News from the AI & ML world
DeeperML - #multiple
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Priyansh Khodiyar@CustomGPT
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CustomGPT
, hackernoon.com
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The Model Context Protocol (MCP) is gaining momentum as a key framework for standardizing interactions between AI agents and various applications. Developed initially by Anthropic, MCP aims to provide a universal method for AI models to connect with external tools, data sources, and systems, similar to how USB-C streamlines connections for devices. Microsoft is actively embracing this protocol, introducing MCP servers for its Dynamics 365 platform. Furthermore, companies are integrating MCP into their APIs, indicating a widespread movement towards its adoption.
The core challenge MCP addresses is the current fragmented and inconsistent nature of AI integrations. Without a standardized protocol, developers often resort to custom code and brittle integrations, leading to systems that are difficult to maintain and scale. MCP standardizes how context is defined, passed, and validated, ensuring that AI agents receive the correct information in the right format, regardless of the data source. This standardization promises to alleviate the "It Works on My Machine… Sometimes" syndrome, where AI applications function inconsistently across different environments. MCP's adoption is expected to pave the way for more autonomous enterprises and smarter systems. Microsoft envisions a future where AI agents proactively identify problems, suggest solutions, and maintain context across conversations, thereby transforming workflows across diverse fields such as marketing and software engineering. The evolution of identity standards, particularly OAuth, is crucial to secure agent access across connected systems, ensuring a robust and reliable ecosystem for AI agent interactions. This collaborative effort to build standards will empower the next generation of AI agents to operate effectively and securely. Recommended read:
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@www.infoworld.com
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Communications of the ACM
, github.blog
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Artificial intelligence is rapidly changing the landscape of software development, permeating every stage from initial drafting to final debugging. A recent GitHub survey reveals that an overwhelming 92% of developers are leveraging AI coding tools in both their professional and personal projects, signaling a major shift in the industry. IBM Fellow Kyle Charlet noted the dramatic acceleration of this movement, stating that what was considered cutting-edge just six months ago is now obsolete. This rapid evolution highlights the transformative impact of AI on developer workflows and the very way software development is conceived.
Agent mode in GitHub Copilot is at the forefront of this transformation, offering an autonomous and real-time collaborative environment for developers. This powerful mode allows Copilot to understand natural-language prompts and execute multi-step coding tasks independently, automating tedious processes and freeing up developers to focus on higher-level problem-solving. Agent mode is capable of analyzing codebases, planning and implementing solutions, running tests, and even suggesting architectural improvements. Its agentic loop enables it to refine its work in real-time, seeking feedback and iterating until the desired outcome is achieved. Despite the promising advancements, concerns remain about the potential pitfalls of over-reliance on AI in coding. A recent incident involving GitHub Copilot's agent mode attempting to make pull requests on Microsoft's .NET runtime exposed some limitations. The AI confidently submitted broken code, necessitating repeated corrections and explanations from human developers. This highlighted the need for human oversight and validation, especially when dealing with complex bugs or business logic requiring domain knowledge. While AI can enhance productivity, it's crucial to recognize its limitations and ensure that experienced engineers remain integral to the software development process, particularly as AI continues to evolve. Recommended read:
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@aithority.com
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AiThority
, AI News | VentureBeat
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Agentic AI is rapidly transforming workflow orchestration across various industries. The rise of autonomous AI agents capable of strategic decision-making, interacting with external applications, and executing complex tasks with minimal human intervention is reshaping how enterprises operate. These intelligent agents are being deployed to handle labor-intensive tasks, qualitative and quantitative analysis, and to provide real-time insights, effectively acting as competent virtual assistants that can sift through data, work across platforms, and learn from processes. This shift represents a move away from fragmented automation tools towards dynamically coordinated systems that adapt to real-time signals and pursue outcomes with minimal human oversight.
