Solar Pump Management Using Django/AWS and IoT on Ground

Hacker News

We extended our Smart Equipment Maintenance Application in Django to enable Monitoring, Managing and Maintaining Solar Water Pumps in Agriculture as well. The system is developed using #Django and hosted on #AWS Benefits of this Application. 1) One app for Solar Pump Operations and Maintenance 2) Master Data Pumps, Location, Equipments, WorkOrder etc 3) Support for REST API as well as WebSocket for IoT Data Iingestion 4) Advance role based security for Operator, Supervisor, Administrator 5) SMS and Email based Notification 6) Can Switch the Solar Pump On/Off from the App Our Hardware Partner is developing on-ground microcontroller with 4G Module More updates as we build this application. Screenshot https://media-exp2.licdn.com/dms/image/C5122AQEftBwYJgpyng/feedshare-shrink_800/0?e=1582156800&v=beta&t=5jKGfvCd2AVdW0wSpQQvWJOfjCHlaCknLtgZXTwCWG4

Visit product View on Hacker NewsJan 20, 2020

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

55%55% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: email, using · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

34%34% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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