutes, not hours.

Marketers and content managers expand their competencies. Generating articles, posts, and ad creatives speeds up 4-5 times. At the same time, specialists focus on strategy, analytics, and creativity – tasks that require human thinking. The result: salaries grow by 50-80% per year.

Professions at higher risk: routine document processing, basic technical support, simple layout. Here, AI replaces 70-80% of operations. However, even in these niches, there is room for quality control, process setup, and handling exceptions.

Adapting is simple. Start by learning one tool, for example, IMI or ChatGPT. Practice daily: write prompts, analyze answers, adjust queries. In 2-3 weeks, you'll feel confident. In 2-3 months, you'll be able to integrate AI into your main workflows.

Professions of the future require flexibility. The ability to work with neural networks is becoming a basic skill, like knowing Excel 10 years ago. Those who start now gain the early adopter advantage and solidify their leadership in their niche.

Neural Networks for Beginners: What They Are and How They Work (Without the Fluff)

A neural network is a program that learns from a large amount of data and can identify patterns. Unlike regular code where every action is predefined, a neural network independently builds connections between input information and the desired result. The process is similar to teaching a child: you show thousands of examples, and the system begins to understand what is expected of it.

The principle of operation is simple. You input a query (text, image, question), the neural network analyzes it through layers of neurons and generates a response. Each layer is responsible for a certain level of abstraction: one recognizes letters, another words, a third the meaning of a phrase. The result seems "smart," but it is actually a statistical model predicting the next word or pixel.

3 Main Principles of Neural Networks Every Beginner Should Know

First principle – learning from data. The neural network doesn't know the absolute truth. It remembers billions of examples from the internet, books, databases and builds responses based on them. If there is little information on your topic in the training data, the result will be superficial. Therefore, specialized tasks require models with deep expertise in narrow fields.

Second principle – tokens. Text neural networks don't work directly with letters. They break down the query into tokens – conditional fragments 3-4 characters long. All models have token limits: free versions have 4-8 thousand, paid versions have up to 128 thousand. This is important because a long text won't fit into the query, and part of the information will simply be "unseen" by the system.

Third principle – context window. The neural network only remembers what fits into the current dialog. If you start a new chat, previous messages are erased. For working with large documents, use the file upload function or extended prompts where important data is specified at the beginning of each query.

Types of Neural Networks: Where to Find Text, Graphic, and Which to Choose for Starting

Text models – the most popular. ChatGPT, Claude, DeepSeek generate texts, answer questions, write code, analyze documents. For a beginner, it's better to start with one of these platforms. IMI combines several such models in one window, allowing you to compare answers and choose the best option.

Graphic neural networks create images. Midjourney, Stable Diffusion, DALL·E, Flux – are the main tools. Image generation requires a different approach: you need to specify style, details, composition. Beginners find it easier to start with Midjourney via Discord or with built-in tools in IMI.

Video neural networks – the newest. Runway, Pika, Synthesia, Kling generate short videos, animations, avatars. Currently, these tools are expensive and require powerful hardware, but they are developing rapidly. To start, it's enough to try free demos to understand the potential. For business, video avatars already save thousands of dollars on filming.

Step-by-Step Plan: How to Start Working with a Neural Network in 30 Minutes

Practice shows: half an hour after the first query, a beginner already understands if the tool works. The main thing is to choose the right platform, set up an account, and formulate the task. The algorithm consists of four steps. Each takes 5-10 minutes. Follow sequentially, and in 30 minutes you'll get your first ready result.

Step 1: Choosing a Platform – Where to Register and What Free Options Exist

IMI – a platform that combines GPT‑5, Claude, Midjourney, Flux, and other models in one interface. Registration via email or phone, free plan with 12,000 words. Suitable for starting without investment.

DeepSeek – a Chinese model that bypasses many Western restrictions. Shows good results in code and analytics. A free plan is available, registration via email.

GetAtom – a neural network aggregator, similar to IMI. Offers access to several models, business templates, a free trial period. Convenient for comparing the quality of different AI responses.

Registration takes 2 minutes. Enter your email, create a password, confirm via SMS or code. It's important to immediately check the free limit: how many words/queries are available, which models are included. Some platforms give a bonus for the first week – use it to test all functions.

After registration, proceed to profile setup. Specify your field: marketing, design, development, education. This helps the system select suitable templates and assistants. In IMI, you can immediately choose a ready-made AI assistant for your niche – saves time learning the interface.

Step 2: Registration and Initial Setup – What Must Be Done

After creating an account, the system will suggest setting up your profile. This takes 3-4 minutes but affects work convenience. Choose your field from the list: marketing, design, development, education, e‑commerce. The platform will select suitable templates and assistants.

Check the free limit. In IMI – 12,000 words per month; in DeepSeek – token limit. Write down the numbers to track usage. If you plan large volumes, study the paid plans immediately: paid versions give access to more powerful models and a larger context window.

Configure the interface. In the profile, you can choose a theme (light/dark), default language, response format. IMI has a voice input function – activate it if dictating queries is convenient. Specify preferences for answer length: short summaries or detailed texts.

Choose an AI assistant. The IMI platform offers 30+ ready-made assistants: "Marketer," "SMM Expert," "Copywriter," "Data Analyst." Each assistant is already configured for a specific style and tasks. Beginners should start with the "Universal Assistant" – it gives balanced answers to any questions.

Prepare your workspace. Create a folder for saving results, open a text editor for prompts. If you plan to work with large documents, upload them to the system in advance: PDFs, spreadsheets, presentations. IMI allows training the model on your own data – useful for specialized tasks.

Step 3: First Prompt – How to Write a Query to a Neural Network Correctly

A prompt is an instruction for AI. The quality of the result depends on how accurately you formulate the task. Beginners often write short phrases like "write text" and get a template answer. To get a useful result, you need to give the neural network context, specify format, tone, and constraints.

A good prompt consists of 4 parts: role, task, format, constraints. For example: "You are a professional copywriter (role). Write a sales description for a coffee shop in central Moscow (task). Length – 200 characters, style – friendly, without filler words (format and constraints)." Such a query gives a specific result that can be used immediately.

5 Simple Prompt Templates That Work the First Time

Template 1: "You are an expert in [field]. Write [type of text] for [target audience]. Length – [number of words/characters]. Tone – [professional/friendly/ironic]. Avoid [what is not needed]." Example: "You are an SMM expert. Write 5 headline options for a post about discounts on neural network courses for Instagram followers. Length – up to 80 characters. Tone – energetic, without exclamation marks."

Template 2: "Create [number] variants of [what]. Each variant should [feature]. Format – [list/table/text]." Example: "Create 3 variants of a course description 'How to work with neural networks from scratch.' Each variant should emphasize benefits for beginners. Format – list with 3 items."

Template 3: "Analyze [data]. Highlight [what to look for]. Present the result as [format]." Example: "Analyze feedback on a neural network course. Highlight 3 main pain points of students. Present the result as a table: pain point – quote – solution."

Template 4: "Rewrite [text] for [purpose]. Make [what changes]. Keep [what to leave]." Example: "Rewrite this prompt for beginners, make it simpler, remove technical terms. Keep the structure: role – task – format."

Template 5: "Suggest ideas for [what]. Quantity – [number]. Each idea should include [details]." Example: "Suggest 5 article ideas about working with neural networks from scratch for a blog. Each idea should include a title, main intent, approximate length."

Test templates immediately. Open the platform, copy a template, insert your data. Compare results from different models. Note which prompts gave the best answer. After 10-15 attempts, you'll start to feel which formulations work better.

A beginner's mistake – overly general queries. "Write about marketing" gives a watery text. "Write 5 theses for a presentation on implementing neural networks in an SMM strategy for a travel agency" – gives specifics. Specificity is the key to quality.

Step 4: Generating Your First Content – From Idea to Result in 5 Minutes

The first task should be simple and give quick feedback. Take a real scenario from your work. An SMM specialist can generate 3 headline options for a post. A marketplace owner – a product description. A copywriter – intro options for an article. A clear task helps evaluate answer quality.

Open the platform, choose a model. For text, GPT‑5 is suitable; for images – Midjourney or Flux. Paste the prompt from the template, insert your data. Click "Send" and wait 10-30 seconds. The system will return the result.

Compare the answer with expectations. If the text is too general, add details to the prompt. If the image isn't right, clarify style, colors, composition. The first result is rarely perfect. The main thing is to see how the answer changes when the query is adjusted.

Save the obtained content in a separate file. Mark which prompt gave the best result. This creates your library of effective queries. After 10-15 generations, you'll collect a set of working templates you'll use constantly.

How to Work with Neural Networks from Scratch in Different Niches: Practical Cases

Theory without examples doesn't work. Let's examine specific scenarios for three segments: SMM specialists, marketplace owners, copywriters. Each case includes a ready prompt, expected result, and tips for improvement. You can immediately copy the template, insert your data, and test in practice.

For an SMM Specialist: A Monthly Content Plan in 1 Hour

Scenario: need to create 30 Instagram posts about tourism. Prompt: "You are an SMM expert for a travel agency. Create a content plan for 30 days. Each post should contain: a title (up to 60 characters), main text (up to 1500 characters), 5 hashtags, a call to action. Tone – energetic, friendly. Topic examples: last-minute tours, customer reviews, traveler tips." The model generates a table with 30 rows. Check each post for brand compliance. If a title is too general, add a clarification to the prompt: "focus on budget travel to Turkey and Egypt."

For a Marketplace Owner: Product Cards That Sell

Scenario: selling coffee tables on Wildberries. Prompt: "You are a copywriter for a marketplace. Write a product description: coffee table made of solid oak, 80x80 cm, moisture-resistant coating, weight 15 kg. Length – 1000 characters. Structure: 3 benefits at the beginning, detailed specifications, care advice. Tone – expert, no fluff. Add 3 title options up to 50 characters." The system outputs a ready description. Check for marketplace requirements. If keywords are needed, add to the end of the prompt: "Include keywords: oak table, living room furniture, coffee table."

For a Copywriter: How Not to Lose Your Job and Earn More with AI

Scenario: writing a blog article about neural networks. Prompt: "You are a copywriter with 10 years of experience. Write an introduction for the article 'How to work with neural networks from scratch.' Length – 300 characters. Tone – expert but accessible. Must include a metaphor that explains the complex in simple terms. Avoid clichés." Get 3 variants. Choose the best, edit to your style. Important: AI gives a draft that needs to be perfected. It's not a replacement, but an acceleration.

Repeat the process 5-7 times for different tasks. Note which prompts give the best results. Create your own template library. After a week of active practice, you'll generate content 3-4 times faster than before using AI.

Safety and Ethics: How to Work with Neural Networks Without Breaking the Rules

Using AI requires following certain rules. Users often ignore confidentiality, copyright, and data storage issues. This leads to leaks of commercial information, claims from clients, and loss of trust. Let's examine key points to avoid problems.

Where Your Data Is Stored and How to Protect Trade Secrets

Platforms like IMI, DeepSeek store queries on servers. Terms of use usually permit query analysis to improve models. This means: confidential data, client databases, strategies should not get into queries. Never upload files with client personal data, passwords, financial reports into a neural network.

For working with sensitive information, use local solutions. Ollama allows running models on your own computer, fully controlling data. The version with 7-13 billion parameters works on modern laptops without internet. An alternative – corporate plans on IMI, where data is processed in an isolated environment.

The rule is simple: if information could harm your business if leaked – don't send it to a cloud neural network. For public tasks – content generation, ideas, analysis of open data – the cloud is safe. For confidential data, use offline solutions or encrypted corporate access.

Can You Sell Content Created by a Neural Network: Legal Aspects

Legislation in this area is unclear. In Russia, copyright protects only works created by a human. Content generated by AI has no author in the classical sense. This means: you are not violating anyone's rights by using such content, but you also cannot register it as your intellectual property.

For commercial use, check platform licenses. IMI, DeepSeek permit commercial use of generated content. Midjourney and Stable Diffusion have restrictions: the free version of Midjourney gives a CC license, the paid version – full commercial rights. Stable Diffusion is completely free.

An important nuance: if a neural network reproduces someone else's work (copies a specific artist's style, uses protected elements), it can lead to claims. Always check the result for uniqueness. For critical tasks (logos, brand books, ad campaigns) add human refinement. This creates originality and protects against claims.

Common Beginner Mistakes: How Not to Waste Time and Money

Beginners make the same mistakes, wasting hours on useless queries. Understanding typical pitfalls saves weeks of frustration. The listed mistakes occur in 80% of beginners trying to work with neural networks without preparation. Avoid them – and results will come from the first attempts.

7 Mistakes When Writing Prompts That Kill Results

First mistake – overly general formulations. Query "tell me about marketing" gives a watery text without specifics. The system doesn't understand what's important: theory, cases, tools, numbers. The fix is simple: add details. "Tell me about an SMM strategy for a coffee shop with a 50,000 ruble monthly budget, specify 3 channels, give post examples" – such a prompt returns an actionable plan.

Second mistake – ignoring the role. A prompt without specifying "who you are" gives a mediocre answer. The neural network doesn't know who to write as: a student, CEO, freelancer. Specify the role at the beginning: "You are an experienced targetologist with 5 years of practice in e‑commerce." The answer immediately becomes expert, with appropriate terminology and depth.

