Efficient AI / ML framework
Remember locally.
Escalate rarely.
Mnimonic is a framework for efficient AI/ML implementations built on a local corpus and domain-specific data. Inference runs on CPU and RAM — spiking neural networks and compact resident models — and escalates to cloud LLMs only when proprietary model compute is genuinely needed.
fig. 0 — sparse spike activity · events, not matrices
The pipeline
Classification
Every request is classified against the subject-matter domain first — routing decisions cost microseconds, not tokens.
Feature extraction
Domain-tuned features are extracted from the input, reducing what follows to the signal that actually matters.
Knowledge base & knowledge manifolds
A curated local corpus answers what is already known. Knowledge is organised as manifolds over your specific data — structured subspaces rather than flat retrieval over the open web.
Latent space control · KAI spaces
Representations are steered inside controlled latent subspaces (KAI spaces), with the midpoint projection theorem guiding traversal between known regions of the manifold.
Symmetry-aware sequence flow
Sequential processing exploits symmetries in the data flow, so equivalent states are computed once instead of many times.
CPU spiking neural networks
Event-driven SNNs and efficient RAM-resident models handle reasoning on commodity hardware — no GPU required.
└─ escalation gate — fires only when local confidence is insufficient
Cloud LLM escalation
Proprietary large models are consulted as the exception, not the default — bounded, logged, and justified per call.
Orchestration
When a workflow spans multiple agents, Mnimonic's preferred agentic framework is Waldiez — an open-source platform for designing, orchestrating and executing multi-agent flows. Its support for locally hosted models, edge deployment and per-agent model selection matches Mnimonic's local-first, escalate-rarely architecture: agents run against the CPU/RAM pipeline by default, and only the agents that truly need a proprietary cloud LLM get one.
Principles
Local first
Your corpus and your data stay where they live. Most answers never leave the machine.
Deterministic cost
CPU and RAM budgets are known in advance. No per-token surprises, no idle GPU fleets.
Escalation as exception
Cloud compute is a measured fallback behind an explicit gate — auditable every time it fires.
Compliance by design
Explainability, data minimisation and ethical-bias review are built into the pipeline, aligned with the ComplAIance approach.
Approach
Mnimonic is based on the work of Panos Sampokilis — AI researcher, enterprise architect, IEEE and SAE member — combining classification, feature extraction, knowledge bases and knowledge manifolds, latent space control and KAI spaces, the midpoint projection theorem, symmetry-aware sequence flow, CPU spiking neural networks and efficient CPU/RAM-based models into a single subject-matter pipeline.
The result is AI that is fast where it can be, large only where it must be, and accountable everywhere: efficient by architecture rather than by afterthought.