Tootfinder

Hey... it's snowing again... what a surprise.
Our first winter in Barrie has been eventful as we've now gone 12 days with 2cm or more snow accumulation per day, and had a streak broken yesterday of 10 straight days of heavy snow / snow squall warnings.
According to local media the 2025 November/December snowfall amount is the highest in 25 years. Based on my substantial lack of meteorological knowledge I am predicting less snow than normal for the rest of the winter. The lakes are cooling quickly and icing over due to the colder than normal temperatures. That should reduce the lake effect snow squalls coming off of Georgian Bay. (Delivered in my Cliff Clavin voice).
#Snowmageddon #IceAge

I was just thinking about how the fact that #Musk named his AI "Grok" is evidence that he "reads sci-fi" in the same way he "plays video games." Like, he claims to do it but when it comes time to show the evidence it's clear he does not actually "grok" it.
Like... To grok something is to have a layer deeper than simply knowledge, but mathematically encoding statistical relationships between words is pretty obviously not even understanding much less qualifying as "groking" it. In the book, the ability to grok something is also the ability to annihilate that thing with a thought. Just pretending that an LLM actually *was* something that could become AGI (which it's not), this name would imply the AI would have the power to annihilate reality. That's bad. That's a bad name for an AI.
And why would a greedy fascist name something of his after something an anarchist communist space Jesus taught to the hippie cult he started? There are so many layers of facepalm to this. It's some kind of php-esque fractal of incompetence.
Like, there's no reason to talk about this but my brain does this to me sometimes and now it's your problem.

Easy Adaptation: An Efficient Task-Specific Knowledge Injection Method for Large Models in Resource-Constrained Environments
Dong Chen, Zhengqing Hu, Shixing Zhao, Yibo Guo
https://arxiv.org/abs/2512.17771 https://arxiv.org/pdf/2512.17771 https://arxiv.org/html/2512.17771
arXiv:2512.17771v1 Announce Type: new
Abstract: While the enormous parameter scale endows Large Models (LMs) with unparalleled performance, it also limits their adaptability across specific tasks. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a critical approach for effectively adapting LMs to a diverse range of downstream tasks. However, existing PEFT methods face two primary challenges: (1) High resource cost. Although PEFT methods significantly reduce resource demands compared to full fine-tuning, it still requires substantial time and memory, making it impractical in resource-constrained environments. (2) Parameter dependency. PEFT methods heavily rely on updating a subset of parameters associated with LMs to incorporate task-specific knowledge. Yet, due to increasing competition in the LMs landscape, many companies have adopted closed-source policies for their leading models, offering access only via Application Programming Interface (APIs). Whereas, the expense is often cost-prohibitive and difficult to sustain, as the fine-tuning process of LMs is extremely slow. Even if small models perform far worse than LMs in general, they can achieve superior results on particular distributions while requiring only minimal resources. Motivated by this insight, we propose Easy Adaptation (EA), which designs Specific Small Models (SSMs) to complement the underfitted data distribution for LMs. Extensive experiments show that EA matches the performance of PEFT on diverse tasks without accessing LM parameters, and requires only minimal resources.
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