Open source, end-to-end encrypted task management for activists

Hacker News

It's called "Pursuance" and it's built using React, Go, and Postgres. This project was envisioned by Barrett Brown, whose 2011 crowdsourced investigative journalism endeavor, Project PM, was so successful in uncovering corruption (involving Palantir, WikiLeaks, and the DOJ) that he was targeted by the state and was sentenced to 63 months in prison. We won the National Magazine Award when in solitary confinement. In this Wired article[1], Barrett says that he, Project PM, and Anonymous could have been even more successful if they would have had better tools. Pursuance is that tool. We just began a closed alpha, with the focus being on improving the usability and nuances of how the features work for users trying to use this for real-world (but not-yet-super-high-security) use cases, so not all the encryption has been added yet. Main repo: https://github.com/PursuanceProject/pursuance Our Kickstarter, which ends today: https://www.kickstarter.com/projects/85883596/barrett-browns... The Kickstarter video includes our clearest explanation of what Pursuance is and what we're trying to enable. I'm the tech lead and I'm happy to answer any questions you may have. P.S. We're not rolling our own crypto, of course; the libraries we're using have been audited by a 3rd party and/or were written by a cryptography expert (like https://github.com/dchest ). [1] https://www.wired.com/2016/12/anonymous-barrett-brown-free-r...

Visit product View on Hacker NewsJul 15, 2018

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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: open source, ide, io · Missing: https docs, excited, just released

80%80% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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