Vibe coder with 3+ years of experience and a B.S. Computer Science degree in progress — grounded in databases, software engineering, and programming languages. I let AI handle the boilerplate; I handle the architecture, security, and quality.
A B.S. Computer Science student building production software — grounded in databases, software engineering, and systems thinking, with a generalist's willingness to pick up something new mid-project.
End-to-end automations on real public APIs — each one its own repo with source, tests, CI schedule, and docs. Same loop I bring to client work: research → build → harden → ship.
Mirrors every published repo — public or private, personal or org — into timestamped archives with retention policy and a machine-readable manifest.
Pulls the top Hacker News stories each morning, ranks them by points, and auto-publishes a clean markdown digest to a repo — fully on a schedule.
Zero-dependency Go bot that auto-labels issues by keyword, marks stale threads, auto-closes them, and drops a weekly triage report — stdlib only.
Polls CoinGecko on an hourly cron, keeps an append-only history, fires webhook alerts on big moves, and renders a sparkline report each cycle.
I can learn while vibe coding. New tool, new framework, new paradigm — I research it, adopt it, and ship with it.
Before any AI-generated tool goes into the codebase, I research its real-world use case and fit — so it lands intentionally, not by accident.
I run vulnerability scanning on AI-generated code before it ships. Because generated code should be trusted, not assumed.
Fundamentals to back the vibe — databases, algorithms, data structures, and clean software engineering behind every build.
When AI suggests a dependency, I stop and research its [use case] first — docs, maintenance, and whether it actually fits the problem before implementing anything.
Prompt the codegen, vibe through the boilerplate, and iterate on the parts that matter — fast and dirty, but intentional.
Run vulnerability scanning on everything AI produced — check for exposed secrets, injection, and insecure patterns, then patch before it ships.
Deploy, gather feedback, repeat. The loop is fast because the pipeline is sound.