Tootfinder

Dubai-based Huspy, which offers proptech tools to home buyers, agents, and brokers, raised a $59M Series B led by Balderton, after a $40M Series A in June 2022 (Tage Kene-Okafor/TechCrunch)
https://techcrunch.com/2025/07/07/uae-proptech-huspy-r…

UAE proptech Huspy raises $59M to scale in Europe | TechCrunch
Huspy closes $59 million Series B to double down on operations across the Middle East and Europe, backed by Balderton Capital and Peak XV.

going through my months-old screenshots and this motherfucker is a recurring pattern

In #Graz wird diesen Sommer kräftig gebaut.
Nun gibt's offenbar anlässlich der Totalsperre der Tramlinien über die Hauptbrücke weitere Maßnahmen am Kaiser-Franz-Josef-Kai als auch der Erzherzog-Johann-Brücke:
https://…

Man hätte zu Beginn der Pandemie nicht nur (Zugriff auf) Masken haben, sondern auch mit Jens Spahn gut befreundet sein müssen, um reich zu werden. Und zweiteres ist halt schon hart…
Dafür scheint ersteres zumindest teilweise optional gewesen zu sein…
#pol #depol

Had some fun exploring and understanding design of deepfakes over the weekend, by making a satire musical video, using a wide range of tools, and producing in the context of current politics. I learned a lot through exploring. Hope you find the result intersting.
https://youtu.be/dYv_AZdHvGI

From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations
Pulkit Bansal, Raghvendra Kumar, Shakti Singh, Sriparna Saha, Adam Jatowt
https://arxiv.org/abs/2507.05179

From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations
In an era of rampant misinformation, generating reliable news explanations is vital, especially for under-represented languages like Hindi. Lacking robust automated tools, Hindi faces challenges in scaling misinformation detection. To bridge this gap, we propose a novel framework integrating Direct Preference Optimization (DPO) with curriculum learning to align machine-generated explanations with human reasoning. Fact-checked explanations from credible sources serve as preferred responses, whil…

Can Large Language Models Automate the Refinement of Cellular Network Specifications?
Jianshuo Dong, Tianyi Zhang, Feng Yan, Yuanjie Li, Hewu Li, Han Qiu
https://arxiv.org/abs/2507.04214

Can Large Language Models Automate the Refinement of Cellular Network Specifications?
Cellular networks serve billions of users globally, yet concerns about reliability and security persist due to weaknesses in 3GPP standards. However, traditional analysis methods, including manual inspection and automated tools, struggle with increasingly expanding cellular network specifications. This paper investigates the feasibility of Large Language Models (LLMs) for automated cellular network specification refinement. To advance it, we leverage 200,000+ approved 3GPP Change Requests (CRs)…

Learning AI Auditing: A Case Study of Teenagers Auditing a Generative AI Model
Luis Morales-Navarro, Michelle Gan, Evelyn Yu, Lauren Vogelstein, Yasmin B. Kafai, Dana\'e Metaxa
https://arxiv.org/abs/2508.04902

Learning AI Auditing: A Case Study of Teenagers Auditing a Generative AI Model
This study investigates how high school-aged youth engage in algorithm auditing to identify and understand biases in artificial intelligence and machine learning (AI/ML) tools they encounter daily. With AI/ML technologies being increasingly integrated into young people's lives, there is an urgent need to equip teenagers with AI literacies that build both technical knowledge and awareness of social impacts. Algorithm audits (also called AI audits) have traditionally been employed by experts to a…

Sources: Mistral AI is in talks to raise as much as $1B in equity from several investors including Abu Dhabi's MGX; it has raised €1B since its 2023 founding (Bloomberg)
https://www.bloomberg.com/news/articles/2025-…

Reinforcement Learning for Automated Cybersecurity Penetration Testing
Daniel L\'opez-Montero, Jos\'e L. \'Alvarez-Aldana, Alicia Morales-Mart\'inez, Marta Gil-L\'opez, Juan M. Au\~n\'on Garc\'ia
https://arxiv.org/abs/2507.02969

Reinforcement Learning for Automated Cybersecurity Penetration Testing
This paper aims to provide an innovative machine learning-based solution to automate security testing tasks for web applications, ensuring the correct functioning of all components while reducing project maintenance costs. Reinforcement Learning is proposed to select and prioritize tools and optimize the testing path. The presented approach utilizes a simulated webpage along with its network topology to train the agent. Additionally, the model leverages Geometric Deep Learning to create priors …