I kept running into the same issue whenever starting a new project: the setup phase consumed more time than the actual feature work. Preparing boilerplate, restructuring directories, performing refactors, resolving dependency conflicts, and wiring framework logic consistently took hours before I could write meaningful code. Even with modern tooling, the early-stage workload was nearly identical across projects.

To remove that bottleneck, I built Bleenk, an AI coding agent that operates directly on real repositories. Instead of generating isolated snippets, it performs multi-step modifications across the codebase. It creates branches, edits files, restructures modules, rewrites components, and prepares pull requests. The agent is designed to understand the full repository context, not just the last prompt.

Bleenk uses an isolated Vercel Sandbox environment to execute and validate generated code, ensuring changes run correctly before they ever reach a PR. It also includes Bleenk-Mini, a model optimized for repository-level reasoning, multi-file coordination, and incremental refactoring.

The result is that I can ship full-stack application features in minutes instead of hours. In internal use, tasks that previously required three to five hours of manual engineering now complete in under fifteen minutes with consistent diffs and minimal manual cleanup.

I am interested in technical feedback: which repository-level operations do you think an automated agent should prioritize, and what guardrails or review patterns are most important for safe code modification?