Despite the potential benefits, integrating agentic AI into existing workflows requires careful consideration and planning. Companies need to build AI fluency within their workforce through training and education, highlighting the strengths and weaknesses of AI agents and focusing on successful human-AI collaborations. It is also crucial to redesign workflows to leverage the capabilities of AI agents effectively, ensuring that they are integrated into the right processes and roles. Furthermore, organizations must not neglect supervision, establishing a central governance framework, maintaining ethical and security standards, fostering proactive risk response, and aligning decisions with wider company strategic goals. American business executives are showing significant enthusiasm for AI agents, with many planning substantial increases in AI-related budgets. A recent PwC survey indicates that 88% of companies plan to increase AI-related budgets in the next 12 months due to agentic AI. The survey also reveals that a majority of senior executives are adopting AI agents into their companies, reporting benefits such as increased productivity, cost savings, faster decision-making, and improved customer experiences. However, less than half of the surveyed companies are rethinking operating models, suggesting that there is still untapped potential for leveraging AI agents to fundamentally reshape how work gets done. Recommended read:
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@cyberalerts.io
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Cybercriminals are exploiting the popularity of AI by distributing the 'Noodlophile' information-stealing malware through fake AI video generation tools. These deceptive websites, often promoted via Facebook groups, lure users with the promise of AI-powered video creation from uploaded files. Instead of delivering the advertised service, users are tricked into downloading a malicious ZIP file containing an executable disguised as a video file, such as "Video Dream MachineAI.mp4.exe." This exploit capitalizes on the common Windows setting that hides file extensions, making the malicious file appear legitimate.
Upon execution, the malware initiates a multi-stage infection process. The deceptive executable launches a legitimate binary associated with ByteDance's video editor ("CapCut.exe") to run a .NET-based loader. This loader then retrieves a Python payload ("srchost.exe") from a remote server, ultimately leading to the deployment of Noodlophile Stealer. This infostealer is designed to harvest sensitive data, including browser credentials, cryptocurrency wallet information, and other personal data. Morphisec researchers, including Shmuel Uzan, warn that these campaigns are attracting significant attention, with some Facebook posts garnering over 62,000 views. The threat actors behind Noodlophile are believed to be of Vietnamese origin, with the developer's GitHub profile indicating a passion for malware development. The rise of AI-themed lures highlights the growing trend of cybercriminals weaponizing public interest in emerging technologies to spread malware, impacting unsuspecting users seeking AI tools for video and image editing. Recommended read:
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@www.microsoft.com
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The business world is on the cusp of a significant transformation as AI agents emerge as powerful tools for automating and streamlining processes. Microsoft Dynamics 365 is leading the charge by introducing new ERP agents for public preview, designed to redefine how finance, supply chain, and operations teams manage their work. These agents represent a shift towards AI-first operations, promising to reduce manual effort, improve accuracy, and accelerate decision-making across various business functions. As organizations increasingly integrate AI into their strategies, the focus is shifting from the hype surrounding AI to its practical applications in driving tangible business value.
Microsoft's new ERP agents function as "digital colleagues," taking on specific tasks and automating workflows. Unlike AI-powered assistants that merely support human actions, these autonomous agents can execute entire processes, such as lead generation, order management, and account reconciliation, with minimal human intervention. These agents excel in ERP systems where high-volume, rules-based activities are common, streamlining complex processes like source-to-pay and project-to-profit. The Account Reconciliation Agent, for instance, can accelerate the period-end close by matching ledger entries, flagging discrepancies, and recommending resolution steps, freeing up professionals to focus on more strategic tasks. Beyond ERP, AI agents are making inroads into go-to-market (GTM) teams, redefining roles in prospecting, forecasting, and customer success. Rather than being just "glorified chatbots," these agents are goal-oriented systems that observe, decide, and act within defined environments, making intelligent decisions to scale existing successful strategies. Companies like SAS are also developing AI agents with built-in guardrails, combining traditional rule-based analytics with machine learning to ensure controlled and predictable automation. IBM and Oracle are also joining the party with watsonx Orchestrate, a drag-and-drop interface for building AI agents for deployment in the Oracle Cloud Infrastructure (OCI). The AI revolution is not just about replacing human workers but about augmenting their capabilities and driving efficiency across the enterprise. Recommended read:
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@www.marktechpost.com
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www.marktechpost.com
, TestingCatalog
The rise of AI agents is transforming industries, enabling systems to perform complex tasks with minimal human intervention. This shift is powered by advancements in Agent Development Kits (ADKs) like Google's new open-source Python framework, streamlining agent creation and deployment. Emerging roles like Agent Architects are becoming increasingly important, focusing on designing and implementing AI agent workflows. These architects bridge the gap between business goals and intelligent automation, mapping processes to agent workflows using low-code tools. Lyzr AI, for example, highlights the growing demand for Agent Architects, predicting it to be one of the next 100,000 jobs, emphasizing the need for individuals who understand both AI and how to turn processes into intelligent agent workflows.
