Software engineer with a focus on tooling and developer workflows.
Founder / CTO of Railon (listed in conference/speaker materials) and has held senior engineering roles; some listings indicate a transition to principal staff engineering at Mailchimp (see Sources).
Gained visibility through deep, practical testing of AI coding models and agents.
present
Runs the YouTube channel “GosuCoder”, producing frequent, hands-on reviews, bench tests, and demonstrations of AI coding assistants and coding-focused LLMs (Claude, Grok, Gemini, Qwen, etc.).
Produces technical content that emphasizes realistic evaluation: plan-driven prompting, agent orchestration, and codebase-level testing rather than synthetic benchmarks.
Regular participant/guest in industry podcasts and conference talks (e.g., DevCon Fall 2025, Rate Limited podcast appearances).
Active on X/Twitter as @GosuCoder and maintains a LinkedIn presence (see Sources).
Practical usage examples (how to learn from his work)
Follow his plan-mode and “plan-first” walkthroughs to adopt a structured, task-planning approach to using LLMs for engineering tasks (useful for building reliable agent workflows).
Watch his side-by-side comparisons (e.g., Claude Code vs. Gemini vs. Grok) to understand real-world tradeoffs between model cost, latency, and code correctness.
Use his codebase-indexing and semantic-search demos (RooCode, Augment Code, Cursor) as examples of integrating LLMs into an IDE or development pipeline.
Study his evaluations when designing your own model-evaluation harness: he stresses that harness and toolchain choices can change perceived model rankings.
Connections to other people and companies
Railon (founder / CTO listed in event materials).
Mailchimp (listed as principal staff engineer in conference listing).
Appears on or contributes to developer/AI industry podcasts (e.g., Rate Limited) and speaks at conferences (DevCon Fall 2025).
Engages with AI tooling companies and projects through product reviews and testing (Cursor, RooCode, Claude, Augment Code, various model providers).
Expectations for the future
Will likely continue to focus on rigorous, pragmatic evaluations of coding LLMs and agent workflows.
Expected to keep producing content that highlights where coding agents are genuinely useful vs. overhyped — useful for teams adopting AI-assisted development.
May publish more structured benchmark suites or talks that formalize his evaluation methodology (he has indicated work on more robust evaluation frameworks in talks).
Interests
Practical application of LLMs to software engineering tasks: code generation, refactoring, debugging, and agent orchestration.
Tooling and editor integrations that make LLMs productive for daily developer workflows (e.g., Cursor, Zed, RooCode integrations).
Improving evaluation methods for coding models and encouraging reproducible, realistic testing.