Self-delivering autonomous bicycle

Hacker News

I interviewed for the YC summer batch yesterday with my cofounder justincorbett for our company Weel, we did not get in. The idea is that you order an ebike on your phone, it delivers itself to you, you ride it like a regular electric bicycle, and when you are done it rides itself away autonomously. The feedback we got during the interview and in the email last night was that the idea was interesting but the partners were not convinced that this would become something many people would want given how many other alternatives people had for last mile transportation. Basically, it's overteched for the problem. Today we are regrouping and taking a fresh look at our own biases and views. I'd love any feedback from HN folks on the idea itself, the feedback we got from YC, and any criticisms or ideas you might have. Landing demo with absurd music: https://youtu.be/hXDXjav3XD4

Visit product View on Hacker NewsMay 1, 2018

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

77%77% 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 · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

39%39% 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 · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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