You Write. We Quantify – thoughtly.co

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I created Thoughtly because many of my hobbies and interests - things I spend a lot of my time learning and writing about - are not reflected in my resume or in any single online profile. Written discussions online are generally insulated within specific communities (Stack Overflow, Reddit, Blogs, etc.), making it difficult for users to retain their credibility as they cross platforms. So we set out to quantify written discussion online and display them in a centralized portfolio. By enabling people to quickly display the extent of their involvement in a field, we provide context for online discussion. Your Thoughtly signature is a link to your online profile - which displays a summary of your written contributions online. It took a better part of a year to spec out everything and build a tool that sufficiently processes and accurately aggregates written content. We also think that natural language processing is a hole in the quantified self movement. Words can be valuable data too. Thanks and I'm excited for feedback. :-{ thoughtly.co Update: It currently takes a few minutes for the server to process connected accounts and issue credentials to your profile! However it is working, hang with us for a few minutes.

Visit product View on Hacker NewsJan 27, 2014

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Actual performance

11points

16comments

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

71%71% 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.

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

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

15%15% 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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