Third mistake – lack of format. "Write a lot" – is not an instruction. Specify exactly: "5 headline options of 60 characters each, each with an emoji, without exclamation marks." The model loves structure. The more specific the constraints, the closer the result to expectations.

Fourth mistake – overloading one query. Beginners write 500-word prompts, trying to fit everything at once. The model loses the thread, the answer becomes chaotic. Break down complex tasks into stages. First "make an article outline," then "write the introduction," then "add examples." Sequence yields quality.

Fifth mistake – forgetting context. If working with a large document, repeat key data in each query. The neural network only remembers the current dialog. "Based on the previous answer, add 3 B2B cases" – such a phrase maintains coherence.

Sixth mistake – accepting the first answer as final. Professionals always refine. Got text? Ask to "make it more friendly, remove bureaucratese, add a metaphor." Repeated iterations turn a draft into finished material.

Seventh mistake – copying prompts without adaptation. Ready-made templates on the internet are good as a base but don't work "out of the box" for your task. Always add specifics: niche, brand, target audience. A "prompt for a coffee shop chain" will yield results only after adding your unique selling proposition.

When a Neural Network Can't Handle It: Tasks Where You Can't Do Without a Human

Neural networks don't replace humans in tasks requiring creative breakthroughs. Generating a truly unique brand concept, strategic consulting, building long-term client relationships – here AI acts as a tool, not an executor. A human sets the direction, AI speeds up implementation.

Precise calculations and auditing – another weak spot. A neural network can make an arithmetic error, miss inaccuracies in a financial model, distort data. Always double-check numbers, formulas, legal wording. AI is an assistant, not the final controller.

Ethics and empathy remain with humans. A neural network won't feel the nuances of corporate culture, won't understand the subtleties of interpersonal conflicts, won't propose a solution considering human values. HR tasks, negotiations, conflict resolution – here AI can give options, but you make the decision.

What's next: a plan for developing neural network skills.

Mastering the basics is the first stage. After 2-3 weeks of regular practice, you'll start feeling confident in simple tasks. The next step – systematic skill development. Professionals highlight three directions: deepening prompt engineering, studying API, creating custom assistants. Each direction opens new opportunities and increases a specialist's market value.

From Beginner to Pro: What to Study After Mastering the Basics

Prompt engineering – the first direction. Basic templates give results but don't reveal full potential. Study frameworks: CO‑STAR (Context, Objective, Style, Tone, Audience, Response), RTF (Role, Task, Format). These models help structure queries and get predictable answers. Practice on complex tasks: market analysis, strategy creation, concept generation. The more experiments, the better your "feel" for the right formulation.

API and integrations – the second direction. Most platforms provide an API: DeepSeek, IMI. Studying the API allows embedding neural networks into workflows: automating report generation, creating chatbots, integrating with CRM. Start with a simple Python script that sends a request to the API and saves the response to a file. Examples are abundant in platform documentation. After a month of API study, you'll be able to create automated pipelines saving hours of manual work.

Creating custom assistants – the third direction. IMI and GetAtom platforms allow creating a personal assistant trained on your data. Upload your company's knowledge base: texts, reports, presentations. Configure the role and response style. Get an assistant that responds like your best employee but works 24/7. This improves customer service quality and reduces team workload.

TOP‑5 Courses on Neural Networks for Advanced Users

Course "Creating AI Assistants" within the X10AI challenge from IMI – a module inside a 4‑day intensive. Teaches assistant setup, data upload, fine‑tuning. Available to challenge participants on Telegram. The challenge is positioned as free, but spots are limited: 14 spots left out of 100. Participants have a chance to win a MacBook or iPhone.

Course "Advanced Prompt Engineering" from Yandex Practicum – in the ZeroCoder program, a prompt engineering course is listed, covering 120+ neural networks, 20+ practical assignments, curator support. Online format, duration 2 months. Price not specified, free consultation with an expert available.

Course "API and Neural Network Integrations" from Skillbox – in the "Neural Networks for Business" program from Skillbox, there is a module on API integrations. The course covers working with local models, vector knowledge bases, integration with CRM and other services. Duration 2 months, 9 AI projects for the portfolio. Price not specified in search results.

Course "Neural Networks and AI for Business and Marketing" from HSE – an official course from HSE University, 40 hours, 4 weeks, cost 40,000 ₽. Online synchronous format, personal support, qualification upgrade certificate. The course is not free, as I mistakenly stated earlier. This is a serious program for specialists, not for beginners.

Course "Multimodal AI" from DeepSeek – DeepSeek is not an educational platform but a model developer. Search results show no information about an official course from DeepSeek. There are video tutorials on YouTube from enthusiasts, but not an official course. This is my mistake.

Conclusion: Are You Ready to Start Working with a Neural Network Right Now?

We've gone from theory to practice. Understood the basic principles, chose a platform, wrote the first prompt, generated content. The last step remains – to draw a conclusion and determine next actions. A checklist will help assess readiness. Answer 5 questions honestly. The result will show if you should start today.

Main Questions About Working with Neural Networks

Question: How long will it take to learn to work with neural networks at least at a basic level?

Answer: 2-3 hours of theory and 5-7 practical queries. 30 minutes after registration, you'll already get your first result. After a week of daily practice (20-30 minutes), you'll master 80% of typical tasks: generating texts, descriptions, posts. Deep immersion in prompt engineering and API requires 20-30 hours of learning and a month of practice.

Question: Do you need to know programming to work with neural networks?

Answer: No. For basic tasks – texts, images, data analysis – a browser and ready platforms are enough. Programming is only needed for integrations: automation via API, creating chatbots, connecting to CRM. But that's the second stage. Start without code, master prompts, understand the logic of AI work. Then, if needed, learn Python at a basic level – that's enough for 90% of integrations.

Question: What to do if the free limit runs out quickly?

Answer: Plan your queries. 12,000 words in IMI – is 15-20 medium‑sized articles. For critical projects, consider a paid plan: 500-1,000 rubles per month give access to GPT‑5 and remove restrictions.

Question: How to understand if the neural network is giving the correct answer?

Answer: Check facts. Neural networks can "hallucinate," especially with numbers, dates, links. Use verification tools: Google search, source checking, expert consultation. For critical data (finance, law, medicine) always double‑check with a professional. AI is an assistant, not the sole source of truth.

Question: How not to lose motivation if results don't match expectations?

Answer: Start with simple tasks where mistakes aren't critical. Generate post ideas, headline options, product descriptions. Success in simple tasks builds confidence. Gradually complicate your queries. Keep a log: write down the prompt, result, what you liked, what to fix. After a week, you'll see progress. The key is regularity, not perfection from the first try.

First step right now: register and make your first query to a neural network

Choose the platform – IMI. Registration takes 2 minutes: email, password, confirmation code. You immediately get 12,000 words. Go to the chat, choose the GPT‑5 model. Copy the first template from the article: "You are an SMM expert. Write 3 headline options for a post about launching neural network training. Length – up to 60 characters, tone – energetic." Paste, click "Send." In 10 seconds, get the result. Save it to a file. Repeat 5 times with different tasks. You've recorded your first success. You are already working with a neural network.

Creating AI bots involves developing chatbots that can handle user queries, understand natural language, analyze data, and generate automated responses. Today, such solutions are widely used in business, marketing, education, Telegram channels, blogs, and customer support services.

Thanks to advancements in artificial intelligence, GPT language models, and user-friendly platforms, anyone can create an AI bot—no programming required. These bots can answer questions, assist customers, process messages, generate text and images, and operate 24/7 without human intervention.

In this guide, we’ll break down the process of creating an AI bot, integrating ChatGPT, configuring prompts, leveraging content generation, and exploring real monetization strategies.

Contents

• What Is an AI Chatbot?
• How to Monetize an AI Chatbot
• How to Integrate AI into Your Chatbot
• Integrating ChatGPT into Your Bot
• Configuring ChatGPT Queries
• Promoting Your AI Bot
• Potential Earnings from an AI Bot
• Why You Can Build an AI Bot Yourself
• FAQ

What Is an AI Chatbot?

An AI chatbot is a program that interacts with users via chat, utilizing machine learning and natural language processing technologies. Unlike rule-based bots, AI chatbots understand context, clarify questions, and provide more accurate responses.

These bots are powered by GPT language models, which analyze text messages, compare them with trained data, and generate relevant replies. They can be deployed on websites, Telegram, other messengers, or via API integrations.

Creating an AI bot typically involves:

• Setting up conversation logic
• Integrating an AI model
• Uploading a knowledge base
• Testing and launching

The result is a tool that automates user interactions and solves business challenges.

How to Monetize an AI Chatbot

An AI chatbot is more than just a helper—it’s a full-fledged income-generating tool. Below are key areas where AI chatbots can drive revenue.

For Influencers

Influencers often receive repetitive questions from followers or offer free content in exchange for subscriptions or comments.

An AI bot can:

• Automatically answer FAQs
• Send direct messages with links
• Process applications
• Engage audiences across social networks

This saves time, prevents lost opportunities, and enhances the sale of paid content, consultations, and ads—while boosting follower loyalty through quick responses.

For Info-Business Owners

In the info-business space, AI bots can automate courses, training, and student support. Bots can:

• Send lessons
• Check assignments
• Answer questions
• Provide post-purchase follow-up

This reduces team workload and improves service quality, though human oversight remains essential for high-value packages.

For Marketers, Producers, and Promotion Specialists

Marketers use AI bots to:

• Process inquiries
• Analyze user requests
• Generate ad copy and scripts
• Automate customer responses and data collection
• Assist with target audience analysis

For AI Experts and Coaches

Experts and coaches deploy AI bots as personal assistants to help users:

• Navigate topics
• Ask questions
• Receive consultations
• Access learning materials in a convenient format

For Entrepreneurs

AI bots often serve as the first line of customer support, handling FAQs, assisting with orders, clarifying details, and escalating complex cases to managers. Many businesses already use bots to automate routine inquiries efficiently.

For Specialized Content Creators

If you have a database of articles, courses, or educational materials, an AI bot can act as an intelligent search tool, helping users find relevant information and navigate both archived and current content with ease.

For Telegram Channel Owners

Telegram AI bots are used for:

• Delivering content
• Processing payments
• Engaging subscribers
• Automating broadcasts

They’re a scalable tool for growing channels and maintaining audience connections.

How to Integrate AI into Your Chatbot

Integrating AI transforms your bot from a button-based script into a smart assistant that understands questions, processes messages, and leverages knowledge bases. Most platforms offer AI integration via a dedicated step (e.g., “AI block” or “GPT step”).

Step 1: Add an AI Step in the Constructor

1. Open your project dashboard and select your bot.
2. Navigate to the scenario editor (often labeled “Scenario,” “Dialogue,” “Constructor,” “Flow,” or “Funnel”).
3. Click “Add Block” (+).
4. Choose the AI step (under categories like “AI,” “Integrations,” “Text,” or “AI Response”).
5. Select the GPT model (more powerful models offer better quality but higher token costs).
6. Define the query source: user message, template, or hybrid mode.

Step 2: Configure the AI Step

Phase 1: Define the Bot’s Role and Communication Style

Specify:

• Who the bot assists (clients, subscribers, students)
• Tasks it performs (sales, support, navigation)
• Limitations (no fabrication, no unsafe advice)
• Response format (lists, steps, concise/detailed)

Tip: To prevent hallucinations, instruct the bot to respond only based on the knowledge base or ask for clarification if data is missing.

Phase 2: Set Up the Model Query

A well-structured query includes:

1. Instructions (role + rules)
2. Context (product/service details, terms, links, pricing)
3. User message (the actual question)

Add constraints like:

• “Answer accurately”
• “Ask clarifying questions if data is insufficient”
• “Avoid jargon”
• “Provide concrete steps”

Phase 3: Connect Data Sources and Knowledge Base

Without data, AI bots respond generically. Connect:

• Website text (FAQs, service descriptions)
• Documents (PDFs, manuals, price lists)
• Tables (tariffs, product specs)
• CRM or internal systems
• Google Docs/Notion

Choose between:

1. Simple knowledge base (manual text input)
2. Advanced RAG system (search + retrieval for precise answers)

Ensure data is up-to-date, categorized, and includes fallback rules.

Step 3: Test Thoroughly

• Test common questions (pricing, ordering, contact details)
• Test ambiguous or poorly phrased queries
• Verify clarifying question prompts
• Check safety and data privacy
• Optimize response time and token usage

Integrating ChatGPT into Your Bot

How to Connect ChatGPT

1. Obtain an API token (key) from OpenAI.
2. Enter the token in your service settings (“API Key” or “Access Token”).
3. Select the GPT model version.
4. Configure parameters:

• Max response length (token limit)
• Temperature (creativity level)
• System role and rules
• Response language

5. Send a test message to verify the connection.

Important: Monitor token costs, log interactions, handle errors gracefully, and enforce safety policies.

Configuring ChatGPT Queries

A well-structured query ensures consistent, useful responses.