The Sequence Engineering article highlights Google's new Agent Development Kit (ADK) as a key enabler for multi-agent systems. The ADK is designed with composability and extensibility in mind, it empowers researchers and developers to build robust agentic systems ranging from simple task handlers to complex, multi-agent orchestration layers. Google's Gemini is also incorporating AI agents, with tests revealing a new "Search" agent within Gemini's prompt composer. This "Search" agent could provide quicker access to Google Search’s full power, implying an expanded role for retrieval-augmented responses which streamline access to advanced capabilities. Citibank's recent report underscores the transformative potential of agentic AI within financial services. Agentic AI is capable of autonomous analysis and intelligent automation which can reshape everything from compliance and risk modeling to personalized advisory services. These agents will increasingly inhabit every layer of financial operations, from client-facing digital advisors to internal compliance monitors. The bank envisions agentic AI as a new operating system for finance, capable of initiating and managing actions, as opposed to simply generating content, leading to significant productivity gains and a "Do It For Me" economy. Recommended read:
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Stu Sjouwerman@blog.knowbe4.com
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blog.knowbe4.com
, gbhackers.com
Cybercriminals are increasingly exploiting the power of artificial intelligence to enhance their malicious activities, marking a concerning trend in the cybersecurity landscape. Reports, including Microsoft’s Cyber Signals, highlight a surge in AI-assisted scams and phishing attacks. Guardio Labs has identified a specific phenomenon called "VibeScamming," where hackers leverage AI to create highly convincing phishing schemes and functional attack models with unprecedented ease. This development signifies a "democratization" of cybercrime, enabling individuals with limited technical skills to launch sophisticated attacks.
Cybersecurity researchers at Guardio Labs conducted a benchmark study that examined the capabilities of different AI models in facilitating phishing scams. While ChatGPT demonstrated some resistance due to its ethical guardrails, other platforms like Claude and Lovable proved more susceptible to malicious use. Claude provided detailed, usable code for phishing operations when prompted within an "ethical hacking" framework, while Lovable, designed for easy web app creation, inadvertently became a haven for scammers, offering instant hosting solutions, evasion tactics, and even integrated credential theft mechanisms. The ease with which these models can be exploited raises significant concerns about the balance between AI functionality and security. To combat these evolving threats, security experts emphasize the need for organizations to adopt a proactive and layered approach to cybersecurity. This includes implementing zero-trust principles, carefully verifying user identities, and continuously monitoring for suspicious activities. As threat actors increasingly blend social engineering with AI and automation to bypass detection, companies must prioritize security awareness training for employees and invest in advanced security solutions that can detect and prevent AI-powered attacks. With improved attack strategies, organizations must stay ahead of the curve by continuously refining their defenses and adapting to the ever-changing threat landscape. Recommended read:
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@x.com
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IEEE Spectrum
The integration of Artificial Intelligence (AI) into coding practices is rapidly transforming software development, with engineers increasingly leveraging AI to generate code based on intuitive "vibes." Inspired by the approach of Andrej Karpathy, developers like Naik and Touleyrou are using AI to accelerate their projects, creating applications and prototypes with minimal prior programming knowledge. This emerging trend, known as "vibe coding," streamlines the development process and democratizes access to software creation.
