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North Korean Hackers Combine BeaverTail and OtterCookie into Advanced JS Malware
The North Korean threat actor linked to the Contagious Interview campaign has been observed merging some of the functionality of two of its malware programs, indicating that the hacking group is actively refining its toolset. That’s according to new findings from Cisco Talos, which said recent campaigns undertaken by the hacking group have seen the…
ThreatBook Launches Best-of-Breed Advanced Threat Intelligence Solution
Singapore, Singapore, September 29th, 2025, CyberNewsWire Analyzing over 14 billion cyber-attack records daily, ThreatBook ATI is a global solution enriched with granular, local insights; and can offer organizations a truly APAC perspective. Boasting low false positive rates, the solution is highly compatible with existing security stacks. ThreatBook ATI provides actionable insights for threat detection and…
Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration
class MCPAgent: “””Advanced MCP Agent with evolved capabilities – Jupyter Compatible””” def __init__(self, agent_id: str, role: AgentRole, api_key: str = None): self.agent_id = agent_id self.role = role self.api_key = api_key self.memory = [] self.context = AgentContext( agent_id=agent_id, role=role, capabilities=self._init_capabilities(), memory=[], tools=self._init_tools() ) self.model = None if GEMINI_AVAILABLE and api_key: try: genai.configure(api_key=api_key) self.model = genai.GenerativeModel(‘gemini-pro’) print(f”✅…
MBZUAI Researchers Release K2 Think: A 32B Open-Source System for Advanced AI Reasoning and Outperforms 20x Larger Reasoning Models
A team of researchers from MBZUAI’s Institute of Foundation Models and G42 released K2 Think, is a 32B-parameter open reasoning system for advanced AI reasoning. It pairs long chain-of-thought supervised fine-tuning with reinforcement learning from verifiable rewards, agentic planning, test-time scaling, and inference optimizations (speculative decoding + wafer-scale hardware). The result is frontier-level math performance…
Implementing DeepSpeed for Scalable Transformers: Advanced Training with Gradient Checkpointing and Parallelism
In this advanced DeepSpeed tutorial, we provide a hands-on walkthrough of cutting-edge optimization techniques for training large language models efficiently. By combining ZeRO optimization, mixed-precision training, gradient accumulation, and advanced DeepSpeed configurations, the tutorial demonstrates how to maximize GPU memory utilization, reduce training overhead, and enable scaling of transformer models in resource-constrained environments, such as…
A Coding Implementation of an Advanced Tool-Using AI Agent with Semantic Kernel and Gemini
In this tutorial, we build an advanced AI agent using Semantic Kernel combined with Google’s Gemini free model, and we run it seamlessly on Google Colab. We start by wiring Semantic Kernel plugins as tools, like web search, math evaluation, file I/O, and note-taking, and then let Gemini orchestrate them through structured JSON outputs. We…
Building Advanced Multi-Agent AI Workflows by Leveraging AutoGen and Semantic Kernel
In this tutorial, we walk you through the seamless integration of AutoGen and Semantic Kernel with Google’s Gemini Flash model. We begin by setting up our GeminiWrapper and SemanticKernelGeminiPlugin classes to bridge the generative power of Gemini with AutoGen’s multi-agent orchestration. From there, we configure specialist agents, ranging from code reviewers to creative analysts, demonstrating…
How to Build an Advanced BrightData Web Scraper with Google Gemini for AI-Powered Data Extraction
In this tutorial, we walk you through building an enhanced web scraping tool that leverages BrightData’s powerful proxy network alongside Google’s Gemini API for intelligent data extraction. You’ll see how to structure your Python project, install and import the necessary libraries, and encapsulate scraping logic within a clean, reusable BrightDataScraper class. Whether you’re targeting Amazon…
ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks
LLMs primarily enhance accuracy through scaling pre-training data and computing resources. However, the attention has shifted towards alternate scaling due to finite data availability. This includes test-time training and inference compute scaling. Reasoning models enhance performance by emitting thought processes before answers, initially through CoT prompting. Recently, reinforcement learning (RL) post-training has been used. Scientific…
Enhancing AI Inference: Advanced Techniques and Best Practices
When it comes to real-time AI-driven applications like self-driving cars or healthcare monitoring, even an extra second to process an input could have serious consequences. Real-time AI applications require reliable GPUs and processing power, which has been very expensive and cost-prohibitive for many applications – until now. By adopting an optimizing inference process, businesses can…
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