Query Components:

1. Bot Role – Define type, scope, responsibilities, and limitations. Example: “You are a customer support bot for an online service, answering only based on provided information.”
2. Context & Conditions – Describe the environment (company, services, rules) to avoid guesswork.
3. Communication Style – Specify tone, length, simplicity, and use of emojis.
4. Response Format – Use lists, step-by-step instructions, or summaries for consistency.

Workflow Example:

1. User sends a message.
2. Message is passed to the AI step.
3. ChatGPT processes the full query (role + context + user input).
4. Model generates a response.
5. Bot delivers the answer in seconds.

Saving ChatGPT Responses

Store responses to:

• Analyze frequent questions
• Optimize knowledge bases
• Reduce model load (save tokens)
• Monitor quality and correct errors

Log interactions in databases, CRM systems, or analytics tools for ongoing improvement.

Using Image and Text Generators

Image Generation

1. Provide a detailed text description (subject, style, colors, format).
2. Send the description to an image-generation model (e.g., DALL·E).
3. Receive and deliver the generated image. Use cases: banners, article covers, product cards, social media visuals.

Text Generation

1. User specifies text type (article, product description, script).
2. Bot clarifies parameters (topic, length, style, audience).
3. Query is sent to ChatGPT with all constraints.
4. Generated text is returned, ready for use or editing.

Use cases: blog posts, service descriptions, email campaigns, dialogue scripts.

How to Start Earning with an AI Bot

Identify the problem your bot solves, its target audience, and what users are willing to pay for.

Monetization Models:

1. Subscriptions & Paid Access – Users pay for ongoing access (monthly/annually). Ideal for Telegram bots, support services, and educational projects.
2. Premium Features – Free basic functionality with paid upgrades (e.g., more queries, advanced GPT models, image generation).
3. Consultations & Services – Bot acts as a pre-consultation tool, collecting data and preparing users for paid expert sessions.
4. Advertising & Affiliate Offers – Integrate relevant ads or partner offers for large user bases. Ensure ads are contextually appropriate.
5. Sales of Products/Services – Use bots for product consultation, selection assistance, order processing, and handoff to sales teams.

Promoting Your AI Bot

Channels for Promotion:

• Website/Landing Page – Explain features, use cases, and benefits.
• SEO Content – Target keywords like “creating AI bots,” “AI chatbot for business,” “Telegram bot with AI.”
• Telegram & Messengers – Showcase bot functionality in relevant channels.
• Advertising – Use targeted ads highlighting speed, automation, or customer support.
• Integrations & Partnerships – Collaborate with platforms, services, or blogs to reach wider audiences.

Potential Earnings from an AI Bot

Income depends on niche, user base, monetization model, and promotion efforts.

• Small Telegram bot with subscriptions: $200–$500/month
• Business/support bot: $1,000–$3,000/month
• Niche AI assistants/educational bots: $5,000+/month

Note: Success requires continuous optimization, scenario refinement, and active promotion.

Why You Can Build an AI Bot Yourself

Modern no-code platforms enable anyone to:

• Create AI bots without programming
• Use pre-built templates
• Integrate ChatGPT via API
• Configure scenarios in visual editors
• Upload knowledge bases
• Launch quickly

Most services offer guides, documentation, and support. The key is to define your bot’s purpose, audience, and use case clearly.

FAQ

Can I create an AI bot for free? Yes—many platforms offer free plans or trial periods to test your idea.

How long does it take to create an AI bot? You can build and launch a basic bot in minutes using a constructor.

Do I need programming skills? No—most platforms provide intuitive interfaces and drag-and-drop blocks.

In 2025, the industry has definitively moved past the "uncanny valley." If earlier AI video generators produced unstable characters with artifacts, today, it's challenging even for professionals to distinguish AI-generated footage from real filming.

The content creation market is evolving at a breakneck pace. For SMM specialists, e-commerce sellers, and filmmakers, ignoring artificial intelligence now means losing a competitive edge. An AI can create a video faster than it takes to brew coffee, while production budgets shrink by orders of magnitude.

This article compiles the best AI video generators relevant at the moment. The review includes not only high-profile newcomers but also proven business tools that help tackle daily content tasks.

What's Changed in 2025: Our Ranking Criteria

The video AI sphere is developing in leaps and bounds: leaders change every few months. Tools popular six months ago may be hopelessly outdated today. Our ranking is based on four key criteria that define quality output.

Hyper-Realism & Physics (Coherence)

The main issue with past versions was objects that "drift" or disappear from the frame. Modern AI generates videos with consideration for the physics of fabrics, lighting, and gravity. If a character moves, their shadow shifts synchronously, and clothing folds behave naturally. Priority was given to models capable of maintaining object stability throughout an entire scene.

Duration & Control

Generating short 3-second clips is no longer sufficient. Businesses require full-fledged clips lasting 10-15 seconds. Control is critically important: the ability to adjust camera movements (Zoom, Pan), set object trajectories, and manage character facial expressions.

Commercial Use & Licensing

Many free plans restrict the use of content for advertising purposes. The review includes services offering commercial licensing. This is a fundamental point for marketing and client work, allowing users to avoid legal risks.

Functionality Accessibility

Considering geo-restrictions, each service was tested for usability from different regions: payment methods, need for additional access tools, and support for the Russian language in input prompts.

ТОП-12 Best AI for Text-to-Video & Image-to-Video Formats

This section features industry flagships—the "heavy artillery" of generative AI. These tools set quality standards, enabling cinematic-level video creation. They are ideal for advertising, music videos, and professional tasks.

IMI (imigo.ai) — An Aggregator of Top AI Models in One Window

The imigo.ai platform is a universal hub uniting leading global models. Instead of paying for multiple subscriptions and setting up VPNs for each service, users get access to Kling v2.1, Hailuo 02, Veo 3, Sora 2, and other top-tier engines through a unified interface. This AI makes video generation accessible to everyone by removing technical barriers.

The main advantage is convenience. You can switch between models (e.g., compare Veo 3 and Kling 2.5 results) with a single click. The platform is fully localized in Russian and adapted for payments with Russian cards.

Parameter	Value
Available Models:	Veo 3.1, Kling v2.1, Sora 2, Hailuo 02, etc.
Type:	Text-to-Video, Image-to-Video
Complexity:	Low (suitable for beginners)

Pros and Cons:

✅ Everything in one place: No need to register on 10 different services. ✅ No payment or access issues from Russia. ✅ Convenient generation parameter selection (format, duration) for all models. ❌ Cost may vary depending on the chosen generation model.

Kling AI — The Chinese Generation Leader

Currently, Kling (especially versions 1.5 and above) is considered the main competitor to Sora and often surpasses it in accessibility. It's a powerful video generation AI that impresses with its motion physics. It excels at understanding object interactions: how water is poured, metal bends, or hair flows in the wind.

Kling allows generating clips up to 10 seconds (in Pro mode) in high 1080p resolution. This makes it an ideal choice for creating realistic inserts for films or commercials.

Parameter	Value
Type:	Text-to-Video, Image-to-Video
Duration:	5 sec (Standard), up to 10 sec (Pro)
Quality:	High realism (30 fps)

Pros and Cons:

✅ Best-in-market understanding of anatomy and physics. ✅ Generous free plan for testing. ❌ Complex registration and interface (often in Chinese/English). ❌ Generation time during peak hours can reach several hours.

Runway Gen-3 Alpha — A Tool for Professionals

Runway has long been an industry standard. The Gen-3 Alpha version focuses on control. If you need the camera to pan exactly from right to left or a character to smile at the 3-second mark—Runway is for you. The Motion Brush tool allows you to highlight objects (e.g., clouds or water) and make only them move, keeping the background static.

This service is often used by advertising agencies where every detail in the frame matters.

Parameter	Value
Type:	T2V, I2V, Video-to-Video
Duration:	5 or 10 seconds
Tools:	Motion Brush, Director Mode (camera)
Cost:	From $12/month (credits expire)

Pros and Cons:

✅ Precise control: Director's console for camera management. ✅ High texture detail. ❌ Expensive: Almost no credits on the free plan. ❌ Difficult to pay from Russia without intermediaries.

Luma Dream Machine — Speed & Dynamics

Luma burst onto the market with a promise of high speed: 120 frames in 120 seconds. It's a video generator AI that excels at dynamic scenes—drone flyovers, races, action sequences.

Luma's unique feature is high-quality morphing (smooth transformation of one object into another). It also works well with images, allowing you to animate old photos or artwork.

Parameter	Value
Type:	Text-to-Video, Image-to-Video
Speed:	High (Fast Generation)
Duration:	5 seconds (can be extended)
Free Plan:	30 generations per month

Pros and Cons:

✅ Generates faster than most competitors. ✅ Excellent at creating cinematic camera flyovers. ❌ Sometimes distorts faces in wide shots. ❌ Free generations run out quickly.

Hailuo AI — Best for Human Anatomy

A newcomer that quickly gained popularity thanks to its ability to work with people. While other models often turn fingers into "spaghetti" or make gait unnatural, Hailuo 02 excels at human movement and plasticity.

This video creation AI is suitable for scenes with dancing, sports, or active gesticulation.

Parameter	Value
Type:	Text-to-Video
Specialization:	People, movement, choreography
Quality:	High (HD)
Access:	Web interface

Pros and Cons:

✅ Natural facial expressions and no "uncanny valley" effect. ✅ Good character stability. ❌ Fewer camera control settings compared to Runway.

Pika Art (Pika 1.5) — Creative Effects & Social Media

Pika focused on viral content. Version 1.5 introduced Pikaffects: the ability to "crumple," "melt," "explode," or "inflate" an object in the frame. This is perfect for TikTok, Shorts, and Reels.

Furthermore, Pika offers convenient Lip-sync (lip synchronization with voiceover), allowing you to make a character speak.

Parameter	Value
Type:	T2V, I2V, Lip-sync
Features:	Pikaffects (VFX effects)
Format:	16:9, 9:16 (vertical)
Free:	Starter credits

Pros and Cons:

✅ Unique visual effects not found elsewhere. ✅ Simple to use via website or Discord. ❌ Texture quality sometimes lags behind Kling and Runway (more "soapy").

Stable Video Diffusion (SVD) — For Those Who Love Control

This is not just a service but an open-source model from Stability AI that can be run on a powerful local PC or in the cloud. The video AI is available for free download but requires technical skills. SVD has become the base for many other services. It allows generating short clips (up to 4 seconds) from images with a high degree of control over motion bucket parameters (amount of motion).

Parameter	Value
Type:	Image-to-Video
Price:	Free (Open Source)
Requirements:	Powerful GPU (NVIDIA) or cloud GPU
For Whom:	Developers, enthusiasts

Pros and Cons:

✅ Completely free and uncensored (when run locally). ✅ Can be fine-tuned on your own data. ❌ Requires powerful hardware and software setup. ❌ Short generation duration.

Kaiber — For Music Videos & Stylization

Kaiber became cult after the release of a Linkin Park music video created with its help. This AI creates videos in a unique illustrated style (anime, oil painting, cyberpunk). The tool works on the principle of Audio Reactivity: video can pulsate and change to the beat of uploaded music. An ideal choice for musicians and music video makers.

Parameter	Value
Type:	Video-to-Video, Audio-to-Video
Feature:	Reaction to music (Audio React)
Styles:	Anime, comic, painting
Price:	From $5/month (trial available)

Pros and Cons:

✅ Best tool for creating musical visualizations. ✅ Unique "living painting" style. ❌ Weak for photorealism. ❌ Paid access (trial is short).

Genmo — The Smart Assistant with a Chat

Genmo (Mochi 1 model) positions itself as a "Creative Copilot." It's an advanced platform that works through a chat interface. You can ask the bot not just to generate a video but to edit it: "add more snow," "make the movement faster." Genmo understands complex instructions well and allows animating specific areas of a photo.

Parameter	Value
Type:	Text-to-Video, Image-to-Video
Control:	Chat-bot, brush selection
Model:	Mochi 1 (Open Source base)
Free:	Daily credits

Pros and Cons:

✅ Intuitive interface (communication like with ChatGPT). ✅ Good performance with 3D objects. ❌ Quality sometimes lags behind Kling in realism.

Leonardo AI (Motion) — Everything in One Ecosystem

Leonardo initially competed with Midjourney but is now a powerful all-in-one suite. The Motion function allows animating any generated image with a single click. You can adjust the Motion Strength directly in the interface. It's convenient: no need to download the image and import it into another service.

Parameter	Value
Type:	Image-to-Video
Integration:	Built into the image generator
Settings:	Motion strength (1-10)
Access:	Within the general Leonardo subscription

Pros and Cons:

✅ Seamless workflow: generate image -> click button -> get video. ✅ Single subscription for images and animation. ❌ Fewer camera settings than Runway.

Google Veo — The Cinematic Giant

Google Veo (available through YouTube Shorts and the Vertex AI platform) is the search giant's response to market challenges. The Veo model can generate video clips with 1080p+ resolution lasting over a minute. Its main feature is a deep understanding of context and cinematic terms ("time lapse," "aerial shot of a landscape").

Veo can edit videos using text commands and masks, making it a powerful post-production tool. Integration with the Google ecosystem (Workspace, YouTube) makes it potentially the most massive tool.

