Tootfinder

End-of-Year Threat Intelligence Sightings Forecast
This report presents an analysis of Threat Intelligence (TI) Sightings aggregated from several key data sources, including social platforms, code repositories, and specialized TI feeds. The primary objective is to visually track historical trends per source and provide a short-term adaptive forecast for a defined period (in days).
#opensource

Viking, n.:
1. Daring Scandinavian seafarers, explorers, adventurers,
entrepreneurs world-famous for their aggressive, nautical import
business, highly leveraged takeovers and blue eyes.
2. Bloodthirsty sea pirates who ravaged northern Europe beginning
in the 9th century.
Hagar's note: The first definition is much preferred; the second is used
only by malcontents, the envious, and disgruntled owners of waterfront
property.

»You already have a git server:
If you have a git repository on a server with ssh access, you can just clone it.«
Actually, this is only logical and correspondingly simple, but you should also use this. Now I came across this with the help of a link to this guide.
🧑‍💻 https://maurycyz.com/misc/easy_git/

Manchester’s Local Plan – responding to the consultation
Local Plans are what guide the pattern of a land use and and building across a council area.  In the case of Greater Manchester (excluding Stockport), the Joint Strategic Plan, Places for Everyone sets the scene, as does the government's National Planning Policy Framework.  That means that there are some very real constraints on what can be put into a so-called Local Plan (not very local - in the case of…

Manchester’s Local Plan – responding to the consultation
UPDATE!  11 Nov.  We have now submitted a revised version.  It adds some answers to questions we didn’t address in the previously published draft, and has some additions to some of the other …

Revisiting Mixout: An Overlooked Path to Robust Finetuning
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger, Marco Pedersoli
https://arxiv.org/abs/2510.06982 https://…

Revisiting Mixout: An Overlooked Path to Robust Finetuning
Finetuning vision foundation models often improves in-domain accuracy but comes at the cost of robustness under distribution shift. We revisit Mixout, a stochastic regularizer that intermittently replaces finetuned weights with their pretrained reference, through the lens of a single-run, weight-sharing implicit ensemble. This perspective reveals three key levers that govern robustness: the \emph{masking anchor}, \emph{resampling frequency}, and \emph{mask sparsity}. Guided by this analysis, we…

🇺🇦 Auf radioeins läuft...
Nation Of Language:
🎵 Across That Fine Line
#NowPlaying #NationOfLanguage
https://nationoflanguage.bandcamp.com/track/across-that-fine-line
https://open.spotify.com/track/0naG5PyrfwJQ0xtuQ1BGCM

"First off, that 127.0.1.1 has to go. There are active changes underways to change the 127/8 allocation to an 127/24 one, to free more IPv4 addresses."
lolno
That might happen in Linux and inside some large private networks like cloud providers (e.g. already did happen with 240/4 inside AWS and Verizon), but I'm fairly confident this will never happen in general across the Internet.

Robust forecast aggregation via additional queries
Rafael Frongillo, Mary Monroe, Eric Neyman, Bo Waggoner
https://arxiv.org/abs/2512.05271 https://arxiv.org/pdf/2512.05271 https://arxiv.org/html/2512.05271
arXiv:2512.05271v1 Announce Type: new
Abstract: We study the problem of robust forecast aggregation: combining expert forecasts with provable accuracy guarantees compared to the best possible aggregation of the underlying information. Prior work shows strong impossibility results, e.g. that even under natural assumptions, no aggregation of the experts' individual forecasts can outperform simply following a random expert (Neyman and Roughgarden, 2022).
In this paper, we introduce a more general framework that allows the principal to elicit richer information from experts through structured queries. Our framework ensures that experts will truthfully report their underlying beliefs, and also enables us to define notions of complexity over the difficulty of asking these queries. Under a general model of independent but overlapping expert signals, we show that optimal aggregation is achievable in the worst case with each complexity measure bounded above by the number of agents $n$. We further establish tight tradeoffs between accuracy and query complexity: aggregation error decreases linearly with the number of queries, and vanishes when the "order of reasoning" and number of agents relevant to a query is $\omega(\sqrt{n})$. These results demonstrate that modest extensions to the space of expert queries dramatically strengthen the power of robust forecast aggregation. We therefore expect that our new query framework will open up a fruitful line of research in this area.
toXiv_bot_toot

Testing and Enhancing Multi-Agent Systems for Robust Code Generation
Zongyi Lyu, Songqiang Chen, Zhenlan Ji, Liwen Wang, Shuai Wang, Daoyuan Wu, Wenxuan Wang, Shing-Chi Cheung
https://arxiv.org/abs/2510.10460

Testing and Enhancing Multi-Agent Systems for Robust Code Generation
Multi-agent systems (MASs) have emerged as a promising paradigm for automated code generation, demonstrating impressive performance on established benchmarks by decomposing complex coding tasks across specialized agents with different roles. Despite their prosperous development and adoption, their robustness remains pressingly under-explored, raising critical concerns for real-world deployment. This paper presents the first comprehensive study examining the robustness of MASs for code generatio…

PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs
Manuel Frank, Haithem Afli
https://arxiv.org/abs/2510.06730 https://

PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs
Current evaluations of sentence embedding models typically rely on static test beds such as the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can inflate reported performance and obscure real-world robustness. We introduce the Paraphrasing Text Embedding Benchmark (PTEB), a dynamic protocol that stochastically generates meaning-preserving paraphrases at evaluation time and aggregates results across multiple runs. Using a cost-efficient LLM-based met…