TG

TGIFound.com - Geolocating lost and found community

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TGIFound.com - Geolocating lost and found community

TGIFound (http://tgifound.com) is a community-based service that lets people report found items and search for them by location. If you see a lost item on the street you can report it on the mobile app or on the website along with the GPS coordinates, which lets people search for items for a given location. The idea is unique in that it lets people report items as found, rather than lost. It relies on the community to report items as found. The exact location is not revealed but translated to an approximate location to prevent people from gaining possession of someone else's item. This is a free service and I intend to keep it free for as long as I can manage. The site was built in Django in a couple of months of spare time as I was learning Django along the way. An iPhone app is also available at (http://itunes.apple.com/us/app/tgifound/id555479564?ls=1&mt=8). The iPhone app was written in Titanium. It uses quite a few APIs, such as Google Maps so was a useful learning experience for me. I hope that it's a feasible method of reuniting people with their lost items, and I hope that this project can make the world a slightly better place. The project is still in a very early stage and this is one of the first times I've searched publicity for this. I welcome any feedbacks, criticism and bug reports, or that you simply give it a try. Thanks!

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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: apple, google, apis · Missing: mac, agents, macos
68%68% 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
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.
TrustMRRLess likely to generate early MRR · Strong signals: month, google, way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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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