NEW Google EmbeddingGemma is INSANE (FREE)! 🤯

New Google Embedding Gemma is insane and free. Google just dropped embedding Gemma and it's about to change everything. I'm talking about a tiny AI model that runs on your phone. It beats models twice its size and it works without internet. Plus, Google just made it completely free. This could kill billion dollar AI companies overnight. So, Google just released something that nobody saw coming. It's called embedding Gemma. This model has only 308 million parameters. That's tiny, but it performs better than models with 600 million parameters. Most AI models need the cloud. They need internet. They need massive servers. Embedding Gemma runs on your phone. It runs on your laptop. It works completely offline. This model uses less than 200 MGB of RAM. That's smaller than most photos on your phone. But it can understand over 100 languages. It can search through documents and it does all of this in 15 milliseconds. Every AI company charges for embeddings. Open AAI charges you. Anthropic charges you. But embedding Gemma is completely free, open- source. Download it right now. This thing scored the highest on the Massive Text embedding benchmark. That's the gold standard for testing embedding models. It beat every other open-source model under 500 million parameters. We're talking about performance that usually costs thousands of dollars per month. Let me explain what embeddings actually are. You know how humans understand that dog and puppy are similar words, but computers see them as completely different. Embeddings fix this. They turn words into numbers that capture meaning. Speaking of possibilities, if you want to stay ahead of these AI developments and scale your business, check out the AI Profit Boardroom. It's the best place to get more customers and save hundreds with AI automation. We currently have 1,000 members who are crushing it with these exact technologies. Most embedding models need the cloud. Your data goes to Google servers or Open AI's servers. That's a privacy nightmare, especially for businesses. But embedding Gemma changes everything. Your data stays on your device. No cloud, no servers, no privacy risks. This is huge for companies handling sensitive information. Let me show you what this means with real examples. Box, the intelligent content management platform, is already integrating Gemini embedding technology to answer questions and extract insights from complex documents. They're keeping all that sensitive business data secure while getting AI powered insights. Or look at the financial sector. A company called Recap uses embeddings to classify huge volumes of B2B bank transactions. They tested Google's embedding technology against previous models. They saw their F1 score increase by 1. 9%. In finance, that's millions of dollars in better decisions. Embedding Gemma has a 2K token context window. That means it can process really long documents, research papers, legal contracts, technical manuals. The speed is insane. 15 milliseconds to generate embeddings. That's faster than you can blink. Most cloud models take hundreds of milliseconds plus network latency. Here's the secret most people are missing. Embedding Gemma supports matrioska representation learning. You can adjust the quality. Need super high quality. Use 768 dimensions. Need speed over quality. Use 128 dimensions. It's like having multiple models in one. Most people think embeddings are just for search, but that's wrong. Embeddings power everything. Recommendation systems, content classification, spam detection, customer support. Let's talk about the legal industry. Ever is a platform that helps legal professionals analyze massive volumes of discovery documents. They tested Google's embedding technology on 1. 4 million documents. 87% accuracy in surfacing relevant answers. That's finding the needle in the haystack every single time. This isn't theoretical. This is happening right now. Real companies, real results, real money being made and saved. Here's where embedding Gemma really shines. R A stands for retrieval, augmented generation. You have documents, company policies, product manuals. You create embeddings for all of them. When someone asks a question, you find the most relevant documents. You feed those to an AI model. The model gives an accurate answer based on your data. This is huge for customer support. Instead of training staff on hundreds of policies, you give them an AI assistant. The assistant knows everything. Answers accurately never gets tired. Julian Goldie reads every comment, so make sure you comment below. Now, let me show you how to use this. Download the model from HuggingFace. Just search for Google embedding GMA. You can also get it from Kaggle or Google's Vertex AI. All free. For the simplest setup, use sentence transformers. It's a Python library. Import sentence transformers. Load the model. Encode your text. Done. You can also use it with Llama Index or Langchain or Weev8. Pretty much every AI framework supports it now. Google optimized this for mobile chips. It runs great on Android and iOS. You can literally put AI embeddings in a mobile

