Category: Artificial Intelligence

The line between human and automated cybersecurity is becoming increasingly thin. With the release of HexStrike AI v6.0, language models such as ChatGPT, Claude, or Copilot are no longer just textual assistants but true operators capable of carrying out penetration tests from start to finish. 

The secret lies in its architecture: HexStrike works as an MCP server (Model Context Protocol), a standard that enables language models to interact directly with external tools. In practice, the MCP acts as a translator between the world of AI and the cybersecurity application ecosystem . Thanks to this bridge, an LLM can run tools like Nmap, Burp Suite, or Ghidra, interpret the results, and automatically decide on the next step.

With more than 150 integrated tools —twice as many as in previous versions—, HexStrike can handle everything from web application analysis  to cloud audits ☁️and binary reverse engineering. All of this is driven by an intelligent decision engine that analyzes targets, selects the best tool for each stage, and builds multi-step attack chains with near-human logic.

The platform goes further with its multi-agent architecture. Instead of a single virtual operator, HexStrike deploys a team of specialized modules: one monitors vulnerabilities in real time , another manages bug bounty workflows , another generates custom exploits , and another solves CTF-style challenges . All work together in coordination, like a real team of experts sharing context and goals.

The user experience has also been redesigned. Results are no longer just raw reports but are displayed in a dynamic visual dashboard , with progress bars, vulnerability cards with CVSS metrics, and attack vectors illustrated in real time. A way to transform technical data into strategic insights useful for both researchers and corporate teams.

With this sixth version, HexStrike doesn’t just add features: it marks a paradigm shift. By turning language models into autonomous operators, it accelerates security assessment cycles, reduces the workload on human specialists, and democratizes access to professional-grade capabilities. Already available on GitHub, version 7.0 is expected to bring even more specialized agents, Docker support, and performance improvements.

🔑 The key is not adding more tools, but connecting them with AI to operate with speed, precision, and context.

#CyberSecurity #ArtificialIntelligence #HexStrikeAI #MCP #MultiAgent #PenTesting #BugBounty #EthicalHacking #InfoSec #AIinSecurity #FutureOfAI

HexStrike AI Connects ChatGPT, Claude, Copilot with 150+ Security Tools like Burp Suite and Nmap

OpenAI unveiled GPT-5 as its most advanced model to date. Wrapped in the glow of marketing , GPT-5 was introduced with headlines calling it a leap toward Artificial General Intelligence (AGI). Yet once the announcements fade and real-world testing begins, the picture is less epic: yes, there are tangible improvements , but also setbacks and decisions that raise questions .

The headline feature is its unified architecture. What used to be separate models —GPT-4o, GPT-4.5, o3— now lives inside a single system. An intelligent “router” decides whether a query should be handled by a fast model or a deep reasoning model . This change simplifies life for less technical users and comes with a more polished interface, customization options , and faster responses .

In testing, GPT-5 impresses: in coding , it shows a clear leap forward with fewer errors and better understanding of complex contexts; on sophisticated prompts , its reasoning feels sharper; and in mathematics , although it still stumbles, it delivers more consistent results. There’s even a noticeable reduction in “hallucinations” —those fabricated answers that so often eroded trust— in certain cases.

But the downsides appear quickly. Models like GPT-4o, which excelled in some tasks, are gone. Performance on simple questions is sometimes inconsistent , usage restrictions have increased, and advanced users have lost key tools. For power users, the feeling is clear: accessibility has improved, but at the cost of control and versatility.

So is it the generational leap its name suggests? For many, the answer is no. The “GPT-5” label sounds grand, but in practice it feels more like “GPT-4o Plus” . Even Sam Altman, CEO of OpenAI, admits it is not AGI: it doesn’t learn in real time and remains bound by its training limits.

In daily use, GPT-5 is a powerful tool for those who prioritize speed and simplicity: executives , developers using environments like Cursor , or casual users . But it may disappoint those needing broad context or specific models for niche tasks.

The takeaway : in AI, it’s vital to separate hype from actual value. GPT-5 is a step forward, yes, but not the revolution some want it to be. The challenge is not only building more powerful models, but designing them without forgetting the users who push them to their limits.

