A

A kids book that introduces authorization and permissions concepts

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A kids book that introduces authorization and permissions concepts

A colleague and I made a kids' picture book that introduces authorization concepts. We work at AuthZed and explain these concepts regularly. We thought it'd be fun to put them together in a format accessible and appealing to kids and grownups alike. It would also be helpful when explaining what we do for work and make a unique gift for our families. The goal was a fun story first and foremost. We aimed to present concepts accessibly but made conscious decisions to simplify, knowing we couldn't be comprehensive in a picture book format. We also wanted visually appealing illustrations, so we built a custom tool to streamline exploring ideas with AI. It does reference-weighted image generation (upload references, weight which ones matter most), git-like branching for asset organization, and feedback loops that improve subsequent generations. It was built with Claude Code. Here's a screenshot: https://content.authzed.com/hubfs/screenshot-design-asset-ma... We'd love feedback on where we chose to simplify. Did we get the tradeoffs right or did we oversimplify? And lastly, did you enjoy the story? You can read the book online: https://authzed.com/resources/dibs-and-the-magic-library

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, 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
50%50% 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: claude, visual, code · Missing: mac, agents, macos
36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
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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