De’Anna Green’s Post

⚡️ With its lightning-fast speed, unmatched scalability, and real-time data processing capabilities, #NodeJS emerges as the ultimate powerhouse for #AI integration, reshaping digital landscapes with innovative chatbots, predictive analytics, and beyond. Dive deeper into this transformative synergy in this latest article and embrace the boundless potential of backend development with Node.js and AI #integration.💡 #DigitalInnovation

🚀 Exploring AI and Having Fun with the Vercel AI SDK! 🚀 I built an AI chatbot with Next.js and had a blast doing it! In my latest blog post, I share my journey from understanding the basics to implementing a practical application. If you're curious about AI, love hands-on projects, or just want to see how easy it can be to integrate various LLM providers, check it out! 📚 👉 https://lnkd.in/g64qERjK I'd love for you to read through it, try it out yourself, and share your thoughts! 🤖💬 #AI #VercelAI #Nextjs #LearningInPublic #SoftwareEngineering #Chatbot

Building a Stateless Conversational AI Chatbot with Next.js and ChatBotKit SDK Learn how to build a stateless conversational AI chatbot using Next.js and ChatBotKit SDK. This tutorial will guide you through the process of setting up a Next.js project, creating an API endpoint, and designing a chat user interface. Get started today and build your own chatbot! https://lnkd.in/gvSvf9Yi

Curious about how no-code AI gateways are changing the game? Check out my latest blog post where top AI gateways like Portkey, Cloudflare, Kong, beNovelty, and Codenotary are higlighted. These platforms make it super easy to integrate AI models into your apps without writing any code. Perfect for developers and businesses looking to boost their tech capabilities.

I’m happy to share that first AI GPT-based product of Gutschwald AI Zentrum GmbH is fully live and operational. Introducing 🌀Krugozore AI chatbot - a Telegram-based GPT chatbot providing a user-friendly interface, intuitive logic, and special features such as a predefined library of useful prompts. https://lnkd.in/eaauTJVf What sets Krugozore apart from competitors and even OpenAI itself: - Accessibility: Gain full access directly within Telegram, eliminating the need for additional downloads. - Privacy: Onboard seamlessly using Telegram credentials without the need for phone numbers or emails. - Cost-efficiency: GPT-4 version offers a more cost-effective solution, courtesy of our exclusive pricing arrangement with OpenAI. During MVP testing, Krugozore has demonstrated the same response speed as the OpenAI app, making it an exceptionally useful tool. Following the MVP stage of last 3 months, we've gained interest of 3,000 users, with a 4% DAU (Daily Active Users) and a 17% MAU (Monthly Active Users) rate, surpassing Telegram itself and aligning with industry standards for app engagement. What's under the hood: - 4,000 lines of Python code 🐍 - Support for 9 languages - Voice and text recognition - API integration with OpenAI - API connectivity with Google Search - SQL database - Payment integration with Stripe - Cloud deployment for instant accessibility - Automatic UX We invite you to test and utilize 🌀Krugozore. If you have ideas for collaboration or need assistance in creating an MVP for your project without the coding hassle, please don't hesitate to reach out; we'd be delighted to assist you.

You can’t do AI without API’s! So, are you building multi-LLM apps? Then check out our new AI Gateway - will make your life easier if you are! Here we go!! #Kong #AI #API

We are at a fascinating inflection point in technology. The integration of AI into applications is shaping the future of how we interact with and deploy technology. Enhanced user experiences with Generative AI is unlocking potential we only dreamed of a few years ago. In my latest post, co-authored with Serdar Badem, we explore how to think about getting started with delivering next generation, intelligent applications powered by Generative AI. 🌟 Key Highlights: 🚀Foundational considerations for building a retrieval-augmented generation app 🚀Large language model considerations and dev framework considerations 🚀Operational changes to be addressed with people and technology Curious to learn more? Check out the full post. https://lnkd.in/gNWu_ksA Also be sure to join us at Explore LV August 26-29 to learn more about #Tanzu and AI. https://lnkd.in/gKsEFEvB #AI #AppDelivery #TechRevolution #VMware #Java #Spring #SpringAI Serdar Badem Art Fewell Rita Manachi Danielle Burrow Colleen Green Anu Srinivasa

The best LLM use cases are where you use LLM as a tool rather than expose it directly. As Richard Seroter says, how many chatbots do you need? However, this use case of replacing static product pages by personalized product summaries is like many other LLM use cases in that it faces unique risks due to non-determinism. Imagine that a customer sues you a year from now, saying that they bought the product because your product summary claimed (wrongly) that the product was flameproof and their house burned down. The only way to protect yourself would be to have a record of every generated summary and the storage costs will quickly add up ... One way to avoid this problem (and what I suggest) is to generate a set of templates using LLMs and use an ML model to choose which template to serve. This also has the benefit of allowing human oversight of your generated text, so you are not at the mercy of prompt engineering. (This is, of course, just a way to use LLMs to efficiently create different websites for different customer segments -- the more things change, the more they rhyme with existing ideas). Many use cases of LLMs are like this: you'll have to reduce the non-deterministic behavior and associated risk through careful architecture.

Essentially, this idea is a product recommendation system. Done something similar in life-science industry. The UX quality would depend on how accurate and timely your User Preferences data are, how accurate you can match the preferences and the products, and the output quality of your AI model which I truly believe, still need human-review, due to the hallucination issues/risks. The more specific (niche) the products are, the higher the probability of preference-product mismatches and/or hallucinations occurred (obviously, because the training data of general AI model are too general), which could lead to bad UX.

New tutorial coming up 😍 You will build a website with GitHub as the CMS and an AI chatbot that knows about all your pages. We will use Next.js 14 and Langchain. In this tutorial you will build a blog. But this is also very suited to create a documentation page with an AI chatbot. Or any other kind of website.