Backend engineer building Go/Python systems for Web3, automation, data tooling, and AI-assisted workflows.
I like practical software: services that run, scripts that save time, bots that handle real traffic, and small tools that make messy workflows easier to operate.
| Area | Focus |
|---|---|
| Backend systems | Go services, APIs, workers, PostgreSQL, Redis |
| Automation | Telegram bots, scraping pipelines, workflow tooling |
| Web3 tooling | EVM/Monad experiments, transaction flows, analytics scripts |
| AI-assisted engineering | Codex workflows, project automation, codebase memory tooling |
| Project | What it shows | Tech |
|---|---|---|
| Monad bridge | EVM-to-Monad bridge prototype with backend persistence, Dockerized runtime, and transaction-oriented workflow | Go · PostgreSQL · Docker |
| URL Shortener | Small production-style backend service with persistent links and redirect handling | Go · PostgreSQL |
| IG Reels downloader bot | Telegram bot for media download flows, containerized operation, and reliability work around provider/API limits | Python · Telegram API · Docker |
| PumpFun token snipe | Research tooling for parsing new PumpFun tokens and exploring early token patterns | Python · Pandas |
| not-golang-contest | Flash-sale backend contest solution with database and cache coordination | Go · PostgreSQL · Redis · Docker |
- Build the smallest useful version first, then harden the parts that prove they matter.
- Prefer observable systems: logs, metrics, reproducible commands, and clear failure modes.
- Use AI heavily for acceleration, but keep verification grounded in tests, artifacts, and real runtime behavior.
