Mikhail Ankudinov specializes in developing website parsers, Telegram bots, and automated data processing systems. He gathers information through APIs, HTML scraping, and browser automation, saving the results in formats such as Excel, CSV, JSON, or databases like PostgreSQL. One of his projects is a monitoring system for freelance platforms that operates 24/7 on a VPS. This system collects job postings from Kwork, FL.ru, and Freelance.ru, analyzes them using AI, filters the results, and sends suitable offers via Telegram. He works with tools such as Python, Requests, BeautifulSoup, Playwright, Selenium, Flask, Laravel, Filament, n8n, and others, ensuring regular execution, notifications, and integration with web applications. Before starting a project, he conducts a source analysis to determine the data structure, output format, and limitations.

Reviews

Mikhail Ankudinov is a professional performer who consistently delivers high-quality work: on time, taking into account all client requests, often ahead of schedule, and incorporating creative solutions. Reviews highlight his responsibility, responsiveness, initiative, ability to understand client needs even with vague specifications, and constant availability. There are no negative reviews.

Portfolio projects

Skills and Technologies: web development, automation, Python, PHP, Laravel, PostgreSQL, n8n, AI, Telegram API, Docker, CI/CD, HTML/CSS, JavaScript, Vite, Tailwind CSS, Filament.

Projects and Achievements: The candidate has developed and implemented several high-performance systems, including an automated job vacancy parser with AI analysis that reduced manual labor in job searching and responding, as well as an internal analytics system for a website without using third-party services. As part of the OnFlaude CMS project, a custom Open-Source CMS was created using Lean Architecture, ensuring security and scalability. The candidate also implemented a flexible portfolio system with automatic ZIP archive unpacking and developed a media library for managing website files.

Achievements and Recognition: As a result of the candidate's work, significant improvements in system performance and usability were achieved, including process automation that reduced task completion time by up to 80%. The candidate also ensured a high level of security for servers and applications, as evidenced by the successful integration of Fail2Ban and the configuration of attack protection.

* It is created by a neural network based on a portfolio, information provided by the user about himself and reviews from other users.

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