Hey, Product Hunt community!
We just launched here, and thanks to all of you, we won our #1 POTD. While that is great, we would like to hear your feedback, comments, and ideas.
@Zencoder is just getting started, and with your feedback, we can ensure that we build the best coding agent for developers and creators worldwide. So, feel free to share what you like, dislike, or have any feedback, and we'll do our best to be responsive.
Zencoder
🚀 Hey Product Hunt!
Andrew here. While building our IDE extensions and cloud agents, we kept running into the same problem many of you probably face when using coding agents in complex repositories: agents getting stuck in loops, over-apologizing, and burning time without making real progress.
We tried to paper over this with scripts, but juggling terminals and copy-paste prompting quickly became painful. So we built Zenflow - a free desktop tool for orchestrating AI coding workflows.
It handles the things we kept missing in standard chat interfaces:
Dynamic Workflows: Workflows are defined in simple .md files, and agents can dynamically rewire the next steps based on what they discover mid-execution.
Spec Driven Development: Use formal specs to guide agents, ensuring the implementation matches your architectural intent before a single line of code is written.
Cross-Model Verification: Have Codex review Claude’s output, or run multiple models in parallel to see which one handles a specific codebase or task best.
Blast Mode (Multi-Model Inference): Run up to four different models (Claude, GPT, Gemini, Codex) on the same task simultaneously. Compare their outputs side-by-side and pick the best result.
Parallel Execution: Run multiple approaches on the same backlog item simultaneously mixing human-in-the-loop workflows for hard problems with faster “YOLO” runs for simpler tasks.
Project-Level Kanban: Track and manage all agent work through project lists and kanban-style views, not scattered terminal windows.
What we learned building Zenflow
After running 100+ experiments on SWE-Bench and private datasets, we found that models are increasingly overfit to public benchmarks. Real-world success doesn't come from "smarter" models alone; it comes from the "Goldilocks" Workflow just enough structure to prevent loops without over-orchestrating the creativity out of the AI.
We’ve been dogfooding this heavily to build our own IDE extensions, and we’d love to hear how it handles your toughest repos.
Zenflow is free to use and currently supports Claude Code, Codex, Gemini, and Zencoder.