Ka

Kaedim – API for 3D User Generated Content

Hacker News

Kaedim – API for 3D User Generated Content

Hi HN, I am Konstantina from Kaedim (https://www.kaedim.com). Kaedim is using ML to transform 2D art, sketches or photos into 3D content. We make it easy to integrate 3D User Generated Content in game or metaverses, with our API. Creating digital 3D objects is getting increasingly difficult and expensive. There is a very limited supply of people who are good 3D artists, and the cost of training one is very big. It usually involves years of learning difficult 3D software. However, more and more of the digital experiences around us are turning into 3D. I needed this product myself. The idea for Kaedim was born from a personal frustration when, 2 years ago, I was working on a project for re-creating a cathedral in 3D software for my university degree. Before being hands-on, the concept seemed straightforward to me, “the same way you draw on a piece of paper, you can also draw in 3D, how hard can it be?”. The reality shocked me. Having completely underestimated the task I found myself needing hours to model each 3D object (chairs, tables, walls) using really complicated and steep learning curve 3D software. Every time I wanted to model something new I had to start from a cube and do all the necessary operations on it to achieve the desired shape. Over and over again. Moreover, there were many times when I would bin my creations and start from scratch for better luck. The reality with 3D modelling software is that it’s almost always easier to start from scratch than to try and fix a modelled object. After my personal experience of the problem, I started thinking about game devs. “Game developers have this problem at scale, they need to build whole 3D worlds with millions of objects. How do they do it?”. So we started talking to them. Only to discover, there is no secret. 3D asset production is a big bottleneck for them too. There is a very limited supply of people who are really good 3D artists, and the cost of training one is very big. It usually involves years of training on difficult 3D software. Our solution is an ML algorithm that creates 3D models out of 2D images. We are constantly training on more and more data points to improve the accuracy and we have added a Quality Control step to always guarantee a standard level of quality. We then use the QC results to train our algorithms further. Artists and game devs have used Kaedim so far to quickly prototype, create and iterate their 3D art, in a cost effective way. However, talking to a lot of game developers, we realised something key . For the same reason games like Minecraft and Roblox are very popular, more and more people want the opportunity to customise and contribute 3D content inside their favourite games/metaverses. This is why we created the Kaedim API. Within your app, enable your players to upload their 2D inspiration and easily create their own 3D content for customising and populating the game. Kaedim API Demo Video: https://www.youtube.com/watch?v=k976GJWQrKw Documentation: https://app.archbee.io/public/m370vHO-M7WGXJQLRlIte/AU-DhH6mX0e1sb_FRXH3i#lk-useful-links For signup and more information about onboardings get in touch with us here Discord Server: https://discord.gg/4wN8NSUr Thanks a lot for reading this! We are adding more and more features over time and would love to hear your feedback and ideas on what you’d like to see from the API. If you have any cool app ideas that can be built by using Kaedim, drop them in the comments!

Share card

Actual performance

150points
40comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
95%95% 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: model, user, new · Missing: mac, agents, macos
88%88% 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
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs
58%58% 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
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: training · Missing: arr, mrr, revenue
12%12% 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.

Correct prediction on native model

Similar products

MediaMachine
MediaMachine45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Infrastructure for User-Generated Video Content.

Indie Hackers3ai
TINT
TINT60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

User Generated Content Platform for Marketers

Indie Hackerscommitment-full-time
Shoppable widget
Shoppable widget68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sell more with user-generated content.

Product Hunt+243User Experience
Fr
Free HN User Favorites API Endpoint (MIT)44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Free HN User Favorites API Endpoint (MIT)

Hacker News4
Backswing.com
Backswing.com65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A golf publication built on user generated content

Indie Hackers1content
Dropppin
Dropppin23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Reward your customers for submitting user-generated content to boost brand engagement

AppSumo8
Us
User-Generated Blog (Finimize Your Life)51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

User-Generated Blog (Finimize Your Life)

Hacker News2
Idukki
Idukki60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Collect,Curate & Display User Generated Content via Idukki

Indie Hackerscommitment-full-time
Ma
Markov Generated HN Titles37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Markov Generated HN Titles

Hacker News2
Mo
Moshimoshi, Gravatar for user bios51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Moshimoshi, Gravatar for user bios

Hacker News2