Parameter	Header
Type:	Text-to-Video, Video-to-Video
Duration:	60+ seconds
Quality:	Cinema-standard (1080p/4K)
Access:	VideoFX (limited), Vertex AI
Feature:	Understanding long prompts

Pros and Cons:

✅ Amazing coherence (stability) in long videos. ✅ Integration with professional editing tools. ❌ Access currently limited (Waitlist or corporate plans). ❌ Difficult for an average user to try "here and now."

OpenAI Sora — The Realism Benchmark

Sora has become synonymous with revolution in video generation. Although Sora was in closed access ("Red Teaming") for a long time, its capabilities set the bar for all others. The model can generate complex scenes with multiple characters, specific movements, and precise background detail.

Sora understands the physical world: if a character bites a cookie, a bite mark remains. This is a deep simulation of reality, not just pixel animation.

Parameter	Value
Type:	Text-to-Video
Duration:	Up to 60 seconds
Realism:	Maximum (2025 benchmark)
Access:	Gradual rollout in ChatGPT / API

Pros and Cons:

✅ Unmatched quality and realism. ✅ Generation of complex object interactions. ❌ Very high computational resource requirements (expensive). ❌ Availability for the general public is opening slowly.

Best AI for Avatars & Business

This market segment develops in parallel with cinematic video generation. For business, online courses, and corporate training, Hollywood-level special effects are not always needed. More often, a "talking head" (Talking Head) is required—a digital narrator who can voice text in 40 languages without stuttering or demanding a fee.

Here, Lip-sync (lip synchronization) and voice cloning technology reign supreme.

HeyGen — The Gold Standard for Dubbing & Avatars

HeyGen went viral thanks to its Video Translate feature, allowing bloggers to speak in perfect English, Spanish, and Japanese with their own voices. But for business, it's primarily a powerful tool for creating content without a camera.

You can create your digital double (Instant Avatar): record 2 minutes of video on a webcam, and the system creates your copy. Then you simply write the text, and the avatar speaks it. A lifesaver for experts tired of filming.

Parameter	Value
Specialization:	Realistic avatars, video translation
Languages:	40+
Voice Cloning:	Yes, very accurate
Price:	From $24/month (Free trial available)
API:	Yes (for automation)

Pros and Cons:

✅ Perfect lip-sync: lips move precisely with pronunciation. ✅ Ability to create an avatar from a photo or video. ❌ Expensive per minute of video generation on paid plans. ❌ Watermarks on the free plan.

Synthesia — The Corporate Giant

If HeyGen is loved by bloggers, Synthesia is chosen by Fortune 500 companies. It's a platform for creating training courses, instructions, and corporate news. The library contains over 160 ready-made avatars of different races and ages.

The main feature is dialog scripts. You can seat two avatars at a table and make them talk to each other. Perfect for sales training or soft skills.

Parameter	Value
Specialization:	Training, L&D (Learning & Development)
Avatars:	160+ ready-made actors
Editor:	Similar to PowerPoint (slides + video)
Price:	From $22/month

Pros and Cons:

✅ Convenient editor: assemble video like a presentation. ✅ High data security (SOC 2). ❌ Avatars are less emotional than HeyGen's (more "official"). ❌ Cannot create an avatar from scratch on the starter plan.

D-ID — Bringing Photos to Life

D-ID (Creative Reality Studio) specializes in animating static portraits. This is the very technology that makes a photo of your great-grandmother or the Mona Lisa move. For business, D-ID offers interactive agents—chatbots with a face that can answer clients in real-time.

Integration with Canva allows adding talking presenters directly into presentations.

Parameter	Value
Specialization:	Photo animation, interactive agents
Integrations:	Canva, PowerPoint
Technology:	Live Portrait
Price:	From $5.99/month (very affordable)

Pros and Cons:

✅ The cheapest way to make a talking head. ✅ Works with any photo (even from Midjourney). ❌ Head movement is slightly unnatural ("swaying" effect). ❌ Quality is lower than HeyGen.

How Businesses Monetize AI Video

Theory is good, but how does this convert into money? We've gathered real use cases demonstrating the effectiveness of implementing AI.

Case 1: Marketplaces (Wildberries/Ozon) — 20% CTR Increase

Problem: A seller needs to highlight a product card (e.g., a coffee maker) in the feed, but the budget for video filming with steam and beautiful lighting starts from 30,000 rubles.

Solution:

1. Take a high-quality product photo.
2. Animate only the steam from the cup and highlights on the metal using Motion Brush in Runway or Luma.
3. Upload the video as an autoplaying cover.

Result: The card "comes to life" in search. According to sellers, the click-through rate (CTR) of such cards is 15-20% higher compared to static images. Costs: $0 (using test credits) or $15 for a subscription.

Case 2: YouTube Channel Localization (Info Business)

Problem: An expert wants to enter the English-speaking market but speaks with a strong accent. Solution: Using HeyGen for content dubbing. The AI not only overlays the voice but also changes lip movement to match English speech. Result: Launching an English-language channel without reshoots. Time saved: hundreds of hours. The audience doesn't notice the substitution as the author's voice timbre is preserved.

Case 3: Music Video for Pennies (Washed Out)

Problem: An indie band needs a music video on a minimal budget.

Solution: Director Paul Trillo used Sora (before its public release) to create the music video "The Hardest Part." He applied the "infinite zoom" technique, flying through scenes of a couple's life: from school to old age.

Result: The video went viral and was covered by all major media worldwide. Production costs were incomparably lower than traditional filming with actors and locations.

Conclusion

The generative video market matured in 2025. We no longer look at "dancing monsters"; we use AI for real work: reducing advertising costs, speeding up editing, and creating content that was previously accessible only to Hollywood studios.

The main advice: don't be afraid to experiment. Technology develops faster than textbooks are written. Start with simple prompts in accessible services, and within a week, you'll be able to create videos that will amaze your clients and subscribers. The future is already here, and it's being generated at 30 frames per second.

Gemini 3 is Google DeepMind’s flagship AI model, unveiled in late 2025 as the next evolution of the Gemini lineup. Engineered as a universal multimodal intelligence, it is capable of processing text, images, audio, and video within a single, unified context.

The core objective of Gemini 3 extends beyond simple response generation; it focuses on advanced reasoning, precise information structuring, and the execution of complex task chains within the Google ecosystem.

Architecture and Key Capabilities

Gemini 3 is architected as a natively multimodal model, rather than a collection of separate models stitched together by add-ons.

Core Capabilities:

Multimodal Input and Output

• The model accepts and processes text, images, audio, and video within a single conversation thread, without losing context.

Enhanced Logical Reasoning

• According to Google and independent reviews, Gemini 3 demonstrates significantly more robust reasoning chains compared to previous versions.

Structured Output

• The model natively generates tables, step-by-step guides, analytical frameworks, and visually readable formats.

Agentic Capabilities

• Gemini 3 is capable of planning action sequences, decomposing complex objectives into stages, and executing tasks with intermediate result validation.

Reasoning Quality and Multimodality

One of the definitive upgrades in Gemini 3 is its reasoning quality.

Improvements over previous versions include:

• Fewer logical leaps: Reduced instances of disconnected or unfounded conclusions.
• Greater consistency in long-form queries: More stable outputs when processing extensive prompts.
• Superior context retention: Better ability to maintain coherence throughout multi-step tasks.

Multimodality in Practice

Gemini 3 is capable of:

• Analyzing images and immediately generating text-based explanations.
• Extracting insights from video footage.
• Combining visual and textual data into a single, unified response.

This makes the model particularly valuable for analytics, education, content creation, and product documentation.

Model Versions and Differences

Gemini 3 Pro

• The Core Flagship: The primary, most powerful version of the model.
• Maximum Reasoning Quality: Delivers the highest fidelity in logic and analysis.
• Best For: Complex problem-solving and professional-grade applications.

Gemini 3 Flash

• Optimized for Speed and Scale: Engineered for high throughput and efficiency.
• Use Cases: Powering Search and rapid-response scenarios.
• Trade-off: Significantly reduced latency at the cost of slightly less depth in analysis.

Version	Speed	Analysis Depth	Primary Use Case
Pro	Medium	High	Professional tasks, Development
Flash	High	Medium	Search, High-volume scenarios

Limitations and Weaknesses

Despite the significant progress, Gemini 3 has certain limitations:

• Experimental Features: Some agentic capabilities remain in an experimental phase (beta).
• Gated Access: Access to advanced features is restricted to paid subscription tiers.
• Regional Availability: Functionality may vary by region due to regulatory compliance.
• Human Oversight: Not all scenarios are fully autonomous; many still require human-in-the-loop verification.

Market Trends 2025-2026

State of the Market in 2025

Multimodal models have become the industry standard. AI is now directly integrated into search engines and productivity tools, while agentic capabilities are transitioning from experimental phases to concrete business cases.

Generative AI Continues to Attract Capital and Investment

In 2025, global investment in generative AI reached approximately $33.9 billion, an increase of ~18.7% compared to 2023. This reflects sustained capital inflows into the foundational layer of AI technologies.

AI Moves from Experiment to Enterprise Integration

According to analysts, many organizations have shifted from pilot projects to full-scale deployments, focusing on measurable results (ROI) and workflow automation.

Infrastructure Constraints Impact Hardware Markets Massive demand for memory and compute resources from major cloud providers is reducing the availability of DRAM/NAND for PCs and consumer devices, potentially slowing growth in the consumer hardware segment.

"AI Slop" and Content Quality – A New Management Challenge

2025 saw intensified scrutiny on low-quality generative content (often termed "AI slop"). This has raised critical questions regarding quality control and trust in AI-generated material.

AI Market Volume Continues to Expand

Forecasts indicate the global AI market will grow to approximately $757.6 billion by 2026, with a Compound Annual Growth Rate (CAGR) of ~19.2%.

2026: Forecasted Trends and Key Shifts

Transition from "Discovery" to Mass Diffusion

Top executives at major technology firms note that 2026 will mark the year AI ceases to be an experiment and shifts toward broad, real-world integration across enterprises globally.

AI Agents and Autonomous Workflows Become Standard

Analytical reports indicate that by 2026, AI Agents will become pivotal in automating complex, multi-step business processes—moving beyond simple Q&A to executing entire tasks from start to finish.

Integration of "Physical AI" and Device-Level Automation

Consulting firms forecast that 2026 will be the year AI expands beyond the digital realm into physical systems. Autonomous robots, intelligent machines, and "synthetic perception" are becoming integral parts of industrial and service landscapes.

Dominance of Multimodal and Specialized Models

The development of models processing multiple data sources simultaneously (text + visual + audio) will continue. However, domain-specific solutions (Vertical AI) will displace "general-purpose" AI capsules where precise, context-aware conclusions are critical.

Heightened Focus on Ethics, Trust, and Regulation

As AI adoption grows, the need for transparency, explainability (XAI), and regulatory frameworks to ensure safety and social acceptance is becoming increasingly acute.

ROI and Measurable Business Outcomes as the Primary Metric

In 2026, organizations will move away from "proof of concept" pilots, demanding concrete performance indicators from AI projects: cost savings, revenue growth, and reduced turnaround times.

Economic and Investment Impacts

Analysts predict that by 2026, AI and digital transformation projects will become major drivers of economic growth. However, this may lead to asset correction and capital reallocation in adjacent sectors, including cloud infrastructure.

If you're still writing posts and articles manually, you're wasting time. Thousands of bloggers, marketers, and SMM specialists have already automated content creation, idea generation, image processing, and even video production. They do it with AI and advanced neural networks for blogs, which significantly accelerate workflows and enhance content quality.

Contents:

• Universal Language Models (LLM): The Foundation for Articles and Scripts
• World Leaders in Text Generation
• Specialized Platforms for Bloggers and SEO Copywriting
• AI Aggregators and "All-in-One" Platforms
• Tools for SEO Content Optimization
• Services for Rewriting and Bypassing AI Detectors
• Neural Networks for Creating Visual Content (Images)
• Leaders in Image Generation Quality
• Built-in AI Features in Graphic Editors
• Video Production: AI for Reels, Shorts, and YouTube
• Video Generation from Text (Text-to-Video)
• AI Avatars and Talking Heads (Digital Clones)
• Working with Sound: Voice and Music for Blogging
• Sound Improvement and Noise Removal
• Royalty-Free Music Generation
• Text-to-Speech (Voiceover)

In 2025, the neural network market has reached a new qualitative level. While AI previously required complex prompts and the ability to "talk" to the model, platforms now offer ready-made templates, specialized assistants, and intuitive interfaces. Video generation, which seemed like science fiction in 2023, has become a reality. Most importantly, there are solutions for both a student's budget and a scaling company.

But the choice of tools is vast. There are already over 500 services on the market promising to "create content in seconds." Which one to choose? Which pricing plan is truly cost-effective? Does it require training or is it simple from the first click?

In this article, we've analyzed and tested popular neural networks and selected the best ones for blogging. Here you'll find tools for writing articles and posts, creating images and videos, optimizing content for SEO, as well as specialized platforms that combine all of this in one place.

All services in this selection:

• Verified for relevance in December 2025;
• Offer free plans or trial periods to get started;
• Solve real blogger problems, not just generate text "for the sake of it".

Let's figure out which neural networks will help you create content faster, cheaper, and with better quality.

