We

We need your feedback on hellotrekker.net website

Hacker News

We need your feedback on hellotrekker.net website

Hello founders, This is Vicia from hello trekker. Although it is still in the tuning process, we just launched the website. I would love to hear from your suggestions! Here is our website: www.hellotrekker.ent hello trekker provides local touring consultation to business travelers while they are at the break visiting customers, going to shows/exhibitions, etc. Here are questions I need your help: 1. do you have a clear idea what hello trekker tries to sell and achieves through the website? 2. is the website easy and straightforward to use? 3. any other comment? Please feel free to respond us here or send me an email: support@hellotrekker.net Thanks so much for your kind help~ Vicia (www.hellotrekker.net)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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 · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% 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
34%34% predicted probability of success on TrustMRR, 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
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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.

Correct prediction on native model

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