Aleksandr Filippov

Aleksandr Filippov

Aleksandr Filippov is an AI Product Manager and hands-on LLM engineer based in Limassol, Cyprus. He writes the production code behind the AI products he leads: retrieval systems that ground answers in knowledge you can trust, the agent orchestration around them, Model Context Protocol servers that put both in front of any AI client, and the Python services underneath. The product side — vision, stakeholders, delivery — sits on top of work he can build himself.

Away from work he trains at the gym on a programme he runs himself, high intensity and low volume, and spends the rest of his time with his family. He keeps a close eye on where AI is actually moving, tries each shift against whatever he is building at the time, and has a few ideas of his own in progress that are not ready to show yet.

This site gathers his projects, his writing, and the thinking behind them.

2.6k commits, 760 merged pull requests, 16 external repositories, 11 organizations, 82 active days in the last 90plus 8,375 private contributions not shown — every number here is a floor, not a ceiling

on GitHub

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Agent Experience: Preparing Your Site for Its Second Reader

Agent Experience: Preparing Your Site for Its Second Reader

Every page on your site is two documents: the one a browser renders for people and the one a parser reads. AI agents read the second one. Here is what I learned making this site legible to them -- and what a machine reader finds on the personal sites of eight of the best-known people in AI, from a hidden Order of the Unicorn to a robots.txt that redirects into itself forever.

Dynamic Workflows: When the Agent Writes Its Own Harness

Dynamic Workflows: When the Agent Writes Its Own Harness

For a few weeks now, Claude Code has been writing its own multi-agent harness for each task -- and it quietly retired the orchestration code I used to maintain by hand. Here is what changed, the patterns worth knowing, and the research idea I think sits underneath it.

MCP Context Server: Persistent Memory for Your AI Coding Agents

MCP Context Server: Persistent Memory for Your AI Coding Agents

Your AI coding agent loses its plan every time the context window compacts. MCP Context Server fixes that with persistent, searchable memory -- one Docker command and your agents remember everything.