Disclosure first: I’m the author of this page (香蕉痞 Yang), and I used an AI assistant to help write this post. It’s free, no account, no ads.
I kept getting asked “why does site X still recognise me when I’m on a VPN?”, so I built a page that shows the answer from the website’s side. It reads what a normal site can read from your browser without any permission prompt (118 signals: timezone vs. IP location, languages, fonts, GPU/canvas, screen, network, etc.), then writes it up as one plain paragraph, the kind of profile a tracker could build.
The part I find most useful is the contradiction checks: WebRTC leaks, DNS leaks, and mismatches such as a US exit IP with an Asia/Shanghai timezone and zh-CN fonts, which is usually what gives away a VPN, proxy or a spoofed user agent.
Privacy notes, since this is the right crowd to ask: no IP is stored, only anonymous counts of how common each value is (used for the “how unique are you” part). The page itself is not open source yet; the wider 1ms.dog network toolset it belongs to is MIT on GitHub (yt1988/1ms.dog).
Link: https://who.1ms.dog/
Genuinely curious what people here see. Do Tor Browser / Mullvad Browser / LibreWolf with resistFingerprinting come out looking “common”, and are there signals you think I’m missing or over-weighting?
Claude built it, not you, meat proxy
Fair point, and that’s why the post says AI helped. I used it for a lot of the code and the English text. What the page checks, and how it handles data (checks run in the browser, no IP or full fingerprint kept, only anonymous per-value counters that ‘Don’t count me’ or GPC turns off), was my call, and so is any bug you find in it.
Thaanks for making this. Very eye opening. 😥
Correction to my post: I wrote that the page itself isn’t open source. That was wrong. The source is public under MIT at https://github.com/yt1988/who-are-you, so you can check what it collects instead of taking my word for it. Sorry for the mix-up.
