Can you create web services and applications without deep programming knowledge? With the advent of powerful language models and AI assistants — yes. All you need is to clearly formulate the task. This approach is called vibecoding (vibe coding).
It gained particular popularity after OpenAI co-founder Andrej Karpathy publicly demonstrated in February 2025 how he fully delegates programming to neural network agents. His workflow requires almost no manual code input. He formulates an idea — the model writes, checks, and refines the project.
In this article, we will:
Our goal in this material is not just to describe the trend, but to give a practical understanding of how to use vibe coding in work or business, what limitations and opportunities it offers, and why this direction is becoming part of the future of technology.
Vibecoding (vibe coding) is a programming style where the developer does not write code manually, but describes the task in natural language, and artificial intelligence itself creates the working code. This approach lowers the technical barrier: there's no need to know language syntax, understand architecture, or manually debug the project — these tasks are performed by an AI assistant.
This approach is called "code by vibe" because the basis is not compiler logic, but the context, intent, and result that the developer describes as a thought, goal, or command.
The term "vibecoding" was introduced by Andrej Karpathy — a scientist, developer, and co-founder of OpenAI. In 2025, he described his methodology where the code is not important, the result is, and the entire process can be delegated to AI.
"I don't touch the keyboard. I say: 'reduce the left indents by half' — and the agent does everything itself. I even process errors through chat, without diving in."
-- Andrej Karpathy, February 2025
He claims that development becomes similar to managing an interface through dialogue, rather than writing lines manually. For example, his project MenuGen (a web service that generates dish images from a menu photo) is completely written by AI: from authorization to the payment system.
To start using vibecoding, you need an editor or development environment with AI support. Below is a list of popular tools in 2025 that allow you to generate code, create applications, fix errors, and run projects directly in the browser or on a local machine.
Junie – Assistant for code snippets. AI Assistant – Programming chat.
You can connect different language models in each vibecoding tool. But not all are equally good with code. Some are better for text generation, others for development, others for bug fixing and API work.
For a quick guide, here is a comparison of the most popular models for vibecoding:
| Model | Suitable for | Advantages | Restrictions | Where is it used | | ------ | ------ | ------ | ------ | ------ | | GPT‑4o | Daily tasks, routine code | Stable, fast, understands prompts well | Limited context window | Cursor, Replit, JetBrains AI | | GPT‑4.1 | Full-scale programming | Deep analysis, creates architecture | Slower, more expensive | Devin AI, Cursor (Pro, Ultra) | | Claude Code (Opus 4) | Code generation & refactoring | Writes excellent code | CLI interface, not for beginners | Claude Code CLI | | DeepSeek-Coder | Research, structural tasks | Generates complex queries and SQL | Less known, unstable | Cursor, via Cline | | Gemini (Google) | Web interfaces, API integration | Strong logic, API knowledge | Can "hallucinate" | Via Cline or Replit | | GPT‑3.5-turbo | Quick prototypes, pet projects | Lightweight, cheap, good with basic tasks | Weak on architecture and complex logic | Free mode in Cursor, Replit |
The fastest way to understand vibecoding is to try it yourself. Below is a step-by-step guide on how to create a Telegram bot that, given a link to a GitHub repository, sends a brief summary: name, author, stars, release, and other data.
We'll use the Cursor editor with the GPT‑3.5 model. Everything is done right in the editor — no manual coding required.
Step 1: Set up the environment. Install Cursor, choose a plan (Pro recommended for full access), and enable Agent mode with the GPT‑3.5 model. Step 2: Describe the task. Formulate a clear prompt in the chat, specifying the bot's function, language (Python), and libraries (Aiogram, requests). Step 3: Generate the project. The AI assistant creates the project structure: bot.py, requirements.txt, README.md, .env.example. Step 4: Correct errors. If errors appear when running, copy the terminal text into the chat with the words: "Fix the errors." The AI will make corrections. Step 5: Launch. Run the bot with python bot.py. It will successfully start and respond to links in Telegram. Step 6: Study and improve. The finished project can be uploaded to GitHub, deployed (e.g., via Replit), and extended with features.
✅ Advantages:
❌ Disadvantages:
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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).
This is the "gateway" that receives incoming messages from messengers and forwards them to the agent. It:
A simple web interface based on Vite and Lit. Through it you can:
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:
Skills can be written yourself or downloaded from ClawdHub / MoltHub.
Moltbot's memory is simple yet powerful. It is implemented as regular text files:
This allows for manual memory editing, control over what the bot "remembers," and copying or transferring data between devices.
Moltbot does not contain its own model but connects to external APIs:
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.
During installation, Moltbot:
This is a critically important component:
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.
Moltbot supports connections to numerous services and applications via "skills":
Moltbot's key feature is that it is not limited to just answering but can perform actions at the system level.
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.
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.
Moltbot is capable of performing real tasks, but most stories are still experimental:
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.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
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.
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.
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.
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
Software & Tools
You'll need software to manage and interact with your models. These tools generally fall into three categories:
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
One of the easiest pathways for beginners and experts alike.
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:
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.
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.
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?
The journey to powerful, private, and personalized AI begins on your own machine.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Claude 4 Sonnet is a multilingual AI model from Anthropic, engineered to tackle complex tasks, analyze data, and generate high-quality content. Positioned strategically between the more powerful Opus and the lighter Haiku, Sonnet leverages an extended context window. This allows it to process large documents, manage long chains of reasoning, and handle queries that demand precise answers.
This model is built for developers and professionals who require fast and reliable data processing. Claude 4 Sonnet supports file uploads (including images and JSON), processes inputs step-by-step, and is proficient in over 20 programming languages. It uses tokens efficiently, delivers structured responses, and streamlines workflow management.
