AI & ML Streaming Accessibility

Cognition UI

An adaptive interface for large language models designed to humanize synthetic intelligence through fluid typography and streaming latency reduction.

model: dev-gpt-4.0-preview

Explain the concept of "optimistic rendering" in the context of generative AI streams.

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Roles

  • Product Design
  • Prompt Engineering
  • Frontend Architecture

Tech Stack

  • React Server Components
  • Tailwind CSS
  • OpenAI API (Stream)
  • Radix UI

Stats

40ms
TTFB
99%
A11y Score

The Friction

Interacting with Large Language Models often feels robotic. The standard "loader spinner" creates anticipation anxiety, and raw text dumps overwhelm the user. The goal was to simulate a "thought process" essentially creating a UI that feels like it's breathing rather than just processing.

Fluid Typography & Streaming

Token-by-Token Rendering

Instead of buffering chunks, we utilized a custom readable stream reader that paints characters the microsecond they arrive. This creates a hypnotic cadence that keeps the user engaged during generation.

Markdown Stability

Streaming Markdown often causes layout shifts as bold tags or code blocks close. We built a parser that predicts block closures and reserves vertical space, eliminating layout jank during generation.

useStream.ts
async function* streamResponse(reader) {
  const decoder = new TextDecoder();
  
  while (true) {
    const { done, value } = await reader.read();
    if (done) break;
    
    // Yield decoded chunks for optimistic UI
    yield decoder.decode(value, { stream: true });
    
    // Artificial aesthetic delay for readability
    await wait(getRandomDelay(10, 30));
  }
}
"The interface should be invisible. The user isn't talking to a computer; they are exploring a thought space. The latency is the texture of that exploration."

Visual Modules

Semantic Search

Vector embedding visualization

Code Highlighting

Custom Prism.js implementation

import { Spark } from '@cognition/core';
// AI logic handling const init = () => { return Spark.connect(); }
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