Confidential AI browser extension – alternative to OpenAI Atlas

Hacker News

Hi HN, We published our experimental browser extension for Chrome just a few hours before OpenAI announced its Atlas browser. Ours takes a different approach: privacy first. Atlas makes “memory” and cross-tab context a core feature. OpenAI acknowledges the privacy concerns and promises users full control over what gets stored and sent to the cloud. In practice, most users won’t maintain that control. Our vision is full privacy by design, so it doesn’t matter what data flows to the cloud. The extension connects to (1) a cloud-hosted inference service (Privatemode AI) running gpt-oss-120b in carefully architected confidential-computing environments and (2) a local document store with a vector DB. The inference service leverages AMD SEV-SNP and the confidential-computing features of the Nvidia H100. The environment is designed so that we (Edgeless Systems) and no one else can access user. All data stays encrypted even during processing in main memory. The client automatically verifies the integrity of the backend using a process called "remote attestation". All relevant server-side software can be found on GitHub and can be built reproducibly. It’s an early developer version. If you want to play with it, you’ll need to clone the repo and run it together with our encryption/verification proxy and the aforementioned document store. You'll also need to sign-up for a free API key at www.privatemode.ai. Looking forward to your feedback! Repo: https://github.com/edgelesssys/privatemode-for-chrome-demo Blog post: https://www.privatemode.ai/articles/browser-extension-demo-l... Demo video: https://youtu.be/4j77YHXDqAQ

Visit product View on Hacker NewsOct 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, context, openai · Missing: mac, agents, macos

88%88% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, users · Missing: mobile apps, ios, personal

43%43% 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

12%12% 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.

Incorrect prediction on native model

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