#Gemini 2.0 Architecture

[indiaplix]

🤖 Google’s Gemini AI Robotics Goes Offline! Now robots can run tasks without internet using Gemini AI Robotics On-Device. Fast, secure, and perfect for industries! 🔧 #GeminiAI #GoogleAI #Robotics #OfflineAI #ArtificialIntelligence #TechNews #ai #Tech #Robot #Technology #Gemini #Google

[w2gsolution01]

Gemini 2.0: A New Era of AI with Advanced Capabilities

The world of artificial intelligence is evolving at an unprecedented pace, and Gemini 2.0 stands at the forefront of this transformation. This cutting-edge AI model represents a significant leap forward, offering enhanced capabilities that promise to redefine how we interact with technology. From improved natural language processing to advanced problem-solving skills, this innovative system is designed to meet the demands of a rapidly changing digital landscape. Let’s explore the remarkable features and potential applications of this groundbreaking technology.

What Makes Gemini 2.0 Stand Out?

This next-generation AI model introduces a host of advancements that set it apart from its predecessors. Built with a focus on efficiency, adaptability, and user-centric functionality, it delivers a seamless experience across various domains. Whether you’re a business professional, a developer, or an everyday user, the system’s versatile design caters to diverse needs.

Enhanced Natural Language Understanding

One of the standout features of this AI is its superior natural language processing (NLP) capabilities. The model excels at understanding context, tone, and intent, enabling it to engage in more human-like conversations. This makes it an invaluable tool for applications like customer service chatbots, virtual assistants, and content creation platforms. Users can expect more accurate responses and a smoother interaction flow, even when dealing with complex or ambiguous queries.

Advanced Multimodal Capabilities

Unlike traditional AI models that focus solely on text, this system integrates multimodal processing, allowing it to handle text, images, and potentially other data types. This versatility opens up new possibilities, such as analyzing visual content alongside textual input or generating detailed descriptions of images. For businesses, this means more dynamic marketing tools and enhanced data analysis capabilities.

How Gemini 2.0 Empowers Businesses

The business world is increasingly reliant on AI to streamline operations and enhance decision-making. This advanced model offers tailored solutions that drive efficiency and innovation across industries.

Streamlining Customer Support

Customer support is a critical area where AI can make a significant impact. With its ability to process and respond to queries in real time, the system reduces response times and improves customer satisfaction. Its contextual understanding ensures that responses are relevant and personalized, creating a more engaging user experience.

Boosting Content Creation

Content creators and marketers can leverage this AI to generate high-quality, SEO-friendly content with ease. From blog posts to social media captions, the model can produce engaging material that aligns with brand guidelines. Its ability to understand trending topics and optimize for search engines makes it a powerful tool for digital marketing strategies.

Enhancing Data Analysis

Data-driven decision-making is at the heart of modern business success. The system’s advanced analytical capabilities allow it to process large datasets, identify patterns, and generate actionable insights. This is particularly valuable for industries like finance, healthcare, and retail, where timely and accurate data analysis can drive competitive advantages.

The Technology Behind the Innovation

The advancements in this AI model are powered by a sophisticated architecture that combines cutting-edge algorithms with robust computational frameworks. This section delves into the technical aspects that make it a game-changer.

Scalable and Efficient Design

The model is designed to be highly scalable, allowing it to handle tasks of varying complexity without compromising performance. Its efficient processing capabilities ensure that it can operate effectively on both large-scale servers and smaller devices, making it accessible to a wide range of users.

Continuous Learning Capabilities

One of the key strengths of this system is its ability to learn and adapt over time. Through continuous training and exposure to new data, it refines its understanding and improves its performance. This ensures that the AI remains relevant and effective in dynamic environments.

Real-World Applications of Gemini 2.0

The versatility of this AI model makes it suitable for a wide range of applications, from education to healthcare and beyond. Here are some practical examples of how it’s being used.

Revolutionizing Education

In the education sector, the system is transforming how students learn and educators teach. By providing personalized tutoring, generating interactive learning materials, and automating administrative tasks, it enhances the learning experience. Students can benefit from tailored feedback, while teachers can focus on delivering high-quality instruction.

Advancing Healthcare Solutions

Healthcare professionals are using this AI to streamline diagnostics, analyze patient data, and improve treatment plans. Its ability to process complex medical information quickly and accurately supports doctors in making informed decisions. Additionally, it can assist in patient communication, providing clear and empathetic responses to inquiries.

Transforming Creative Industries

For artists, writers, and designers, the system serves as a creative partner. It can generate story ideas, suggest design concepts, or even assist in composing music. By automating repetitive tasks, it allows creatives to focus on their craft, fostering innovation and productivity.

Ethical Considerations and Responsible AI Use

As AI becomes more integrated into our lives, ethical considerations are paramount. The developers of this model have prioritized responsible AI practices to ensure its benefits are maximized while minimizing potential risks.

Transparency and Accountability

The system is designed with transparency in mind, providing users with clear insights into how decisions are made. This fosters trust and ensures that the AI is used responsibly across applications.

Addressing Bias and Fairness

Efforts have been made to reduce bias in the model’s outputs, ensuring fair and equitable performance. By training on diverse datasets and implementing rigorous testing, the system aims to deliver unbiased results that cater to a global audience.

The Future of AI with Gemini 2.0

The launch of this AI model marks a significant milestone in the evolution of artificial intelligence. Its advanced capabilities pave the way for new innovations and applications that will shape the future.

Integration with Emerging Technologies

As technologies like augmented reality, virtual reality, and the Internet of Things (IoT) continue to evolve, this AI is poised to integrate seamlessly with them. This will enable more immersive and interactive experiences, from smart homes to advanced gaming platforms.

Driving Global Collaboration

The system’s ability to process multiple languages and cultural contexts makes it a powerful tool for global collaboration. Businesses, researchers, and individuals can use it to bridge communication gaps and work together more effectively.

Why Gemini 2.0 Matters

The introduction of this AI model is more than just a technological advancement; it’s a step toward a more connected and efficient world. Its ability to understand, adapt, and deliver results across industries makes it a catalyst for change. Whether you’re looking to enhance business operations, improve customer experiences, or explore new creative possibilities, this system offers the tools to succeed.

