Anthill 🐜 — A Distributed LLM Experiment

Anthill is a small experimental project exploring whether large language model inference can be split into tiny encrypted tasks and processed across volunteer machines — similar in spirit to how torrents distribute data. This is not a formal research initiative; it is simply a technical playground to try ideas without expectations.

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🐜 Concept Overview

Anthill imagines a system where: • Prompts remain on the user’s device • The device plans to break the prompt into tiny, unreadable micro-tasks • These micro-tasks could be sent to volunteer nodes • Each volunteer node would check the task for safety flags • Safe tasks would be processed locally via a small LLM • Partial outputs would then be returned to the main device • The main device would plan to compose the final answer from many tiny responses

All processing is planned to stay decentralized and privacy-aware by design.

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🗺️ Planned Architecture:

This diagram represents the direction of exploration, not a finished system.

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🎯 Project Goals (Exploratory, Not Commitments)

Anthill aims to: • Explore distributed inference techniques • Experiment with prompt sharding and encrypted micro-tasks • Understand decentralized coordination patterns • Prototype unusual or playful LLM architectures • Encourage non-centralized, volunteer-assisted computation

The project has no deadlines, guarantees, or production intent.

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📌 Current Status • Concept: drafted • Architecture: early sketch • Implementation: not started • Timeline: undefined • Purpose: experimental learning and exploration

Development will be purely exploratory and may change direction at any time.

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🤝 Contributions

Suggestions, discussions, and experimental pull requests are welcome. Since this is a relaxed playground project, contributions do not require strict formality — ideas and curiosity are enough.

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📜 License: MIT

Anthill is released under the MIT License, allowing free use, modification, and experimentation.