Attach a poll to a tweet with PollAll.me

Hacker News

http://www.pollall.me/ Often we receive tweets with which we don't agree and we want to poll our followers about their opinions. Ideally we should be able to attach a poll to a tweet without leaving Twitter. With this thoughts in mind I made PollAll.me. It allows to attach a poll to a tweet as easy as send a tweet. If you don't agree with a tweet, just type @pollall ? in your reply and receive a poll with default question and quotation of the replied tweet. Or you can provide your own questions and answers for a poll, just reply with a tweet like: @pollall Will Java replace C in 5 years: Yes, No, Never? PollAll.me runs on Google App Engine. I deployed three projects on GAE already and I like it very much for the robustness and stability. Server side is wrote on Python with Jinja2 as template engine. On client-side I use JQuery, Bootstrap and CanJS, fantastic JavaScript framework to which I switched after using Backbone.js. Blog post: http://www.cliws.com/e/aIqkK7ZknDeBkM4JbatPpQ/

Visit product View on Hacker NewsDec 5, 2012

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Launch Intel predictions

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

67%67% 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

58%58% predicted probability of success on Indie Hackers, 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 · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

17%17% 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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