Our thick client Java SaaS

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Hi Folks, We've adjusted our networked Java desktop time and expense tracking system (Senomix Timesheets) to provide a cloud-hosted option for our customers and I thought I'd post it up as an example of how Java and Java Web Start can be used in SaaS for Windows and Mac OS X. In our solution, applications run from user desktops to provide a thick client which works outside of web browsers, with the server component deployed to a host in the cloud. The result provides the connectivity (and centralized web page installation) of a web application while still giving users the speed and responsiveness of desktop software. Since the client apps use Java Web Start for operation, we were able to reuse an established desktop Java code base in the SaaS system which had already been created for a standard licensed model. The additional development to shift things to the cloud was relatively minimal and the common code base also made it easy for us to continue with a self-installed licensed system in parallel with the hosted SaaS option. If you have a set of Java applications operating with a desktop one-time-charge licensing model and are wondering how you might add a SaaS business model to that mix, taking a look at Java Web Start might be worth your while. If you'd like to check our solution for yourself, you can find a summary of screen shots, system features and a 30-day trial at: http://www.senomix.com

Visit product View on Hacker NewsAug 20, 2012

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, apps · Missing: agents, macos, agent

89%89% 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 · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · 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 · Strong signals: host, users · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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