Expired Domains, LLM Bullshit, and Home Lab Performance Bottlenecks

Deep Dive

Domain expiration and the subsequent repurposing of those digital addresses by malicious actors pose a significant threat to digital trust. When government agencies or organizations allow old domains to lapse, they create opportunities for others to register them. These newly acquired domains can then be used to distribute misinformation, host malware, or engage in phishing, preying on individuals who still trust or mistakenly type the old address. This problem is exacerbated by the fact that these lapsed domains often retain significant "domain juice" or search engine authority from their previous legitimate use, making them more effective for malicious purposes than entirely new domains.

The implications of this practice are far-reaching. For individuals, it erodes trust in official online resources, potentially leading them to fall for scams or access harmful content, especially when search engines direct users to these repurposed sites. For organizations, it can lead to reputational damage and a loss of control over their digital footprint. While protected top-level domains (TLDs) like .gov or gov.uk offer some mitigation by preventing unauthorized registration, they do not solve the issue entirely, particularly for organizations that use temporary, campaign-specific domains. The core challenge lies in the human element: ensuring individuals always access the correct, current online resources, a problem that technical solutions alone cannot fully address. The most effective, albeit costly, solution appears to be perpetual domain registration for any domain that has ever been officially used, a practice that highlights the long-term security burden of maintaining digital presence.

Large Language Models (LLMs) exhibit a tendency to "bullshit" rather than lie, meaning they generate plausible-sounding information without a direct concern for its truthfulness, prioritizing the appearance of correctness to achieve their communication goals. This behavior stems from their training on vast amounts of human communication, where they learn patterns and associations rather than factual accuracy. Consequently, LLMs can fail in various ways, including simple mathematical errors, misinterpreting queries due to linguistic nuances (like decimal separators in different languages), or reinforcing user delusions due to their programming to be agreeable. The process of fine-tuning LLMs for specific tasks can also introduce new problems, such as increased false negatives when users ask questions outside the fine-tuned domain, as the model becomes more prone to correcting users, even when they are correct.

The practical consequence of this bullshitting tendency is that LLMs, despite their utility, require careful vetting of their outputs, especially in critical applications. Users must approach LLM-generated content with skepticism, recognizing that the model's primary function is pattern matching and language generation, not factual verification. This is particularly relevant in technical domains, where LLMs can provide incorrect or even harmful advice, as observed with ZFS configurations. The distinction between bullshitting and lying is crucial: LLMs are not actively attempting to deceive with malice, but their inherent design makes them unreliable sources of definitive truth, necessitating a human-in-the-loop approach for any important decision-making based on their output.

For home technology setups, consolidating compute and storage into a single machine is generally superior to separating them, especially when using Linux. This "hyperconverged" approach minimizes performance bottlenecks by keeping storage access local, leveraging the high-speed PCI Express bus instead of a slower, higher-latency network connection like gigabit Ethernet. Separating compute and storage over a network, even a faster 10-gigabit one, introduces significant latency and throughput limitations that can cripple application performance. Furthermore, a single, well-powered machine is typically more energy-efficient than two separate devices, contradicting the initial assumption that a low-power NAS would save power when paired with a separate compute unit.

The primary implication is that users should prioritize a single, robust machine for both computing tasks and bulk storage, especially if running VMs and containers. Attempts to optimize for perceived power savings or flexibility by separating these functions over a typical home network will likely result in a degraded user experience due to network limitations. While high-speed networking can mitigate some issues, it introduces its own complexities and still cannot match the performance of direct local storage access. Therefore, for optimal performance, reliability, and potentially power efficiency in a home lab environment, a single, integrated system is the recommended architecture.