Track product alternative requests on Reddit to find product ideas

Hacker News

I often browse through subreddits like r/iosapps, r/macapps to find product opportunities & market gaps. I noticed a pattern: users frequently ask for alternatives to existing tools when they're frustrated with current solutions. These posts are a great source of inspiration for product ideas as users are actively searching for alternatives to products that they already use & they describe their pain points with the the current solution. So I build a free tool, AlternativeWatch.com to collect & curate such posts. What it does: - Monitors product-focused subreddits for "alternative to X" posts - Displays live feed of recent alternative requests - Lets you browse historical data of past requests - Sends curated opportunities twice a week via email Looking for feedback on: - Which additional subreddits would be valuable to monitor? - Better ways to categorize & enrich the opportunities - What other data sources can I use to add more value?

Visit product View on Hacker NewsJan 16, 2025

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

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, existing · Missing: https docs, excited, just released

47%47% 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: ios, apps, users · Missing: mobile apps, personal, entrepreneurs

40%40% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

10%10% 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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