Daf·thunk – open-source Editor for Prototyping Workflows on Cloudflare
Over the past few months, a student and I set out to explore the impact of vibe coding on web development. Somewhere along the way, we ended up building Daf·thunk, a visual workflow editor. It leverages Cloudflare’s fantastic infrastructure (Workers, D1, KV, Workflows, AI, etc.) to create surprisingly robust workflows that can be triggered manually, via HTTP requests, by email, or on a schedule. During development, we primarily used Cursor with its agent and tab modes, alongside Claude Sonnet 3.5, 3.7, 4, and Gemini 2.5 Pro. Occasionally, we switched to MAX Mode when working on or reviewing…
In plain words
Daf·thunk is an open-source visual workflow editor built on Cloudflare's infrastructure. It lets users create and manage workflows that can be triggered manually, via HTTP requests, email, or scheduled tasks. The editor integrates with Cloudflare Workers, D1, KV storage, and AI capabilities to build automated processes. It is designed for developers who want to prototype and deploy workflows without extensive backend setup.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Over the past few months, a student and I set out to explore the impact of vibe coding on web development. Somewhere along the way, we ended up building Daf·thunk, a visual workflow editor. It leverages Cloudflare’s fantastic infrastructure (Workers, D1, KV, Workflows, AI, etc.) to create surprisingly robust workflows that can be triggered manually, via HTTP requests, by email, or on a schedule. During development, we primarily used Cursor with its agent and tab modes, alongside Claude Sonnet 3.5, 3.7, 4, and Gemini 2.5 Pro. Occasionally, we switched to MAX Mode when working on or reviewing more complex changes. We tried to regularly refine our Cursor rules and began applying specific rules to different parts of the codebase (backend, frontend, database, etc.). We also indexed documentation and used it extensively in prompts. For large refactors, we often referenced previous commits to reapply patterns elsewhere in the code. Overall, we feel that prompting for small, incremental, and easy-to-review changes scales well when coding with LLMs and the results are really impressive. In this regard, Andrej Karpathy’s talk “Software Is Changing (Again)” resonates deeply. John Ousterhout’s concept of deep modules has also been a useful mental model. Our Cursor rules ask for simple APIs that hide rich internal logic, and avoid wide interfaces that mirror implementation details. Aside from frequent commits, we didn’t document our process much, as we were mainly exploring and building intuition around what works and what doesn’t. Since we chose to trust the LLM more than usual, we’ve released everything under an open source license and without warranty. As models improved, our reliance on unit tests dropped, which will probably come back to haunt us soon... contributions are welcome ;) GitHub repository: https://github.com/dafthunk-com/dafthunk Product Hunt page: https://www.producthunt.com/products/dafthunk?launch=dafthun...
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