#GPT5 #OpenAI #ArtificialIntelligence #AI #DigitalFuture #DigitalTransformation

In a new experiment that seems to anticipate the direction of creative computing, Google has introduced Opal, a tool that allows users to build small AI-driven applications using only natural language. No programming knowledge is required. No installation needed. You just describe what you want to achieve, and the system does the rest.

Opal is available in public beta — currently only in the United States — and is part of the Google Labs experimentation program. Its concept is simple yet ambitious: turn written ideas into functional workflows powered by AI. From generating product descriptions to sending automated emails or creating images, everything can be organized into an app that runs directly in the browser.

💡 How do you build an app without coding?

Opal works on a very familiar idea for anyone who has used language models like ChatGPT or Gemini: writing a prompt. The difference is that this prompt is transformed into a visual structure — a chain of steps representing inputs, processes, and outputs that make up an app.

For example, if you ask “create a name generator for design companies,” Opal not only returns suggestions — it builds a reusable flow you can run anytime. It also offers a visual interface to edit, rearrange, or enhance each step.

For those who prefer not to start from scratch, Opal includes a gallery of templates ready to be customized. Just duplicate an app, modify the text or components, and share it like any Google document.

🧩 An interface for the present, a language for the future

What sets Opal apart isn’t just that it’s code-free — it’s how it combines visual editing with conversational language. Instead of dragging components manually, you can just type: “Add a step that translates this into English” or “Show the result in a table.” The AI understands and updates the flow in real time.

This aligns with what some in the industry call “vibe-coding” — a way of building software based more on intention than syntax. Rather than coding line by line, you converse with the system to shape what it does. The idea comes first — not the technical process.

🔭 How far can it go?

Like any experiment, Opal has limitations. It’s not designed to build complex commercial applications or replace full development frameworks. Users can’t access the underlying source code, and its availability is geographically limited.

Also, as with many of Google’s recent tools, Opal appears to be backed by models from the Gemini family, offering powerful language understanding and generation — although Google hasn’t disclosed the exact versions used.

The launch of Opal is another sign of where the tech industry is heading: making software creation more accessible, assisted, and collaborative. Tools like Opal are redefining how we build — and proving you don’t need code to make something useful.

#GoogleOpal #NoCode #AI #VibeCoding #Innovation #AItools #NoCodeDevelopment #LowCodeNoCode #Gemini

https://www.techrepublic.com/article/news-google-opal-no-code-ai-app-builder

Just days before WWDC 2025, Apple took an unusual step: instead of announcing new features, it published a study titled “The Illusion of Thinking”, questioning whether so-called “reasoning models” (LRMs) can actually think through complex problems. Models from OpenAI, Anthropic—with Claude 3.7 Sonnet—DeepSeek and Google Gemini were tested on logic puzzles like the Tower of Hanoi and the river crossing challenge. The results were surprising: in simple tasks, standard LLMs like GPT-4 performed better. At moderate levels, LRMs had the edge—but as complexity rose, both collapsed, with near-zero accuracy 🧠📉.

Researchers noticed that as tasks became more complex, LRMs reached a threshold where they reduced their “reasoning effort,” even when resources were available. They called this a “complete accuracy collapse,” where the models, instead of thinking harder, simply “gave up” before solving the puzzle.

OpenAI, Anthropic, and Google pushed back, claiming that current models are already laying the groundwork for tool-using agents capable of solving increasingly difficult problems. The observed “collapses,” they argue, are tied to safeguards meant to avoid excessively long or unstable responses 🧪🛑.

Apple’s team ensured clean data by designing puzzles with no known solutions during training. They didn’t just evaluate final answers—they analyzed the intermediate steps in reasoning, highlighting a deeper issue 🧩🧬.

This approach raises a central question: do LRMs really “think,” or do they follow learned patterns only up to a certain threshold? For some, this casts doubt on the path toward Artificial General Intelligence (AGI), suggesting we may be hitting fundamental limits.

Yet Apple’s stance is not just critical—it’s constructive. The company is calling for more scientific rigor in evaluating AI, challenging benchmarks based solely on math or coding tasks that may be biased or contaminated 🧭🔬.

What does this mean for AI’s future?

• Transparency & evaluation: Apple sets a new bar by questioning how and why we measure machine “intelligence”.
  
• Design vs. ability: The industry may be limiting AI more by architecture than by true potential.
  
