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Traffic Generation Idea Feedback

Hacker News

Traffic Generation Idea Feedback

Hey Everyone, To start, I'd like to say that it's my hobby to design and program websites and web applications. My ultimate objective is to make something really valuable such that it generates enough income that I can work on it full time. The problem is I usually make things that most people just don't want. So, this time around, I thought it would be cool to propose the idea first using a landing page and judge the demand for the product based on how many emails I collect. Please check out the following landing page I made that presents the idea in just a few slides. If you like the idea, please submit your email address to help me judge demand and to be informed if/when I release the product. Also, feel free to provide any feedback in this thread so that I may improve the idea before starting to implement it. The idea is here: http://www.trafomatch.com Thanks so much everyone and I look forward to your feedback!

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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: email, using · Missing: mac, agents, macos
80%80% 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: lua, ide, io · 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, 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
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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