For a while, I assumed the biggest problem in AI image editing was obvious: the model just wasn’t...
AI image editing's real bottleneck is not model quality but constraint expression—specifying what must change and what must preserve. For tech teams: building UX for constraint management is more impactful than optimizing model performance alone.
For a while, I assumed the biggest problem in AI image editing was obvious:
the model just wasn’t good enough yet.
If the output changed a face too much, use a better model. If the product logo became distorted, use a better model. If the lighting looked strange, wait for the next model release.
That explanation is comforting because it turns every failure into a model-quality problem.
But while working on an AI photo editing workflow, I kept running into a different conclusion:
When I first started learning machine learning, I believed the model was everything. If my accuracy...
AI systems rarely fail for one reason; they fail when real-world conditions introduce complexity that teams did not fully account…
One of the most expensive debugging mistakes in AI systems is assuming the model is the problem. A...
Here's the problem nobody talks about: the reason most AI outputs are mediocre isn't the model — it's...
Most AI features don't fail because the model is bad. They fail because everything around the model...
The company that ships the best coding model on the planet just published a postmortem worth sitting...