• AGI roadmap: If models break down with complex reasoning, we may need to rethink how we train and structure them.
  

In short, Apple isn’t just criticizing—it’s proposing a new direction: toward explainable, scientifically evaluated AI that must show not just what it does, but how it thinks—or fails to 

#AppleAI #AIreasoning #AGIdebate #Claude3 #GPT4 #DeepSeek #GoogleGemini #IllusionOfThinking 

https://www.ctol-es.com/news/study-challenges-apple-ai-reasoning-limitations

Artificial intelligence is advancing rapidly, but not all approaches follow the same path. While companies like OpenAI, Anthropic and DeepSeek compete with ever-more powerful, closed models, the French startup Mistral AI is choosing a different direction: transparency, logical reasoning and native multilingualism. With the launch of Magistral, its new language model family, Mistral not only delivers answers but also exposes the reasoning behind them 🔍.

Unlike GPT‑4 or Claude 3, which typically present final results without explaining intermediate steps, Magistral breaks down its reasoning in clear, auditable stages. This is more than a technical upgrade—it’s a philosophical shift that makes AI understandable to its users, ideal for education, research, and highly regulated industries.

⚙️ Magistral doesn’t just match its peers in performance. In tests like AIME 2024, the enterprise-grade variant Magistral Medium exceeded 90 % accuracy using multiple‑vote sampling, rivaling proprietary systems like GPT‑4 and Claude Opus. The open-source Magistral Small also achieves impressive results, outperforming models like Meta’s LLaMA 3 70B and Mistral 7B in logic tasks .

🌍 What truly sets it apart is its multilingual design. Many models—GPT‑4 included—internally convert input to English before processing. Magistral, in contrast, reasons directly in Spanish, French, Arabic or Chinese, enhancing accuracy for non‑English speakers.

The comparison with DeepSeek is especially revealing. The Chinese startup also focuses on chain‑of‑thought reasoning and open-source models, but remains firmly rooted in Mandarin contexts. Mistral, meanwhile, offers a genuinely pan‑European, open, multilingual intuition.

⚡Another standout feature is speed . In “Flash Answers” mode, Magistral can process up to ten times more tokens per second than many competitors, making it suited for real‑time chatbots, assistants and enterprise services.

🧑‍💻.While OpenAI and Google continue deploying increasingly closed-off models, Mistral is offering a powerful alternative. Magistral Small is released under Apache 2.0, enabling researchers, developers—and curious learners—to explore at will.This contrasts with proprietary APIs like Google’s Gemini or Anthropic’s Claude.

With Magistral, Mistral AI is not merely launching a model—it’s proposing a new paradigm for interacting with AI: one where the journey to the answer matters as much as the answer itself.

#MistralAI #Magistral #DeepSeek #GPT4 #Claude3 #GeminiAI #OpenAI 

https://www.notebookcheck.org/Mistral-AI-lanza-Magistral-su-primera-IA-capaz-de-razonar.1038697.0.html

EdgeCortix has announced that its SAKURA-II AI accelerator (M.2 format) is now compatible with a broader range of ARM-based platforms, including the Raspberry Pi 5 and Aetina’s Rockchip RK3588 platform. This compatibility expansion marks a significant step towards democratized access to advanced generative AI capabilities at the edge—combining high performance, energy efficiency, and deployment flexibility.

The value of this integration lies in its ability to run generative AI models—such as Vision Transformers (ViTs), Large Language Models (LLMs), and Vision-Language Models (VLMs)—directly on the device, without reliance on the cloud. This enables ultra-low latency, reduced power consumption, and enhanced data privacy—critical factors in sensitive or connectivity-constrained environments.

🔍 The SAKURA-II accelerator is specifically designed for efficient operation in embedded and low-power systems. Its M.2 form factor allows for seamless integration into compact devices while delivering high computational performance without excessive space or thermal demands. This makes it ideal for robotics, smart surveillance, precision agriculture, industrial automation, and portable devices.

Moreover, the energy-efficient design does not compromise processing power: thanks to its optimized architecture, SAKURA-II executes complex generative AI tasks in real time. This combination of power, efficiency, and low operational cost makes it a strategic solution for developers and companies looking to build smart edge devices without costly data centers or infrastructure.

