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de4er00/README.md

AI engineer in Kazakhstan (UTC+5). I build LLM pipelines with automated quality checks and run them in production: generative video, multimodal LLM-as-judge, RAG over code. MSc from MIPT, PhD candidate in computer vision.

  • code-rag-mcp: local hybrid code search for coding agents, measured on a 70 questions with held-out sets
  • narration-pipeline: writes narration for explainer videos with fact statuses and about 25 deterministic checks, then cuts it into frames with an exact dynamic program
  • procedural-films: short films where every frame and sound is computed in plain JavaScript and Canvas

Open to remote roles. laido59@gmail.com · t.me/deselection

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  1. playable-ads playable-ads Public

    Two playable ads in TypeScript (Three.js + PixiJS): a squad runner and a merge tower defense. One code base builds 3 A/B variants for 8 ad networks (AppLovin, Unity, Google, Meta, TikTok, Mintegral…

    TypeScript

  2. code-rag-mcp code-rag-mcp Public

    Local hybrid code search (BM25 + embeddings) for coding agents over MCP, with a reproducible retrieval benchmark

    Python

  3. narration-pipeline narration-pipeline Public

    LLM narration for explainer videos: facts with confidence status, ~25 deterministic checks, exact frame cut

    Python

  4. procedural-films procedural-films Public

    Two short films where every frame and sound is computed in plain JavaScript and Canvas 2D

    JavaScript