I'm building a guidebook for turning an idea into an application

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Hello there! I have been working on creating a guidebook that will help walk me through the process of designing and developing a full-stack application. The main goal behind this project was to collect and condense down the information and steps I've gather from design books, and online courses and combine them with the technical planning steps I've gathered from different books and courses. I felt that it was too much information to know and remember at any given time so I wanted this to act as a checklist of things to think about and directions to focus on when building out ideas into tools and toys. Any book recommendations, feedback or advice is welcomed. Here is a link to the WIP version: https://docs.google.com/document/d/1XA3DDaZsMhBHjWt76Mt05we5...

Visit product View on Hacker NewsApr 24, 2023

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

77%77% 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.

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

53%53% 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: google · Missing: mac, agents, macos

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

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.

Incorrect prediction on native model

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