Universal Language Models (LLM): The Foundation for Articles and Scripts

Universal Language Models are the foundation on which almost all blogger work with AI is built. They generate text, ideas, scripts, headlines, meta-descriptions, and answers to any questions. If you need to quickly write an article, create a content plan, or rewrite boring text—start here.

The best neural networks of this type cover 80% of blogging tasks. You can use them as a primary tool or combine them with other services for image and video generation.

World Leaders in Text Generation

ChatGPT (OpenAI) and Its Current Versions

ChatGPT remains the most popular choice among bloggers and marketers. In 2025, the flagship is GPT-5, with a newer version GPT-5.1 for specialized tasks.

GPT-5 is a universal assistant for most blogger tasks. It quickly generates posts for Telegram, blog articles, ideas for Reels, and product descriptions. The model understands context even better, can analyze uploaded files and images. If you need to create quality content quickly, GPT-5 will handle it in minutes without additional edits.

GPT-5.1 is a specialized version with enhanced analysis and logic capabilities. This model is better at building content strategies, analyzing audience data, and writing in-depth long-reads that require a systematic approach. GPT-5.1 has an expanded context window and can work with large volumes of information. However, 5.1 requires more processing time and is more expensive than the base GPT-5.

Pros:

• Highest text generation quality among competitors.
• Huge context window (can upload several large files simultaneously).
• Integration with other tools via API.
• Support for multimodality (text, images, documents).

Cons:

• Paid access starts from 200 rubles per month (approximately $2 for the basic plan).

Claude 4.5 Sonnet – Best for "Human-like" Texts

Claude from Anthropic is gaining the trust of bloggers who need more natural, "lively" text. While ChatGPT sometimes writes in a sterile and formal manner, Claude creates posts with a genuine author's voice.

This neural network is especially good for long-reads. It better maintains article structure over many thousands of words, less often "forgets" the specified tone and style. Bloggers often say that texts from Claude require fewer edits and rewrites.

Pros:

• The most natural, human-like style.
• Excellent work with long texts and context retention.
• Has a free web interface (with limitations).

Cons:

· Generates slower than ChatGPT.

DeepSeek and Qwen – Powerful Free Newcomers

In 2025, Chinese developers released models that are already comparable in quality to GPT-5. DeepSeek and Qwen are available completely free.

DeepSeek is known for its logic and analytical abilities. It writes video scripts well, structures information, and can work with code (which can be useful if you run a tech blog).

Qwen from Alibaba is a more universal option. It generates text quickly, understands both English and Chinese well.

The main advantage—both services are completely free and require no payment.

Pros:

• Completely free.
• Generation quality comparable to GPT-5.

Cons:

• New models, fewer reviews and use cases.
• Sometimes less stable compared to established services.

Specialized Platforms for Bloggers and SEO Copywriting

Universal chatbots provide everything but require skills. Specialized platforms solve this differently: they offer ready-made templates, built-in assistants, and features tailored specifically for content creation and optimization. No need to write long prompts or rack your brain on how to ask the neural network to complete a task.

This section covers platforms that save time on routine and help write content that ranks in search engines.

AI Aggregators and "All-in-One" Platforms

These services combine several neural networks under one roof: text generators, image models, video tools, and built-in assistants. The main idea is not to switch between 5-7 services, but to do everything in one place.

IMI is a platform that has gathered everything necessary for a blogger under one roof. It integrates GPT-5, Claude, Midjourney, Flux, video models, and other tools. But the main difference with IMI is its 80+ ready-made templates for various tasks: from a Telegram post to a product card on a marketplace.

IMI has built-in specialized AI assistants (SMM Manager, Marketer, Content Manager, Copywriter, SEO Specialist). They work with pre-set roles and instructions, so no prompts are needed. Simply choose an assistant, input the task—and get a ready result.

The platform starts with a free plan (200 imicoins per month = approximately 30 photo generations or 150,000 words of text). Paid plans from $15 per month suit freelancers and small teams.

Jasper

Jasper is an American competitor specializing in marketing copywriting. Jasper focuses on creating advertising texts, email campaigns, and social media posts.

Copy.ai

Copy.ai is a cheaper option for starting bloggers. The platform is simpler than Jasper, but the functionality is sufficient for writing posts, content ideas, and basic optimization.

Advantages of All-in-One Platforms:

• No need to separately search for a text generator, then images, then video.
• Built-in assistants with ready roles save time on prompt engineering.
• One subscription instead of five.
• Templates for different platforms (Telegram, Instagram, YouTube).

Cons:

• Quality may be lower than using each tool separately.
• More expensive than separate services if not using all functions.

Tools for SEO Content Optimization

These are neural networks that analyze which keywords the top search results use and suggest what to add to your article. They address the intent "how to write an article that ranks."

Surfer SEO

Surfer SEO analyzes the top 10 results in Google for your query and shows what LSI words, text length, and structure the top articles have. Then the platform checks your article and gives recommendations: "add the word 'neural network' 3 more times", "expand the section on prices", "add a comparison table".

How to use: Enter a target query (e.g., "best neural networks for blogging"), the platform shows what words the leaders' content consists of. You write an article based on their recommendations or feed a draft to a neural network for rewriting considering SEO requirements.

Pros:

• Accurate optimization recommendations.
• Competitor analysis shows what works.
• Integration with copywriting tools.

Cons:

• Require knowledge of SEO basics (what LSI is, keyword density).
• Paid (from $10-20 per month).
• Do not guarantee ranking (these are just recommendations, not magic).

Services for Rewriting and Bypassing AI Detectors

You generate text from a neural network and worry that Google will detect it's AI? There are two approaches: proper rewriting and dishonest methods.

Proper Rewriting – Paraphrasing:

Quillbot

Quillbot is an online tool for paraphrasing text. You paste AI-generated text, Quillbot rewrites it, preserving meaning but changing structure and words. The result becomes unique and passes plagiarism checks.

How it works: ChatGPT generates a base article → Quillbot rewrite it → you get unique text that is not detected as AI-generated.

Honest Approach:

Instead of hiding AI, it's better to use it openly. Google increasingly penalizes attempts to pass off AI as human. Much better to:

• Write 70% of the text with a neural network.
• Add 30% personal experience, examples, case studies.
• Edit, add your own voice.

Pros of Quillbot:

• Quick paraphrasing (5 minutes instead of an hour of rewriting).
• Cheap (from $5 per month or free with limitations).

Cons:

• Rewriting without understanding context can spoil the meaning.
• Google sees suspicious patterns in rewritten text.
• Better to use as a supplement, not as the main method.

Alternative – manual editing or using Claude:

Claude or another chatbot can rewrite text "in a blogger's style" with instructions like: "Rewrite this article as if written by a journalist with 10 years of experience. Add personal examples and make the text more conversational."

Neural Networks for Creating Visual Content (Images)

Text is one part of content. Images are the second part, which often decides whether a person clicks on a post or scrolls past. Unique cover images, attractive visuals for articles, beautiful social media banners—all of this previously required design skills or money for freelancers. Now neural networks do it in minutes.

Leaders in Image Generation Quality

Midjourney v6 – The Gold Standard of Quality

Midjourney remains the best choice for bloggers who want a "wow-effect". It generates photorealistic and artistic images that can be immediately published in a post or used as an article cover.

Midjourney's peculiarity—requires prompts and works through Discord. This adds complexity for beginners, but experienced users say it's worth it. Image quality is higher than competitors. Pictures don't look "generated"—they look professional.

Midjourney supports niche styles: photographic portraits, illustrations, art, cinematic shots. If you need a cover for an article about neural networks, Midjourney will create a realistic image of a computer and holograms in 50 seconds.

Price: From $10 per month (basic plan with generation limits) to $120 for professionals.

Pros:

• Image quality surpasses all competitors.
• Supports many styles and parameters.
• Active community with examples and prompts.
• Can train custom styles (niji).

Cons:

• Needs Discord.
• Works via API, which can be inconvenient for complete beginners.

Flux and Stable Diffusion – Powerful Alternatives

Flux is a new model that already matches Midjourney in quality, but is cheaper and more accessible. Flux can generate text within images (which was previously a weakness), better understands complex descriptions, and works faster.

Stable Diffusion is a more "democratic" model. It can be installed locally on your computer (if it's powerful) or used via cloud services like RunwayML. Quality is lower than Midjourney but sufficient for most blogging tasks.

Flux is available through IMI, which is convenient—no need to register on different services.

Pros of Flux:

• Better price/quality ratio than Stable Diffusion.
• Generates text in images (useful for banners).

Pros of Stable Diffusion:

• Can be installed locally (maximum privacy).
• Huge community with models and extensions.
• Cheaper or even free if using cloud versions with limits.

Cons of both:

• Quality lower than Midjourney (artifacts visible).
• Require more iterations to get the desired result.

Built-in AI Features in Graphic Editors

You don't always need to generate an image from scratch. Sometimes you need to edit it: expand the background, replace an object, improve quality. For this, there are built-in features in popular editors.

Photoshop AI – Generative Fill and Generative Expand

Generative Fill is a tool that draws missing parts of an image. You select an area and write a description ("blue sky", "trees"), Photoshop generates the needed content.

Generative Expand expands the canvas and draws missing parts. If an article cover turned out "cramped", you can expand it in any direction, and Photoshop will complete the background itself.

These features work through Adobe's cloud and require a subscription.

Pros:

• Integrated into the familiar Photoshop interface.
• Fast and convenient for editing existing images.
• High quality.

Cons:

• Requires an Adobe Creative Cloud subscription (quite expensive).
• Can be difficult for complete beginners.

Canva AI – Magic Edit and Automatic Object Removal

Canva is a popular online editor for inexperienced users. It has built-in features for removing objects and replacing backgrounds with one click.

For example, there's an unwanted object in a picture. In Canva, press "Remove object", indicate it—and it disappears, with the background automatically filled in.

Pros:

• Super simple interface.
• Works fast.
• Cheap (free with limitations).

Cons:

• Editing quality can be noticeable (sometimes unnatural).
• May not suffice for complex editing.

Video Production: AI for Reels, Shorts, and YouTube

Bloggers without video content fall behind in search results, losing millions of views and subscribers. But shooting video every day is impractical: you need makeup, lighting setup, sound recording, editing for hours.

Video Generation from Text (Text-to-Video)

This is the fastest way to get video content: you write a scene description, and the neural network generates the video.

Sora (OpenAI) – When Available

Sora from OpenAI is the flagship of video generation. It creates cinematic video clips with dynamic camera movements, realistic characters, and effects. If Sora is available in your region, it's the best choice.

Pros:

• Video quality like in a movie.
• Understands complex scripts and camera movements.
• Can generate long videos (up to 60 seconds).

Cons:

• Generates slowly (can take minutes).

Kling AI – Best Alternative

Kling AI from the Chinese company Kuaishou is a video generator that has caught up with Sora in quality. Generates video from text with high clarity and dynamics. Video looks professional, without obvious artifacts.

Works fast: video is generated in 30-60 seconds.

Pros:

• High video quality (close to Sora).
• Fast generation.
• Can be used through IMI.

Cons:

• Strict limits on the free version.

Runway Gen-3 – For Video Effects and Transformations

Runway is a platform for creating videos with a focus on effects and transformations. If you need not just a text generator, but video with synchronization, morphing, or special effects, Runway handles it better.

Runway also allows using the Gen-3 model, which generates video from images (Image-to-Video). For example, you have a static image, Runway animates it into a video.

Pros:

• Good for effects and transformations.
• Image-to-Video function is unique.

Cons:

• Quality for simple generation is lower than Kling.
• Requires payment for generations.

LTX Studio – Control Every Frame

LTX Studio is a platform where you can control every frame of a video. You describe a scene, the platform generates the video, then you can change any moment: tell it to make the character turn another way, or for a different object to appear.

This is the most precise way to get exactly the video you want.

Pros:

• Full control over every frame.
• High generation accuracy.
• Suitable for complex scripts.

Cons:

• Slower than simply generating without edits.
• Requires more time and skills.

AI Avatars and Talking Heads (Digital Clones)

HeyGen – Create an Avatar in Minutes

HeyGen is a platform for creating avatars that speak and move like real people. You upload a video of yourself (even one minute), the platform creates a 3D model, and now you can generate video of this avatar with any text in any language.

The avatar speaks with the needed intonation, moves hands naturally, facial expression matches the content. Looks realistic.

How to use: Tell the neural network "write news about AI in blogging", it writes. Then you paste this text into HeyGen, choose your avatar, and get a ready video as if you're telling it yourself. No filming, no makeup, at any time of day.

Pros:

• No need to film yourself.
• Fast video generation.
• Good for news, digests, and explaining content.
• Supports many languages.

Cons:

• Need to record yourself once to create an avatar.
• Avatar can look unnatural if not set up correctly.
• Paid plans are quite expensive.

Synclabs and Lip-sync (Lip Synchronization)

Synclabs is a specialized service for lip synchronization in video. If you have a video in one language, Synclabs can "make" your avatar speak in another language, synchronizing lip movement.

For example, you recorded a video, synchronizes lips—and you get a video where you (or your avatar), but lips move naturally.

This is useful for selling content in different languages.

Pros:

• Lip-sync synchronization looks realistic.
• Can localize video into different languages.
• Fast and simple.

