A new open-source browser with Android TEE based enforcement for agents

Hacker News

So we contributed our Android TEE based browser enforcement to the community. the PR is here - https://github.com/wootzapp/wootz-browser/pull/373 . I’ve been deep in the weeds on our browser, and we just merged something that felt worth sharing with this community. We got Android’s hardware keystore (TEE / StrongBox) working end-to-end so that client certificates are truly non-exportable. The device generates the key inside the secure enclave, we enroll it, issue a device identity cert, and from then on the browser can only present that cert for mTLS handshakes. No chance of stealing or exporting the private key. The idea is simple: if you want to enforce zero-trust access at the browser level, you need strong device identity. Passwords and tokens leak, but hardware-backed certs with attestation give you a much higher bar. We had to solve for Android quirks, avoid the trap of server-supplied keys, and make sure auto-selection doesn’t leak certs to the wrong sites. It’s live in our Wootz.app browser

Visit product View on Hacker NewsSep 19, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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