Episodes

In 1994, my MSc thesis system bred a new generation of designs slowly enough that you went and made a cup of tea. Selection and generation were separate acts because they had to be.

This week, thirty-two years later, that gap closed.

On Monday a full 9x9 world - 81 stages, over 6,000 building objects - took 12.8 seconds to breed a new generation. Not bad. But profiling showed the real cost wasn't the architecture at all: it was thousands of near-identical materials being created and destroyed on every click, one at a time.

By Friday: 0.3 seconds. Forty times faster, at nearly double the population.

And that speed unlocked something the 1994 system could never do. The new Rapid Stage Select mode collapses picking and breeding into one gesture: click a design you like, and the next generation grows from it instantly. Click, click, click - pause - orbit, inspect - resume. No buttons, no panels, no looking away.

What surprised me most in testing wasn't the speed. It was the engagement. When the tool disappears, you stop operating software and start conducting an evolution. Your eye never leaves the design.

Every click is still recorded - every decision becomes data, the founding principle since 1994. The design memory is complete even when the designing feels effortless.

And this week GAAD got its first public home: the story and its full record - every decision from the 1994 thesis to now - now live at 4bim.com/gaad.

Next up: if every click is a commitment, you need a way back. Episode 4 is about Backtrack - rewinding an evolution to any earlier moment and branching again.

Question for the designers here: when a tool gets fast enough to keep up with your intuition, does it change WHAT you design, or just how quickly?

#GAAD #GenerativeDesign #Architecture #DesignTechnology #Blender

A week ago I shared the first steps of reviving my 1994 MSc thesis - a genetic algorithm that evolves 3D forms and records every design decision along the way. Episode 1 ended with four next steps: a 3D evolution tree, an automatic “what changed” report, a one-click tool and scored fitness. Three are now being tested. The fourth - scoring - quickly became a much bigger topic and deserves its own episode.

In GAAD, every generation you select a parent, and that choice is recorded. From that, the next generation is created. The Design Tree reconstructs the evolutionary path as a navigable 3D structure in Blender from saved JSON data. My MSc thesis called this “the genetic path.” Thirty years later, it has returned as the Design Tree. Alongside it runs an automatic Design History report showing floor area, volume, height, shape mix, timestamps and how every generation differs from the last. Even object intersections are now detected and recorded as neutral design data.

The original thesis argued that design decisions should be recorded, not lost. So I applied the same principle to the development of GAAD itself. Every design choice becomes a Decision, every turning point a Pivotal Moment and every bug a Ticket. So far that has produced 84 decisions, 36 pivotal moments, 20 quotes, 6 tickets and feature requests - all structured, searchable and generated from a single JSON source of truth.

The original prototype evolved four designs on a 2×2 grid, with each generation taking around five minutes. This week I tested a 5×5 grid (3×3 shown below): twenty-five evolving stages generated in just a few seconds.

I also audited every feature from the original thesis against GAAD Rebirth. There are 41 features across seven categories. 21 are complete, 5 are partially complete and 15 remain to be developed. The biggest gap is also the most important: navigation and tracking - backtracking, forward tracking and seed storage. These are the features that make GAAD a Design Memory Platform, not simply another design generator.

The images show where the project stands today:

• Generator – Generation 9 with live mutation sliders.
• Design Tree – Eight generations reconstructed from saved JSON.
• Feature Audit – Original thesis features mapped against Rebirth.

This project has quietly started using its own tools to document itself. More on that next time.

It's been surprisingly satisfying to be writing code again and seeing ideas from thirty years ago evolve into something I never imagined. I'd love to hear your thoughts, especially if you've seen anything similar to GAAD.

GAAD = Genetic Aided Architectural Design. A Design Memory Platform, revived from my 1994 MSc thesis Digital Evolutions, University of East London.

Every design project I have worked on or observed keeps the final model and design deliverables. Almost none keep the reasoning — the alternatives weighed, the options rejected, the decisions made along the way. Once the deliverable goes out the door, that thinking is gone.

In 1994, for my MSc at the University of East London, I built a tool to fix exactly that. I called it GAAD — Genetic Aided Architectural Design. The idea was quietly radical: don't just record what you designed — record how it came to be.

I grew a family of design alternatives, chose one, and bred the next generation from it — an evolutionary tree, with myself as the fitness function, every decision saved so I could replay the whole journey.

It ran on AutoCAD, AutoLISP and a DOS 486. Thirty-two years — and a career in CAD, BIM and digital design leadership — 32 years later, I decided to bring it back.

The goal wasn't finished architecture. It was to prove the concept could live again. By the end of the first stretch I had:

• Reconnected the 1994 vision to 2026 tools.
• Settled the language - World, Stage, Variant, Generation, Design Parameters. The vocabulary the whole project now rests on.
• Built the first working GAAD World in Blender. The concept visible again after three decades.
• Completed a full evolution cycle - generate, evaluate, select, evolve. The core loop from the original thesis, working.
• Started recording decisions as data, not just designs. This has become the most important idea of all.

• The design lineage rebuilt as a navigable 3D evolution tree.
• An automatic “what changed” report at every generation, with real metrics.
• GAAD as a one-click tool rather than a set of scripts.
• The first steps toward scored fitness running alongside human judgement.

And the bigger realisation: if GAAD records design lineage, it needn't be limited to architecture at all.

• Today's AI learns from finished artefacts. GAAD asks a different question: could AI learn from the design journey itself. The decisions, the alternatives, the paths not taken?
• A record of how things were designed, not just what was designed.
• A genuine question for the architects, engineers and designers here: in your field, is the reasoning behind a design ever really captured — or does it vanish the moment the deliverables go out the door?

#GenerativeDesign #Architecture #BIM #AEC #AI #DesignThinking #Blender #ComputationalDesign #GAAD