Blender Scripting Cookbook

Hacker News

A collection of recipes for the Blender Python API. Each recipe presents a (mostly) self-contained solution to a well-defined problem. I started working on this book mostly for myself – as a companion that I can refer to when building non-trivial things using the API. While Blender has an excellent API documentation, certain techniques and workflows are hard to discover just by reading it. Often, you need to dig into Blender's source code or study existing addons to figure things out. This book is an effort to document those practical, but sometimes obscure solutions. The goal of each recipe is to give you just enough information to solve a problem quickly. When programming, most of the time we are looking for solutions in bits and pieces while tackling a larger problem. If I can express those bits and pieces clearly and concisely, I consider that a win. Learn how to use the API to build addons, manipulate geometry, create custom interactive operators, build user interfaces, write custom CLIs, and more...

Visit product View on Hacker NewsJun 25, 2025

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

70%70% 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: exist, existing, ide · Missing: https docs, excited, just released

61%61% 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: user, using, code · Missing: mac, agents, macos

46%46% 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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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

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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