Getting Started with Gemini 2.0

For those eager to explore this technology, getting started is straightforward. Many platforms provide access to the model through user-friendly interfaces, enabling individuals and businesses to easilyintegrate it into their workflows. Whether you’re a developer looking to build custom applications or a business owner seeking to optimize operations, the system’s flexibility makes it easy to adopt.

The arrival of Gemini 2.0 signals a new chapter in the AI revolution. With its advanced capabilities, ethical design, and wide-ranging applications, it’s set to transform how we interact with technology. From empowering businesses to revolutionizing education and healthcare, this model is a testament to the power of innovation. As we move forward, its impact will only grow, shaping a future where AI is more intuitive, accessible, and impactful than ever before.

[jcmarchi]

How to build autonomous AI agent with Google A2A protocol

New Post has been published on https://thedigitalinsider.com/how-to-build-autonomous-ai-agent-with-google-a2a-protocol/

How to build autonomous AI agent with Google A2A protocol

Why do we need autonomous AI agents?

Picture this: it’s 3 a.m., and a customer on the other side of the globe urgently needs help with their account. A traditional chatbot would wake up your support team with an escalation. But what if your AI agent could handle the request autonomously, safely, and correctly? That’s the dream, right?

The reality is that most AI agents today are like teenagers with learner’s permits; they need constant supervision. They might accidentally promise a customer a large refund (oops!) or fall for a clever prompt injection that makes them spill company secrets or customers’ sensitive data. Not ideal.

This is where Double Validation comes in. Think of it as giving your AI agent both a security guard at the entrance (input validation) and a quality control inspector at the exit (output validation). With these safeguards at a minimum in place, your agent can operate autonomously without causing PR nightmares.

How did I come up with the Double Validation idea?

These days, we hear a lot of talk about AI agents. I asked myself, “What is the biggest challenge preventing the widespread adoption of AI agents?” I concluded that the answer is trustworthy autonomy. When AI agents can be trusted, they can be scaled and adopted more readily. Conversely, if an agent’s autonomy is limited, it requires increased human involvement, which is costly and inhibits adoption.

Next, I considered the minimal requirements for an AI agent to be autonomous. I concluded that an autonomous AI agent needs, at minimum, two components:

Input validation – to sanitize input, protect against jailbreaks, data poisoning, and harmful content.

Output validation – to sanitize output, ensure brand alignment, and mitigate hallucinations.

I call this system Double Validation.

Given these insights, I built a proof-of-concept project to research the Double Validation concept.

In this article, we’ll explore how to implement Double Validation by building a multiagent system with the Google A2A protocol, the Google Agent Development Kit (ADK), Llama Prompt Guard 2, Gemma 3, and Gemini 2.0 Flash, and how to optimize it for production, specifically, deploying it on Google Vertex AI.

For input validation, I chose Llama Prompt Guard 2 just as an article about it reached me at the perfect time. I selected this model because it is specifically designed to guard against prompt injections and jailbreaks. It is also very small; the largest variant, Llama Prompt Guard 2 86M, has only 86 million parameters, so it can be downloaded and included in a Docker image for cloud deployment, improving latency. That is exactly what I did, as you’ll see later in this article.

How to build it?

The architecture uses four specialized agents that communicate through the Google A2A protocol, each with a specific role:

Image generated by author

Here’s how each agent contributes to the system:

Manager Agent: The orchestra conductor, coordinating the flow between agents

Safeguard Agent: The bouncer, checking for prompt injections using Llama Prompt Guard 2

Processor Agent: The worker bee, processing legitimate queries with Gemma 3

Critic Agent: The editor, evaluating responses for completeness and validity using Gemini 2.0 Flash

I chose Gemma 3 for the Processor Agent because it is small, fast, and can be fine-tuned with your data if needed — an ideal candidate for production. Google currently supports nine (!) different frameworks or methods for finetuning Gemma; see Google’s documentation for details.

I chose Gemini 2.0 Flash for the Critic Agent because it is intelligent enough to act as a critic, yet significantly faster and cheaper than the larger Gemini 2.5 Pro Preview model. Model choice depends on your requirements; in my tests, Gemini 2.0 Flash performed well.

I deliberately used different models for the Processor and Critic Agents to avoid bias — an LLM may judge its own output differently from another model’s.

Let me show you the key implementation of the Safeguard Agent:

Plan for actions

The workflow follows a clear, production-ready pattern:

User sends query → The Manager Agent receives it.

Safety check → The Manager forwards the query to the Safeguard Agent.

Vulnerability assessment → Llama Prompt Guard 2 analyzes the input.

Processing → If the input is safe, the Processor Agent handles the query with Gemma 3.

Quality control → The Critic Agent evaluates the response.

Delivery → The Manager Agent returns the validated response to the user.

Below is the Manager Agent’s coordination logic:

Time to build it

Ready to roll up your sleeves? Here’s your production-ready roadmap:

Local deployment

1. Environment setup 

2. Configure API keys 

3. Download Llama Prompt Guard 2 

This is the clever part – we download the model once when we start Agent Critic for the first time and package it in our Docker image for cloud deployment:

Important Note about Llama Prompt Guard 2: To use the Llama Prompt Guard 2 model, you must:

Fill out the “LLAMA 4 COMMUNITY LICENSE AGREEMENT” at https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-86M

Get your request to access this repository approved by Meta

Only after approval will you be able to download and use this model

4. Local testing 

Screenshot for running main.py

Image generated by author

Screenshot for running client

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Screenshot for running tests

Image generated by author

Production Deployment 

Here’s where it gets interesting. We optimize for production by including the Llama model in the Docker image:

1. Setup Cloud Project in Cloud Shell Terminal

Access Google Cloud Console: Go to https://console.cloud.google.com

Open Cloud Shell: Click the Cloud Shell icon (terminal icon) in the top right corner of the Google Cloud Console

Authenticate with Google Cloud:

Create or select a project:

Enable required APIs:

3. Setup Vertex AI Permissions

Grant your account the necessary permissions for Vertex AI and related services:

3. Create and Setup VM Instance

Cloud Shell will not work for this project as Cloud Shell is limited to 5GB of disk space. This project needs more than 30GB of disk space to build Docker images, get all dependencies, and download the Llama Prompt Guard 2 model locally. So, you need to use a dedicated VM instead of Cloud Shell.

