I am an AI-native builder focused on turning ideas into working systems through AI-assisted engineering.

My work combines local language models, coding agents, automation, APIs, browser/desktop tooling, persistent memory, knowledge graphs and verification loops. I use AI as an implementation collaborator while I define the product behavior, architecture, constraints, workflows and acceptance criteria.

My main project is EIDOS, a local-first persistent agent runtime exploring long-lived memory, reusable capabilities, multi-agent coordination, controlled computer use, action-effect verification and rollback-oriented self-repair.

I am especially interested in systems where AI does more than answer questions: it can use tools, observe effects, retain state, coordinate work and improve repeatable workflows.

EIDOS is my main research and product project.

It explores a system architecture built around:

• persistent memory and graph state;
• multi-agent / Colony deliberation;
• one arbitration layer for final decisions;
• browser and desktop interaction;
• action → observed effect → verification loops;
• reusable capability learning;
• local model and tool adapters;
• isolated self-edit and rollback research;
• a dedicated Desktop 2 workspace for agent interaction without taking over the user's primary desktop.

A convincing answer is not the same thing as a verified effect.

The public repository deliberately separates implemented, wired, executed, measured and reused capabilities instead of treating documentation as proof.

Explore EIDOS →

A guided automation toolkit for the OnePlus 7 Pro GM1913 covering recovery-oriented workflows, Android/Windows ARM preparation, Kali NetHunter tooling, backup/verification and device setup.

This project reflects another part of how I work: combining existing low-level tools into a safer, reproducible workflow for a real device.

Current areas of focus

• AI automation and internal tools
• agentic workflows and multi-agent systems
• local/private AI
• persistent memory and knowledge graphs
• browser and desktop computer use
• MCP / tool integration
• rapid AI product prototyping
• Linux automation
• Android / Windows-on-ARM experimentation
• verification, rollback and safer autonomy

I keep a broad GitHub workspace because I actively study and compare tools across AI, automation, operating systems, security and agent infrastructure.

These repositories are kept as forks, references or research material. They are not presented as my original authorship.

UI-TARS Desktop · Neural Memory · ClawTeam · OpenClaw · SkillOpt · MiMo Code · CodeGraph · ToolNeuron / Android AI · PrivateLM · Ollamacode CLI

Windows for OnePlus · WOA Tools · QuickRotate WOA · Rucky / NetHunter

My account also contains security and OSINT tooling used for learning, comparison and authorized research. A repository appearing in this account should not be interpreted as original authorship unless explicitly stated.

Browse all repositories →

My workflow is deliberately verification-oriented:

idea
  ↓
specify behavior
  ↓
use AI / agents to implement
  ↓
run it
  ↓
observe the real effect
  ↓
verify
  ↓
document
  ↓
keep, improve or roll back

I care about the difference between:

• code existing;
• code actually being connected;
• a path being executed;
• a measurable effect being observed;
• a learned capability being reused later.

That distinction is central to EIDOS and to how I evaluate AI-generated software.

I am interested in full-time, contract and freelance work involving:

• AI Automation Specialist
• Agentic Systems / AI Agents
• Generative AI implementation
• AI workflow automation
• internal AI tools
• local/private AI
• rapid AI product prototyping
• AI-assisted operations and process automation

Remote · Slovenia · EU

If your team has a manual process that could become an AI-assisted workflow, an agent that needs tools and memory, or an AI prototype that needs to become a working system, that is the kind of problem I want to work on.