We initially built it for Shopify, but now it’s fully embeddable, supports headless implementations, and integrates with tools like Klaviyo, Zapier, n8n, and Snowflake. One thing we’re especially proud of is how fast and unobtrusive it is: polls load async, don’t block rendering, and are optimized for mobile and low-latency responses.
From a tech angle:
Frontend is all React, optionally SSR-safe.
Backend is Node.js + Postgres, with a heavy focus on queueing + caching for real-time response pipelines.
API-first design (public API just launched: apidocs.zigpoll.com).
We recently open-sourced our n8n integration too.
If you're a dev working on ecom, SaaS, or even internal tooling and need a non-annoying way to collect structured feedback, happy to chat or get you set up. Feedback welcome — especially critical stuff. Always looking to improve.
I'm now supporting over 30K clients and over 40 million survey responses. Naturally lots of things come up when scaling a project solo heres a few:
- Optimizing existing reporting dashboards
- Improving onboarding experience
- Tapping new growth channels (Organic SEO, Paid ads, Integration marketplaces)
- Resolving customer support tickets and minimizing ticket flow in general
Lots of things come up which always keeps the work interesting. It's probably time to scale past one person though so that's next on the docket!
In a past life I would have thought this would be the easy part given the product market fit but it's hard to figure out growth channels that are scalable and cost-effective at this stage. Burning what would otherwise be a large salary month on month in search of growth is mentally taxing when it doesn't deliver. Metrics across the board only seem to tell part of the story so it's tricky to figure out what needs changing and what's worth doubling down on.
If anyone has experience doing this sort of thing - please get in touch!
Claude 3.5 did it from the first shot.
1) Don't ask for large / complex change. Ask for a plan but ask it to implement the plan in small steps and ask the model to test each step before starting the next.
2) For really complex steps, ask the model to write code to visualize the problem and solution.
3) If the model fails on a given step, ask it to add logging to the code, save the logs, run the tests and the review the logs to determine what went wrong. Do this repeatedly until the step works well.
4) Ask the model to look at your existing code and determine how it was designed to implement a task. Some times the model will put all of the changes in one file but your code has a cleaner design the model doesn't take into account.
I've seen other people blog about their tricks and tips. I do still see garbage results but not as high as 95%.