Hi

HippoMod – AI-powered social media comment moderation/engagement

Hacker News

HippoMod – AI-powered social media comment moderation/engagement

Hello HN! I'm Casey, and I built HippoMod, an AI-powered comment moderation tool for Facebook and Instagram Ads, after struggling to keep up with ad comments at another startup. It learns your brand voice for auto-reply suggestions and filters out irrelevant or misleading comments (you choose how aggressive this is). I kept the UI simple hoping it would cater to less technical users. If you've used similar tools or deal with a lot of comments, I could use your feedback. Reach out to me directly on LinkedIn, through our contact page, etc., I appreciate hearing from you. A few learnings so far: - Dogfooding: I built a prototype that my co-founder and I used for a few months. No surprises, forcing familiarity with the basic reqs and the pitfalls of the polling approach we started with was a huge help. - Meta app review: it's a mess. The test user feature has been disabled "temporarily" for at least a couple of years, but Meta disables fake accounts. You'll get guidance from the review team to create a user; then that user will be immediately banned. Tip: spend $20 for a sim card and a month of cheap service and dig an old phone out of the junk drawer. I wasted several days trying email accounts, digital phone numbers, etc. that were later deleted. - Product hunt: Start your launch page and marketing (whatever that means for you) early. Like a month early, not a few days in advance. We just launched on Product Hunt, and I didn't realize how much it works like a mini Kickstarter. - Marketing is hard: my engineer brain sees all the features that could be added or improved. It's so much more comfortable to nvim the day away. I'm resisting, sure you all understand the allure haha Thanks again, HN!

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1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
97%97% 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, email · Missing: mac, agents, macos
89%89% 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, way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
41%41% 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: users · Missing: plus, platform, intuitive
38%38% 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 · 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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