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The Complete AI Business System: Build a Funnel That Actually Converts (Without Paid Ads)
A year ago, I thought I had a traffic problem.Continue reading on Medium »
How AI is Transforming Chartered Accountancy: The Future of Smarter CA Practices
Ask any Chartered Accountant what eats up most of their week, and you ll rarely hear strategic advisory work. More often it s the Continue reading on Medium »
A mini robot to simplify dental treatment
A miniature robot developed at the University of Basel could help prepare teeth for a crown. Photo: University of Basel, Catherine Weyer. By Angelika Jacobs A routine check-up at the dentist ends with bad news: tooth decay has left a large cavity, and the tooth needs a crown. The treatment requires several follow-up appointments. During [ ]
Microsoft pressures LG into killing unwanted McAfee ads
Microsoft has intervened to stop Windows 11 users with LG monitors from being bombarded with annoying McAfee trial pop-ups. In response to complaints about the LG bloatware, Microsoft's Windows chief, Pavan Davuluri, said that LG has agreed to immediately disable the McAfee pop-up from its LG Monitor App Installer, and pledged that Microsoft will "keep improving here with our ecosystem partners." This crackdown follows backlash about large pop-up ads for McAfee antivirus and LG's own apps appearing on Windows 11 PCs after being connected to LG monitors. Upon connection, a Windows driver update was silently installing LG driver updates and t … Read the full story at The Verge.
If coding has been solved, why does software keep getting worse?
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Best AI Search Engines for Fast Everyday Research
Traditional search engines give users a list of webpages. AI search engines go a step further by reading multiple sources, summarizing the Continue reading on Medium »
ServiceNow Course | ServiceNow Online Training
Is Service-Now ITSM a Good Career Choice for Freshers in 2026?Continue reading on Medium »
How to Get Your Business Featured in ChatGPT & Google’s AI Search
Search behavior is changing. People are no longer relying only on traditional search engines to find information. Today, users are asking Continue reading on Medium »
Where Are Teams Most And Least Ready For AI-Powered Email?
Email remains one of the most powerful communication and marketing channels for businesses. Whether it s nurturing leads, engaging Continue reading on Medium »
HERMES: Heterogeneous Edge-Relational Multi-Head Embedded SSM Attention for Traffic Conflict Prediction at Signalized Intersections
arXiv:2607.20505v1 Announce Type: new Abstract: Surrogate safety measures (SSMs) enable proactive traffic safety assessment, but many existing methods evaluate pairwise interactions independently or flatten multi-agent scenes into fixed feature vectors, limiting their ability to represent heterogeneous interaction structure and evolving scene-level risk. This study formulates traffic conflict assessment as temporal heterogeneous scene-graph classification and proposes HERMES, a heterogeneous edge-relational graph neural network with SSM-informed multi-head attention. Vehicles and pedestrians are represented as heterogeneous nodes, while vehicle-vehicle, vehicle-pedestrian, and pedestrian-pedestrian interactions are encoded as relation-specific edges with continuous kinematic and surrogate-safety descriptors. Relation-specific attention, dynamic node-edge updates, safety-aware graph pooling, and temporal sequence learning are jointly used to estimate scene-level conflict probability. HE
DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
arXiv:2607.20465v1 Announce Type: new Abstract: The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end. We view LLM-driven data preparation as comprising two complementary capabilities: data construction, which transforms raw sources into supervised training data, and data quality evaluation, which predicts the training value of candidate datasets before downstream training; throughout, "quality" refers to downstream training utility rather than surface-level textual properties. We introduce DataPrep-Bench, the first unified benchmark that jointly evaluates both capabilities under a shared downstream-grounded protocol over six domains and multiple base models. For data construction, methods consume identical raw sources and are scored by fine-tuning a base model on their outputs jointly with Dolly-15k; al
AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
arXiv:2607.20452v1 Announce Type: new Abstract: Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud environments. This paper presents AINTMA (Agentic Intelligent Test Management Architecture), a multi-agent agentic AI system that transforms traditional test management into an autonomous quality intelligence ecosystem. AINTMA deploys six specialized AI agents (Test Discovery, Risk Assessment, Reinforcement Learning Prioritization, Execution Orchestration, Generative Quality Intelligence, and Cloud Security Monitor) coordinated through a secure multi-agent communication framework over a cloud-native microservices infrastructure. The Generative Quality Intelligence agent employs large language models to produce plain language quality narratives, defect risk summaries, and data-augmented test recommendations. The RL Prioritization agent models test selection as a Markov Decision Process, learning contextual