⚙️ The arrival of SAKURA-II on platforms like Raspberry Pi and Rockchip opens new possibilities for innovation across sectors—from academic research to heavy industry.

#EdgeCortix #SAKURAII #RaspberryPi5 #GenerativeAI

https://www.edgecortix.com/en/press-releases/edgecortixs-sakura-ii-ai-accelerator-brings-low-power-generative-ai-to-raspberry-pi-5-and-other-arm-based-platforms

ByteDance (the company behind TikTok) has introduced a new artificial intelligence model called BAGEL‑7B‑MoT, and while the name may sound complex, its purpose is clear: to combine text, images, and video into a single intelligent system that can understand and generate content as if it were “seeing” and “thinking.”

What is BAGEL?
BAGEL is a multimodal AI model, which means it can work with different types of information at the same time—texts, images, or even videos. Instead of using one model for text and a separate one for images, BAGEL brings them together.

This kind of technology can:

• Describe what appears in an image.
  
• Create images from text.
  
• Edit photos with simple instructions.
  
• Understand and answer questions about visual or audiovisual content.
  

What makes it special?

• It has 7 billion active “digital neurons”, making it incredibly powerful.
  
• It was trained on a huge dataset: text, photos, videos, and websites.
  
• It learns in stages: first how to “see,” then how to interpret, and finally how to create or modify what it perceives.
  
• It can perform tasks like editing images or imagining different angles of an object (as if rotating it in 3D mentally).
  

Why does it matter?
This breakthrough opens many doors. It can help:

• People with visual impairments understand images.
  
• Designers generate sketches from a written idea.
  
• Companies automate tasks like content moderation or visual editing.
  

And since it was released as open-source, anyone can use, study, or adapt it for their own projects.

BAGEL‑7B‑MoT is a big step toward a more versatile, accessible, and creative artificial intelligence. It doesn’t just “read” or “see”—it understands, imagines, and helps create.

#ArtificialIntelligence #AI #Technology #Innovation #DigitalTransformation

https://apidog.com/blog/bagel-7b-mot/?utm_source=chatgpt.com

While the race for AI supremacy is dominated by giants like OpenAI, Google, and Anthropic, from China, the startup DeepSeek is moving forward—quietly but confidently. Its latest move: an update to its flagship model, DeepSeek R1-0528, a version that proves there are no minor players left in the AI arena.

Far from being just a technical upgrade, R1-0528 is a declaration of ambition. The company claims to have doubled the model’s capacity for complex reasoning and halved its rate of misinterpretations—known in the field as “hallucinations.” But beyond the percentages, what stands out is the strategic approach: combining technical power with an open philosophy. The model is not only smarter; it’s also more accessible. Its code is available under the MIT license and can be found on platforms like Hugging Face.

DeepSeek also introduced an optimized version, R1-0528-Qwen3-8B, capable of running on a single 16 GB GPU. A clear nod to developers and researchers who may lack access to high-end infrastructure but are eager to experiment with cutting-edge tools.

This update doesn’t shout, but it speaks clearly: the AI ecosystem is no longer a two- or three-player game. China wants a seat at the table. And DeepSeek—without promises of revolutions or flashy headlines—proves that innovation can also speak with an Asian accent and an open-source spirit.

#ArtificialIntelligence #AI #DeepSeek #OpenSource #ChineseAI #FoundationModels #OpenSourceAI #TechNews #AI2025 #SoftwareDevelopment

¡Ballena libre! DeepSeek R1-0528 sacude el tablero y pone contra las cuerdas al imperio OpenAI

The world of artificial intelligence keeps moving forward at lightning speed, and now it’s Claude 4’s turn — the new model family introduced by Anthropic, a company quickly gaining ground in the AI field. With this new generation, they’re stepping up to compete directly with giants like OpenAI (ChatGPT) and Google (Gemini).
Is it worth your attention? Here’s why we think so.

What is Claude 4?

Claude 4 isn’t just one model — it’s a family of three versions, each designed for different use cases:

• Claude 4-opus: the most powerful, perfect for complex tasks and advanced reasoning.
  
• Claude 4-sonnet: a balanced model offering solid performance without sacrificing speed.
  
• Claude 4-haiku: the fastest and lightest, built for quick responses and simpler tasks.
  

This variety lets individuals and companies choose the model that best fits their needs.

