It

Iterative architectural design using generative AI

Hacker News

Iterative architectural design using generative AI

Hi everyone, I'm Joachim, and together with my co-founder, we're building Visoid - a tool for rendering architectural designs in an iterative manner. We firmly believe that the future of this type of rendering, especially during the ideation stage of a project, will be heavily reliant on generative AI. Traditionally, you would send your designs to a professional and potentially wait several days for the renders to be ready, all the while going back and forth with consultations. With Visoid, you can now receive instant, beautiful renders at a fraction of the cost. Our platform is designed for architects and rendering professionals who work with visualizations of architectural designs. Visoid enables our users to iteratively modify the scene, materials, and elements in a sketch, 3D model or similar, controlled with a simple prompt. We're still in the early stages, and while the output isn't always perfect, we've made significant strides in improving the quality and the control of the output images. We're confident that it's only a matter of time before we can consistently deliver higher quality images and with even more control. We're still figuring out how to fit this product into the workflow of our users and would love to get your feedback. I will try to answer any questions you have. For more example images, have a look at our Instagram https://www.instagram.com/visoidai/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% 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, visual · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal
59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
37%37% 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
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.

Correct prediction on native model

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