Share Slicer Settings for 3D Printers

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3D printers need slicer software to turn 3D models into print commands. Slicers have lots of configuration parameters, any of which could screw up a print. A couple months back I was looking for slicer settings to get my new k8200 printer working with anything other than PLA. A kind Redditor shared her settings on a private Github with me, but only after a couple weeks of forum hunting and Googling. That got me thinking - there has to be a better, faster way to do this.. I put together www.slicershare.com to make sharing slicer setting files dead easy. You can browse for slicer settings by filament, printer, slicer software or any keywords. You can leave feedback on any settings you try. For those of you that have dialed in slicer settings for a new filament or a particular model, you can host settings on SlicerShare and point back to them easily, without needing to go to Github, Dropbox, etc. This is still early in development, so expect improvements to the UI and functionality (particularly in managing the profiles you've uploaded). I’d love to get your feedback the website, as well as answer any questions you might have. Thanks for taking the time to check this out!

Visit product View on Hacker NewsJan 6, 2015

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

86%86% 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: para · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

56%56% 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 · Missing: plus, platform, intuitive

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

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

30%30% 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

11%11% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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