updt_me - delivers RSS/Atom feeds straight to your Twitter DM inbox.

Hacker News

Hey HN, newbie programmer here, back for yet another show-and-tell. Would love some feedback! # What [@updt_me](http://twitter.com/updt_me) is meant as an RSS reader via twitter. The idea is pretty simple - you follow @updt_me and send a tweet mentioning the handle, a keyword ("START") and a feed URL. Now, whenever there's a new post on the feed, @updt_me sends you a DM with a link to the post. (Hence, the need to follow...) # Why? Well, I don't use twitter frequently but I do have an app on my phone. My RSS reader slowly grew into a mammoth pile of feeds that I eventually stopped caring about. My source of news these days, is aggregators and forums like Reddit and HN. However, I do pay attention to my DMs on twitter. So I decided to create an RSS feed reader with Twitter as the interface point. # How Whenever you see a feed you want to follow, all you have to do is follow the user @updt_me and send out a tweet of the form "@updt_me START <feed_url_here>", for example: "@updt_me START http://barrettsheridan.com/feed". Whenever the feed has a new post, you'll receive a DM with the link and the title of the post. The site is currently running on a free Heroku instance with tweets being grabbed every hour or so. Might increase the frequency if the project picks up traction. :) What do you guys think? Any ideas? Suggestions? Feedback? Thanks, in advance!! :)

Visit product View on Hacker NewsAug 20, 2013

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

73%73% 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

68%68% 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

57%57% 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: interface · 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

24%24% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

19%19% 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.

Incorrect prediction on native model

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