Anthropic's official release notes state that the latest updates have enhanced the model's speed, stability, and reasoning quality. This new version offers superior context understanding, improved code generation capabilities, and seamless integration for web applications and API use. These improvements make Sonnet a powerful tool for business, research, and software development.
Use Claude 4 Sonnet when you need accurate solutions, fact-checking, document processing, or to generate clear text in Russian and other languages. The model respects user-defined constraints, supports visual analysis, and consistently delivers high-quality, reliable results.
Claude 4 Sonnet is built for practical application, delivering high-quality input processing, accurate user intent understanding, and structured, step-by-step solutions. It's the ideal choice for developers, students, analysts, and businesses that prioritize stability, speed, and precise control over their information workflows.
Below, we explore the key areas where Claude 4 Sonnet delivers superior performance.
Claude 4 Sonnet excels at generating and refining text in Russian and other languages. It supports editing for both short-form and long-form content and simplifies complex writing tasks. Use it to craft articles, resumes, email copy, product reviews, and internal documentation. The model processes text modifications instantly, even with large data volumes.
Leverage Claude 4 Sonnet to enhance text clarity, precision, and readability. It adeptly understands style, context, and formatting requirements, producing well-structured summaries and helping users eliminate errors.
Claude 4 Sonnet efficiently analyzes large documents, including PDFs and images. With its advanced visual understanding capabilities, it processes tables, text files, and performs fact-checking to draw meaningful conclusions. The model maintains high accuracy across documents of any size and complexity.
Use Sonnet to get comprehensive document overviews, identify key issues, propose actionable solutions, and prepare concise summaries. It is an powerful tool for information verification, data comparison, and multi-source analysis.
The model employs advanced reasoning techniques, constructing clear logical chains and explaining its thought process for transparent, auditable results. Claude 4 Sonnet is designed for tasks that require deep analysis, hypothesis testing, input structuring, and sequential processing.
In its Extended Thinking mode, Sonnet processes massive amounts of information to deliver calm, precise, and well-reasoned answers. This is critical for professionals working on deep research, strategic planning, or creating detailed instructional guides.
Claude 4 Sonnet delivers exceptional results in programming and is a benchmark leader on challenges like the SWE-bench. It assists in writing functions, refactoring and improving code, debugging, explaining complex concepts, and supports all major development languages.
Sonnet is particularly useful for code snippet analysis, code generation, and structural validation. It provides intelligent improvement suggestions and helps build functional files step-by-step. Implement this model in your projects where speed, accuracy, and code security are paramount.
Beyond technical tasks, Sonnet generates creative ideas, produces engaging content, assists with visual analysis, and develops innovative textual approaches. It brainstorms options, suggests styles, and delivers solutions for advertising campaigns, marketing copy, social media, and web projects.
The model adapts to user requirements, understands brand voice, and adheres to specified formats. Claude 4 Sonnet streamlines the entire creative process, enabling you to produce high-quality content consistently, reliably, and at scale.
Claude 4 Sonnet delivers its best performance when it receives simple, clear, and structured inputs. The model performs poorly with vague or ambiguous phrasing. The golden rule is: minimum words, maximum clarity.
Use this proven framework for your prompts:
Example Prompt:
«Context: I have a long research document on climate change policies. Task: Create a concise summary of the key findings.
Format: Provide 5 bullet points.
Criteria: Use short, direct sentences and avoid filler words.»
This simple formula works for 90% of tasks, from data analysis to code generation.
Many users make simple errors that reduce the model's accuracy. Below is a short list of common problems with easy solutions to help you use Sonnet more effectively.
The Problem: Prompts like "Improve this text," "Explain this topic," or "Make it better" lack direction. Sonnet doesn't understand your criteria and produces a generic, unfocused result.
The Fix: Always specify the format and purpose.
Example: "Rewrite this paragraph to be more persuasive for a business audience. Use three bullet points and focus on ROI."
The Problem: Asking a question without providing the source text, examples, or necessary context.
The Fix: Provide data directly or give clear sourcing instructions.
Example: "Based on the email thread provided below, extract the action items and list them in a table with 'Owner' and 'Deadline' columns."
The Problem: Prompts with incompatible instructions, such as "Explain in great detail, but keep it very short and fit it into one sentence."
The Fix: Break complex requests into sequential steps. Sonnet handles multi-step tasks well when they are clearly separated.
Example: "First, provide a detailed explanation of how neural networks learn. Then, create a one-sentence summary of that explanation."
The Problem: The model returns a randomly structured response if no format is requested.
The Fix: Use explicit formatting instructions
Example: "List the pros and cons in a two-column table." or "Output the data as a valid JSON object."
The Problem: Accepting an initial, suboptimal result without seeking refinement.
The Fix: Sonnet can improve its output if you ask for clarifications or revisions. A simple instruction can dramatically increase accuracy.
Pro Tip: Add this line to your prompts: "If the provided data is insufficient for a high-quality answer, please ask clarifying questions before proceeding."
Claude 4 Sonnet establishes itself as a versatile and highly functional AI model, engineered to tackle complex tasks with remarkable efficiency. It excels in data analysis, content generation, and code improvement, all while leveraging an extended context window for deep, comprehensive understanding.
The model delivers a compelling combination of high-speed processing, reliable performance, and cost-effective token usage, offering significant value for its operational cost.
Key Takeaway: Integrate Claude 4 Sonnet into your business operations, software development, research initiatives, and content projects. It is a powerful tool for obtaining precise solutions, streamlining workflows, and consistently achieving high-quality, dependable outcomes.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
Max Godymchyk
Entrepreneur, marketer, author of articles on artificial intelligence, art and design. Customizes businesses and makes people fall in love with modern technologies.
