What Haystack does:

-- Builds a clear narrative. Changes in Haystack aren’t just arranged as unordered diffs. Instead, they unfold in a logical order, each paired with an explanation in plain, precise language

-- Focuses attention where it counts. Routine plumbing and refactors are put into skimmable sections so you can spend your time on design and correctness

-- Provides full cross-file context. Every new or changed function/variable is traced across the codebase, showing how it’s used beyond the immediate diff

Here’s a quick demo: https://youtu.be/w5Lq5wBUS-I

If you’d like to give it a spin, head over to haystackeditor.com/review! We set up some demo PRs that you should be able to understand and review even if you’ve never seen the repos before!

We used to work at big companies, where reviewing non-trivial pull requests felt like reading a book with its pages out of order. We would jump and scroll between files, trying to piece together the author’s intent before we could even start reviewing. And, as authors, we would spend time to restructure our own commits just to make them readable. AI has made this even trickier. Today it’s not uncommon for a pull request to contain code the author doesn’t fully understand themselves!

So, we built Haystack to help reviewers spend less time untangling code and more time giving meaningful feedback. We would love to hear about whether it gets the job done for you!

How we got here:

Haystack began as (yet another) VS Code fork where we experimented with visualizing code changes on a canvas. At first, it was a neat way to show how pieces of code worked together. But customers started laying out their entire codebase just to make sense of it. That’s when we realized the deeper problem: understanding a codebase is hard, and engineers need better ways to quickly understand unfamiliar code.

As we kept building, another insight emerged: with AI woven into workflows, engineers don’t always need to master every corner of a codebase to ship features. But in code review, deep and continuous context still matters, especially to separate what’s important to review from plumbing and follow-on changes.

So we pivoted. We took what we’d learned and worked closely with engineers to refine the idea. We started with simple code analysis (using language servers, tree-sitter, etc.) to show how changes relate. Then we added AI to explain and organize those changes and to trace how data moves through a pull request. Finally, we fused the two by empowering AI agents to use static analyses. Step by step, that became the Haystack we’re showing today.

We’d love to hear your thoughts, feedback, or suggestions!