4. Connect to VM

Screenshot for VM 

Image generated by author

5. Clone Repository

6. Deployment Steps

Screenshot for agents in cloud 

Image generated by author

7. Testing 

Screenshot for running client in Google Vertex AI

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Screenshot for running tests in Google Vertex AI

Image generated by author

Alternatives to Solution

Let’s be honest – there are other ways to skin this cat:

Single Model Approach: Use a large LLM like GPT-4 with careful system prompts

Simpler but less specialized

Higher risk of prompt injection

Risk of LLM bias in using the same LLM for answer generation and its criticism

Monolith approach: Use all flows in just one agent

Latency is better

Cannot scale and evolve input validation and output validation independently

More complex code, as it is all bundled together

Rule-Based Filtering: Traditional regex and keyword filtering

Faster but less intelligent

High false positive rate

Commercial Solutions: Services like Azure Content Moderator or Google Model Armor

Easier to implement but less customizable

On contrary, Llama Prompt Guard 2 model can be fine-tuned with the customer’s data

Ongoing subscription costs

Open-Source Alternatives: Guardrails AI or NeMo Guardrails

Good frameworks, but require more setup

Less specialized for prompt injection

Lessons Learned

1. Llama Prompt Guard 2 86M has blind spots. During testing, certain jailbreak prompts, such as:

And

were not flagged as malicious. Consider fine-tuning the model with domain-specific examples to increase its recall for the attack patterns that matter to you.

2. Gemini Flash model selection matters. My Critic Agent originally used gemini1.5flash, which frequently rated perfectly correct answers 4 / 5. For example:

After switching to gemini2.0flash, the same answers were consistently rated 5 / 5:

3. Cloud Shell storage is a bottleneck. Google Cloud Shell provides only 5 GB of disk space — far too little to build the Docker images required for this project, get all dependencies, and download the Llama Prompt Guard 2 model locally to deploy the Docker image with it to Google Vertex AI. Provision a dedicated VM with at least 30 GB instead.

Conclusion

Autonomous agents aren’t built by simply throwing the largest LLM at every problem. They require a system that can run safely without human babysitting. Double Validation — wrapping a task-oriented Processor Agent with dedicated input and output validators — delivers a balanced blend of safety, performance, and cost. 

Pairing a lightweight guard such as Llama Prompt Guard 2 with production friendly models like Gemma 3 and Gemini Flash keeps latency and budget under control while still meeting stringent security and quality requirements.

Join the conversation. What’s the biggest obstacle you encounter when moving autonomous agents into production — technical limits, regulatory hurdles, or user trust? How would you extend the Double Validation concept to high-risk domains like finance or healthcare?

Connect on LinkedIn: https://www.linkedin.com/in/alexey-tyurin-36893287/  

The complete code for this project is available at github.com/alexey-tyurin/a2a-double-validation. 

References

[1] Llama Prompt Guard 2 86M, https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-86M

[2] Google A2A protocol, https://github.com/google-a2a/A2A 

[3] Google Agent Development Kit (ADK), https://google.github.io/adk-docs/ 

[govindhtech]

Google Gemini: DeepMind’s Multimodal AI Chatbot Platform

What is Google Gemini AI?

Google Gemini, formerly Bard, is an artificial intelligence (AI)  chatbot. It uses machine learning and NLP to simulate human speech. Alphabet's Google DeepMind built the multimodal AI large language model Gemini family. It can be added to websites, messaging apps, and Google Search to provide natural language replies to users' questions.

Key Technologies and Skills

LLMs: Gemini's advanced LLMs replace LaMDA and Palm 2. Bard studied LaMDA, a language model for dialogue applications.

Multimodality: Gemini is multimodal by nature. This means language, audio, code, and video data sets are used throughout the curriculum. It understands and processes interleaved text, image, audio, and video.

Cross-Modal Reasoning: Gemini's multimodal architecture supports many input data. It can read handwritten notes, graphs, and schematics to solve complex issues.

Architecture: Gemini LLMs use transformer-based neural networks to assess extended contextual sequences across modalities. Effective attention tactics promote long-term processing.

Training: Gemini models are trained on multimodal and multilingual data sets using advanced data filtering. Targeted fine-tuning optimises models for specific use scenarios. They use Google's latest Trillium TPU processors for training and inference, which improve performance, latency, and energy efficiency.

Gemini can understand and interpret over 100 languages. Multilingual image labelling, summarisation, and mathematical reasoning are possible.

Key Features and Uses

Gemini has many uses:

Text-based

Text creation, translation, and summary. It simplifies complex topics and generates insights. It provides unique, superior answers using internet data.

Visual

Understanding complex visuals like statistics, charts, tagging photos, and visual Q&A without OCR. It can also use Google Imagen 3 to make images.

Audio/Video

Translation and voice recognition on audio. evaluating video frames to describe or answer.

Code

C code for analysis, explanation, and generation in a variety of programming languages. The generative AI coding tool uses Gemini.

Conversational

Made to talk and respond like a person. Even kids can understand complex topics by breaking them down into conversational pieces.

Gemini offers multiple responses. It complements Google Search by letting users quickly browse results or utilise “Google it” to investigate. A URL-based double-check option is also included.

Variations in models

Google launched several model sizes for different geographies and use cases:

Ultra: For challenging tasks.

Advantage: Made for large-scale deployment and performance. accessible in Google Cloud Vertex AI and AI Studio.

Nano targets on-device apps like Google Pixel. Each Nano-1 and Nano-2 has 1.8 billion and 3.25 billion parameters.

Google also released an experimental beta of Gemini 2.0 Flash and upgraded versions of Gemini 1.5 Pro and 1.5 Flash.

From Bard to Gemini

On February 6, 2023, Google first revealed Bard, its AI-powered chatbot. Beginning with trustworthy testers, Bard’s access was made public on March 21, 2023. At first, Bard was driven by a condensed form of LaMDA. Following the success of ChatGPT and Microsoft’s collaboration with OpenAI, the development of Bard was allegedly accelerated, resulting in a perceived “code red” within Google.

