Updesh.

What I Build

Real systems solving real problems. Each project documented honestly — what worked, what failed, what it actually costs to run.

In DevelopmentPersonal Automation · AI Infrastructure

AI Command Center

The Problem

Property income and expenses across multiple units. WEG statements. Bills, receipts, and documents across dozens of categories. Eight years of self-filing complete German tax returns. All of it important. All of it repetitive. All of it eating my weekends.

The Approach

Hybrid architecture — local Ollama models running on-device for privacy-sensitive document processing, Claude API reserved for tasks requiring genuine reasoning. Seven specialist agents governed by a machine-readable Constitution that enforces a hard monthly budget cap and privacy rules. Seven agents: · Supervisor Orchestrator — routes tasks, enforces Constitution, tracks spend · Tax Consultant — German tax law, Finanzamt documents, Elster filing · Financial Planner — portfolio tracking, ETF rebalancing, P&L reports · File Manager (The Librarian) — document classification, Google Drive organisation · Health Agent — reimbursement claims, health document management · Innovation Agent — AI news digest, research, idea capture · Business Operations Agent — invoice management, scheduling, client documents Routing logic: Sensitive documents processed locally via Apple Vision OCR — raw files never leave the device. Only anonymised values reach the cloud API. Cost governance enforced in real time.

The Result

60–70% of tasks handled locally at zero cost. Total monthly AI infrastructure targeting under €30. GDPR compliant by design. System runs autonomously — agents operate without user intervention.

Stack

Mac Mini M4 Pro (24GB)OllamaQwen3 14BGemma 3 12BClaude HaikuClaude SonnetPythonSupabaseApple Vision OCRHetzner VPS FrankfurtGoogle Drive API
Active DevelopmentProperty Management · Web Application

PropTech Platform

The Problem

Property management across multiple units involves repetitive document-heavy workflows — Mietverträge, WEG statements, utility bills, maintenance records, rental income tracking. All managed manually. All taking time that compounds across multiple properties.

The Approach

Custom web application with AI document processing pipeline. Automated intake via a drop-zone folder — documents are classified, data extracted, and filed automatically. German property document types handled natively including Mietverträge, Betriebskostenabrechnungen, and WEG Protokolle. Foundation architected for commercial scale from day one.

The Result

Platform in active development. Automated document intake pipeline operational. Architecture designed to scale from personal use to commercial PropTech product.

Stack

Next.jsSupabaseVercelClaude APIGitHub CI/CDPythonApple Vision OCR
Paper Trading LiveInvestment Automation · Financial AI

AI Trading System

The Problem

Investment portfolio monitored manually. No systematic rebalancing. No real-time monitoring of EU and US market positions. No automated response to news, fund flows, or technical signals.

The Approach

Automated trading system connected to Interactive Brokers Pro API. Three automation layers: · Rule-based layer — systematic execution of rebalancing, stop-loss, position limits. Zero AI tokens. Runs 24/7. · Signal layer — Claude analyses technicals, news sentiment, institutional fund flows. Generates buy/sell signals. · Autonomous layer — Claude Desktop with IBKR MCP for on-demand natural language execution. Infrastructure on Hetzner Frankfurt VPS for 24/7 uptime and stable IP. Multi-currency account holds EUR and USD natively — EU trades execute directly in EUR with no forced conversion.

The Result

Paper trading live on IBKR Pro. 60-day validation period before real capital deployment. Full data pipeline connected: Finnhub news sentiment, FRED macro data, ECB rates, SEC EDGAR 13F institutional flows.

Stack

PythonInteractive Brokers APIIBKR MCPHetzner VPS FrankfurtSupabaseClaude APIFinnhub APIFRED APIECB API