GiftsBuffer.com

Hacker News

GiftsBuffer.com

If you know a person only online and not in real life and find that you would like to send a gift to that person, how would you do it. The other person woud not be comfortable giving out his or her home address, telephone no. or a real name. I was stuck in such a situation once, which gave me the idea to come up with http://giftsbuffer.com. GiftsBuffer is a way to send and receive gifts while maintaining your privacy. No need to exchange private details like address, telephone no. or even your real name with real life strangers, to send or receive gifts. GiftsBuffer as the name says, acts as a buffer between the two parties. This service is powered by Amazon.com amazing service, so you can gift from among thousands of products that Amazon.com sells. How it works is that if you want to send a gift to someone, select the gift you want to send and enter the e-mail address or registered username of the person you want to send the gift to. The other person adds his or her home address, telephone no. and real name to the gift. We gather and update the shipping charges and tax information for the gift. You make payment for the gift based on the updated info. After we receive payment confirmation, we place the order for the gift items with Amazon.com. Amazon delivers the gifts to the person you wished to send the gifts to. Neither party will ever know the address, telephone no. or real name of each other ever, thereby enabling a safe way to send and receive gifts online. I am posting this here with hope that the good people at Hacker News would give me their valuable feedback, a little bit of encouragement and lots of constructive criticism. Your thougths on what you think about the idea, the implementation, the design etc would be really invaluable. Looking forward to your opinions ...

Visit product View on Hacker NewsFeb 22, 2012

Share card

Actual performance

4points

9comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

80%80% 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 NewsMay not resonate with HN audience · Strong signals: lua, hacker news, ide · Missing: https docs, excited, just released

45%45% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

23%23% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real life · 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

Verelo.com49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Verelo.com

Hacker News6

dcmntr.com43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

dcmntr.com

Hacker News1

campusmemes.com to compile all those pics63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

campusmemes.com to compile all those pics

Hacker News2

Appsinmylanguage.com44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Appsinmylanguage.com

Hacker News1

Cleanicons.com52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cleanicons.com

Hacker News10

Commandlinefu.com from the commmandline44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Commandlinefu.com from the commmandline

Hacker News106

placezombies.com44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

placezombies.com

Hacker News3

Claimyourscore․com45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TrustMRRMobile App

esthetio.com53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ton institut mérite mieux qu'un DM

TrustMRR1$69/moSaaS

Lethimcookai.com45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TrustMRROther

Browse all products Analyze your launch