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Why we killed imwith and launched GIFted (Guggy YC S17)

Hacker News

Why we killed imwith and launched GIFted (Guggy YC S17)

Hey everyone, 6 months ago we launched imwith - a NLP-based GIF messenger. I’d like to share with you why we decided to discontinue imwith and launch GIFted. imwith enabled you to chat and get automatic suggestions for relevant GIFs based on what you type. The experience for adding GIFs to your conversation was sleek, seamless and much much better than other messengers. The UI was unique as well. For example, there wasn’t any ‘send’ button. You’d send the message by swiping it. It was extremely novel, but it didn’t really take off (retention wise). We tried a lot of things and nothing really worked. We had endless debates of whether we should optimize the product or start from scratch, it was pretty frustrating. At some point we decided to follow a hunch we had for a while, and enabled users to direct and act GIFs for pre-defined tags (#smile, #grouphug etc.). We were excited about the idea that our NLP engine will offer users their own GIFs. The results were great! Users recorded 50, 60 and even 90 GIFs in their first session and under 15 minutes, they loved that part. But unfortunately they didn’t use the GIFs in the messenger. They actually didn’t use the messenger at all. We decided to discontinue our messenger and to launch GIFted, a GIF community in which people GIF themselves to express their take on specific situations, emotions and gestures. A month ago, we silently launched our early beta and so far thousands of teens across the US have created so many hilarious, creative GIFs…repeatedly, check the user @maxdd4l. He’s going to become famous one day :) GIFted users can browse GIFs, act their own GIFs and participate in a daily GIF challenge. Things look much better now (retention wise). What do you think about GIFted? I’d be happy to get any feedback and answer questions about our journey. https://itunes.apple.com/us/app/gifted-gif-yo-self/id1435132...

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
91%91% 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, ide, io · Missing: https docs, just released, exist
74%74% 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: apple, user · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
41%41% 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
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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