Hands-on CTO at Livable Places

At Livable Places I am not working on a generic "AI-first" story. I am working on a real delivery system for a proptech product with actual operational responsibility. That spans monorepo product development, distributed scoring and data workflows, self-hosted platform operations, geo/census services, and the evidence-led preparation of a new office-risk product line.

The product thesis is bigger than a score dashboard: make societal demand for real estate uses measurable and comparable at location level. The operating logic is Demand -> Supply -> Property, so financing, acquisition, ESG, and portfolio decisions can rely on more legible signals.

My leverage is connecting product logic, data pipelines, AI/ML research, geo services, queue orchestration, and platform operations instead of treating them as separate workstreams. The monorepo, workers, auth, observability, GitOps-style deploys, backups, and runbooks form one system that can be built, shipped, and operated. Collaboration is distributed, including design capability from Ukraine.

The AI/ML work stays deliberately evidence-bound. Office-risk fingerprinting, PCA, k-means, Location Absorption Score, benchmarking, and feature ablation are used for characterization and screening until target quality, residualization, and cross-city stability can support stronger claims.

Since late 2025, most of my implementation has been produced through agent workflows. My work has shifted from writing most code line by line to orchestration: framing outcomes, context, and constraints, directing agents, reviewing changes and evidence, and remaining accountable for product and technical decisions.

• Built and evolved a pnpm/Turborepo monorepo for the web app, services, workers, and operating tools
• Testing base: used Cypress briefly and then moved to Playwright as the primary browser/E2E testing stack
• Worked hands-on on distributed scoring and data workflows with Bun, BullMQ, FlowProducer, QueueEvents, and Redis
• Used Redis for locking, Pub/Sub, and job-completion signaling instead of fragile glue logic
• Built authenticated async report export paths with service-side auth guards, shared export domain logic, and XLSX generation
• Owned a self-hosted bare-metal platform with Docker Swarm, Traefik, Authentik, Ansible bootstrap, Docker Secrets, restic backups, WireGuard, and a full observability stack
• Established IaC/GitOps-style GitHub workflow paths for build, release, deployment, rollback, scaling, backup, restore smoke tests, maintenance, and self-healing
• Worked with distributed external roles, including a designer in Ukraine, through explicit artifacts, review paths, and visible delivery goals
• Worked hands-on on the geo/census service layer and shaped its architecture around Kotlin, Spring Boot, PostGIS, OpenAPI, FastAPI, Valhalla, OSM, and external POI imports
• Personally built a Python data pipeline for external doctor and school data: S3/CSV ingestion, validation and normalization, retry logic, temporary tables and bulk import into PostgreSQL/PostGIS with JSONB tags, tests, containers, and GitHub Actions
• Prepared a new office-risk product line through evidence-led product and delivery work
• Designed and challenged ML workflows around PCA, k-means clustering, LAS calibration, benchmark matrices, target quality, residualization, and feature ablation
• Used multi-agent research workflows and multi-agent pipelines (asset-research and factsheet-review skills, observation -> inference -> hypothesis -> experiment) as part of the product evidence layer, not only as developer convenience
• Since late 2025, delivered most implementation through Claude Code, Codex, Cursor, Antigravity, OpenCode, and Pi Coding Agent; outcomes, context, constraints, review, and evidence gates remain my responsibility, complemented by local agent skills and plugin-style skill development
• Translated strategy into visible goals, scopes, work items, development boards, releases, demos, and customer-facing feedback loops
• Covered S-Core product clarification and story writing for several months without an assigned Product Owner
• Built demo and campaign measurement paths with anonymous access, UTM tracking, event analytics, and conversion-funnel thinking
• Built internal planning and reporting tooling with Nuxt, Vue, SQLite, Zod, and localized product surfaces

• Delivery as a system: monorepo, platform, queues, geo services, Cypress-to-Playwright test migration, and product work are not optimized in isolation
• Decision quality over dashboards: product work is judged by whether it helps customers make better financing, acquisition, ESG, or portfolio decisions
• Infrastructure as code: platform state, auth baselines, stack definitions, secrets, backups, and operations are encoded and repeatable
• Operability by design: logs, metrics, traces, alerts, and auth are part of the product, not follow-up work
• Distributed orchestration with explicit dependencies, locking, and visibility instead of silent background jobs
• AI-first where it matters: use agents, LLM tooling, and research automation to improve evidence quality and delivery speed, but keep claims bounded by data
• ML with evidence gates: fingerprint, benchmark, calibrate, residualize, ablate, and only then claim predictive value
• Geo/data products as infrastructure: OSM, census, POI, routing, and market data are treated as versioned, observable product dependencies
• Visible operating model: strategy, roadmap, goals, scope, work items, board, release, demo, and customer feedback stay connected
• Small batches and visible ownership instead of roadmap and process signaling
• Evidence-first product work: observation, hypothesis, experiment, then scale