1.1 Introduction. Who is this guide for, and why does it focus on private and enterprise development using LLMs?

Hi, friends! I started a new blog and decided to begin with the most relevant topic today: AI-powered product development. This guide is aimed at a broad audience: regular developers, people just starting their programming journey, and those who have been asleep for the past few years and have not paid much attention to AI. I will try to explain all the important aspects briefly and clearly, and share recipes and tools for writing maintainable, extensible, production-ready code that humans can understand.

Why will we follow enterprise coding standards?

When you are vibecoding and building your own app, you are limited only by the boundaries you set for yourself. And any problems that arise as you develop your project are solely your pain.

If you take any product company with more than one developer, then without established development processes, the product will exponentially turn into chaotic, unmaintainable crap with every passing month. Everyone will suffer: developers, testers, and users.

In an enterprise environment, there are certain requirements for the product and its development:

• A unified code style, documented in a Code Style Guide

• The ability to quickly replace one employee with another and rapidly onboard them to the project, as well as retain project knowledge

• Every task, even the smallest one, must go through a multi-stage acceptance and quality assurance process consisting of AI and human review and testing

• A long list of requirements at every stage of development and testing

• Ensuring a more or less predictable result by every available means

As you have probably already realized, we are going to build a system that will make it possible to achieve predictable results and help us work on a product long-term without pain in the ass.

I would like to implement a development flow that is effective both for a single person and for an entire team. In search of a magic bullet, we will try different approaches and methodologies. It will be fun!

Problems with subscription-based cloud models

We have literally become hooked on cloud models; they are convenient and still relatively inexpensive.

The biggest problem is your dependence on third-party infrastructure. One political decision, and you may be deprived of a tool you have grown accustomed to and can no longer imagine your work without. Under the guise of sanctions or anything else.

The second problem is unstable performance and a black box under the hood. If you work extensively with cloud LLMs, such as Claude Code or Codex, you have definitely noticed periodically massive drops in generation speed during peak hours. But what is worse is that you may have Claude Opus enabled, while in reality an extremely weak model is running in low-effort mode, and tasks that used to be completed in one or two prompts are no longer completed at all. At such times, you cannot do your work tasks the way you used to or at the same speed. And there is nobody you can complain to about it.

Pay up and shut the fuck up!

Many Reddit users also keep noticing that when a new model is released, the older models become much dumber. There is a good article on this topic — https://habr.com/ru/articles/1023020/

The third problem - is privacy. If you are one of those people who "have nothing to hide," then congratulations! Any state is a super-system that churns out laws every year, allowing it to dig ever deeper into your underwear and use the information already collected to take away your money and freedoms. And you will be the first in the crosshairs. With AI, this process will accelerate significantly. When it comes to commercial companies, the risks increase greatly. Where there is a lot of money, there is a lot of attention from various players in different organizations, as well as from those seeking to seize resources. And if you believe that the data sent to Anthropic and OpenAI is inaccessible to anyone, you are very naive. This is highly valuable information provided voluntarily.

Advantages of local open models, using Qwen 3.6 27B as an example

The biggest advantage is stable performance in the morning, during the day, and at night. The model does not get dumber in the evening hours; its speed is always the same.

Of course, local models are not as smart as giant cloud-based ones. But there is also a non-obvious advantage here. You plan the task more carefully, breaking it down into smaller ones that even a very dumb model can handle. You will not be able to vibe-code mercilessly; you will have to interact with it like an engineer, knowing its weak points. As a result, you will write code more deliberately.

I argue that models Qwen 3.6 27B is quite enough for high-quality product development. It is a dense model, with all 27B parameters active at the same time. And it can be run locally on a GPU with 32 GB of VRAM or on a Mac with Unified Memory.

I ran an experiment. I had a task for one project: implement a dark theme. I first used Qwen 3.6 27B, then GPT 5.6 Sol. That is, I completed the same task twice using different models. Each implementation took me about 2 hours. The results were similar. But I liked working with Qwen more—it wrote code faster and did not periodically get stuck. The result was a pull request with 110 modified files. Doing this task manually, without AI, would have taken about 5 working days.

The boost is astronomical! Moving on!