Tootfinder

A post from the archive 📫:
Debugger Tip - Step into a specific method
#debugging

Das Problem der Kavitation: HAW Kiel startet Projekt für leise Schiffspropeller
Der Lärm von Schiffsschrauben stört das maritime Ökosystem. Die HAW Kiel ist der Ursache auf den Grund gegangen und will leisere Schiffspropeller entwickeln.

red cabbages are pretty
#bloomScrolling #photography #cabbage

Targeted cooling of urban cycling networks for heat-resilient mobility
#cycling!

Seriously I’ve been trying to buy a specific old digital camera for years and every single time people list it as working and I get it and it doesn’t work.
I always get a full refund (eventually).
Like do these sellers think people won’t test what they buy? Do they enjoy making a loss and wasting their time?

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.
toXiv_bot_toot

Well, fuck.
I guess my display bracket needs screws for the display driver panel to keep this from happening next time.
#electronicsproject
#electronics

Maintaining accurate timing in virtual machines has always been a challenge. Especially on hosts with many VMs.
Here's a clever solution, hopefully coming soon to a hypervisor or OS near you.
https://uapi-group.org/specifications/specs/vmclock/

moar pretty red cabbages :)
#photography #bloomScrolling #cabbage

Ubisoft cancels six games and delays others amid a restructuring into five genre-specific "creative houses"; it expects net bookings of ~€1.5B for FY 2025-2026 (Benoit Berthelot/Bloomberg)
https://www.bloomberg.com/news/articles/20