Nono Martínez Alonso

Excited to share what we've been working on. AI rendering—now in Motif (@buildwithmotif). youtube.com/watch?v=E9wSt3…

@CharlesPattson The tool used on this video is Krea.ai @krea_ai

I’m telling you, the tools we have right now will all feel outdated within a few months. You’ll be building your own tools. The future designer has their own unique set of tools that they’ve built themselves.

Motif made the 2025 AI Disruptors 60 list, curated by @GreenfieldVC and live on @TechCrunch. @BuildWithMotif disruptors60.ai

Great teamwork getting the Motif 'single click' AI rendering functionality integrated into the Motif collaboration tool for Architects: aecmag.com/visualisation/… Stay tuned, more goodness on the way...

Motif: AEC software that actually works the way you do. Real-time 3D collaboration. Live Revit + Rhino integration. AI renderings optimized for architecture. Browser-based. Built by the industry, for the industry.

Welcome to a new frontier of generative storytelling. Gen-4 is available now.

Ex-Autodesk execs snag $46M to build the next gen of architecture design tcrn.ch/40Vr7BS

We don't expect a photographer to capture the perfect image with a single click. We understand that photography involves setting up the scene, adjusting the lighting, choosing the right lens, and taking multiple shots from different angles. The same principle applies to filmmaking, where hundreds of hours of footage are often captured to produce a final film. Yet, when it comes to generated content, there's often an implicit expectation of instant perfection—a demand for a finished product with a single prompt: "Make my idea. Go." This expectation is not only unrealistic but also fundamentally misunderstands the nature of AI as a creative medium. AI is not a magic wand that conjures perfect results on the first try; it's a medium, an apparatus, that requires iterative exploration and refinement to achieve the desired outcome, even with pixel-level control and steerability. Just as a director chooses the best takes from many, artists using AI must select and refine the most promising outputs from multiple generations. Generating multiple outputs, then, is like taking multiple takes, exploring different variations and possibilities within the creative landscape. The best results rarely come from the first attempt. Instead, they emerge through a process of careful selection, refinement, and combination of different outputs. Just as a photographer might stumble upon a stunning composition by experimenting with different angles, an artist using AI might discover a unique aesthetic by exploring different regions of the latent space. This is a different kind of expectation and control: control over the overall aesthetic direction, control over the exploration of ideas and style, and control over the selection and refinement of outputs. Control leads to iteration. Quantity leads to quality.

Today we are launching VE2 — a brand new model for Visual Electric. It can produce hyper realistic photos, accurate text, and generate images 2.5x faster. This is the biggest update ever to Visual Electric. 🔊 SOUND ON

Gen-3 Alpha Text to Video is now available to everyone. A new frontier for high-fidelity, fast and controllable video generation. Try it now at runwayml.com

In car manufacturing, there is the idea of production-intent cars and concept cars. Concept cars are made for customer reaction and marketing. A very small proportion of concept cars are functional to any useful extent, and some cannot even really move. Production cars, on the other hand, require a different approach for mass production and manufacturing. They are made to be used. I think we have a very similar development happening with AI today. There are concept models and production models. The goal of building concept models is to gauge customer reaction to new capabilities and show design skills. It's a game of appearances. You tour around with a car that looks cool, but is not practical, there is no driver experience, and the car might not actually move. Concept cars may look impressive but lack true drivability. Form and no function. The tricky thing here is that concept cars and concept models can lead to overpromising and under-delivering. Production models serve a different purpose. They have to work. They need to be used by drivers. Driver experience matters. Function and form. Production cars need to look nice, work well, and make people happy. Same with AI models. Most importantly, they get you from point A to point B. The trick is to scale operations and logistics to deliver to as many customers as possible. The development cycles and processes for building AI models are different than for cars, of course. Models can be updated and refined over time more readily than overhauling a physical car design. But I do think concept models (gauging reaction, showcasing potential) versus production models (functionality, scalability, real-world use) are different games, with different long-term goals.

@pixel_poppie Glad that helped, Poppie! =)

@nonoesp Just stopping by to say thank you for solving my React port 5000 issue, much appreciated.

AI is thinking...

And people mentioned. #RajaaIssa @bobmcneel @kangaroophysics @jasonkellyjo @futureformslab @DoyleSE111 @capsulewire

Links at gettingsimple.com/73

Here’s my conversation with Andy Payne on Grasshopper, Rhino Compute, Teaching, Learning to Code & Generative AI. 🍿 youtu.be/StRrzZeo2ic

AIFF is approaching! Get tickets @ aiff.com

🔴 We're now live! Chat with Andy Payne and me as my latest podcast conversation with Andy airs. youtube.com/watch?v=StRrzZ…

@johntpierson Thanks so much for sharing, John! ❤️