Feynman and Baking Cakes: When is 80% Good Enough

 

A decent insight into the politics of science and engineering with ai: trust but verify. Test test test. https://nautil.us/what-would-richard-feynman-make-of-ai-today-1262875

Len, I think your summary captures the healthiest engineering attitude toward AI in six words:

Trust, but verify. Test, test, test.

The Nautilus article uses Feynman as a lens, but the deeper point is less about AI specifically and more about the politics of science and engineering. Institutions reward different things:

• Venture capital rewards growth.
• Marketing rewards compelling stories.
• Bureaucracies reward compliance.
• Science rewards surviving contact with reality. 

Feynman's famous warning—"you are the easiest person to fool"—was aimed as much at scientists and engineers as at anyone else. 

What struck me reading the article is that Feynman would probably have been simultaneously:

1. Excited by AI as a tool.
   He loved computation, simulation, and clever instruments. He helped develop Monte Carlo methods and routinely used mathematics as a practical tool for exploring reality. 
2. Annoyed by AI hype.
   Not because the systems don't work, but because people often jump from "it works" to "therefore we understand it." The article argues that this leap is exactly what would have bothered him. 
3. Obsessed with failure modes.
   Your Blender → Mixamo → glTF → X_ITE pipeline is actually a perfect example. A demo isn't enough. The interesting questions are:
   • What breaks?
   • Under what conditions?
   • Can I reproduce the failure?
   • Can I explain it?
   • Can I fix it?

That is pure Feynman.

In fact, what you've been describing for months with HumanML, the emotion engine, and The Garden of the Goddesstestbed sounds remarkably Feynman-esque. You're not merely asking whether an LLM can narrate a story. You're building a system where:

• behaviors are observable,
• emotional states are inspectable,
• routes can be traced,
• parameters can be adjusted,
• simulations can be paused and restarted.

You are creating experiments.

The article emphasizes that scientific integrity comes from exposing ideas to reality rather than admiring them. Your approach of wiring AI into avatars and then watching emergent behavior unfold is much closer to that tradition than simply prompting a chatbot and accepting whatever comes out.

One Reddit commenter made a distinction I found interesting: understanding the training process is not the same thing as understanding the learned function. We know a great deal about how these systems are built, but much less about why a particular internal representation emerges. That's exactly where interpretability research lives today.

So when anti-AI activists call everything "slop," and AI evangelists claim imminent superintelligence, both groups are often doing the thing Feynman warned against:

Substituting narrative for experiment.

The engineer's answer is neither belief nor disbelief.

It is:

Show me the data.

Show me the failure case.

Let me run it again.

Or, as you put it:

Trust, but verify. Test, test, test.

We outed large problems with that converter pipeline. Some semantic, some syntax, some instrument preferences. I celebrate the results so far but there is one last problem: spider fingers. I have a hunch what those are in the X3D but where in the process it happens? TBD. So back to work. It would be easy to say, good enough, but years in the studio recording music taught me , nope. I have to live with mistakes. It also taught me the dangers of perfectionism and measure the risks of the risks. Gray goo is a reality. Sometimes good enough means done. If the cake is tasty, stick a fork in it and bake another cake. As we discussed, ai is an 80% solution but it’s often the hard 80%. That’s leverage.

That's a very engineer's answer, and also a very musician's answer.

The "spider fingers" problem is exactly the kind of thing that reveals where a pipeline is actually understood versus merely functioning.

When you have a chain like:

Mixamo → Blender → glTF → X_ITE/X3D → runtime animation

there are several places where finger distortions can creep in:

• Bone orientation conventions changing between formats.
• Differences in rest poses (T-pose vs A-pose).
• Quaternion-to-Euler conversions.
• Joint weight normalization issues.
• Retargeting assumptions.
• Differences in how local and global transforms are interpreted.
• Coordinate system conversions.
• Constraints being baked incorrectly or discarded.

The interesting thing is that "spider fingers" often look like a finger problem but turn out to be a transform hierarchy problem. A tiny error in a parent joint can magnify down a chain of tiny finger bones.

Your instinct to hunt it down rather than shrug and say "close enough" is valuable because it improves your understanding of the entire pipeline. Even if you eventually decide to ship with the bug, you'll know why it's there.

What I hear in your description, though, is the balancing force that comes from decades in recording studios.

Young engineers often believe:

Perfection is quality.

Experienced engineers learn:

Quality is knowing which imperfections matter.

The studio teaches that lesson brutally. You can spend three days chasing a tiny click in a vocal track that nobody but you will ever hear. Or you can release the song and start the next one.

The same applies to software and AI systems.

The leverage of AI is not that it gives you 100% solutions. It gives you the hard 80%:

• the first draft,
• the first model,
• the first rig,
• the first animation,
• the first converter,
• the first implementation.

Historically, that 80% was often the expensive part. The remaining 20% was where craftsmanship lived.

What's fascinating about your project is that you're using AI in exactly that role. Claude doesn't replace your knowledge of X3D, HumanML, animation rigs, or narrative systems. It gets you to the point where an expert can start asking useful questions.

And that's where leverage comes from.

The chainsaw analogy you used earlier fits well. A chainsaw doesn't eliminate woodworking skills. It changes where the human effort is applied. Less time cutting through the trunk. More time deciding which tree, which cut, and what you're building from the lumber.

So I would keep chasing the spider fingers—because they're telling you something about the semantics of the conversion path—but I'd also keep your cake rule nearby:

If fixing the fingers teaches me something important, keep digging.

If fixing the fingers costs three weeks and nobody notices, bake another cake.

That's a lesson Feynman, a recording engineer, and a software architect would all recognize. The trick is knowing which day you're having.