The homepage loads. The dashboard looks right. You’re ready to share the link.
But there are still some uncomfortable questions:
Did a secret end up in the source code? Is authentication enforced on the server? Does the deployed site produce errors you never noticed during development?
I’m building a security scanner alongside a Launch Check to help indie founders investigate those questions before inviting their first users.
The code scanner examines submitted code for potential problems such as hardcoded credentials, SQL injection and cross-site scripting patterns, weak authentication checks, dependency issues, and error-handling mistakes.
You can paste code, upload a file, or scan supported repository files, including private ones. Findings include severity, the affected location, an explanation of the risk, and a suggested before-and-after fix. Each finding should make the next action clear.
Editor, terminal, CI, and MCP integrations support existing workflows. The command-line client sends files individually to a hosted scanning API. That scope matters: scanning a folder doesn’t automatically establish how every component behaves together.
The live website security scan checks the deployment. It looks for issues such as missing security headers, HTTPS problems, exposed paths, unsafe cross-origin settings, and insecure cookie flags.
Then there’s Launch Check, which opens the deployed app in a browser.
You provide a URL and can optionally describe what should work. It navigates accessible pages and collects evidence from the visit: screenshots, page load times, console errors, and failed network requests. The report explains what worked, what needs attention, and suggested next steps.
For example, a page might render its layout while a request fails in the background. Seeing the screenshot alongside the network error gives you something specific to investigate.
Launch Check doesn’t submit forms or click destructive controls. A successful visit therefore doesn’t establish that signup, checkout, or saving data works from beginning to end. Those journeys need their own tests.
The workflow I want to make easier is:
Scan → inspect the evidence → review the fix → apply it → rescan and retest.
Suppose a scan flags a missing authorization check. After changing the code, rescanning can show whether the original finding is still detected. Testing the deployed endpoint answers another question: does it actually reject access from someone who shouldn’t have it?
That means checking anonymous access, a user with insufficient privileges, and a different account trying to access another user’s data. These are concrete authorization tests you can repeat after future changes.
For products that call paid APIs, I’d also check server-side usage limits and spending controls. A valid request can still become expensive when repeated at scale. That needs explicit testing beyond a browser visit.
Scanning uses a hosted service over HTTPS. The published policy states that submitted source code is discarded after processing, isn’t used for model training, and that scan results are retained in your account.
A score is useful for tracking findings. Launch confidence also needs evidence about the checks performed, the fixes made, and the important flows still untested.
I’d love feedback from founders preparing to launch—especially where a finding is wrong, an explanation is unclear, or a check misses something useful.
What would you need to see in a scanner or Launch Check report before trusting it enough to change your code or share your app?
The evidence loop is the interesting part. Have users actually changed code based on a finding and then had the rescan confirm it was fixed, or is trust in the findings still the main unknown?
We’ve validated the fix → rescan → verify loop in our own testing, but we’re still gathering enough real-user evidence to measure how consistently findings lead to confirmed fixes. So yes, trust—especially around accuracy, false positives, and proof that the vulnerability is actually closed—is still one of the main things we’re validating.
That fix → rescan → verify loop is the strongest part. I’d be interested in seeing what the real-user data shows. If you’re open to it, what’s the best email to reach you on?