See how AI instructions decay, then write ones that hold

This is a submission for Weekend Challenge: Passion Edition

What I Built

I told an agent Never write directly to the database. A long session later, context window full, it wrote directly to the database. The rule loading mark was still sitting in the prompt. The model had just stopped weighting and attending to it.

It's an invisible failure. No error is being thrown. The task comes back subtly wrong, and the rule reads perfectly fine when you go back and check it. I wanted to make it visible, so I built an interactive field you can drag around.

Every rule you write for an agent is a hill. Its height is how well the rule is written: a directive-led, backtick-anchored rule stands tall, a hedged and vague one sits low. Then you raise the water. The water is context load. As it rises the low rules go under first, in order of how well they were written. The weak ones drown while you watch.

Three of the hills are high-stakes prohibitions, the Never... rules. They drown too. That is the whole point of the piece. A rule you cannot afford to lose does not belong in prose at all; it belongs on a runtime hook that runs as code, not attention. The field flags those in red the moment they go under.

Underneath the field is a second tool: a client-side lint that reads an instruction and names the surface tells (hedges, shouting, politeness, a ban placed before its directive). It is deliberately not a score. It catches what a little regex can honestly catch, and points at the real analysis for the rest.

Demo

Play it on its own page. Drag to orbit, drag the load slider to raise the water:

▶ Open the live demo

Each of the nine instruction patterns in the demo links to its rule page on reporails.com/rules.

Code

Code is available on Codepen: https://codepen.io/editor/G-bor-M-sz-ros-the-reactor/pen/019f4cad-e344-78bf-b7bc-919972f42a4e

The whole thing is one self-contained HTML file: no build step, no dependencies, no backend. The CodePen above is the full source, so you can read every line and fork it right there.

How I Built It

The 3D field is hand-rolled on a 2D canvas. No WebGL, no three.js. It is a Gaussian height field sampled over a grid, rotated by yaw and pitch, projected with a perspective divide, and drawn back-to-front with the painter's algorithm. Each tile gets flat Lambert shading against a fixed light. The water is a second translucent plane tessellated over the same grid, so a hill reads as submerged the moment its local height drops below the water line.

The hill heights are burned in, not computed. Each rule's height is a fixed, hand-reviewed number, placed by how well the rule is written against a set of instruction-quality patterns: directive-before-constraint ordering, a required concrete anchor, imperative modality, lowercase prohibitions, and so on. I deliberately did not run the live scorer to place the hills. Reviewed numbers stay fixed and inspectable; a field that recomputes its own heights every frame just drifts every time you touch the scorer.

The lint is multiplicative and ordering-aware. Weaknesses compound rather than add, so a rule that is hedged and vague and buried lands near zero instead of "a bit lower." It also checks order: a ban that stands before its directive is penalized, because naming the forbidden thing first activates it before the model learns what to do.

The honest part. Nothing here claims a hook is immune to context. That would be false; nothing is immune. The demo makes one narrow claim and only that one: a hook runs as code, not attention, so the attention-fade that erodes prose under load does not apply to it. The fade is a property of attention. Code does not have it.

No prize-category cloud tech in this one. It is a single client-side file on purpose, because the whole argument is that the interesting failure happens in the prompt, before any infrastructure gets involved.

The passion behind it: I spend my time trying to make agent behavior measurable instead of a matter of vibes. This was a weekend spent turning one measurable, invisible failure into something you can grab and drag.