Feb 11, 2026
Current operating systems are passive resource allocators designed for a pre LLM era. I propose NeuraOS, a paradigm shift where the OS moves the LLM from the user space to the kernel space. This solves the fragmentation of context and the inefficiency of static resource management.
Traditional schedulers use heuristic based logic. NeuralOS implements a Transformer based scheduler that predicts workload spikes by analyzing user intent streams. It pre allocates CPU cycles and manages thermal throttling before the execution bottleneck occurs, reducing perceived latency.
Memory is no longer just blocks of bytes. My system implements Semantic Paging. The OS understands the priority of weights and KV caches across different active agents, dynamically swapping low relevance embeddings to NVMe and keeping critical hot context in VRAM to prevent inference stalls.
I am deprecating the hierarchical folder structure for a Deep Index Kernel. Every write operation triggers an asynchronous embedding process. The file system becomes a queryable vector database, allowing the OS to provide cross application context without explicit data silo integration.
Security shifts from signature matching to Neural Anomaly Detection. By establishing a baseline of normal system calls for a specific user/developer profile, the kernel can identify and sandbox malicious processes (e.g., unauthorized data exfiltration) in real time based on intent deviation, rather than known malware patterns.
Recursive Language Models for Infinite Context. The most significant bottleneck in AI-First systems is the context window. NeuraOS integrates the Recursive Language Model (RLM) framework proposed by Zhang & Khattab as its native inference strategy. Instead of feeding the entire system state into a single LLM call, the kernel's root model interacts with context through a REPL environment where state is stored as a variable, it can programmatically peek, grep, partition, and spawn recursive sub-queries over it, effectively scaling to unbounded context lengths while mitigating context rot, without ever requiring a single model call to handle the full context.
You can find more of this idea on GitHub.
@article{lafalce2026aifirstos,
title = "AI-First OS Concept",
author = "Mateo Lafalce",
journal = "https://mateolafalce.github.io/",
year = "2026",
month = "Feb",
url = "https://mateolafalce.github.io/2026/AI-First%20OS/index.html"
}