Thoughts on our Hackathon Project?

Hacker News

http://textwingman.herokuapp.com/ Doing a hackathon right now and we'd really like some feedback on the idea/design/anything. The idea is called Wingman. WingMan helps people set up amazing dates, and provides on-demand support and situational advice via text. (Zach) Design: http://imgur.com/a/CcKyG Really just as minimal as possible. I want most of the customer facing UX through text. Anonymity is also important so we're not asking for names just location and phone number. (Bobby) Tech: RoR stack, chose it because wanted to learn RoR a bit better. This invisible app uses Twilio, of course to send users messages and to route messages to our operators. I just finished writing an automated text distribution system that matches operators with user. Currently, it's routes messages to operators who are online and who have the least number of messages. However, I currently improving the matching process to match the operators to the users depending on location, likes, and lifestyles. I'm testing out Twilio's new CoPilot feature for Geo-Matching numbers in order to give a number to text that's the same area code as the number it's texting from and the sticky sender function that makes sure all texts are sent from the same number to the user instead of multiple numbers. I'm also wanting to add some degree of NLP to help the operators. If you guys like it please sign up!

Visit product View on Hacker NewsApr 17, 2016

Share card

Actual performance

1points

1comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

81%81% 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: user, new, code · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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

48%48% 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 · Strong signals: users · Missing: plus, platform, intuitive

27%27% predicted probability of success on AppSumo, 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Crowdsourced Thoughts from Uber Drivers in London53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Crowdsourced Thoughts from Uber Drivers in London

Hacker News1

Thoughts as NFTs53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Thoughts as NFTs

Hacker News3

A Penny for Your Thoughts39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Penny for Your Thoughts

Hacker News3

Penny for Your Thoughts40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Penny for Your Thoughts

Hacker News4

ChatGPT thoughts on my thoughts on their thoughts43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ChatGPT thoughts on my thoughts on their thoughts

Hacker News4

Sapien's Muse, my project to collect unfiltered thoughts47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sapien's Muse, my project to collect unfiltered thoughts

Hacker News12

Howard – Text him your thoughts45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Howard – Text him your thoughts

Hacker News2

My afternoon project, Clustergram55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

My afternoon project, Clustergram

Hacker News1

Project Maya72%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Project Maya

Hacker News88

My 24hour hack project50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

My 24hour hack project

Hacker News1

Browse all products Analyze your launch