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 rely on tokenization, which is often an arbitrary and lossy chopping of inputs. Embeddings provide high-dimensional but opaque vectors, such as BERT’s 768-dimensional spaces, while system prompts offer 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: First, semantic dimensions ensure that each axis is chosen for meaning and utility. Second, deterministic compression allows domain data to be reduced without losing interpretability. Finally, procedural expansion enables the rebuilding of 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 text, images, and video as effectively as it handles audio, multimodal sensor data, and structured datasets.

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, offering an infinite choice-space without breaking logic. It also 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: Imagine loading any dataset and mapping it into a tunable geometric space, where you can author rules and agents that evolve in real time while maintaining coherence across infinite branching paths,

...what would you build?

Building Durable AI Systems Today

While we wait for the "geometry" revolution, we still have to ship software with the tools we have. The key is to treat current LLMs as a transitional runtime—the /cgi-bin of our era—rather than the final architecture. Use them, but insulate your core logic from them.

The "Sandwich" Pattern

Don't let the LLM permeate every layer of your stack. Instead, sandwich the probabilistic AI layer between two deterministic layers. The outer layer (input) should be rigorous validation and context retrieval. The inner layer (logic) should be the heavy lifting done by the model. The final layer (output) should be strict schema validation and policy enforcement. If the AI hallucinates, the outer layers should catch it, not the user.

Avoiding the "Prompt Engineering" trap

Don't fall in love with your prompts. They are temporary hacks for a primitive model. Invest your engineering time in evaluation pipelines rather than prompt tuning. A good eval suite survives the next model update; a perfectly tuned prompt breaks the moment the API changes version.

Signs of a mature AI strategy

You know you are escaping the brittle era when you stop relying on "vibe checks" to deploy. If your release process requires a human to "chat with the bot" to see if it feels right, you are still in the cgi-bin days. When you have a deterministic evaluation set that scores your model's reasoning on 500 edge cases automatically, you have started building engineering rigor.