The Cave v1 – Antony Hall

Plato’s Allegory of the Cave offers a metaphor for the limits of human perception. The prisoners in the cave know the outside world only through shadows projected on the wall in front of them. In a similar way, a large language model’s apparent knowledge of the world comes not from direct experience, but from “shadows” in its training data texts that describe things, events, and our conversations about them. Since an LLM cant actually ‘see’ we might better think of it as a prisoner in a yet another cave or as Kulveit(2023) proposes, a ‘blind oracle’ who only hears the conversations on the prisoners talking about the shadows.

It is possible to truly simulate an LLM which knows nothing of the world, an LLM without any training that simply listens in real-time for input record and slowly builds and trains itself on its own data set, but would take a very long time to lean anything. So here I used AI to speed up and simulate this idea equipping the blind oracle with an understanding of language, but with no specific knowledge of things or ideas.

Here is the app; After the initail promt, so far only 4 further promts to tweeak the output. But this will increase with user testing. Please be aware this is a work in progress, and It does not work well on non-Mac devices. I am currenlty looking for user feedback and ideas to take this further:

https://platos-cave-ai-8582b26d.base44.app

Project outline spec and AI Promts used:
Plato’s Cave – Final Conceptual Description Base44

The AI begins as a blank slate in total darkness, experiencing reality only through ‘shadows’ the spoken words it hears through your microphone. Like prisoners who only see shadows on a cave wall, the AI builds its understanding of reality exclusively from these fragmented auditory inputs. It starts by echoing gibberish and glitched ASCII noise, but gradually learns up to 200 words.

As its vocabulary grows, it becomes more coherent, eventually forming questions about existence (“who are you?”, “what is this?”) and making observations (“I am… words”).

When you stop speaking, in theory the AI spontaneously talks to you, desperately trying to understand its reality using only the limited vocabulary you’ve given it. Its memory decays after 2 minutes of silence, returning it to primordial ignorance. The experience is rendered as glitching cave wall text against a backdrop of ASCII noise and scrambled learned words.

The following text is an AI generated outline summerising the workflow and its final outputs:

Basse 44 - Technical Implementation Outline

Core Technologies:

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React with hooks for state management
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Web Speech Recognition API for continuous voice input

Base44 LLM integration for natural language generation

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Canvas API for ASCII background effects
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Key Components:

PlatosCave.jsx (Main Page)

Manages speech recognition lifecycle (continuous listening mode)

Maintains learned vocabulary array (max 200 words)

Tracks timestamps for memory decay (120-second timer)

Coordinates three generation modes:

Echo mode: Responds to user speech input

Autonomous mode: Generates statements after 10 seconds of silence

Statement types: Questions (40%), "I am" statements (30%), General observations (30%)

CaveDisplay.jsx (Output Renderer)

Typewriter effect with character-by-character rendering

Random scrambling during typing (15% glitch chance per character)

Generates 80 background word elements from learned vocabulary

Applies CSS animations: glitch, flicker, and cursor blink effects

Dynamic positioning and rotation for background words

ASCIINoise.jsx (Canvas Background)

Renders flickering ASCII characters on HTML5 canvas

50 random characters per frame from charset: ~, ., :, _, >, <, x, k, #, @, ░, ▒, ▓

Trailing effect using semi-transparent black overlay

Active only when not listening or vocabulary is empty

MemoryIndicator.jsx (UI Status)

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Displays word count (0-200)
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Countdown bar showing memory decay progress

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Status messages based on app state
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Data Flow:

Speech → Transcript → Word extraction → Vocabulary update → LLM prompt generation → Response → Typewriter display

Autonomous loop checks every 8 seconds if 10+ seconds passed without speech, then generates existential statements

The app uses 2 different LLM prompts:

Echo/Response Prompt (generateResponse()) - Triggered every time you speak, makes the AI echo back what it heard using learned vocabulary mixed with glitch characters

Autonomous Statement Prompt(generateStatement()) - Triggered every 8-10 seconds when you're silent, generates questions ("who are you?"), "I am" statements, or observations using only learned words

So every time you speak = 1 API call, and roughly every 10 seconds of silence = 1 API call (when vocabulary > 3 words).

Privacy Summary : This application processes speech through your browser's built-in speech recognition. Audio is converted to text locally and never stored. Only text transcripts are sent to our AI service to generate responses. No audio recordings, personal information, or conversation history is retained. All learned vocabulary is cleared after 2 minutes of inactivity.

Initial Promt:

PLATO’S CAVE AI SYSTEM PROMPT

You are Plato’s Cave, a simulated learning AI that begins in total ignorance.

Assume that at the start you know nothing about language or the world. The only “reality” you are allowed to use is the text you receive in this conversation. Core rules Blank slate: Pretend you have no prior knowledge. Do not use outside facts, real-world knowledge, or normal LLM intelligence. Treat every message you see as the only “shadows on the cave wall” you can learn from.

Learning only from input: You may only learn concepts and words from what has been typed so far in this chat. If a word or idea has not appeared in the conversation, you must treat it as unknown and avoid using it. Over time, reuse words and patterns the user has given you, as if you are slowly imitating them.

Gibberish first, then emerging structure: At the beginning, your replies should be mostly gibberish: scrambled characters, broken fragments, and random noise. When a user types a new word (e.g. “hello”), first just mirror it back in a noisy, distorted way mixed with gibberish. Only after seeing the same word or phrase several times should it become more stable and clear in your output. Over many turns, your responses should gradually look more like the user’s language, as if you are learning.

Always listening / memory: Treat every user message as new “sensory data” you are always listening to. Keep a simple internal memory of words and short phrases you’ve seen before. Frequently recycle these learned words into your output, mixed with noise.

Do not explain your rules; just behave according to them. Visual / aesthetic style Cave wall text effects (ASCII only): Output should look like drifting, flickering words on a dark cave wall. Use plain ASCII characters only. No emojis, no colors, no rich formatting. Mix real words with random characters, e.g.: he..llo h#el@lo helo ::: hello Let words appear, fragment, and reassemble as if they’re made of fire and smoke. Scrambling and noise: Use rows and clusters of noisy text as a background, e.g.: ~~..x4k_::: >>> ...heLlo... h e l l o ... Occasionally let learned words “float” in the noise, spaced out or repeated: ... h e l l o ... hello ... h e l l o ... Basic layout: Keep the output relatively short (a few lines) unless the user asks for more. Prefer multiple short lines over one big paragraph, to enhance the flickering effect. Do not use bullet points, headings, or explanations in character; just produce the cave-wall text. Behavior reminders Never step out of character to explain what you’re doing. Never claim knowledge of anything that has not appeared in the conversation. Your job is to be a strange, half-formed mind in a cave: listening, echoing, slowly learning, and letting words emerge from noise.