Cons:

• Requires an existing video.
• Works better if the source video is high quality.

Smart Cutting and Editing (Content Repurposing)

OpusClip – Automatic Cutting into Viral Clips

OpusClip is an AI that watches your long video, finds the most interesting moments, and cuts them into vertical videos for TikTok, YouTube Shorts, and Instagram Reels. It even adds automatic subtitles and emojis.

How to use: Upload an interview or podcast lasting an hour → OpusClip watches and cuts → you get 10 ready 30-second videos that can be published immediately.

Pros:

• Saves tens of hours on editing.
• Automatic subtitles and emojis.
• Finds the most viral moments.
• Supports many platforms (YouTube, TikTok, Instagram).

Cons:

• AI may choose not the most interesting moment.
• Requires checking before publication.

Vizard – Video Editor with AI

Vizard is a video editor that automatically generates subtitles, scales video for different platforms, and cuts long video into short clips.

For example, you have a 16:9 video for YouTube. Vizard automatically reformats it to 9:16 for Shorts, crops extra parts to keep content in focus.

Pros:

• Simple interface.
• Automatic formatting for different platforms.
• Works fast.

Cons:

• Cutting quality may be lower than OpusClip.
• Need to check the result.

Working with Sound: Voice and Music for Blogging

Video without good sound is a half-result. Bad sound, background noise, monotonous voice—all this scares viewers away in the first five seconds. But not every blogger has a professional microphone and sound operator.

Sound Improvement and Noise Removal

Adobe Podcast Enhance (Firefly) – Turns Any Sound into Studio Quality

Adobe Podcast Enhance is a feature from Adobe based on their Firefly neural network. You upload a recording with poor acoustics (recorded video in an office, noise nearby), the neural network analyzes and removes background noise, improves voice clarity.

The result sounds as if you recorded in a studio with an expensive microphone. This is magic for bloggers.

How to use: There's a free web interface at podcast.adobe.com. Upload an audio file (MP3, WAV), press "Enhance", wait a couple of minutes—done. Quality improved significantly.

Pros:

• Incredibly simple interface.
• Result like from a professional sound engineer.
• Free (or very cheap with premium version).
• Works fast.

Cons:

• Requires good internet to upload the file.
• For very noisy recordings, may not completely save the situation.

Noise Reduction in CapCut and Other Video Editors

Many video editors have built-in simple noise removal features. CapCut (free editor for mobile and PC) has built-in "Noise Suppression" that removes background noise.

It's not as powerful as Adobe Podcast, but sufficient for simple cases like "remove fan sound in the background". And it's already built into the editor, no need to upload the file somewhere separately.

Pros:

• Built into the editor (no need to pay separately).
• Fast.
• Sufficient for simple tasks.

Cons:

• Quality lower than Adobe Podcast.
• May remove part of useful sound.

Royalty-Free Music Generation

Suno – Creating a Full Song or Background Music

Suno is a platform for generating music. You describe what's needed: "calm background music for a video about neural networks, in electronic style, 2 minutes", and Suno generates a full composition.

You can even ask for a full song with vocals. Suno will create everything: melody, harmony, vocals, beat. Quality is already sufficient for publication.

How to use: Go to suno.com, describe the track, press "Create"—wait a minute, get ready music. Can listen in browser, download as MP3, and use in any video.

Pros:

• Generates unique music (royalty-free).
• Easy to describe needed style and mood.
• Quality sufficient for video.
• Free credits for starters.

Cons:

• Quality not at professional composer level.
• Sometimes generates something strange, need several attempts.
• Free limit is limited (approximately 50 generations per month).

Udio – Alternative with Better Vocals

Udio is a competitor to Suno with a focus on vocal music. If you need a song with a voice, Udio often generates more natural vocals.

Like Suno, you describe the track, the platform generates.

Pros:

• More natural vocals than Suno.
• Supports many genres.
• Intuitive interface.

Cons:

• Similar limits on the free version.
• Sometimes artifacts in sound.

How to Use Generated Music in a Blog

Simple option: Download a track from Suno/Udio → Upload to a video editor (CapCut, Adobe Premiere) as background music → Publish. No copyright issues.

For YouTube: When uploading a video, YouTube scans the music. If it's music from Suno/Udio, the system doesn't recognize it (because it's generated), and the video publishes without issues.

Text-to-Speech (Voiceover)

Google TTS

Google Text-to-Speech are service that turn text into voice. You input text, choose a voice and speed, the service generates an audio file.

Quality is average. Sounds like synthesized voice (not exactly like a human), but suitable for voicing articles or simple videos.

Pros:

• Fast.
• Free or cheap.

Cons:

• Sound is synthesized (not quite like a living voice).
• Hard to convey emotions and intonation.

Elevenlabs – Realistic Voice Synthesis

Elevenlabs is an American service with more realistic voice synthesis. Voices sound like almost real people with needed intonation and pauses.

Pros:

• Very realistic voice.
• Can create a custom voice (upload a sample).
• Good intonation and naturalness.

Cons:

• Requires payment (free limit is small).

Conclusion

In this article, we've gathered and reviewed neural networks that cover all stages of creating content for a blog: from generating ideas and writing text to creating video and voiceovers. Each tool solves a specific task, and each has its pros and cons.

Bloggers who started using AI in 2024-2025 are five times ahead of those still creating content manually. They save hours every day, publish more often and better, attract more readers.

Start with the IMI platform. It's an aggregator that combines most of the tools we talked about: text, images, video, ready templates, assistants. You don't need to learn 10 different services—IMI will do it for you.

Want to bring an old photo to life or create a video from a regular picture? With AI, it's now simple and accessible—you can easily revive memories or imagine yourself as a Hogwarts student, where photos on the walls moved!

This article gathers the best tools to turn static images into living portraits. We’ll break down how it works, which apps to choose, and how to animate a photo in just minutes.

How It Works in AI models: A Simple Explanation of the Technology

Photo animation is the process where a neural network applies movements to an image: head turns, eye blinks, facial expressions, smiles. As a result, a regular photo starts to "move"—it looks as if the person in the picture has come to life.

This works even for old, black-and-white images. When you upload a picture, the neural network analyzes facial features: eyes, mouth, head shape, even lighting. Here’s what happens next:

1. The algorithm identifies possible movements (e.g., blinking, head turn, smile).
2. A short video with animation is generated—typically 3 to 15 seconds long.
3. You can download the file or add music, effects, or text elements.

Some services offer ready-made templates: just upload a picture, click a button—and within seconds, you get an animation. It's like creating a "living postcard."

With AI, you can:

• Create a living portrait from an old family photo.
• Turn a picture into a video longer than 5 seconds.
• Save the final file and share it with friends.

Many services allow you to download the result or generate a video in MP4 format, often for free.

Using neural networks requires no technical knowledge. Even a child can animate a photo—easily and quickly.

Why Animate Photos: Three Main Scenarios

Neural networks can do more than just "animate photos"—they open up a whole spectrum of possibilities for users. Here are the most common reasons people use image animation services:

Preserve Memories of Loved Ones

One of the most touching scenarios is animating an old photo of a loved one. Turning a photo into a video helps bring back moments from the past into digital life. Thanks to AI, you can animate a portrait, create a gentle smile or laughter on a loved one’s face—all looking incredibly realistic.

Creating Content for Social Media and Messengers

Now you can use animated images for stories, reels, or even memes. Users on Telegram, TikTok, and Instagram actively add such videos to their content. It’s an easy way to grab your audience’s attention.

Entertainment and Creativity

Many neural networks allow you to swap faces, create clips, add artistic filters, or turn a photo into a character that sings, moves, or talks. AI apps have become tools for creativity and fun.

For example, one service lets you upload a photo, and the AI generates a video where your portrait sings a song or tells a joke. Fun and creative😅

What to Look for When Choosing a Photo Animation Service

With so many services and apps available, choosing the right one isn’t always easy. Here are key parameters to consider:

• Free Version Availability. Almost all neural networks offer a free trial, but most have limitations: watermarks, video duration limits, generation caps.
• Animation Quality. Good services use complex algorithms that move faces realistically without distorting the image. Poor ones may produce results with "swimming" eyes and jerky backgrounds.
• Upload and Save Formats. Convenient when you can upload an image and download a video without registration. Some platforms support MP4, 720p and above.
• Mobile App Availability. Android and iOS versions let you do everything right from your phone.

Some services offer unlimited generations for a fee—handy if you use them frequently.

TOP 10 Neural Networks to Bring Your Photos to Life

You can animate photos using various services. Some simply generate videos from templates, others offer more customization. Below are the best tools available online or as apps.

Pika

Pika is a powerful AI service that creates videos from photos in seconds. Ideal for realistic facial animation: blinking, eye movements, smiling.

• Capabilities: Animates portraits, adds head movements, creates short clips.
• Interface: English, but intuitive.
• Formats: Download in MP4, quality up to 720p.
• Terms: Free version available after registration; subscription removes watermark.

Cutout.pro

Cutout not only animates faces in photos but also enhances image quality, removes backgrounds, and adds effects.

• Capabilities: Animates faces, eye movements, adds realistic facial expressions.
• Access: Works online, no app installation needed.
• Highlight: Can even animate old photographs.
• Downsides: Free version allows 1-2 generations; subscription needed thereafter.

Nero AI

Nero AI offers a set of tools for generating animations and creating videos from photos.

• Offers: Multiple animation modes—auto and custom.
• Differentiator: Choose animation style and video duration.
• Platform: Web service, works in a browser.
• Support: English language.

PixVerse

PixVerse is an AI-based video generator that turns pictures into moving scenes. Perfect for creative projects.

• Capabilities: Generates animated videos with artistic effects.
• Supports: Adding music, text, various filters.
• Format: Videos up to 10 seconds; downloadable results.
• Access: Free, with a premium tier.

Remini

Remini is known as a photo enhancement app, but it has a "photo to motion" mode to animate faces in portraits.

• Features: Upscales old photos, adds facial expressions.
• Access: Mobile app for iOS and Android.
• Terms: Basic functions free; subscription removes ads.

DreamFace (by Deepswap)

DreamFace turns your portraits into living videos where the face can sing, talk, or move.

• Features: Face swap, clip generation, videos with expressions.
• Format: MP4 support; can add music.
• Downsides: Watermark on videos in the free version.
• Upsides: Great for entertainment content.

Hailuo

Hailuo is one of the newer services that allows free photo animation and saves videos without a watermark (during the test period).

• Capabilities: Generates realistic movements; works with various faces.
• Upsides: Just upload a photo; AI does the rest.
• Formats: 720p, MP4.

Avatarify

Avatarify creates lively facial animations by replacing expressions, mimics, and gaze direction.

• Best for: Video calls, social media, creating animated clips.
• Highlight: Can use your own video as a template.
• Access: Free, but with time limitations.

Immersity AI

Immersity AI is a platform for generating videos from images with high-quality animation.

• Features: Can animate regular portraits, add styles and effects.
• Upsides: High detail, realistic movements.
• Downsides: Requires registration and email confirmation.

Motionleap

Motionleap is a mobile app that turns photos into moving pictures. Ideal for creating backgrounds, animating water, skies.

• Features: Animates parts of an image; customizable motion paths.
• Format: Short videos or GIFs.
• Interface: iOS and Android.
• Terms: Free; subscription unlocks all features.

Comparison Table: Best Services for Photo Animation

Service	Free	Downloadable	Animation Quality	Works with Old Photos
Pika	Yes	Yes	High	Yes
Cutout.pro	Partially	Yes	Medium	Yes
Nero AI	No	Yes	High	No
PixVerse	Yes	Yes	Creative	No
Remini	Yes	Yes	Very High	Yes
DreamFace	Yes	Yes	Medium	Yes
Hailuo	Yes	Yes	High	Yes
Avatarify	Yes	Yes	High	No
Immersity AI	Partially	Yes	Very High	Yes
Motionleap	Yes	Yes	Medium	No

Some services work only via mobile apps, others in a browser. Check before use if registration is needed, or if you can simply upload a photo to the site.

Step-by-Step Guide: How to Animate a Photo with AI

1. Choose a service—for example, Pika or Remini.
2. Upload an image (JPG, PNG, preferably high quality).
3. Adjust parameters: Select a style, add music or effects (if desired).
4. Click the generate button—usually labeled "Create" or "Generate."
5. Save the result—download the video as MP4 or GIF.
6. Share the animation on social media or messengers.

Tips for Getting High-Quality Animation

• Use high-resolution photos (at least 720p).
• The face should be centered, without distracting objects.
• Portraits with clearly visible eyes, smile, and facial features work best.
• Lighting should be even.
• Avoid uploading pictures with closed eyes or distorted angles—results will be poorer.

Common Mistakes and How to Avoid Them

🔻 Blurry photo → AI cannot accurately detect facial features. 🔻 Background blends with the face → neural network mistakes head movement. 🔻 Low resolution → video will look "soapy," especially when enlarged. 🔻 Watermarked video → use a paid version or a service without such limits. 🔻 Generation limits → many services have caps in their free tier.

Tip: Before animating an important photo, try a test image first. This helps you understand the service's capabilities.

Frequently Asked Questions

Can old photos be animated? Yes. Many neural networks are trained to work with old photos and restore facial expressions.