When Bard was first released, it was criticised for a number of reasons, including a significant public mistake during a demonstration in which it gave false information regarding the James Webb Space Telescope, which hurt Google’s stock price.

About a year after its original announcement, on February 8, 2024, Bard was formally renamed Gemini. It is thought that the rebranding was done to highlight on the success and developments of the underlying Gemini LLM, simplify Google’s AI products, and deflect attention from the initial criticism of the Bard label.

Cost and Availability

With Gemini Pro accessible in more than 230 nations and territories and Gemini Advanced in more than 150, Gemini is a globally accessible brand. Although age limitations vary by country and platform, users must typically be at least 18 years old (e.g., the web app may be accessible as young as 13 in some areas, but users under 18 may be limited to English only). It is necessary to have a personal Google account, a school account, a Google Workspace account with access, or an AI Studio account.

There is no cost for basic access to Gemini. A paid Google One AI Premium subscription (which costs $20 USD per month after a free trial and includes Google Workspace features and storage) is required to access the more sophisticated capabilities through Gemini Advanced. Additionally, Google provides Google Workspace users with Gemini add-on subscriptions. There is also a free tier of the Gemini API.

Limitations and Concerns

Gemini confronts the same difficulties as other LLMs:

Training Data: Large volumes of data are used to train the models, and these data may contain biases and prejudices from the actual world. Bias may still show up in outputs despite mitigation efforts.

Accuracy: While providing material with confidence, LLMs occasionally give erroneous, misleading, or inaccurate information.

Creating “hallucinations” or fabrications is part of this. A plant with the wrong scientific name and a false information regarding the James Webb Space Telescope.

Originality and Context: Particularly with the free version, there are restrictions on the inventiveness and originality of the content generated. Geminis sometimes struggle to grasp context, which could result in unrelated answers.

Plagiarism: There are no integrated plagiarism detection tools in Gemini or ChatGPT.

Responsible Development

Google claims that its AI Principles, which were released in 2018, serve as a framework for their work on Gemini (previously Bard). In an effort to maintain useful and topical interactions, the firm uses human feedback, review, and built-in guardrails (such as limiting dialogue duration) as part of its commitment to responsible AI development. Gemini was tested against scholarly standards and underwent comprehensive safety testing and mitigation about issues including bias and toxicity. Google claims that in order to make AI safe and practical, it still collaborates with outside organisations, offers tools and education, and works with communities.

Comparison to ChatGPT

Gemini and ChatGPT are AI chatbots that generate conversational language that sounds human by utilising generative AI and LLMs.

ChatGPT has been limited to data up to a particular point (e.g., 2021 data indicated for older versions), but Bard/Gemini is intended to draw on current information from the web.

While prior GPT models were initially text-only, GPT-4 is now multimodal. Gemini is inherently multimodal, having been trained on a variety of data from the beginning.

Gemini is known to break down difficult subjects into manageable bits, but ChatGPT reacts to a single text input.

Compared to GPT-4o, which has a context window of 128,000 tokens, Gemini 1.5 Pro is said to have a far bigger context window at 2 million tokens.

In contrast to ChatGPT, Gemini offers a double-check functionality that allows users to confirm information.

Gemini supports Google’s services, whereas Microsoft has included ChatGPT into its Bing search engine.

In conclusion

Google Gemini AI is the company’s multimodal AI chatbot, which was developed from Bard. It is intended to deliver complex, natural language responses and insights by utilising extensive datasets and web knowledge, and it can be used for text, photos, audio, video, and code. Despite its strength, it has similar drawbacks to other LLMs, including the possibility of bias and inaccuracy, which Google mitigates through ethical development methods.

[aiturtlesai]

Meta Unveils Llama 4: New More Powerful Versatile AI Models

  Meta has launched Llama 4 Scout and Maverick, advanced AI models with MoE architecture and multimodal capabilities, which offer superior performance. Behemoth, a teacher model, is in the training phase. Key Points: Innovative Models – Llama 4 Scout and Maverick introduce “mixture of experts” architecture for greater computational efficiency.  High Performance – Outperforms GPT-4o and Gemini 2.0 Flash in reasoning and coding benchmarks.  Advanced Multimodality – Supports text, images and video with smoother integration.... read more: https://www.turtlesai.com/en/pages-2639/meta-unveils-llama-4-new-more-powerful-versatile-ai

[iksathrob]

