Escaping the /cgi-bin Era of AI

The Problem with Today’s AI Primitives

If you’ve worked with modern LLMs, you know the feeling: they’re magical, but built on duct tape. Tokenization, embeddings, attention, system prompts, they work, but they’re crude hacks. The architecture is fragile, opaque, and fundamentally limited.

Like the early web’s CGI scripts, they get the job done but lack robustness and structure. Just as web frameworks replaced ad-hoc scripts, AI needs a foundational leap.

Current primitives: Tokenization often involves arbitrary, lossy chopping of inputs. Embeddings rely on high-dimensional but opaque vectors (e.g., BERT’s 768-dimensional spaces). System Prompts provide brittle steering instructions with no persistent knowledge update.

Transformers make these workable but don’t solve the absence of self-learning, long-term coherence, or interpretability.

Starting with Geometry Instead of Attention

Rather than focus solely on attention and context windows, we can start with geometry: Imagine semantic dimensions where each axis is chosen for meaning and utility. We could employ deterministic compression, reducing domain data without losing interpretability. Then, through procedural expansion, we could rebuild rich representations from compressed states without hallucinations.

Here, embeddings aren’t mysterious, they’re structured, interpretable spaces.

Any Data, Any Sensor, Any Domain

A geometry-first framework can ingest: A geometry-first framework can ingest text, images, video, and audio. It handles multimodal sensor data and structured datasets with equal ease.

Outputs could be modular, reusable context with interpretable environments tuned to specific domains, editable at runtime.

Why This Matters for Coherence and Agency

Coherence becomes part of the substrate, not an afterthought. Relationships between concepts, events, and entities are encoded explicitly.

This enables: This enables real-time agency evolution within narrative or operational bounds. It allows for an infinite choice-space without breaking logic, and supports human-in-the-loop reasoning with full visibility into the system’s understanding.

Toward a Post-/cgi-bin AI Stack

Like web frameworks replaced CGI, a geometry-first reasoning substrate could replace today’s brittle hacks, not by eliminating transformers or prompts, but by integrating them into a coherent, interpretable pipeline.

Knowledge could evolve incrementally, patterns could persist without retraining from scratch, and interpretability would be a core feature.

The Big Question

If you had access to a platform where you could: If you had access to a platform where you could load any dataset, map it into a tunable geometric space, author rules and agents that evolve in real time, and maintain coherence across infinite branching paths... what would you build?

...what would you build?