app, no cloud required. Think about the possibilities. A translation app that works offline. A document scanner that understands content. A voice assistant that knows your personal information. Every SAS company needs embeddings, customer support systems, knowledge bases, document search, content recommendations. Right now, they pay thousands per month to OpenAI or Google. But with Embedding Gemma, they can run it themselves. No monthly fees, no usage limits. You could build a competing product, charge half the price, keep more profit, and offer better privacy. It's a huge opportunity. Is this as good as GPT embeddings? For most use cases, yes. But the real advantage isn't performance. It's control. You own the model. You control the data. No company can change the pricing or shut down the service. Google released this as open source. That means developers will improve it. We're already seeing integrations with popular tools. Olma supports it. LM Studio supports it. MLX supports it. The real opportunity is fine-tuning. You can train this model on your specific data. A medical company could fine-tune it on medical documents. A legal firm could tune it on legal cases. Fine-tuning is way cheaper than training from scratch. Hundreds of dollars instead of millions. Let me tell you what this means for SEO. Embedding models power search engines. Google uses them. With embedding Gemma, you can analyze your content like Google does. Find content gaps. Optimize for semantic search. You could build internal tools for SEO analysis, content optimization, keyword research, all powered by the same technology Google uses. Now, I want to talk about the competitive landscape. Open AAI has embedding models, but they're cloud only and expensive. Microsoft has embedding models. Meta has embedding models, but none of them run on device. None work offline. None are this efficient. Google just changed the game. They made highquality embeddings accessible to everyone. Small businesses, individual developers, startups, anyone can compete now. And if you want to learn how to implement this stuff properly, we have an SOP and process inside the AI money lab in the comments and description with the links plus over 100 different use cases. You can get all the video notes and training materials to actually put this into practice. Let me give you specific use cases. Customer support chat bots. Instead of scripted responses, use R A with your knowledge base. Content management systems automatically tag and categorize content. E-learning platforms match students with relevant courses. HR systems match job candidates to positions. The applications are endless. Any business that deals with text can benefit. And that's pretty much every business. Let me address some concerns. Model size, 308 million parameters sounds big, but it's actually small by modern standards. Accuracy. Some people worry that smaller models are less accurate, but embedding Gemma proves that wrong. It outperforms many larger models. How does Google make money if the model is free? Simple. They want you to use their cloud services for everything else. It's like giving away the Razer to sell the blades. But it benefits us, too. We get amazing technology for free. Here's what I predict will happen. and other companies will scramble to compete. Meter will release better open models. Microsoft will do the same. Open AAI will lower prices. So, here's my advice. Start experimenting now. Download the model, build some prototypes, learn how it works, position yourself for the future. Let me give you a specific example. You run a marketing agency. Your clients always ask for content ideas. With embeddings, you could analyze all their existing content. Find content gaps. Suggest topics that are semantically related but underexplored. way more valuable than basic keyword lists. Or you run an online course business. Students ask for help finding relevant lessons. With embeddings, you could create semantic search. Students ask natural language questions. The system finds the most relevant lessons even without exact keywords. These aren't futuristic ideas. This is possible today with free open-source technology. If you want to scale your business and get more customers, check out the AI profit boardroom. It's the best place to save hundreds with AI automation. We currently have 1,000 members who are crushing it. And if you need help with SEO, book a free SEO strategy session link in comments and description. I'll show you exactly how to get more organic traffic and leads. But remember, we also have that SOP and process inside the AI money lab. Link in the comments and description with the links plus over 100 different use cases. You see how I show a checklist of 100 different tutorials that were given away as freebies every day inside the school feed. Also that they can get all the video notes from there and the other stuff that they get along with all the trainings in the AI money lab. On top of that, I also show that it has 19,000 members because then people feel like they're missing out and they want to be part of something bigger than them. Embedding Gemma is just the beginning. The AI revolution is accelerating. Make sure you're ready for what comes