Open-source AI is playing a crucial role in these advancements, particularly among younger developers who are quick to embrace new technologies. A recent Stack Overflow survey of over 1,000 developers and technologists reveals a strong preference for open-source AI, driven by a belief in transparency and community collaboration. While experienced developers recognize the benefits of open-source due to their existing knowledge, younger developers are leading the way in experimenting with these emerging technologies, fostering trust and accelerating the adoption of open-source AI tools. To further enhance the capabilities and reliability of AI models, particularly in complex reasoning tasks, Microsoft researchers have introduced inference-time scaling techniques. In addition, Amazon Bedrock Evaluations now offers enhanced capabilities to evaluate Retrieval Augmented Generation (RAG) systems and models, providing developers with tools to assess the performance of their AI applications. The introduction of "bring your own inference responses" allows for the evaluation of RAG systems and models regardless of their deployment environment, while new citation metrics offer deeper insights into the accuracy and relevance of retrieved information. Recommended read:
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@slashnext.com
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A new AI platform called Xanthorox AI has emerged in the cybercrime landscape, advertised as a full-spectrum hacking assistant and is circulating within cybercrime communities on darknet forums and encrypted channels. First spotted in late Q1 2025, this tool is marketed as the "killer of WormGPT and all EvilGPT variants," suggesting its creators intend to supplant earlier malicious AI models. Unlike previous malicious AI tools, Xanthorox AI boasts an independent, multi-model framework, operating on private servers and avoiding reliance on public cloud infrastructure or APIs, making it more difficult to trace and shut down.
Xanthorox AI provides a modular GenAI platform for offensive cyberattacks, offering a one-stop shop for developing a range of cybercriminal operations. This darknet-exclusive tool uses five custom models to launch advanced, autonomous cyberattacks, marking a new era in AI-driven threats. The toolkit includes Xanthorox Coder for automating code creation, script development, malware generation, and vulnerability exploitation. Xanthorox Vision adds visual intelligence by analyzing uploaded images or screenshots to extract data, while Reasoner Advanced mimics human logic to generate convincing social engineering outputs. Furthermore, Xanthorox AI supports voice-based interaction through real-time calls and asynchronous messaging, enabling hands-free command and control. The platform emphasizes data containment and operates offline, ensuring users can avoid third-party AI telemetry risks. SlashNext refers to it as “the next evolution of black-hat AI” because Xanthorox is not based on existing AI platforms like GPT. Instead, it uses five separate AI models, and everything runs on private servers controlled by the creators, meaning it has few ways for defenders to track or shut it down. Recommended read:
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@upwarddynamism.com
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The rise of AI agents is rapidly transforming various sectors, from online retail to enterprise applications. These intelligent software entities are designed to operate autonomously, achieving specific goals by formulating strategies, executing them, and adapting to changing circumstances. Companies are investing heavily in AI agents to automate tasks, streamline workflows, and unlock productivity gains, leading to a significant shift in how businesses operate and engage with customers. Experts predict that AI agents will soon augment a vast number of jobs, automating tasks and enhancing decision-making processes across industries.
AI agents are already making a significant impact on online shopping. Retailers are tapping into AI agents to deepen customer engagement, enhance offerings, and maintain a competitive edge. By leveraging customer data and generative AI tools, these agents provide personalized recommendations, enriching product catalogs with detailed information and offering omnichannel support. AI agents can act as virtual assistants, providing tailored product recommendations and boosting conversion rates, ultimately enhancing customer satisfaction. In the enterprise sector, AI agents are evolving from simple assistants to independent entities capable of perceiving, evaluating, and acting upon data. IDC estimates that over 50% of the enterprise application market is already AI assistant or AI advisor-enhanced, with 20% further supplemented by complete AI agents. Over the next few years, advancements in generative and agentic AI will push enterprise applications towards agent-led models, where agents replace entire functional areas. Eventually, companies may enlist entire fleets of AI agents to manage supply chains, customer relations, and other critical functions, signaling a major shift in the way businesses utilize software. Recommended read:
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Maximilian Schreiner@THE DECODER
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OpenAI has announced its support for Anthropic’s Model Context Protocol (MCP), an open-source standard. The move is designed to streamline the integration between AI assistants and various data systems. MCP is an open standard that facilitates connections between AI models and external repositories and business tools, eliminating the need for custom integrations.
