THE PERSONAL NOTEBOOK OF SHELPER
So I write.
Ideas, projects, and things
learned along the way.
Latest writing
29 ENTRIESAI Will Evolve Like a Society, Not Just a Model
A thought has been on my mind: what if we are still looking at AI too much like an individual brain? We often discuss progress in terms of the next model: its benchmark scores, reasoning ability, context window, or parameter count. These things matter. But I suspect that language models and today’s agents are only the beginning. The more consequential evolution may be in how AI systems organize work, share knowledge, use tools, and build on one another’s results.
A Graph as a Model Harness: Let Context Correct Vision
Part 3 of AI Beyond the Model. Part 2 explains the representation layer. Most discussion of scene graphs asks how to turn visual detections into structured state. I am interested in the reverse direction too: can the structure help correct the detections and associations that produced it? This was the focus of a discussion about retail shelf images. The initial models might detect products and price labels, produce top-k product candidates, and propose which label belongs to which product.
An AI That Learns to Build Its Own Specialists
Part 4 of AI Beyond the Model. I have been thinking about a different way to organize AI. Instead of asking one ever-larger model to perform every task directly, what if the large model learned to create, teach, select, and coordinate specialists? This idea emerged in several conversations. In July 2025, I considered a practical feedback loop in which a cloud model teaches a smaller model running on a device. In August 2026, I broadened that into heterogeneous AI infrastructure: large models working with detectors, trackers, 3D algorithms, and other specialized components.
Can AI Learn How to Search?
Part 5 of AI Beyond the Model. Part 4 introduces the specialist-building system. In the previous article, I imagined a general model that discovers repetitive tasks, creates specialized components, and coordinates them. That still leaves one of the deepest questions from my August discussion unanswered: who designed the space in which the system searches for solutions? Choosing among existing models is one kind of intelligence. Training a new model for a specified task is another.
From Next-Token Prediction to Persistent World Models: Notes on the Future of AI
Part 1 of AI Beyond the Model. On September 2 and 3, I had a conversation that began with a practical question: if so many language models seem to share the same architecture, why can one inference engine run so many of them? It ended somewhere less practical and more interesting: perhaps the next step in AI is not one model that directly answers every question about its input, but a system that builds and maintains a model of the world, then lets other components reason over it.
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