Queen Mashudu Mudau’s Post

🚀 This is groundbreaking! ElevenLabs is revolutionizing the voice technology landscape, launching its Reader app globally with support for 32 languages. 🎙️ By collaborating with leading AI experts and developers, ElevenLabs has achieved a remarkable milestone in voice synthesis and editing. 💡 With cutting-edge AI algorithms and robust infrastructure, the Reader app offers unparalleled versatility and efficiency in creating synthetic voices. 🌐 But wait, it gets crazier. The app can seamlessly generate high-quality voices in multiple languages with remarkable accuracy and nuance in just seconds! 🕒 This innovation marks a significant leap forward in the evolution of voice technology and sets a new standard for global accessibility and user experience. 🌍 This is just the beginning for the future of voice technology. 🚀 P.S. Streamline your workflow with our 30+ bot templates! Giga Bots work on pre-made script templates that run in the background. All you need to do is import your bot file, connect your tools, and adjust. Giga Bots auto-prompt and complete the rest. Check out 🔔 [bot.gigabai.com] 🔔 to automate your workflow today. Source: gigabai.com

Box is taking a major step forward in AI and automation. At the recent Box Works event, they introduced Box AI Studio and Box Apps – tools designed to help businesses harness unstructured data and improve workflows. With Box AI Studio, users can build custom, no-code AI agents powered by top LLMs from Anthropic, Google, and Microsoft, tailored for roles like sales advice or contract review. These tools are expected to disrupt how organizations manage content and documents, offering huge potential for productivity and customer experience improvements. https://lnkd.in/guhiEaXE #AIagents

The rise of app sprawl, compounded by the integration of generative #AI, poses a significant challenge for most organizations. “As generative AI applications join the enterprise equation, observability tools are a must-have for facilitating the delivery of higher-quality software at a faster pace.” To ensure continuous availability and improve customer satisfaction, VP, Product Management, IBM Automation, Bill Lobig suggests #observability as an essential solution for combatting app sprawl and understanding #AI app behaviors and dependencies: https://ibm.co/3WXotKH

Are they truly innovating, or are they just repackaging existing tech? For an AI startup to stand out, they must: • Solve unique, high-impact problems. • Demonstrate deep expertise in applying AI tools to real-world challenges. • Build layers of proprietary value (e.g., unique datasets, workflows, or UX designs). It’s fine to use existing LLMs, but the value proposition should be more than just “we use ChatGPT.” It should be about how they use it to create meaningful differentiation.

Sometimes you nail the complex parts, but get stuck on choices that can make or break your product - like the tech stack. A client of ours nailed their research and built some top-notch AI models but got stuck choosing the right tech stack for building web application for their unique data and use case. We stepped in, dug deep into their project, and laid down the perfect foundation for them. Now, their product is running like a charm. The right tech stack is important. Don't let it hold you back. Let's build the ideal foundation for your project together. Contact us today to discuss your project needs. [https://lnkd.in/dK5YGPuP] #SoftwareDevelopment #AI #Chatbots #AIAutomation #KueenzTechnologies #WebDevelopment #Innovation

Building a perfect RAG solution is not easy! With each iteration, the bar for optimality keeps rising. And it’s not just the technical challenges that stand in the way—there are non-technical hurdles too. Here are 20 lessons I've learned guiding customers who are driving real impact with RAG 1. Focus on accuracy of results before optimizing costs. 2. Security is non-negotiable; protect data and indexes. 3. Preprocess data and indexes—clean data is key. 4. Include a feedback loop from UX for continuous improvement. 5. Start small with a prototype and 3-4 clear requirements. 6. Define evaluation metrics early—groundedness, relevance, custom, etc. 7. Guide users with prompts to avoid cognitive overload. 8. Establish a gold standard for user questions and optimize for it. 9. Leverage built-in Vector DB features like hybrid search and semantic ranking. (Azure AI Search) 10. Use effective chunking; adjust based on document type. 11. Reduce latency for better user experience. (depends on use case) 12. Opt for higher-dimension embedding models when needed. 13. Use scoring profiles to keep data fresh. (Azure AI Search) 14. Don't overload indexes with diverse data; split them if needed. 15. Start simple, then move to custom code. 16. Chain models based on tasks and for cost optimization 17. Use user-friendly filters for easier search. 18. Cache frequent answers to save on API costs. 19. Chat UX isn't always needed; add controls based on use case. 20. Provide more context for better accuracy of outputs Bonus Tips: - Define system rules for context-specific answers. - Use few-shot samples to guide LLM behavior. RAG solutions have become the top AI application in recent years, offering significant organizational benefits when implemented correctly. Curious if you're facing any other challenges with building RAG solutions? Let me know in the comments! #rag #ai #techstack #optimization

I'm building custom AI chatbots with no code tools for select businesses trained on their own data.... This is an early concept of an AI sales assistant I'm creating for my own agency. This chatbot is trained on a very small amount of information, being my landing page, and my API database. Its trained to answer basic questions about Influence Labs and to give recommendations for API's for your app build. It pulls data from my Airtable database and returns relevant info based on your question. Again, this is an extremely early concept I'm working on, so expect some bugs and hiccups. Check out this 2 minute demo here: https://lnkd.in/eZdSyyAM #nocode #nocodelowcode #AI

🤖 Ever wished your browser could learn to do your repetitive tasks while you sleep? Just discovered Autotab - a fascinating new tool where AI watches you work, learns your web workflows, and replicates them with impressive accuracy - even adapting when websites change. The game-changer? It doesn't need complex coding or perfect instructions. Just like a human assistant, it learns by watching you work once, then handles the repetitive stuff automatically. 🔑 Key insight: The future of automation isn't about writing perfect scripts - it's about showing AI what to do once and letting it handle the rest. Check it out: https://autotab.com What repetitive web tasks would you love to automate in your daily work? #AI #Automation #ProductivityHacks #FutureOfWork

Developing and deploying AI applications at scale requires a flexible platform that can create solutions grounded in an organization's organizational data. How would you describe your current AI app process? If there's room for improvement, take a look at this Microsoft blog post outlining the new capabilities of the Phi-3 small language models that give your dev teams greater choice and flexibility when creating AI solutions.