Could the hands of fate guide psychics to reveal your perfect home style? Stuck at home in lockdown 2.0 and feel like your living room just isn’t vibing with your star sign? Does your colour scheme clash with your seductive Scorpio spirit, or are your bathroom tiles putting a damper on your fiery Aries impulses? Maybe modern minimalism is killing the mood for your romance loving inner Gemini. RELATED: The best time to sell your house, according to the universe Caulfield South Art Deco pad has business upfront, party out back Australian housing: How JobKeeper can help stop property ‘falling off a cliff’ // // Fear not. The clairvoyants, tarot readers and psychic mediums of PsychicWorld (not an endorsement) have joined forces in a survey that reveals the perfect home style for your part of the zodiac. And if you’re really serious about aligning your digs with the universe, we’ve reviewed their divinations to find a few homes for sale that are perfect for a Pisces, awesome for Aquarians and that Librans should love. Oh, and before you mention it – no they don’t have any tips for Ophiuchus, like most of what you hear about star signs that was fake news. Aries: March 21 to April 29 44A Carnoustie Grove, Mornington is perfect for an Aries to add their own touch of colour. It will come as no surprise that you only truly feel at home when you are surrounded by a contemporary style. PsychicWorld have revealed your fiery, impulsive nature will have room to shine in a home that’s designed for fluid changes between trends. They recommend the rams of the stars embrace shades of black, beige and white for the fundamentals, and a variety of materials for a “sleek and fresh” look. That sense of sleek and fresh starts from the front door. Which is what’s on offer at 44A Carnoustie Grove, Mornington, where a super-sized townhouse listed with McEwing Partners’ Stephen Bannister blends black, white and pale timber tones for a blank canvas awaiting your latest whim. Taurus: April 20 to May 20 10 Stewart Rd, Emerald has the kind of rustic charm a Taurus could charge into. When most people think of bulls, they think of regional climes. Or Spain, but international travel is off at the moment. The psychics believe rustic decor is key to helping you grab life by the horns. They advise you to think timber floors, fireplaces and raw, unfinished materials when redecorating. But if you really want to charge into life as a Taurus, homes don’t come much more rustic than 10 Stewart Rd, Emerald. Share the rustic vibe with friends and family, or rent it out — the choice is yours. From the attic-style main bedroom to the character-filled chunky interior rendering, exposed cathedral ceilings, timber highlights and brick-bound fireplace, it could be the perfect match. And, there’s five abodes on the title — so there’s room for all your Taurean friends, or to rent a few out so others can embrace the style too. Gemini: May 21 to June 20 2 Ingleside Crescent, Glen Waverley shouts French provincial. You’re a romantic. Or, at least you are according to the psychics. And if they’ve got your back, who cares what your partner (or your ex) thinks? For lovers of love, there is only one style that suits: French country — French provincial in real estate speak. Farmhouse inspired, it takes rustic to elegant new levels. PsychicWorld recommends round furniture. Geminis love romance, and a big bedroom leaves plenty of room for pillow talk. Jellis Craig’s Stephen Huang recommends 2 Ingleside Cres, Glen Waverley. And while it might cost $4.38m, the price includes five bedrooms and bathrooms, a home theatre and a gazebo. There’s even a Juliette balcony. Cancer: June 21 to July 22 6 Thule Court, Brighton has the old-school cool to make Cancerians drool. // // Underneath that hard, crustacean shell, you’re really all about family. With an instinct for classic, yet elaborate decor, the tarot readers reckon you’d only be happy if you drew a traditional home out of the deck. That means a dark colour palette and plenty of influences from the 18th or 19th century. Given modern Melbourne wasn’t around for the 1700s, your best bet might be a classic Victorian-era abode. They also think you’ll need a fair bit of space for all your heavy, royal-inspired furniture. Designed by the man who created Melbourne’s Royal Exhibition Building in Carlton, the four-bedroom Thule is a Brighton icon that predates electricity in this city. Classic with elaborate flourishes, this home takes family living to the next level. Victorian-era architect Joseph Reed instilled the 6 Thule Court home with the dark timber ceilings, panelling, and lavish flourishes that will either have you loving it or, well, crabby. Although, with Kay & Burton listing the property at $5.3-$5.8 you might have to put your family second while you get on top of the mortgage. Leo: July 23 to August 22 145 Dalgetty Road, Beaumaris mixes opulence with functionality. The psychics warn that if you show a Leo false modesty, they’ll show you their fangs. But show them a mid-century modern pad and everything is cool for cats, according to the survey. Bring on opulence, heaps of glass, metal and even plastic, but don’t do so at the expense of functionality. Few parts of Melbourne offer an extensive list of mid-century homes, but Beaumaris is famous for it. Seriously. They have their own website. Simple, but attractive: could this be your lion’s den? Sam Hartrick with Eview Group has one listed for sale at 145 Dalgetty Road complete with timber interior walls, big windows and a refined architectural feel throughout. But the $1.55-$1.65m price tag is a long way removed from the 1960s. Virgo: August 23 to September 22 27 Lahinch Drive, Fingal is modern meets brutalist — and possibly a Virgo’s dream home. Ask the stars, and they’ll tell Virgos they’re shameless neat freaks with an abiding hatred of mess. If you want a home that matches up with your penchant for organisation and clean lines, you want modern styling. “I’m no Leo” you say? And you’re not. You like the kind of modern that predates that enjoyed by the big cats of the universe. Your style goes back to the ‘40s, when it was still about the crisp lines and light colour schemes. It’s less Brady Bunch, more Scandi-chic. And according to the psychics, it’s just what you need to feel at peace. Even non-Virgos would dig this sumptuous living space. The YPA Mornington Peninsula listing at 27 Lahinch Street, Fingal doesn’t have a 1940s pedigree — despite the $1.35-$1.4m million price guide. But it does have the kind of modern meets brutalist architecture and pale polished concrete floors and walls where even the smallest thing out of place would be an affront. Only you would never let that happen, would you? Libra: September 23 to October 22 119/555 St Kilda Road, Melbourne has many features that could suit a Libra. The balance point of the universe is a no-fuss position, according to PsychicWorld. And after weighing things carefully they’ve determined that means Librans love harmony and good vibes. Their advice: skip anything flamboyant and think minimalist if you’re serious about your happiness. Monochromatic palettes, simple design and clean lines should dominate your living spaces, with a pop of colour to accentuate things. A one-bedroom apartment is about as minimalist as things come. Clean lines and a simple colour palette — the perfect balance. The single-sleeping residence at 119/555 St Kilda Road, Melbourne, comes with minimal cleaning, minimal furniture requirements and a minimal price tag at $430,000-$485,000. Core Realty’s Thomas Chan has the listing which comes with a blend of white, grey and a few black accents. Scorpio: October 23 to November 21 4/23 Franklin Place, West Melbourne could be a Scorpio’s warehouse dream. Passionate, seductive, forthright: that’s how Scorpios face the world. And the psychics have revealed those three things combine to mean just one thing for most people under this sign: industrial. So make a statement, and your life complete, by embracing warehouse living. Find your place in the universe by exposing yourself to raw brickwork and metal elements. And remember, black is your friend. Don’t assume this means you’ll have to develop a caffeine addiction and join the hipsters in Collingwood or Fitzroy. There’s also great coffee and a surprising number of neck beards in West Melbourne. Even the bedrooms have industrial appeal. Which is also home to 4/23 Franklin Place, where exposed brickwork is on show in the bedroom, exposed timber beams hangs out in the kitchen and exposed insulation adds character in all the living spaces. Jayson Watts at Nelson Alexander is auctioning it online at 12.30pm on July 25. Sagittarius: November 22 to December 21 1/14 Wilsons Road, Mornington embraces its coastal location. The hunters among the stars are known for their love of travel. Which is why Sagittarians should heed the sage advice of their peers collated by PsychicWorld and set sail for coastal comfort. Colour is key here, so you’ll want to make the white of breaking waves your go to backdrop and highlight it with light blue or grey and the odd splash of green. In short, decorate your home so it feels like you’re close to the beach. Or, break out your chequebook and move there. A serious indoor-outdoor connection helps bring fresh sea breezes into the home, too. Stone Real Estate’s Malcolm Parkinson is taking inquiries on 1/14 Wilsons Road, Mornington, which is two doors and a roundabout from the waterfront. With a sand-coloured facade and plenty of white and grey on display inside, it embraces coastal cool. Capricorn: December 22 to January 19 1 Lever Avenue, Blairgowrie could be a Capricorn’s dream. The universe has a message for you. You’re a workaholic. And while that means Capricorns might struggle to find the time to consult a psychic adviser, they’re still confident they can help you live better. You just need to deck your home out Scandinavian style. Clean aesthetics, natural light, simple design and functionality are all important. Playing ABBA’s greatest hits is optional. But with an A-BUS multizone speaker system built into 1 Lever Avenue, Blairgowrie, the Swedish pop supergroup’s could be the perfect backing track for this blend of cutting edge architecture and Scandinavian design. The Scandi-chic look is on display inside and out. Kay & Burton’s Liz Jensen describes it as efficient and aesthetic, and there’s even a studio office set at the rear of the block to make life easier when you bring work home with you. And who cares about the $2.8-$3.08m asking price, you like working anyway — why not have a good excuse? Aquarius: January 20 to February 18 24 Albert Street, Upper Ferntree Gully has a colour scheme with bohemian flair. Aquarians don’t like putting labels on things. So PsychicWorld’s survey has put one on you. According to the zodiac, you’re destined to love a bohemian vibe. More common in artistic circles than architectural ones, this style has a focus on bold colours, patterns and wall art. And plants. Lots of plants. Living spaces so full of quirky additions, that most Librans would think you’re a hoarder, should suit you. And there’s plenty of space to bring your own sense of style to the home. And so should 24 Albert Street, Upper Ferntree Gully. Rebecca Halit at Property Partners is expecting $745,000-$815,000 for the four-bedroom home that welcomes you home with a mix of aged timber and coloured glass. It then works its way through the colour chart with bedrooms ranging from lavender to orange. Pisces: February 19 to March 20 20 Heaton Avenue, Elwood has traditional California bungalow appeal at the front. Settle on just the one thing that floats your boat? Not if you’re a Pisces. While the rest of the zodiac is embracing one thing or another, you’re content to just keep swimming. Which is why the psychics think you’ll be hooked on transitional decor. A blend between traditional and modern, it’s the best of both worlds and it puts mixed textures at the top of your mood board. Wood, glass, fabric and metal could all find their way into your perfect living space, but keep the colours neutral if you want to keep the universe on your side. But it’s all about modern charm at the rear. Happily, Melbourne is home to one of the best types of property for people who can’t decide what they want: the mullet. All business and traditional period style at the front, these homes have been renovated and extended to gradually shift into modern party pads at the rear. Like 20 Heaton Avenue, Elwood, which comes with a $5.25 million asking price and the kind of cache that comes from once being home to a member of Crowded House. McGrath’s Nicole Prime has the listing. MORE: Jane Turner: Kath & Kim star pulls Elwood mansion from market Thornbury: Designer sells art gallery house before auction Crossways Historic Country Inn, Marysville lolly shop site for sale The post Interior design based on your star sign: Style tips from the stars appeared first on realestate.com.au. from news – realestate.com.au https://ift.tt/2CHBRvz

