---
schema: 1
type: term
title: '**vibe code specifications**'
description: '**vibe code specifications** as used in the channel''s captured material.'
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/vibe-code-specifications-95ff45875e81
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/vibe-code-specifications-95ff45875e81.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \*\*vibe code specifications\*\*

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/vibe-code-specifications-95ff45875e81

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**vibe code specifications** as used in the channel's captured material.

: After this chapter you will be able to identify which problems fall outside the limits of LLMs — verifiable correctness, spatial reasoning, exact determinism — and select the newer architectures and form factors built to solve them: energy-based models, hybrid diffusion–autoregressive video models, and asynchronously orchestrated coding agents. — in which the term points at written inputs that drive those agents, we situate the entry within its assigned topic: *Beyond LLMs: New AI Architectures and Capabilities*. What you'll take away from this conversation: why LLMs are architecturally awkward for mission-critical work, what energy-based models (EBMs) are and how they differ, and how one company is betting that verifiable, inspectable AI fills a gap LLMs can't.