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Instagram caption/hashtag evaluation tool – HashtaStat

Hacker News

Instagram caption/hashtag evaluation tool – HashtaStat

Hello everyone. I have been experimenting with more heavy use of Instagram for the past couple of months and in doing so ran into some tasks that I found myself doing over and over again. Most people will probably not have any use for it, but it solves my annoyances and may help others, so that is why I have put it out there. You can go look at it directly: http://hashtastat.com/ or read a little description below. The first thing I found myself doing over and over again was count hashtags. Instagram has a 30 hashtag limit and numerous times I found myself counting hashtags to see if I was over the limit. HashtaStat counts hashtags and further more tells you and removes duplicates. If you have duplicate hashtags, your post will be marked as spam and will not show up under any of the hashtags you provided. Secondly I almost always check hashtags to see which types of pictures they contain. On HashtaStat you will get links to each one that you can click and easily see which pictures they contain. Finally, and this may be an even smaller thing. I like the “look” of a caption text followed by 5 bullets and then the hashtags. This “hides” the hashtags until people click to actually see them. On an iPhone writing these bullets and separating them with line breaks is annoying as you have to change keyboard layout several times. This led to the final and then so far complete functionality of HastaStat. I usually do most of my “research” on desktop and then make the caption on HashtaStat, which I save to Evernote and use from my phone when posting the picture. Why the name HashtaStat as it has very little to do with the functionality explained above? I wanted to make a MVP that I could “launch” and get out into the public. If people like it and use it I will work on functionality to help you select hashtags. But to have a concrete goal I decided on this basic functionality as it also solved a problem and annoyance for myself. Thanks for your time. /Jesper

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
93%93% 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: tasks, plain · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way, para · 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 · Missing: plus, platform, intuitive
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
19%19% 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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