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Lists.sh – A Microblog for Lists

Hacker News

Lists.sh – A Microblog for Lists

Greetings, creator here! I've been working on a new blogging platform specifically for lists on and off for a few months now and I'm excited to officially announce its launch. After seeing https://charm.sh a few months ago, I've been enamored by the idea of SSH apps. I decided that a blogging platform focused on developers could be the perfect use case for an SSH app. Also, I love writing lists. I think restricting writing to a set of lists can really help improve clarity in thought. The goal of this blogging platform is to make it simple to use the tools you love to write and publish lists. There is no installation, signup is as easy as SSH'ing into our CMS, and publishing content is as easy as copying files to our server. Check it out and let me know what you think! source: https://github.com/neurosnap/lists.sh

Visit productView on Hacker NewsApr 25, 2022
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
75%75% 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
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
36%36% 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
17%17% 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.

Correct prediction on native model

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