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How to market promises

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How to market promises

I've been using Node for over three years. In the beginning I looked for callback helpers like Async and tried everything I could find. Like many I settled into using vanilla callbacks. It has not been a problem. Back then I checked into promises and they looked promising (sorry). The simple examples looked great. But then I read a number of write-ups about promises and they would quickly make them appear complex and painful to learn. Even blogs saying "Promises look complicated but here's a simple explanation" would then quickly present complex explanations. I've just started using selenium with the node driver. This solution pretty much forced me to use promises. In one day I now feel comfortable with them and look forward to trying them in my apps. But this is the key point: I still don't understand the last half of the promises tutorials and they still look complex to me. I used javascript as a front-end developer successfully for years with little idea what closures and other advanced topics were and I didn't care. I learned the subset that worked for me and I was happy. Actually I think this flexibility is one of the strongest features of JS. Even with my current limited understanding of promises I am now using them effectively like I did javascript. I'm writing lots of selenium code with promises and love it. I think there should be introductory cookbooks that just tell you how to chain lines using `then` and put the error-catching `then` at the end. The hardest part would be explaining how to get library functions to return promises. There could be a cookbook for that also. If I had a cookbook that just told me what to do and what benefits I'd get then I would have used promises years ago. I would have learned them in depth while using them, which is the only way I learn anything in depth.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
87%87% 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: apps, using, code · Missing: mac, agents, macos
87%87% 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
67%67% 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: apps, way · Missing: mobile apps, ios, personal
48%48% 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
33%33% 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
13%13% 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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