BlankTrace – a Rust‑based MitM proxy to anonymize browser traffic

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Hey HN — I’ve built BlankTrace, a cross-platform (Linux/macOS) Rust CLI/daemon that acts as a local HTTP/HTTPS MITM proxy to anonymize your browser traffic. ## What it does / Why you might use it Randomizes browser fingerprint: rotates User‑Agent (via rand_agents) and Accept‑Language headers. - Strips or blocks cookies on requests/responses. - Regex‑based tracker / domain blocking with whitelist support. - Runs as a MITM proxy on localhost:8080 with full TLS interception (generates its own CA) for HTTPS. - Logs proxy activity asynchronously into SQLite (requests, blocked domains, fingerprint rotations, cookie behavior). - Provides a CLI interface: view stats, manage whitelist/blocklist, export data, etc. ## Technical details - Written in Rust. - Uses: hudsucker (for the MITM proxy), rand_agents (for realistic UA rotation), rusqlite (logging), tokio, clap etc. - Configuration via config.yaml, allowing you to tune rotation mode, block patterns, retention policy, and more. - Logging schema includes tables for domains, IPs, cookie attempts, fingerprint rotations, etc. Requires trusting the generated CA certificate in your browser / OS for HTTPS interception. MIT‑licensed. ## Current status - Proxy is fully functional: HTTP/HTTPS interception works. - Fingerprint randomization is working across requests. - Cookie stripping and domain blocking are implemented. - Async logging to SQLite is operational. - Graceful shutdown, CA persistence, and log cleanup are supported. - Known limitation: manual installation of the CA cert is required to intercept HTTPS. ## Why I built this I wanted a lightweight, performant, and configurable proxy for browsing with more privacy — without relying on heavyweight browser extensions. BlankTrace gives you control over fingerprinting, cookies, and trackers, all under your own daemon. ## Get started - GitHub repo: https://github.com/mrorigo/blanktrace - Project homepage / docs: https://mrorigo.github.io/blanktrace/ - Build: cargo build --release → run ./target/release/blanktrace Trust the generated CA certificate in your system / browser to enable HTTPS interception. Use CLI: blanktrace stats, domains, whitelist, export, etc. ## Feedback / Help Wanted I’d love feedback on configuration UX (are the defaults sane? what settings do you want exposed?) It's new — testing on different platforms / browsers would help ensure stability. Contributions (code, documentation, detection rules) are very welcome. If you care about privacy, fingerprint randomization, or want to better understand your browser’s network behavior — I’d appreciate you checking it out.

Visit product View on Hacker NewsNov 23, 2025

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

91%91% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

76%76% 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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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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