What’s New?

🧠 Smarter reasoning and understanding
Claude 4-opus delivers impressive results in programming, math, logic, and reading comprehension benchmarks. In many cases, it even outperforms GPT-4 and Gemini 1.5 — meaning it doesn’t just “answer well,” it actually helps you think through complex problems.

📄 Handles massive text input
One standout feature is its context window of 200,000 tokens. This allows it to read and process very long documents, multiple files at once, or large text databases — without losing track of the conversation.

🤖 Safer and more useful interactions
Anthropic trained Claude using a technique called Constitutional AI, aimed at making the model helpful, ethical, and responsible. It can self-correct, avoid biased responses, and steer clear of harmful or false claims.

🚀 Instant access
You can use Claude 4 right away at claude.ai, no installation needed.
The free version includes the Sonnet model, and if you want the powerful Opus version, it’s available through the Claude Pro plan — similar to ChatGPT’s Plus plan.

What Can You Use It For?

Beyond the tech specs, the exciting part is how it applies in real life. Here are some practical examples:

✅ For developers

• Review and improve code
  
• Detect and explain errors
  
• Help document systems
  
• Translate code across languages
  

✅ For students and teachers

• Summarize long or technical texts
  
• Explain hard concepts with examples
  
• Create educational content or exercises
  
• Plan lessons or presentations
  

✅ For content creators and marketers

• Generate ideas for posts or campaigns
  
• Write and refine blog or social content
  
• Adapt content for different audiences
  
• Quickly proofread for tone, grammar, and style
  

✅ For entrepreneurs and small businesses

• Draft proposals, budgets, or presentations
  
• Assist with admin tasks or customer support
  
• Analyze contracts or policies
  
• Automate replies or internal help
  

✅ For anyone looking to save time

• Read and summarize long documents
  
• Write replies to tricky emails
  
• Organize ideas or plan workflows
  
• Research topics and build reports
  

So, What Does It All Mean?

Claude 4 isn’t just another “ChatGPT alternative” — it brings its own strengths: better reasoning, massive context, safer interactions, and flexible models.
In a world where AI is no longer just a trend but a real productivity tool, having more solid options like this one is great news.

Best of all? You can try it out right now.

#Claude4 #ArtificialIntelligence #AI #TechNews #ClaudeAI #Anthropic #ChatGPT #ProductivityTools #MachineLearning #AIFuture

https://www.xataka.com/basics/claude-4-cuales-novedades-nuevos-modelos-inteligencia-artificial-anthropic

MCP (Model Context Protocol) servers enable natural language agents to interact directly with key infrastructure, automation, and monitoring tools.
This unlocks smarter workflows—where AI not only suggests… it acts.
💡 Here are some MCP servers you can already use today:
🔷 AWS MCP: control Amazon Web Services from an agent → https://github.com/awslabs/mcp
💬 Slack MCP: automate communication, channels, and messages → https://github.com/modelcontextprotocol/servers/tree/main/src/slack
☁️ Azure MCP: manage projects, repos, pipelines, and work items → https://github.com/Azure/azure-mcp
🐙 GitHub MCP: inspect and navigate code on GitHub → https://github.com/github/github-mcp-server
🦊 GitLab MCP: full integration with your GitLab projects → https://github.com/modelcontextprotocol/servers/tree/main/src/gitlab
🐳 Docker MCP: manage containers with natural language commands → https://github.com/docker/mcp-servers
📊 Grafana MCP: get visualizations, dashboards, and alerts → https://github.com/grafana/mcp-grafana
☸️ Kubernetes MCP: operate your cluster using natural language → https://github.com/Flux159/mcp-server-kubernetes

📌 Each of these servers enables tools like GitHub Copilot or custom agents to execute real tasks in your DevOps environment.
AI as a copilot? Yes.
AI as an assistant engineer executing real tasks? Also yes. And it’s already happening.

I invite you to discover MCP Alexandria 👉 https://mcpalexandria.com/en

There you’ll find the entire MCP ecosystem organized and standardized, aiming to connect developers with contextualized, reusable, and interoperable knowledge, in order to build a solid foundation for truly connected intelligence.

#DevOps #MCP #AI #Automation #IntelligentAgents #LLM #OpenSource #DevOpsTools