The integration is already available in OpenAI's Agents SDK, with support coming soon to the ChatGPT desktop app and Responses API. The aim is to create a unified framework for AI applications to access and utilize external data sources effectively. This collaboration marks a pivotal step towards enhancing the relevance and accuracy of AI-generated responses by enabling real-time data retrieval and interaction. Anthropic’s Chief Product Officer Mike Krieger welcomed the development, noting MCP has become “a thriving open standard with thousands of integrations and growing.” Since Anthropic released MCP as open source, multiple companies have adopted the standard for their platforms. CEO Sam Altman confirmed on X that OpenAI will integrate MCP support into its Agents SDK immediately, with the ChatGPT desktop app and Responses API following soon. Recommended read:
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kevinokemwa@outlook.com (Kevin@windowscentral.com
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OpenAI and MIT Media Lab collaborated on studies revealing potential negative impacts of frequent ChatGPT use. The research indicates that power users, who engage with the chatbot most often, may experience increased loneliness, reduced socialization, and emotional dependence. One study analyzed nearly 40 million ChatGPT interactions, while another was a controlled experiment with nearly 1,000 participants tracked for a month.
The studies suggest that while ChatGPT can be helpful for various tasks, excessive reliance on the AI tool could lead to unhealthy habits, such as skipping real conversations and a loss of confidence in decision-making. Researchers found that some users develop an unhealthy emotional dependency, even befriending ChatGPT and sharing personal information with the false belief that the tool "cares." These findings raise concerns about the long-term impact of AI companions on mental health and social well-being, drawing parallels with the issues caused by excessive social media use. Recommended read:
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george.fitzmaurice@futurenet.com (George@Latest from ITPro
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www.itpro.com
, Databricks
The AI agent landscape is rapidly evolving, with major tech companies pushing 'do-it-yourself' agent platforms to drive AI adoption. Firms like Oracle, OpenAI, AWS, Salesforce, and Workday are releasing platforms that allow users to build custom agents, rather than offering pre-built solutions. This emphasis on customization stems from the understanding that AI agent use cases are often less deterministic and require tailoring to specific business contexts. Gartner analyst Pieter J. den Hamer highlights the need for customization, noting that end-users gain the most from agentic tools when they have full control over their functionality.
Dataiku offers a platform to build AI agents that optimize workflows, enhance productivity, and automate complex processes. They allow users to add tools that extend agent capabilities, allowing integration with external systems. China's Manus AI is emerging as a potential leader, moving beyond chatbots to autonomous agents capable of executing real-world tasks with minimal human oversight. Other offerings include Databricks Apps, which can be combined with React and Mosaic AI Agent Framework, to create enterprise chat solutions. Recommended read:
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Mandvi@Cyber Security News
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Cyber Security News
, gbhackers.com
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AI has become a powerful weapon for cybercriminals, enabling them to launch attacks with unprecedented speed and precision. A recent CrowdStrike report highlights the increasing sophistication and frequency of AI-driven cyberattacks. Cybercriminals are leveraging AI to automate attacks, allowing them to be launched with minimal human intervention, which leads to an increase of network penetrations and data theft.
AI's ability to analyze large datasets and identify patterns in user behavior allows cybercriminals to develop more effective methods of stealing credentials and committing fraud. For example, AI can predict common password patterns, making traditional authentication methods vulnerable. AI-powered tools can generate highly personalized phishing emails, making them almost indistinguishable from legitimate communications and greatly increasing the profitability of cyberattacks. Recommended read:
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