597 days of iterating

Hacker News

597 days ago, I posted about the project our team was developing - https://news.ycombinator.com/item?id=3183322 - here on HN, and today we're finally taking the wraps off what we've been working on since then. At it's core, what we've been building is an end-to-end solution for creating, storing, and working with structured content in a way that's accessible to anyone. You can check out an introduction - http://team.marquee.by/introducing-marquee/ and dig into the technical/philosophical details of our approach - http://team.marquee.by/on-content/. When we just started out and got rejected from YC's 2012 Winter class, HN provided us with invaluable feedback that greatly helped inform how we explored the space. Today we sit a solid year and half later with something that we're not only proud of, but are incredibly excited about its potential. We've seen a lot of people enter the publishing space over that time (and seen some of them go), been through an accelerator program (TS NYC 2012), took a since-aborted stab at fundraising, launched one of Time's top 50 sites of 2013 on our platform - http://techland.time.com/2013/05/06/50-best-websites-2013/slide/narratively/ - and are starting to get a good bit of validation for where we had hoped to see the market go. I'd love to get the HN community's thoughts on our progress and, more generally, our approach to the space.

Visit product View on Hacker NewsJun 20, 2013

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

78%78% 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: new · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% 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

30%30% 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

14%14% 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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