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marcocjmr/README.md

hi, I'm Marco πŸ‘‹

Software engineer β€” I build products end to end, from the interface people touch to the systems behind it.

Computer Science engineer, based in Cuenca, Ecuador. I care about clean UIs and things that actually ship. Lately: building with LLMs and agents.

Project Description Focus
logistics-agent-band Nexis β€” multi-agent system for corporate travel planning: four agents, multi-LLM, real-time policy checks AI Β· Agents Β· RAG Β· Frontend Β· Backend
rag-business-assistant RAG chatbot answering from a business's own docs β€” section-aware chunking, pgvector similarity search, streamed answers with cited sources AI Β· RAG Β· pgvector Β· Next.js
hydroponics-fog-lpwan Fog-computing simulation for real-time crop monitoring β€” 3 LPWAN networks, bio-inspired optimization vs ACO/PSO across 54 scenarios. My CS thesis IoT Β· Simulation Β· Research Β· Java
markeeto Where I design & build β€” my studio, my site, client work UI/UX Β· Frontend Β· Design

tools Β roughly where my hours go

typescript   β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘
python       β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘
java         β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘
sql          β–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘β–‘

also Β next.js react node postgres pgvector llm apis figma

elsewhere Β markeeto.dev Β· linkedin

open to remote roles β€” product engineering, AI & agents Β· utc-5

Pinned Loading

  1. logistics-agent-band logistics-agent-band Public

    Multi-agent system designed for complex corporate travel coordination and relocation logistics. β”‚ Built using the Band SDK, AI/ML API, and Featherless AI for Track 1 (Internal Enterprise β”‚ Workflow…

    TypeScript

  2. hydroponics-fog-lpwan hydroponics-fog-lpwan Public

    Fog computing for hydroponic monitoring over heterogeneous LPWAN, with bio-inspired optimization (iFogSim2) β€” CS thesis, UPS Cuenca 2026

    Java

  3. rag-business-assistant rag-business-assistant Public

    RAG chatbot answering from a business's own docs β€” section-aware chunking, pgvector search, streamed answers with cited sources

    TypeScript