Submit your startup in 150 directories in 5 min

Hacker News

Back in November 2023, I faced a significant challenge with my AI startup. I was banking on Product Hunt to boost our visibility, but things didn't pan out as expected. I quickly realized what was missing—a straightforward yet effective strategy to stand out: getting listed in directories. That's when I developed QuickListing. QuickListing simplifies the process of increasing online presence. It’s an automated tool that submits your startup to over 150 directories with just one entry. It’s ideal for founders who need to maximize exposure, but have limited time and resources. QuickListing efficiently handles the directory submission process, ensuring your startup gains visibility and SEO benefits without the need for manual, repetitive tasks.

Visit product View on Hacker NewsJan 29, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently, maximize · 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 HuntUnlikely to reach the leaderboard · Strong signals: tasks · Missing: mac, agents, macos

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

39%39% 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 · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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