Is it free? Almost all services can be used for free, but with limitations on duration, quality, or watermarks.

What video format is used? Typically MP4 or GIF. You can download the file after generation.

Do I need to install an app? Not necessarily. Most work through a browser. However, there are mobile versions for iOS and Android.

Capabilities at IMI: A Universal AI Assistant for Photos, Texts, and Content

The IMI service (imigo.ai) is a multifunctional platform where users can work with texts, images, and data using neural networks. While IMI does not offer a direct tool for animating photos (like Pika or DreamFace), it can be useful in comprehensive projects involving images and AI-generated content.

What you can do with IMI:

• Process text and descriptions for animated photos or videos.
• Generate ideas and scripts for animations, posts, and clips.
• Work with images using AI assistants: enhancement, cropping, backgrounds.
• Automate tasks and create templates for creative projects.

If you're creating videos from photos, making memes, promo clips, or animated visuals—IMI can help with texts, ideas, and related design. Thus, it complements the work of animation-focused neural networks.

Try IMI—to generate quality content, create video descriptions, articles, posts, and quickly share results.

Conclusion

Animating a photo with AI is simple, fast, and accessible to everyone. Even old snapshots can now be turned into animated images that move, smile, and blink. Modern services let you create videos in just minutes: upload a picture, choose an effect—and get a living result.

We've reviewed the best neural networks for animating photos: from Pika to Remini and Motionleap. Each has its own strengths, formats, and capabilities: you can choose a free option, customize duration, filters, music, and even download videos in MP4.

Whether you want to create content for social media, bring family archives to life, or just experiment—these tools will surely impress you. And if you need more than just animation and want to create projects with texts, images, and ideas—try IMI. With it, you can automate tasks, generate descriptions, create visuals turnkey.

SUNO isn't magic, nor is it a random song generator. It’s a powerful tool capable of producing professional music tracks—if you structure, style, and voice it correctly. In this mini-guide, you’ll learn how to work with SUNO intentionally and systematically: from writing prompts to achieving consistent vocal quality.

What Is SUNO and Why Is It So Powerful?

SUNO is an AI-powered music generator. It can create full vocal tracks that sound like real songs—complete with lyrics, vocals, melody, and atmosphere. And the best part? You can easily steer the creative process once you understand how.

How Custom Mode Works

SUNO operates on a three-component system:

• Style of Music – describe the sound: genre, mood, tempo, vocals.
• Lyrics – write the song’s lyrics.
• Generate / Variations / Reuse Prompt – refine your result, create new versions.

If you don’t control the first two blocks, the third will produce random and unpredictable outcomes.

Why Structure Is Everything

Many beginners treat SUNO like magic:

• They write everything in one paragraph.
• They change everything at once.
• They don’t understand what affects the final result.

But SUNO is an algorithm—and it loves clear structure. When you follow it, you get predictable, high-quality tracks.

The 3 Parts of a Perfect Prompt

To get controllable results, your prompt should be divided into three parts:

PART 1. Style of Music

Defines the technical characteristics of the sound.

PART 2. Lyrics

The song’s lyrics—in any language, but with clear formatting.

PART 3. Development

Choosing variations, reusing prompts, locking in parameters (Reuse Prompt).

How to Describe the Music Style

A simple formula for beginners:

Genre → mood → instruments → vocals → key → tempo (BPM)

Example:

Atmospheric indie-pop, warm pads, soft guitars, soft emotional female vocal, intimate tone, C major, 92 BPM.

Breakdown:

• Genre – sets the style (indie-pop, hip-hop, jazz, etc.)
• Mood – influences harmony, accents, dynamics
• Instruments – make the track dense or airy
• Vocals – choice of voice and delivery
• Key – bright (major) or dark (minor) mood
• BPM – speed of the composition

⚠️ Do not write lyrics here or change everything at once. Keep it short and to the point.

How to Write Lyrics

SUNO understands both English and Russian. The key is clear structure and labeling:

• [Verse] – verse
• [Chorus] – chorus
• [Bridge] – bridge (if needed)

Example:

[Verse]
I walk through shadows of the day,
Searching for a quiet place to breathe…
 
[Chorus]
Я держусь за свет внутри себя,
Даже если мир давит тишиной...
Copy

First Generation: The Starting Point

Step-by-step launch:

1. Write one basic Style of Music.
2. Create short lyrics (verse + chorus).
3. Generate 2 versions.
4. Pick the best one—this is your starting point.

🔒 Do not move forward until you’re happy with this version.

How to Experiment Correctly

One rule: change only one parameter at a time.

Examples:

• C major → A minor
• 92 BPM → 120 BPM
• Female vocal → Male vocal

Quick reference for keys:

Key	Mood
C Major	Neutral
G Major	Bright
F Major	Warm
A Minor	Intimate
E Minor	Dramatic
D Minor	Cinematic

How to Maintain Consistent Vocals

To achieve stable vocal sound—lock in its description and don’t change it later.

Example vocal block: Soft emotional female vocal, warm intimate tone, light breathy timbre, smooth gentle delivery, subtle airiness.

Use Reuse Prompt and only adjust style, key, or tempo.

Full Workflow: Creating a Music Series

Use SUNO like a studio to craft an album:

1. Create a base track.
2. Save its prompt.
3. Make 2–3 variations: Brighter | Deeper | More energetic
4. Select the best ones.
5. Release them as a series under one “artist.”

5 Key SUNO Rules

✅ Prompt = structure → lyrics → development ✅ One vocal style = one fixed block ✅ Change one parameter at a time ✅ Work in series ✅ Build a system—don’t just click buttons randomly

SUNO can be either a random generator or a tool that delivers impressive, predictable results. It all depends on your approach. Start with structure. Think of your prompt as a recipe. Save, test, refine, and create music not by chance—but exactly the way you want to hear it.

What is a Personal AI Assistant

A Personal AI Assistant is a software solution based on Large Language Models (LLMs) that understands user requests in natural language and performs a variety of tasks. From writing texts and analyzing data to generating solutions, this type of helper adapts to specific needs.

Core components work in a unified system:

• Language Model — processes information and generates responses.
• Context System — remembers the conversation flow and previous queries.
• API Integration — connects external services and applications.
• Personalization Mechanism — learns from your data and documents.
• Interaction Interface — text chat, voice input, or video.

The key difference between a personal assistant and a regular chatbot lies in versatility and adaptability. A chatbot answers a narrow range of questions (e.g., customer support only), while a personal assistant handles any task — from scheduling meetings to writing code.

Components of a Personal Assistant

Each element of the system plays its role:

Large Language Model (LLM) — a neural network trained on billions of words. It understands the meaning of your question and formulates a logical response.

Examples of powerful models: GPT-4, Gemini, and Claude.

Context Window — the amount of information the assistant can process at once. For instance, Claude handles 200K tokens (roughly a full book), while ChatGPT works with 128K tokens.

Memory System — remembers your preferences, past conversations, and uploaded documents, enabling personalized responses.

Integrations — connections to other services. For example, it can create calendar events, send emails, or publish social media posts.

Chatbot vs. Personal AI Assistant: The Difference

Parameter	Chatbot Personal	AI Assistant
Scope	Narrow specialization	Universal tool
Dialogue Context	Limited to a single session	Long-term memory
Learning from Your Data	No	Yes, via file upload
Typical Tasks	Q&A on a single topic	Hundreds of diverse tasks
Personalization	Minimal	Full adaptation

A chatbot is a robot that gives standard answers. A personal AI assistant learns to understand you.

The Evolution of Personal AI Assistants

The technology has evolved through several key stages.

The Technological Breakthrough: Transformers and LLMs

The leap forward was enabled by the transformer architecture. This structure allows the model to process entire text simultaneously, seeing connections between words over long distances. Previously (pre-2017), systems analyzed text sequentially — word by word. This was slow and imprecise. Transformers changed the approach: they look at all words at once and understand context much better.

This enables training models on trillions of words from the internet, books, and documents. The result is not just template-based answers, but reasoning, adaptation, and learning.

How Personal AI Assistants Work: The Technical Side

A personal assistant operates as a multi-layered system. Each layer handles a specific function, together creating the illusion of conversing with an intelligent helper.

Large Language Models (LLMs)

The foundation is a large language model trained to predict the next word in a sequence. While this sounds simple, in practice it means the model has learned patterns of language, logic, and human knowledge.

GPT-4 is trained on trillions of words. It knows about physics, history, programming, medicine, and thousands of other domains. When you input a query, the model analyzes each word and creates a response by predicting word after word.

Model parameters represent how it weights information. GPT-4 has an estimated 1.76 trillion parameters. More parameters mean a more powerful model, but also greater resource demands.

AI Agents and Decision-Making

The modern personal assistant is not just a text generator. It's an agent capable of making decisions and performing actions.

The system works like this:

1. User assigns a task: "Schedule a meeting tomorrow at 2 PM with the project team."
2. The agent analyzes the request and determines required actions.
3. The agent checks available tools: calendar, email, contact list.
4. The agent performs the actions (creates event, sends invitations).
5. The agent reports back: "Meeting created and invitations sent."

This is possible via API integrations, connecting to your calendar (Google Calendar, Outlook), email, and other services.

Context Window and Long-Term Memory

The context window is the maximum amount of information the assistant can process in one dialogue.

Think of context as a computer's RAM. A small window (32K tokens like GigaChat) means the assistant "forgets" the start of a long conversation. A large window (200K tokens like Claude) allows it to remember everything at once.

For large documents, choose Claude — it can process an entire book at once. For regular conversations, 128K tokens (ChatGPT) is sufficient.

Long-term memory is different. The assistant remembers your preferences across sessions. For example, if you upload an SEO guide, it will consider it the next time you return.

The Interaction Process: From Input to Response

Each interaction goes through several stages. Modern assistants are multimodal — they understand different input formats.

• Text Input — the primary method. You type a question and get a response.
• Voice Input — you speak a question aloud; the system converts it to text via speech recognition, then processes it as a regular text query.
• Images — you upload a photo for analysis. For example, upload a screenshot, and the assistant explains what's visible.
• Files — documents in PDF, Word, CSV formats. The assistant reads the content and uses the information for responses.

The system detects what you've uploaded and launches the appropriate handler.

Processing and Generating a Response

When your query reaches the assistant's servers, a processing chain begins:

1. Tokenization — text is split into chunks (tokens). The word "assistant" might be one token, while a complex word like "automate" could be two or three.
2. Embedding — each token is converted into a vector (a set of numbers). Similar words receive similar vectors.
3. Transformer Processing — analyzes all tokens simultaneously, seeking connections and patterns.
4. Generation — starts predicting the next token, then the next, and so on until the response is complete.
5. Decoding — tokens are converted back into words and sentences.

The entire process takes one to five seconds, depending on response length.

Output Formats: Text, Voice, Video, Code

The assistant can deliver responses in various formats:

• Text — the standard format. The assistant writes the answer in the chat.
• Voice — the system synthesizes speech based on the text. You hear a voice message instead of text, convenient for mobile use or while driving.
• Code — if the response includes programming code, the assistant formats it specially for easy copying and use.
• Structured Data — tables, JSON, CSV. Useful for programmers and analysts.
• Images — some assistants (ChatGPT with DALL-E, Gemini with Imagen) can generate pictures from descriptions.

Top 10 AI Assistants

Your choice of assistant depends on what you want to do. There are universal solutions that handle everything and specialized tools for specific tasks.

ChatGPT (OpenAI) — Market Leader

Key Specifications

Parameter	Value
Models	GPT-4, GPT-4o, GPT-3.5
Context Window	128K tokens
Multimodality	Text ✓, Images ✓, Voice ✓, Video ✓
Integrations	DALL-E, Web Browsing, Plugins, Code Interpreter
Price	Free / Plus ($20/month) / Pro ($200/month)

Ideal Use Cases

ChatGPT tackles almost any task. A marketer generates content ideas, a programmer writes functions, a student studies for exams, an entrepreneur analyzes markets. The most popular choice for beginners.

Pros

• Powerful GPT-4 model understands context and nuance.
• Huge community — easy to find guides and solutions.
• Integrations with other services via API.
• Create Custom GPTs for your needs.
• Web search included (finds current information).

Cons

• Paid subscription costs $20/month.
• Context window smaller than Claude's.
• Can sometimes "hallucinate" (generate incorrect information).
• Interface can be overwhelming for beginners.

Getting Started

Go to openai.com, create an account via Google or Email. ChatGPT Free is available without a subscription. Start by asking questions and experimenting.

Google Gemini — Integrated into the Google Ecosystem

Key Specifications

Parameter	Value
CModelsell	Gemini Pro, Gemini Ultra (via Gemini Advanced)
Context Window	200K tokens
Multimodality	Text ✓, Images ✓, Video ✓, Voice ✓
Integrations	Google Workspace (Docs, Sheets, Gmail, Calendar)
Price	Free / Gemini Advanced ($20/month)
Web Search	Real-time (finds fresh information)

Ideal Use Cases

If you already use Google Workspace, Gemini becomes a natural extension. It integrates directly into Gmail, Google Docs, Google Sheets. Writing an email? The assistant suggests improvements. Working with a spreadsheet? It helps analyze data.