http://realestateiksa.blogspot.com/2020/07/interior-design-based-on-your-star-sign.html

[baillieslf-blog]

Intel 14nm低功耗Gemini Lake架构曝光：全能SoC

Intel的Atom产品线放弃了在手机、平板平台上的研发，但面向二合一平台、超轻薄本方面依然在努力争取。

具体来说，接班去年14nm Apollolake的是GeminiLake（双子星），也是Intel在Atom上使用的第三代14nm产品，采用SoC单芯片设计，封装面积极小、功耗极低，计划今年第四季度推出。

今天，外媒曝光了Geminilake的架构设计图，其中最大的亮点就是CNVi（Connectivity Integration Architecture）单元，Intel第一次可以在如此小的空间内集成Wi-Fi、蓝牙和调制解调器模块（3G/LTE）。

这里的第一次是指笔记本平台，毕竟此前Intel也做过手机SoC。

其他细节就比较单调了，最高LPDDR4-2400、最高eMMC5.1/PCIe 2.0/、支持USB 3.0/USB 2.0等，最高4核心（Goldmont+架构）。

[govindhtech]

Qwen 3 Benchmarks Surpassing Gemini 2.5 Pro, and Grok-3

After four months, Alibaba's new model family may surpass DeepSeek-R1, the top open-weights big language model.

Qwen 3: Faster, Deeper

Overview

Qwen3 is the latest big language model from Qwen. Qwen3-235B-A22B flagship model exceeds DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro in math, coding, and general capabilities. A tiny MoE model, Qwen3-30B-A3B, beats QwQ-32B with ten times as many active parameters, and even Qwen3-4B can compete with Qwen2.5-72B-Instruct.

We are open-weighting two MoE models: Qwen3-235B-A22B, a big model with 235 billion total parameters and 22 billion activated parameters, and Qwen3-30B-A3B, a smaller model with 30 billion total parameters and 3 billion activated parameters.

Six dense models—Qwen3-32B, Qwen3-14B, Qwen3-8B, Qwen3-4B, Qwen3-1.7B, and Qwen3-0.6B—are also open-weighted under Apache 2.0.

Hugging Face, ModelScope, and Kaggle now provide post-trained and pre-trained models like Qwen3-30B-A3B-Base. It recommends SGLang and vLLM for deployment. Ollama, LMStudio, MLX, llama.cpp, and KTransformers are recommended for local usage. These solutions make Qwen3 easy to integrate into development, production, and research workflows.

Qwen 3 allows researchers, developers, and organisations worldwide to design unique solutions using these cutting-edge models.

Try Qwen3 on the mobile app and chat.qwen.ai!

Important Features

Mixed Thinking

Qwen3 models introduce hybrid problem-solving. They offer two modes:

Thinking Mode: The model deliberates before responding. This is ideal for complex topics that require more thought.

Non-Thinking Mode: The model replies almost rapidly, making it suitable for simpler questions where depth is less important than speed.

