My First Web App - A Pizza Coupon Search Tool

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A little background: I'd been throwing this idea around in my head for a while, and finally had some free time between two quarters of school, and decided to make the site. I spent several days over the break laying out the backend for the site, and starting coding, and then the next few weekends finishing it up (and I've been making small changes since then). Where I got the idea: I originally got the idea when trying to look up coupons for some pizza places, and having a lot of trouble finding any that worked at my store since they were all regional, or weekly codes, so I just manually tried codes until one worked. The site is a bit limited at this point in time, as it only contains one site, but I made the backend flexible enough that adding more sites shouldn't be too difficult. How it works: I have a python script running on my server that watches a database, looking for new coupons that need to be added, and updates codes every day. It stores which codes work at which stores in a database that is used by the site to display working codes for each store. I know that this site may not be the most interesting thing ever to everyone here, but I learned a ton about php, mysql, and web crawling with Python in the process of making it, and would love to hear your thoughts on the site. Link: http://abiteofpizza.com

Visit product View on Hacker NewsJul 30, 2012

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

83%83% 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: new, coding, code · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, 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

59%59% 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

38%38% 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

36%36% 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

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

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