Mead – how I'm pushing back against Medium

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https://www.meadapp.com/ TLDR; Mead is a special case static site generator that automates the process of compiling and uploading to your S3 account. Mead is a desktop application that combines the flexibility of static site generators (Jekyll, Hugo, etc) with Medium's ease of publishing. I created Mead because I wanted to write more and the present options put up too many obstacles. Like many others, I don't like the experience Medium offers to readers. Modals and banners distract the user from engaging with the content and in some cases, prevents them entirely. Static site generators are great in terms of their flexibility. They're the only way we can fully control what trackers and advertisements readers are exposed to. However, the writing process is a bit cumbersome. I often forget the commands I need to run, and I'd like to not have to run 3 git commands every time I notice a typo. Mead, at its core, prioritizes simplicity. It was made so I could easily open up an app on my computer, write some text, add an image (automatically uploading to S3), paste in a code snippet, click a button, and have the whole thing public without any other effort. Mead is also an experiment in creating a product that does not need advertisements or trackers to continue existing. My goal here is to fully respect the privacy of others. If I'm failing in some way, please gently point it out and I will make the necessary corrections. If you have any comments, feedback, or feature requests, please feel free to reach out. My email is [my username] at gmail

Visit product View on Hacker NewsAug 21, 2019

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

84%84% 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: created · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

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

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

45%45% 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.

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