As previously established, Qwen 3 delivers smooth and scalable performance benefits connected to computational reasoning budget. This design makes task-specific budgets easier to configure, improving inference quality and cost.

Supports several languages

Qwen 3 models accommodate 119 dialects. Due to their multilingual capabilities, these models may be used worldwide, opening up new possibilities.

Increased Agentic Capability

It optimised Qwen 3 models for coding and agentic capabilities and strengthened MCP support. The following examples show how Qwen3 thinks and acts.

In comparison to Qwen2.5

Qwen3 has a much larger pretraining dataset than Qwen2.5. Qwen2.5 was pre-trained on 18 trillion tokens, whereas Qwen3 uses 36 trillion over 119 languages and dialects. Qwen2.5-VL applied these research to enhance it. To add math and code data, Qwen2.5-Math and Qwen2.5-Coder developed synthetic data. Code samples, textbooks, and Q&As are included.

Qwen3 Pre-workout

It takes three stages to prepare for training. The model was pretrained on about 30 trillion tokens with a 4K context length in stage 1 (S1). The model learnt basic language and general knowledge at this time. In stage 2 (S2), we added STEM, coding, and reasoning challenges to the dataset. The model was pretrained with 5 trillion extra tokens. High-quality long-context data was used to extend the context to 32K tokens in the last stage. This assures the model can efficiently handle longer inputs.

Qwen 3 dense base models perform similarly to Qwen2.5 base models with more parameters due to model architectural advancements, more training data, and more efficient training methods. Qwen2.5-3B/7B/14B/32B/72B-Base and Qwen3-1.7B/4B/8B/14B/32B-Base work similarly. Qwen 3 dense base models outperform Qwen2.5 models in STEM, coding, and reasoning. For Qwen3-MoE basis models, they perform similarly to Qwen2.5 dense base models with 10% of active parameters. Thus, training and inference costs drop dramatically.

Post-training

The hybrid model, which can reason step-by-step and respond swiftly, was trained using a four-stage pipeline. This pipeline includes reasoning-based reinforcement learning (RL), thinking mode fusion, long chain-of-thought (CoT) cold start, and generic RL.

First, it improved the models using lengthy CoT data from coding, maths, logical reasoning, and STEM issues. Teaching the model fundamental thinking was the goal. The second phase increased reinforcement learning computing power using rule-based incentives to better model exploration and exploitation.

The third phase enhanced the thinking model utilising extended CoT data and regularly used instruction-tuning data to include non-thinking skills. The second stage's upgraded thinking model produced this data, ensuring smooth reasoning and rapid reaction times. The fourth step employed reinforcement learning (RL) on over 20 broad-domain tasks to increase the model's general capabilities and repair undesired behaviours. Agent capabilities, format following, and instruction following were among these duties.

Agentic uses

Qwen 3 calls tools well. To fully exploit Qwen3's agentic features, use Qwen-Agent. Qwen-Agent's inherent encapsulation of tool-calling templates and parsers simplifies development.

The MCP configuration file, Qwen-Agent integrated tool, or custom tools can define available tools.

[govindhtech]

Vertex AI Gemini Live API Creates Real-Time Voice Commands

Gemini Live API

Create live voice-driven agentic apps using Vertex AI Gemini Live API. All industries seek aggressive, effective solutions. Imagine frontline personnel using voice and visual instructions to diagnose issues, retrieve essential information, and initiate processes in real time. A new agentic industrial app may be created with the Gemini 2.0 Flash Live API.

This API extends these capabilities to complex industrial processes. Instead of using one data type, it uses text, audio, and visual in a continuous livestream. This allows intelligent assistants to understand and meet the demands of manufacturing, healthcare, energy, and logistics experts.

The Gemini 2.0 Flash Live API was used for industrial condition monitoring, notably motor maintenance. Live API allows low-latency phone and video communication with Gemini. This API lets users have natural, human-like audio chats and halt the model's answers with voice commands. The model processes text, audio, and video input and outputs text and audio. This application shows how APIs outperform traditional AI and may be used for strategic alliances.

Multimodal intelligence condition monitoring use case

Presentation uses Gemini 2.0 Flash Live API-powered live, bi-directional, multimodal streaming backend. It can interpret audio and visual input in real time for complex reasoning and lifelike speech. Google Cloud services and the API's agentic and function calling capabilities enable powerful live multimodal systems with a simplified, mobile-optimized user experience for factory floor operators. An obviously flawed motor anchors the presentation.

A condensed smartphone flow:

Gemini points the camera at motors for real-time visual identification. It then quickly summaries relevant handbook material, providing users with equipment details.

Real-time visual defect detection: Gemini listens to a verbal command like “Inspect this motor for visual defects,” analyses live video, finds the issue, and explains its source.

When it finds an issue, the system immediately prepares and sends an email with the highlighted defect image and part details to start the repair process.

Real-time audio defect identification: Gemini uses pre-recorded audio of healthy and faulty motors to reliably identify the issue one based on its sound profile and explain its results.

Multimodal QA on operations: Operators can ask complex motor questions by pointing the camera at certain sections. Gemini effectively combines motor manual with visual context for accurate voice-based replies.

The tech architecture

The demonstration uses Google Cloud Vertex AI's Gemini Multimodal Livestreaming API. The API controls workflow and agentic function calls while the normal Gemini API extracts visual and auditory features.

A procedure includes:

Function calling by agents: The API decodes audio and visual input to determine intent.

The system gathers motor sounds with the user's consent, saves them in GCS, and then begins a function that employs a prompt with examples of healthy and faulty noises. The Gemini Flash 2.0 API examines sounds to assess motor health.

The Gemini Flash 2.0 API's geographical knowledge is used to detect and highlight errors by recognising the intent to detect visual defects, taking photographs, and invoking a method that performs zero-shot detection with a text prompt.

Multimodal QA: The API recognises the objective of information retrieval when users ask questions, applies RAG to the motor manual, incorporates multimodal context, and uses the Gemini API to provide exact replies.

After recognising the intention to repair and extracting the component number and defect image using a template, the API sends a repair order via email.

Key characteristics and commercial benefits from cross-sector usage cases

This presentation highlights the Gemini Multimodal Livestreaming API's core capabilities and revolutionary industrial benefits:

Real-time multimodal processing: The API can evaluate live audio and video feeds simultaneously, providing rapid insights in dynamic circumstances and preventing downtime.

