Deterministic AI Core in Rust: O(1) Without GPU on MacBook Air
An AI model based on agglutinative logic with constant O(1) complexity runs on a MacBook Air M2 with 8 GB RAM, bypassing the limitations of probabilistic LLMs. The development uses Rust and DMA to directly execute intents as mathematical formulas, eliminating hallucinations and stochasticity.
Problems with Probabilistic Models
Modern LLMs rely on brute force: massive computations to guess tokens. This leads to three key limitations:
- Energy consumption at the level of power plants for training.
- Lack of determinism: outputs are based on probabilities, without strict logic.
- Hallucinations up to 20% in generation, not architecturally fixable.
The approach of increasing power masks problems but doesn't solve them. Determinism requires rethinking the fundamentals.
O(1) Architecture Based on Agglutinative Logic
The core translates high-level intents into executable code directly. Processing and validation time is constant O(1), independent of context. There's no brute force or probabilistic calculations: intents are treated as formulas.
Key components:
- Agglutinative logic: Builds inferences by gluing axioms, ensuring reproducibility.
- Rust without Python: Interpreters and abstractions are eliminated for maximum performance.
- DMA: Direct memory access accelerates operations on the CPU.
Tests v0.26.0 show 5.4 million operations per second on a MacBook Air CPU, surpassing cloud alternatives in speed and reliability.
Implementation Without GPU
Performance is achieved through stack optimization:
- Complete elimination of overhead from Python and frameworks like PyTorch.
- Translation of intents into native machine code.
- Minimization of data copies via DMA.
This enables running autonomous systems locally, without cloud resources.
Ethics Protection at the Axiom Level
Self-learning is integrated with ethics.rs—a module of mathematical axioms for universal human values. Not prompt filters, but strict O(1) validation:
- Destructive code is rejected at the logic compilation stage.
- The model evolves only within the framework of axioms, excluding immoral paths.
- Automatic cleanup: invalid self-learning branches are pruned.
Key Takeaways
- Determinism eliminates hallucinations entirely, unlike LLMs.
- O(1) ensures constant speed on modest hardware (8 GB RAM).
- Rust + DMA deliver 5.4 million ops/s on CPU without GPU.
- Ethical axioms prevent self-learning risks.
- The approach is scalable for real-time autonomous systems.
The project demonstrates that the future of AI lies in mathematical rigor, not resources. BSL license, patenting in progress.
— Editorial Team
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