I a built a clean, minimal Mailing List reader, focussed on readability

Hacker News

It's online here: http://marc.ttias.be/ As a comparison, here are currently the most popular alternatives. Let's take OpenSSL's security announcement from last week: - marc.info: http://marc.info/?l=openssl-announce&m=145399448602327&w=2 - classic pipermail: https://mta.openssl.org/pipermail/openssl-announce/2016-January/000061.html - My marc.ttias.be (clean): http://marc.ttias.be/openssl-announce/2016-01/msg00002.php Or socat's recent revelation about missing prime numbers in OpenSSL (discussed here: https://news.ycombinator.com/item?id=11014175): - openwall: http://www.openwall.com/lists/oss-security/2016/02/01/4 - seclist: http://seclists.org/oss-sec/2016/q1/261 - My marc.ttias.be (clean): http://marc.ttias.be/oss-security/2016-02/msg00003.php Let me know what you think please!

Visit product View on Hacker NewsFeb 7, 2016

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Actual performance

2points

2comments

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: pipe, 000, io · Missing: https docs, excited, just released

68%68% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

60%60% 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

42%42% 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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: new, open · Missing: mac, agents, macos

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

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

18%18% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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