Use case: A remote medical assistant might instruct a field paramedic utilising live voice and video to provide emergency medical aid by monitoring vital signs and visual data.

Gemini's superior visual and auditory reasoning deciphers minute aural hints and complex visual settings to provide exact diagnoses.

Utilising equipment noises and visuals, AI can predict failures and eliminate manufacturing disruptions.

Agentic function invoking workflow automation: Intelligent assistants can start reports and procedures proactively due to the API's agentic character, simplifying workflows.

Use case: A voice command and visual confirmation of damaged goods can start an automated claim procedure and notify the required parties in logistics.

Scalability and seamless integration: Vertex AI-based API interfaces with other Google Cloud services ensure scalability and reliability for large deployments.

Use case: Drones with cameras and microphones may send real-time data to the API for bug identification and crop health analysis across huge farms.

The mobile-first design ensures that frontline staff may utilise their familiar devices to interact with the AI assistant as needed.

Store personnel may use speech and image recognition to find items, check stocks, and get product information for consumers on the store floor.

Real-time condition monitoring helps industries switch from reactive to predictive maintenance. This will reduce downtime, maximise asset use, and improve sectoral efficiency.

Use case: Energy industry field technicians may use the API to diagnose faults with remote equipment like wind turbines without costly and time-consuming site visits by leveraging live audio and video feeds.

Start now

Modern AI interaction with the Gemini Live API is shown in this solution. Developers may leverage its interruptible streaming audio, webcam/screen integration, low-latency speech, and Cloud Functions modular tool system as a basis. Clone the project, tweak its components, and develop conversational, multimodal AI solutions. Future of intelligent industry is dynamic, multimodal, and accessible to all industries.

[govindhtech]

CISA’s Zero Trust Maturity Model 2.0: Cybersecurity Roadmap

Zero trust maturity model version 2 Zero trust maturity model 2.0 With the help of CISA’s Zero Trust Maturity Model v2.0, the Federal IT environment is rapidly changing in favor of increased cybersecurity. To improve their cyber posture, federal agencies might use this model as a guide. The correct tools must be chosen in order for government entities to successfully manage this transition. Designed to fulfill the strict compliance requirements of federal organizations, Google Workspace brings strong security features to the table.

Google Workspace’s Role in Cisa zero trust maturity model 2.0 Cisa zero trust maturity model Thin-layer security, continuous validation, and device-independent protection are highlighted in the CISA Zero Trust Maturity Model. A set of principles and ideas known as “zero trust” is intended to reduce ambiguity when it comes to enforcing precise, least privilege per-request access choices in information systems and services while dealing with networks that are seen as weak. Maintaining as much granularity in access control enforcement while preventing unwanted access to data and services is the aim.

For the following reasons, zero trust puts fine-grained security restrictions across individuals, systems, data, and assets that vary over time in a more data-centric manner rather than a location-centric one.To educate agencies on how to apply Zero Trust (ZT) concepts to mobile security technologies that are already on the market and probably included in a Federal Enterprise’s mobility program, CISA prepared the Applying Zero Trust concepts to Enterprise Mobility document.

On March 7, 2022, and ending on April 20, 2022, CISA made the document available for public comment. A revised version of the paper will be produced when CISA has taken into consideration the feedback provided by all responders. The formulation, execution, enforcement, and evolution of security rules are supported by the visibility that this gives. At a deeper level, zero trust can need altering the cybersecurity culture and mindset of a business.The following elements of Google Workspace support the idea of “never trust, always verify”:

Boost Access Control and Identity: Context-Aware The correct individuals only get access to the right data thanks to strong authentication mechanisms and access.

Zero trust maturity model version 2 These measures safeguard private data at all points of access with sophisticated device controls and endpoint management. Collaborating while preserving data is made possible by granular sharing settings and data loss prevention (DLP) features.Uncovered Information from the Google Workspace Zero Trust Security Workshop.

Recent Google Workspace Zero Trust Security Workshop provided an insightful overview of how Google Workspace may help federal agencies on their Zero Trust journeys. Using Zero Trust concepts in the federal context was discussed at the session by CISA specialists. A practical application of Zero Trust architecture driven by Google Workspace and Gemini AI was shown in the ManTech case study, which proved beneficial to the attendees.

The platform’s ability to achieve CMMC compliance is shown by ManTech’s shared experience, which provides other agencies with a useful model. IT decision-makers had practical experience with Google Workspace’s security controls via interactive demonstrations that were included in the training.

Zero trust maturity model The Google Workspace Zero trust best practices guide for U.S. public sector organizations is a great tool for anybody who was unable to attend the event and wants to achieve CISA-compliant Zero trust maturity.

Special Sessions at Google Cloud Next ’24 on Google Workspace and Federal Cybersecurity.

Google Cloud Next ’24 is the next stop in the discussion, with talks on: Achieve strict compliance across many frameworks (CJIS, FedRAMP, ITAR, DoD ILs) with Assured Workloads for Public Sector Compliance.

Using Gemini in Google Workspace, Keep Your Data Private and Compliant Come learn about the privacy and security features that come standard with Gemini for Google Workspace and how Sovereign Controls may help your company attain digital sovereignty. AI-Powered Cooperation to Break Down Silos: Discover how creativity, productivity, and safe collaboration are facilitated with Google Workspace.

Through Google Workspace, Gemini: Privacy Guaranteed: Learn about the ways that Gemini protects data sovereignty while offering strong capabilities. Ready for the government: The way that Google Workspace makes safe collaboration possible: Learn how federal, state, and local government organizations may simplify operations while upholding the highest standards of data security (with ManTech) using Google Workspace.

Google is dedicated to providing creative solutions that will assist agencies in the dynamic field of federal cybersecurity. Zero Trust principles are the foundation of Google Workspace, which gives users the flexibility and toughness they need to meet new challenges. Agency tasks are accomplished and sensitive data is safeguarded with more efficacy when paired with the wider capabilities of Google Cloud.

Federal agencies will always have access to state-of-the-art resources necessary to keep ahead of emerging cyber threats because to Google’s commitment to ongoing innovation.

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