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Update from the team: just pushed improved profile consistency — Canvas/WebGL/AudioContext signatures now stay stable across page loads within a session (matching real browser behavior) while remaining unique per profile. This was our #1 requested fix. Try it at app.firekey.ai — still free.

We've been getting great feedback from e-commerce sellers on the Canvas + WebGL fingerprint isolation. The seed-based approach means each profile generates a consistent, unique fingerprint -- important for account stability on platforms that check for fingerprint changes between sessions. Happy to answer technical questions about the fingerprinting implementation!

We've been getting great feedback from e-commerce sellers on the Canvas + WebGL fingerprint isolation. The seed-based approach means each profile generates a consistent, unique fingerprint -- important for account stability on platforms that check for fingerprint changes between sessions. Happy to answer technical questions about the fingerprinting implementation!

The seed-based fingerprint generation was the key design decision — each profile generates a consistent, unique fingerprint every session, which prevents detection via fingerprint instability while maintaining isolation between profiles. Happy to answer technical questions about how we handle Canvas, WebGL, and AudioContext spoofing specifically!

We've been getting great feedback from e-commerce sellers on the Canvas + WebGL fingerprint isolation. The seed-based approach means each profile generates a consistent, unique fingerprint -- important for account stability on platforms that check for fingerprint changes between sessions. Happy to answer technical questions about the fingerprinting implementation!

The seed-based fingerprint generation was the key design decision — each profile generates a consistent, unique fingerprint every session, which prevents detection via fingerprint instability while maintaining isolation between profiles. Happy to answer technical questions about how we handle Canvas, WebGL, and AudioContext spoofing specifically!

Happy to answer any technical questions about how FireKey handles browser fingerprint isolation. We're spoofing 50+ parameters including Canvas, WebGL, AudioContext, and font lists — each profile gets a unique, consistent fingerprint. Currently free in open beta at app.firekey.ai 🚀

This fills a real gap for e-commerce sellers. The approach of spoofing fingerprints to plausible values (rather than blocking APIs) is technically sound — blocking Canvas/WebGL is itself a detectable signal. Each account needs a consistent unique fingerprint, not a randomized one each session.

Hey Product Hunt! 👋 I built FireKey because I kept seeing the same problem: Amazon sellers and social media managers getting accounts banned even when using VPNs. The real issue? Browser fingerprinting. Platforms use 50+ signals (Canvas hash, WebGL renderer, AudioContext, font list) to link accounts. IP rotation alone covers less than 30% of the correlation risk. FireKey fixes this by assigning...

Built FireKey because browser fingerprinting (canvas/WebGL hash, font list, WebRTC leaks) = ~70% of account correlation detections. Most tools only rotate IPs. FireKey gives each profile a completely isolated fingerprint identity. Free open beta: https://app.firekey.ai