Raymond Camden’s Post

Day 47 #100DaysOfCode -> Created a basic webpage where users can input brain scans and get a prediction -> Used Flask API to make predictions based on my model inference -> Connected the backend(Flask) to the Frontend(html) for user interface #DeepLearning #AI #ML #connect

You can now index your local files and turn them into your own generative search engine. Nikola Milosevic shows you how to deploy VerifAI and how to start using it. Read more now!

🌊Super excited to announce that we will host our next webinar “From LLM to Large Action Model (LAM): how to build, evaluate, and improve LAMs using open and closed source LLMs”  this Thursday 13th June at 9 am PST on Zoom! Webinar link: https://lu.ma/v2mr2ynn Following the success of our previous webinar on a general introduction to AI Web Agents building with LaVague (https://lnkd.in/e4xVTx5m), we have decided to organize another one that goes into the technical details of our framework. LaVague (https://lnkd.in/e22QkuQz) is an open-source Large Action Model framework to build AI Web Agents. Our framework makes it easy to design agents to perform actions for us on the web, by piloting a driver using Large Action Models. We have two core components to make it happen: - Our World Model: powered by OpenAI GPT-4o multimodal to reason using the user’s objective (Fill this form) on the screenshot of the web driver and provide instruction (Click on Apply button) to our action engine. - Our Action Engine: powered by LlamaIndex to take the instruction of the World model to turn it in #Selenium code. In this webinar, we will dig deeper into the inner workings of our Action Engine to understand how we designed our RAG pipeline to consistently produce the right code to interact with the current page’s web elements. We will show insights and tips on how to improve the retrieval stage on the DOM, and how to best generate the right Selenium code using the retrieved HTML associated to the user instruction. We will do a demo, present our architecture and code, explore evaluation and share potential next steps to improve our framework as a whole, along with tips for users to customize their Large Action Model. So if you are interested in building performant Agents, are interested in Large Action Models, or want to contribute to our open-source framework, don’t hesitate to drop in! You can also join us on our Discord (https://lnkd.in/eKf-mD-m), play with our GitHub (https://lnkd.in/e22QkuQz), or contribute to our project (https://lnkd.in/eYCXZVxV)!

The first time I worked with GPT models to build AI-driven features for our app, I encountered a frustrating challenge: inconsistent response formats from the models. No matter how carefully I crafted the prompts, the responses often came in unreliable formats, making them difficult to integrate into our system. Someone had worked on this part before and had a series of hacks and conditional statements to catch and handle format inconsistencies. For example, we'd request a specific JSON object, only to receive a response with mixed strings and objects. It was a nightmare to parse and use as JSON directly. The breakthrough came when I found a feature called "function calling with structured outputs" for the OpenAI models. Function calling allows you to define expected function parameters, enabling the assistant to respond with reliable, structured data that can be directly parsed and used as needed. I implemented it across the board, and we were able to reduce errors for our users and serve them better. If you're curious about function calling, here's a link to learn more: https://lnkd.in/d-X-EV2H

Some of the best implementations of LLMs that I've seen take something that's already useful and reduce friction by integrating it into an existing workflow. I'm thinking: Gong call summaries Loom automatic title/description generation That's a big reason why this feature has been getting such good feedback - The developer opens a PR, as they would do regardless, and gets a few suggestions right from GitHub. That'd already be enough for a solo developer, but if you've worked as part of a team, you know the challenge: everyone wants their code reviewed before merging, yet few have the time to review others' code. This tool helps bridge that gap, making code reviews more accessible and efficient.

[Workshop + Code] Build Full-Stack AI Apps with GPT-4o: https://lnkd.in/ehSQePRe OpenAI's GPT-4o (along with Vercel and NextJS) is revolutionizing web development, making it possible to create sophisticated, AI-driven applications. In this session, you will witness a live demo and code-share, showcasing the practical applications of these technologies. If you're looking to stay ahead of the curve in AI, this is a vital session to attend. Reserve your free spot here: https://lnkd.in/ehSQePRe Important: Register even if you can't make the live session, you'll get a recording sent over plus any key codes & assets. Thanks to SingleStore for letting us know about this session, and partnering to share it with our network. #datascience #dataengineering #artificialintelligence #genai

The demo project of the FANTASIA Interaction Model is available on GitHub! This is the project used in the live tutorials and it showcases how to use Behaviour Trees, Probabilistic Graphical Models and Graph Databases to build linguistically motivated #ConversationalAI with Unreal Engine and FANTASIA. Installation instructions are available in the Readme while more technical details will be provided in a separate document as we continue the ongoing codebase cleanup and documentation process. Just a few weeks before starting the next phase of our research with the introduction of new AI tools and corresponding theoretical developments.

🚀 Thrilled to have spoken at the GenAI Deep Tech event on an exciting topic: Semantic Search Powered by WASM and WebGPU 🔍⚙️ Key highlights from my talk: 🦀 Rust for Performance: Leveraging Rust to build a high-performance semantic search engine. 🟪 WebAssembly: Compiling the Rust code to WebAssembly (WASM) to run directly in the browser. 🎮 WebGPU for Speed: Utilizing WebGPU to accelerate embedding queries. ⚡ Real-time Search: Achieving efficient, real-time semantic search with no server dependencies, all in the browser. 🌐 Optimized for Modern Web Apps: Bringing advanced ML-powered search closer to the end user with the power of WASM and GPU acceleration. Thanks to everyone who joined the session and engaged in the discussion 🙌. #GenAI #SemanticSearch #WASM #WebGPU #AI #MachineLearning #Rust #WebTech #SearchInnovation

Excited to share my latest project: TrendyThumbs👍 (Link: https://lnkd.in/dCVXVrVD) Leveraging Spring Boot & NextJS, this tool helps content creators dive deep into what makes thumbnails of trending YouTube videos so eye-catching! Here’s how it works: 🔹 Fetches top 50 trending thumbnails via the YouTube API 🔹 Analyzes visual features using Google Vision AI API to extract dominant colors, facial expressions, word count, object labels (coming soon!), etc. 🔹 After further categorization, data is stored in MongoDB for fast retrieval 🔹 Insights are served to the users with the help of charts to help them spot design trends and patterns I believe this definitely has potential for more innovative features!

A new video is out! Part 2 in my #Spring #AI series. Combining #Java23, Spring Boot, Spring Web, and Spring AI as well as OpenAI API in this video, to create an AI-enabled excuses generator. As always, tutorial code is up for grabs as well. https://lnkd.in/dtEZmHB9