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yasdelayu/README.md

  whoami

name:        Maxim
alias:       mmgpro
role:        AI / Automation Engineer
focus:       LLM agents · Telegram bots · Content tooling · Workflow automation
mode:        > freelance projects · long-term contracts · consulting
languages:   Python · TypeScript · Bash
ships:       fast, documented, and actually working

I build the boring stuff so you don't have to. Custom AI agents, scrapers, parsers, Telegram bots, content pipelines, internal tools. Anything that should run by itself — I make it run by itself.

  services.list()

> for_clients/

  • 🤖 AI agents on Claude / GPT / open-source models
  • 📨 Telegram bots — from quick MVP to production
  • 🧲 Parsers & scrapers — TG, web, marketplaces
  • ⚙️ n8n / Make automations — wire your tools together
  • 📊 Content pipelines — generate, post, repurpose
  • 🛠️ Custom internal tools — dashboards, CRM-lite, ops

> for_employers/

  • ✅ Ship-first mindset, no astronaut architecture
  • ✅ Comfortable with the full stack — back, front, infra
  • ✅ Production AI agents that survive contact with reality
  • ✅ Async-friendly, documented PRs, honest standups
  • ✅ Russian (native) · English (working proficiency)
  • ✅ Available remote · part-time · full-time

  arsenal.2026/

Top of the AI / automation stack right now — what I actually ship production with in 2026.

LLM CORE · AGENTS · MCP



RAG · VECTORS · LONG-TERM MEMORY



VOICE · MULTIMODAL · MEDIA



SELF-HOSTED LLM · EVAL · OBSERVABILITY



LANGUAGES · FRONTEND · DATA · INFRA

  agents.deployed()

Production AI agents I've built and operate. Not demos — they run 24/7.

🤖 JARVIS  ·  flagship case study

Always-on autonomous agent running on my own Linux box. Wakes on Telegram messages from a single owner-verified channel, routes tasks to specialist sub-agents (dev / devops / content / security), holds a 4-layer memory that survives across sessions, and is hardened against prompt-injection in the wild.

Stack:   Claude Opus · Python · SQLite · Telegram Bot API · systemd Skills:   persistent memory · agent routing · security audit · code review · content ops Uptime: 24/7  ·  Sessions: continuous  ·  Access: owner-only

 
agent:    Jarvis
runtime:  Linux + systemd
brain:    Claude Opus 4.7
memory:
  ├─ semantic   (facts)
  ├─ episodic   (events)
  ├─ verbatim   (chunks)
  └─ reflection (patterns)
guards:
  ├─ owner chat_id verify
  ├─ prompt-injection log
  └─ secret redaction

> other agents in the field/

Agent Job Channel Status
🧾 Terminator summarises group chats · 24h digests · multi-chat Telegram groups live
📡 Claudebot owner-only AI controller for the home server Telegram DM live
🎬 Content pipeline auto-script · SEO · thumbnail prompts internal live
🔒 Client agents custom AI workers · under NDA private references on request

If you need an agent that actually does the job instead of demoing — that's what I build.

  rag.expertise()

Retrieval-Augmented Generation done right. Hybrid search, two-stage reranking, semantic chunking — the boring details that keep LLMs from hallucinating in production.

> what I deliver

  • 📚 Knowledge bases over docs · PDFs · codebases · chat history
  • 🔍 Hybrid retrieval — BM25 + dense vectors + metadata filters
  • 🎯 Two-stage retrieval with cross-encoder rerankers
  • 🧩 Smart chunking — semantic · parent-child · contextual (Anthropic-style)
  • 📊 RAG eval harness — Ragas + custom golden sets, regression-tested
  • 🧠 Long-term agent memory — semantic · episodic · verbatim layers
  • 🔄 Incremental indexing with dedup, versioning & TTL
  • 🛡 Citation tracing + answer-grounding checks (no silent hallucinations)
  • ⚡ Streaming RAG with prompt-cache warm-ups for sub-second TTFT
 
vector_db:    Qdrant · pgvector · Weaviate
              · Pinecone · Chroma
embeddings:   OpenAI text-embed-3
              · Voyage-3 · Cohere v3
              · BGE-M3 (multilingual)
rerank:       Cohere Rerank 3.5
              · Voyage Rerank · Jina
parsing:      Unstructured · LlamaParse
              · Docling · pdfplumber
frameworks:   LlamaIndex · LangChain
              · Haystack · DSPy
eval:         Ragas · LangSmith
              · Braintrust · custom
memory:       Mem0 · Letta · Zep
              · custom 4-layer
context:      Anthropic Contextual
              Retrieval (49% recall ↑)

Need a RAG that answers from your data and cites sources — not a chatbot that makes things up? DM me.

  now.building()

Two private commercial projects currently shipping. Code under NDA — architecture and stack are open.

🎵   Reaction Engine

music-news automation platform

End-to-end pipeline that turns RSS feeds, Instagram / TikTok accounts and webhooks into ready-to-publish Telegram posts. AI filters relevance, kills duplicates on three levels (URL · title · semantic), generates copy + images, schedules in timezone-aware queues and ships digests from history.

stack:    TypeScript · React 19 · Vite
backend:  Express · Supabase · Caddy
ai:       OpenRouter · Gemini · Firecrawl
ingest:   Apify · RSS · Webhooks
deploy:   Docker · VPS
status:   delivered & running
scope:    full-stack · admin UI · client docs

🎬   Content Factory · SaaS

multi-tool content platform for creators

Telegram Mini App where users turn product links into UGC reels, talking AI avatars, auto-edited videos with subtitles & color, carousels, scripts, voiceovers and competitor analytics. Credit-based billing, render farm on Hetzner, tariffs from Free to Agency.

stack:    Next.js 16 · React 19 · Tailwind 4
data:     Supabase · Redis · BullMQ
workers:  Python · FFmpeg · Whisper · VAD
render:   Remotion · Go ffmpeg-over-ip
ai:       OpenRouter · Gemini Veo
deploy:   Vercel + Hetzner VPS
status:   active development
scope:    full-stack SaaS · billing · workers

Need a system like one of these for your business? DM me — I take projects of any scope.

  stats --dump

  contribution.snake

GitHub contribution snake

  let's build something

Looking for an engineer who ships? Got an idea that needs to become a product?
I'm one message away.



more channels arriving soon

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