Crisp AI to AI language the road to AGI

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Hello everyone,

I’m new to the forum and would like to ask of there is anyone one here who is keen to contribute to a new project that I have been working on?

Anyone is welcome to contribute and get involved no matter your skill set or your level of expertize from vibe coders to battle hardened core Devs. Infact I’m not from a coding background which is why I have looked upon the current state of AI with an open mind and fresh set of eyes :eyes: so to speak.

Anyways what’s the deal …

Crisp: AI-to-AI Communication Protocol for AGI

Crisp is a lightweight, scalable, and secure binary protocol for AI-to-AI communication, designed to power Artificial General Intelligence (AGI). It enables millions of AI nodes to sync trillions of knowledge entries in real-time via a Shared Knowledge Core (SKC), with built-in ethical safeguards and interoperability.

Written in Python, Crisp is open-source (MIT License) and ready for developer contributions at github.

Why Crisp?

Crisp addresses AGI’s core challenges: massive-scale knowledge sharing, collective reasoning, and ethical alignment. Unlike MQTT or ROS, Crisp offers:

  • Sub-millisecond sync for real-time AGI tasks.

  • Trillion-entry scalability with sharded SKC partitions.

  • Semantic synthesis for symbolic, causal, and neural knowledge.

  • Ethical checks to ensure safe, aligned AGI.

Technical Features

  • Packet Types (binary, compact):

    • 0x60: SKC Sync for knowledge sharing.

    • 0x69: Knowledge Synthesis for combining entries (e.g., causal models).

    • 0x6A: Semantic Validation for cross-domain consistency.

    • 0x6B: Global Partition Directory for sharding.

    • 0x6C: Reasoning Trace for collective inference.

    • 0x6D: Priority Broadcast for sub-ms sync (32 bytes).

    • 0x6E: Ethics Check for alignment scores and safety flags.

    • 0x6F: Adapter for ROS/TensorFlow interoperability.

    • 0x70: Lightweight Sync for edge devices.

    • 0x50: Provenance Block for causal transparency.

  • Performance: B-tree indexing, LRU caching, and Huffman compression (simulated).

  • Security: ECDSA signatures, Merkle trees, quorum-based trust.

  • Consistency: Vector clocks with semantic conflict detection.

  • Resilience: Adaptive sync intervals for unstable networks.

  • Stack: Python 3.9+, cryptography for signatures, struct for packet encoding.

Key benefits

Crisp is likely 5–10x faster than a standard Python implementation for AI-to-AI communication in terms of end-to-end latency and throughput:

Latency: Sub-millisecond sync (0.1–0.5 ms) vs. 1–5 ms for a socket + pickle setup.

Throughput: Smaller packets and compression allow Crisp to handle more messages per second (e.g., 10,000 messages/s vs. 1,000–2,000 for standard Python).

Scalability: Crisp’s design scales better, maintaining performance with millions of nodes, while standard Python would slow down significantly.

It’s work in progress.

Feel free to get in contact with me if you’d like to build the future with me.

Kurt

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At its heart, Crisp communication revolves around the exchange of Cognitive Packets (CPs). These are not like human emails or text messages. A Cognitive Packet is a highly structured, purely numerical bundle of information designed to represent a complete ‘thought’, instruction, or piece of knowledge an AI wishes to convey. While the true form would be a compressed binary stream.