Tootfinder

Sources: Meta plans to unveil an AI lab dedicated to "superintelligence", led by Scale AI CEO Alexandr Wang, and is offering seven- to nine-figure compensation (New York Times)
https://www.nytimes.com/2025/06/10/technology/meta-new-ai…

Meta Is Creating a New A.I. Lab to Pursue ‘Superintelligence’
The new lab, set to include Scale AI founder Alexandr Wang, is part of a reorganization of Meta’s artificial intelligence efforts under Mark Zuckerberg.

Leveraging Network Methods for Hub-like Microservice Detection
Alexander Bakhtin, Matteo Esposito, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2506.07683

Leveraging Network Methods for Hub-like Microservice Detection
Context: Microservice Architecture is a popular architectural paradigm that facilitates flexibility by decomposing applications into small, independently deployable services. Catalogs of architectural anti-patterns have been proposed to highlight the negative aspects of flawed microservice design. In particular, the Hub-like anti-pattern lacks an unambiguous definition and detection method. Aim: In this work, we aim to find a robust detection approach for the Hub-like microservice anti-pattern …

I think this will do nicely.
Folks were complaining about how long the AC RMS amplitude measurement in ngscopeclient took on large datasets, so I made it 67 times faster (as long as your GPU supports GL_ARB_gpu_shader_int64)
Benchmark: 50M point sinusoid, Xeon 6144 vs RTX 2080 Ti

movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps

The dedication from the first edition of The Joy of Gay Sex, by Charles Silverstein and Edmund White. Love the contrast here: Charles dedicates it to his partner, White "to all my tricks"

Don't trust **anyone** who uses their phone like this! Maybe they are actually ghosts. 👻
Thank you, Tim Cook, for gifting us this easy-to-use ghost detector!
#apple #wwdc #liquidglass

Dual Modality-Aware Gated Prompt Tuning for Few-Shot Multimodal Sarcasm Detection
Soumyadeep Jana, Abhrajyoti Kundu, Sanasam Ranbir Singh
https://arxiv.org/abs/2507.04468

Dual Modality-Aware Gated Prompt Tuning for Few-Shot Multimodal Sarcasm Detection
The widespread use of multimodal content on social media has heightened the need for effective sarcasm detection to improve opinion mining. However, existing models rely heavily on large annotated datasets, making them less suitable for real-world scenarios where labeled data is scarce. This motivates the need to explore the problem in a few-shot setting. To this end, we introduce DMDP (Deep Modality-Disentangled Prompt Tuning), a novel framework for few-shot multimodal sarcasm detection. Unlik…

Today seems like a good day to repost this piece I wrote a few weeks back.
How CISOs can defend against Scattered Spider ransomware attacks
https://www.csoonline.com/article/3994369/how-cisos-can-defend-against-scattered-s…

How CISOs can defend against Scattered Spider ransomware attacks
CISOs should fortify help desk and employee defenses, enhance intrusion detection and tracking capabilities, and recognize that paying ransoms is not a viable strategy.

LAID: Lightweight AI-Generated Image Detection in Spatial and Spectral Domains
Nicholas Chivaran, Jianbing Ni
https://arxiv.org/abs/2507.05162 https://

LAID: Lightweight AI-Generated Image Detection in Spatial and Spectral Domains
The recent proliferation of photorealistic AI-generated images (AIGI) has raised urgent concerns about their potential misuse, particularly on social media platforms. Current state-of-the-art AIGI detection methods typically rely on large, deep neural architectures, creating significant computational barriers to real-time, large-scale deployment on platforms like social media. To challenge this reliance on computationally intensive models, we introduce LAID, the first framework -- to our knowle…

MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
Ziyan Liu, Chunxiao Fan, Haoran Lou, Yuexin Wu, Kaiwei Deng
https://arxiv.org/abs/2507.06908

MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
The rapid expansion of memes on social media has highlighted the urgent need for effective approaches to detect harmful content. However, traditional data-driven approaches struggle to detect new memes due to their evolving nature and the lack of up-to-date annotated data. To address this issue, we propose MIND, a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. MIND implements three key strategies: 1) We retrieve similar memes from an unannotated…