An

An Idea: Hitchhikr. Uberkiller?

Hacker News

An Idea: Hitchhikr. Uberkiller?

I have an idea, and not much time to execute (just hired as network engineer). This idea response came from https://news.ycombinator.com/item?id=12758113 , and I was going to post a comment about it. Instead, I figure that it's probably more appropriate as an "Ask/Tell HN", and see if the hordes of uber-intelligent (lol, I made a funny, uber) peoples here can run with it. I'm releasing this. There's no patent, copyright, trademark, or kitchen sink. I created it a half-hour ago. If you want to get me something if you use it, I'd like a good bottle of 12+ year scotch :) Site/App: Hitchhiker (probably Hitchhikr.$something) Reason: Uber-like, but drivers set price or offer free "hitchhikr" Monetization: Skim % off of paid trips. Idea: Hitchhiking was cool years ago, but fell by the wayside due to bad actors. The problem is 2-fold: "who are you picking up?", "and who's picking you up?" . Fb, LinkedIn, Twitter solve this. The second problem is "Do you get compensated?" You choose, and we do the hard part. Details: Website and apps are portal to your account. Uses OAuth to Facebook, Google, Twitter, LinkedIN for identity. Personal Security is paramount, and is why we require established ident. Some rides can be free, and others may be paid. This is worked out during "acceptance". There's 2 lists: People offering rides from-to at time (with a cost), OR people needing a ride from-to (and if they're willing to pay,and how much). We can sync bus routes as well, so people always know of slower, more expensive ways back (think of it as fail-safe). Before a ride but after accepting, idents are shared between all parties. During the ride, GPS coords on a realtime map are published. Anyone with that link can view realtime where you are at, increasing personal security and making a timedata-based map log. Freeloaders will be encouraged to pay by a system that keeps track of miles, similar to the way "Magic: The Gathering" cards are traded on Pucatrade.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting
90%90% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, apps, new · Missing: mac, agents, macos
41%41% 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
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, apps, google · Missing: mobile apps, ios, entrepreneurs
32%32% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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