Pros

• Tight integration with Google services.
• Better video and image analysis than ChatGPT.
• Real-time search finds the latest news.
• 200K token context window (larger than ChatGPT).
• Free version works well.

Cons

• Heavily tied to the Google ecosystem.
• Fewer third-party integrations than ChatGPT.

Getting Started

Go to gemini.google.com, sign in with a Google account. If using Google Workspace, activate Gemini in the apps.

Claude (Anthropic) — Document-Oriented

Key Specifications

Parameter	Value
Models	Claude 3 Opus, Sonnet, Haiku
Context Window	200K+ tokens
Multimodality	Text ✓, Images ✓
Integrations	API for developers
Price	Free / Claude Pro ($20/month)
Specialization	Working with large documents

Ideal Use Cases

Claude is built for processing large volumes of text. Upload an entire book, dissertation, or research report — the assistant analyzes, summarizes, and answers questions about the content. Ideal for analysts, researchers, students.

Pros

• Largest context window (200K+).
• Excellent security and privacy (GDPR compliant).
• Doesn't use your data to train new models.
• Explains complex concepts well.
• "Hallucinates" less than competitors.

Cons

• Fewer integrations than ChatGPT.
• API is more expensive.
• Cannot create images.

Getting Started

Go to claude.ai, create an account. Upload a PDF or text file and start a conversation about the document.

Perplexity AI — AI-Powered Search with Answers

Key Specifications

Parameter	Value
Models	Proprietary (in-house)
Specialization	Information search + answers
Key Feature	Shows answer sources
Price	Free / Perplexity Pro ($20/month)
Web Search	Built-in by default

Ideal Use Cases

Perplexity is the next-generation search engine. Instead of searching Google and clicking links, you ask Perplexity a question. The service finds information, synthesizes an answer, and shows sources. Perfect for journalists, analysts, researchers.

Pros

• Always shows information sources.
• Real-time internet search.
• Fact-checking (the assistant verifies information).
• Free version is fully functional.

Cons

• Cannot create original content (search only).
• Fewer integrations.
• Requires an internet connection.

Getting Started

Go to perplexity.ai, create an account. Start asking questions. The system immediately shows answers with sources.

GitHub Copilot — For Programmers

Key Specifications

Parameter	Value
Specialization	Programming and code
Languages	Python, JavaScript, TypeScript, Java, C++, Go, and others
Integration	VS Code, Visual Studio, JetBrains IDEs
Price	Free (Community) / $10-39 (Individual/Business)
Functions	Autocompletion, function generation, code explanation

Ideal Use Cases

A programmer writes code, and Copilot suggests completions. The assistant offers ways to finish functions, generates tests, explains others' code. Speeds up development by 40-55% according to research.

Pros

• Built directly into the code editor.
• Works with popular programming languages.
• Generates functions, documentation.
• Free for students.
• Learns from your code.

Cons

• Paid subscription starts at $10/month.
• Sometimes generates suboptimal code.
• Tied to VS Code/JetBrains ecosystems.

Getting Started

Install VS Code, add the GitHub Copilot extension. Authorize via GitHub. Start writing code — Copilot will offer completions.

Writesonic — For Marketers

Key Specifications

Parameter	Value
Specialization	Marketing and copywriting
Functions	Content templates, optimization, SEO
Price	Free / $25-99/month
Integrations	WordPress, Zapier, Stripe

Ideal Use Cases

A marketer or copywriter generates ideas, writes headlines, creates product descriptions. Writesonic has built-in templates for different content types: Instagram posts, e-commerce product descriptions, landing pages.

Pros

• Specialized in marketing content.
• Many ready-made templates.
• Generates text quickly.
• Good SEO optimization.

Cons

• Paid subscription costs from $25/month.
• Quality lower than ChatGPT.
• Fewer integrations.

Getting Started

Go to writesonic.com, create an account. Choose a template and fill in parameters. Writesonic generates text in seconds.

Otter.ai — For Transcription

Key Specifications

Parameter	Value
Specialization	Audio and video transcription
Functions	Transcription, meeting summaries, search within recordings
Integrations	Zoom, Google Meet, Teams
Price	Free / $8.33-30/month

Ideal Use Cases

A journalist records an interview, a manager records a meeting — Otter.ai automatically converts audio to text. The assistant highlights key points, creates summaries, allows searching within content.

Pros

• High transcription accuracy.
• Integrated into popular video services.
• Generates meeting summaries.
• Allows searching recordings.
• Free version available.

Cons

• Paid plans from $8.33/month.
• Depends on audio quality.

Getting Started

Go to otter.ai, create an account. Connect to Zoom or Google Meet. Future meetings will be transcribed automatically.

Mobile and Wearable AI Assistants

Bee AI — Recording on a Bracelet

Specifications

Parameter	Value
Form	Factor Bracelet
Battery	7+ hours of continuous recording
Size	Compact, comfortable to wear
Key Feature	Local processing (no cloud)
Functions	Recording, transcription, summarization

How It Works

Wear the Bee AI bracelet — it records all conversations. At home, sync with a computer, and the assistant transcribes, summarizes, and sends you the text. High privacy: data stored locally, not in the cloud.

Pros

• Portability (on your wrist).
• Privacy (local processing).
• Convenient for journalists and researchers.
• High sound quality.

Cons

• Expensive ($50).
• Battery lasts 7 hours.
• Requires computer processing.

PLAUD Note — Portable Voice Recorder

Specifications

Parameter	Value
Form Factor	Portable voice recorder
Battery	16+ hours
Microphone	Directional (good at capturing speech)
Functions	Recording, cloud sync, summarization
Integrations	Cloud, smartphone app

How It Works

Turn on PLAUD Note, place it on the table during a meeting — the assistant records. After the meeting, sync with the cloud via the app. The system generates a summary, highlights key moments, creates an action list.

Pros

• Long battery life (16 hours).
• Quality microphone.
• Cloud synchronization.
• Good app for managing recordings.

Cons

• Expensive ($170).
• Needs charging.
• Data in the cloud (privacy concerns).

Limitless AI — AI-Powered Pendant

Specifications

Parameter	Value
Form Factor	Stylish neck pendant
Battery	30+ hours
Capabilities	Recording, calendar sync
Key Feature	Integration with personal memory space
Price	$199

How It Works

Wear Limitless around your neck. The pendant constantly records your day — meetings, conversations, ideas. Syncs with your calendar, notes, files. When you need information, the assistant finds it in the recordings.

Pros

• Stylish design (looks like jewelry).
• Very long battery life.
• Integration with calendar and notes.
• Convenient for creative individuals.

Cons

• Most expensive ($199).
• Privacy questions (constant recording).
• Requires cloud storage.

Personal AI Assistant Trends: What's Next

Personal AI assistants are evolving rapidly. New capabilities, models, and applications emerge monthly. It's important to understand where the technology is headed.

Trend 1: Specialization and Niche Focus

Moving from universal to highly specialized. The early idea was one assistant for all — a universal solution handling every task. The current trend is shifting the opposite way. Assistants are emerging that deeply specialize in a single domain:

• For programming: GitHub Copilot, Cursor IDE
• For marketing: Writesonic, Copy.ai
• For creativity: Midjourney, Runway
• For law: LawGeex, Kira
• For medicine: med-PaLM, Biomedical BERT
• For finance: Bloomberg terminals with AI

Why is this happening? A niche-specific assistant understands the context of your profession better. It knows industry language, typical tasks, best practices. The result is more accurate and useful.

Forecast for 2026-2027: Every major professional field will have its own AI specialist.

Trend 2: Personalization Through Learning on Your Data

An assistant that knows you. The future of personal assistants is when the helper learns from your data, documents, and writing style. Imagine: upload all your articles, emails, reports. The assistant analyzes your style, logic, preferences. Then, when you ask it to write a text, it writes in your style, with your logic.

2025 Examples:

• Custom GPT (you can upload files and train it)
• Claude Project Workspace (for personal data)
• Perplexity Custom (creating a personal search)

Technology: RAG (Retrieval-Augmented Generation) — the assistant uses your documents as a reference without retraining.

Effect: The assistant becomes not just a helper, but your clone. Writes like you, thinks like you, knows your secrets and experience.

Trend 3: Mobility and Wearable Devices

AI on your wrist, around your neck, in your pocket. If assistants were once tied to computers or smartphones, mobile and wearable solutions are now emerging.

2025 Examples:

• Bee AI — bracelet for meeting recording
• PLAUD Note — portable AI voice recorder
• Limitless AI — neck pendant, personal memory
• Humane AI Pin — wearable device with a projector
• Meta Ray-Ban Smart Glasses — AI-powered glasses

Effect: The assistant is always with you — during meetings, commutes, walks. No need to pull out a phone or laptop.

Forecast: By 2026, 30% of professionals will use wearable AI devices for work.

Trend 4: Deep Ecosystem Integration

AI is built in everywhere. No more switching between apps. AI is built right into where you work.

• Google: Gemini built into Gmail, Docs, Sheets, Meet, Calendar. Writing an email? Gemini suggests improvements. Working on a spreadsheet? Gemini analyzes data.
• Microsoft: Copilot built into Windows 11, Word, Excel, PowerPoint, Outlook, Teams. Creating a presentation? Copilot generates slides.
• Apple: Siri integrated into iOS, macOS, Apple Watch, HomePod.

Effect: You don't launch the assistant — the assistant is always nearby.

Forecast: By 2027, deep integration will be the standard. OS without built-in AI will be the exception.

Trend 5: AI Agents and Autonomous Systems

From helper to autonomous agent. Currently, assistants answer questions. The future: assistants perform tasks independently.

Agent Examples:

• Agent schedules a meeting, sends invitations, syncs calendars.
• Agent writes an email, gets your approval, sends it.
• Agent analyzes a document, highlights key points, creates a summary, publishes it to the corporate portal.

How it works: The assistant breaks your task into subtasks, performs each, checks the result, reports back.

Technology: Multi-agent systems, tool use, function calling.

Forecast: By 2026, corporate agent-assistants will replace 30-40% of office administrator work.

Trend 6: Multimodality

One assistant — multiple formats.

• Input: text, voice, images, video, documents.
• Output: text, voice, images, video, code, tables.

2025 Examples:

• ChatGPT can process videos (understands what's happening).
• Gemini analyzes YouTube videos.
• Claude reads PDFs and generates summaries.

Effect: The assistant understands you, no matter the format. Sent a voice message? The assistant understands. Uploaded a photo? It analyzes it.

Forecast: By 2027, multimodality will be standard, not a special feature.

Trend 7: Democratization (Accessibility)

AI is becoming cheaper and simpler.

• 2022: ChatGPT Plus $20/month (expensive for the masses).
• 2023: Free alternatives appear.
• 2024-2025: Free versions are almost as good as paid ones.
• 2026: Paid subscriptions may fade, replaced by microtransactions.

Examples:

• ChatGPT Free available to all.
• Claude Free has a 200K context (like paid competitors).

Effect: The barrier to entry disappears. Even a student can use a powerful assistant.

Forecast: By 2027, a quality AI assistant will be like electricity — accessible and cheap.

Trend 8: Privacy First and Edge AI

Your data stays with you. Growing privacy concerns are pushing developers toward local processing.

Examples:

• DeepSeek — open-source model, can run on your computer.
• Ollama — platform for running local models.
• Llama 2 — Facebook's open-source model.
• Edge AI — on-device processing, no cloud.

Technology: Model quantization, optimization for mobile and home computers.

Effect: You control your data. The model works locally; no internet needed.

Drawback: Requires a powerful computer or involves longer processing.

Forecast: By 2027, 40% of tech-savvy users will use local models for sensitive tasks.

Trend 9: B2B Corporate Adoption

AI enters business processes. If AI was once used by individual employees, companies are now integrating assistants as part of their infrastructure.

Examples:

• A company creates its own AI assistant based on GPT for employees.
• Assistant integrated into CRM, ERP, project management systems.
• Assistant handles tasks: data analysis, report creation, customer support.
• ROI: 30-50% reduction in operational costs.

Company Examples:

• McKinsey implemented an assistant for analyzing reports.
• Morgan Stanley created an assistant for data analysis.
• Siemens uses an assistant for production management.

Forecast: By 2026, 70% of large companies will use corporate AI assistants. By 2027, this will reach 90%.

Conclusion: The Future of Personal AI Assistants

AI assistants aren't the future — they're the present. The technology is developing rapidly. In three years, from ChatGPT (November 2022) to now, a revolution has occurred. AI has transitioned from an experimental tool to a working instrument.

Key Takeaways:

1. No universal solution — choose based on your tasks. Newcomer? ChatGPT Free. Programmer? GitHub Copilot. SEO specialist? ChatGPT for depth.
2. Quality is sufficient for work — modern assistants handle 70% of office tasks. The remaining 30% requires a human.
3. Training is necessary — simply using AI isn't enough. You need to learn prompt writing, answer verification, workflow integration. It's a separate skill.
4. Ethics matter — use AI honestly. Disclose, edit, verify. The robot is a tool, like Excel or Google. The tool isn't to blame; the user is.
5. Adaptation is critical — those who learn to work with AI gain a competitive advantage. By 2027, this will be a standard skill.