Day 2 of Engineering Week 2024 closed out with some sessions on harnessing AI for creativity and efficiency: ➡️ Backend For Frontend (BFF) Pattern: Optimizing User Experiences ➡️ AI: Enhancing Human Creativity and Decision-Making, Not Replacing It ➡️ Getting Started with AI Using OpenAI and Integrating it into Your Projects A big thank you to our speakers, Santiago Salazar Patiño, Juan Carlos Aleman, and Rogelio Ortiz, for sharing their expertise and new ideas on how AI can elevate our work. #EngineeringWeek2024
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We've wrapped up Day 2 of Engineering Week 2024, where we explored some of the perks of adding AI to our toolkit. Big thanks to Santiago Salazar Patiño, Juan Carlos Aleman, and Rogelio Ortiz for sharing these awesome topics: ➡️ Backend For Frontend (BFF) Pattern: Optimizing User Experiences ➡️ AI: Enhancing Human Creativity and Decision-Making, Not Replacing It ➡️ Getting Started with AI Using OpenAI and Integrating it into Your Projects These sessions really got us thinking about how AI can make our work smoother and more creative. #EngineeringWeek2024
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Found the Swarm🐝 multi-agent orchestration library pretty elegant. It’s simple, yet powerful for building AI apps. Documented my takeaways in a quick video. https://lnkd.in/dBEDCsYx
How to Build Multi-Agent Systems: AI Apps in 20 Min | OpenAI Swarm Tutorial 🐝
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🚀 Just completed my Image Recognition app! 📱🤖 This Android app integrates Machine Learning and TensorFlow Lite for efficient real-time object classification using the MobilenetV1 model. Key features: 1. Upload or capture an image and instantly classify it using advanced Deep Learning algorithms 2. Built with TensorFlow Lite, optimizing the model for mobile performance 3. Leverages the MobilenetV1 architecture for efficient object detection and recognition Check out the demo in the video! 🎥 #MachineLearning #TensorFlowLite #AI #DeepLearning #AndroidDevelopment #AppDevelopment #Mobilenet #ObjectRecognition #ImageClassification #AIonMobile
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Build your personal AI assistant, watch the full tutorial! 🤖💡 During our past live coding workshop, we built an app that uses the Gemini family of AI models to provide personalized furniture recommendations based on uploaded images. 🛋️📸 Additionally, we demonstrated a live example of generating video descriptions and identifying top global locations similar to your uploaded video. 🌍🎥 Learn how to leverage Rockset as a vector database to manage and query large datasets efficiently, and how to use Gemini APIs within your apps. Subscribe to our channel and watch the full workshop—link in comments! 📺 #AI #MachineLearning #Gemini #Rockset #VectorDatabase #LiveCoding #Tutorial #TechWorkshop
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Clean Flutter Series: Advanced Permission Management In episode 7, we integrate the Lifecycle Cubit with the Permission Cubit. Using Cursor AI, we'll enhance our permission handling by tracking app state and permission changes. What you'll learn: Cubit integration techniques Bloc to Bloc communication App state and permission synchronization Stream subscription management Dependency injection strategies https://lnkd.in/dqgzBVvY
Integrating Lifecycle with Permission Cubit | Cursor AI in Action| freezed| Geolocation Series | E07
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Last night’s AI coding session was focused on AI Vision 🤖 I created an app called “Vision Quest” that allows users to enter an image url into a form. On-submit, the app feeds your input to an API call that uses GPT-4 Turbo. This model will analyze your image and return a detailed description of what it’s “looking” at. Check it out in the clip: #buildinginpublic #aiengineer #softwareengineer
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It's finally happened - The first AI software engineer is here. A few hours ago, #CognitionAI just launched Devin, an AI that's changing how we code. Unlike anything before, Devin can turn simple commands into fully working websites or programs. It can - create and launch software on its own, breaking down tasks into simple steps. - quickly fix errors, making coding much smoother. - pick up new tech easily, constantly improving. Deven's a glimpse into the future of coding. It's making creating software easier and faster for everyone. #AI #CodingRevolution #Devin https://lnkd.in/dkDMRfbu
Devin, the first AI software engineer by Cognition Ai • subscribe ✨👏
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Hi Connections I have been exploring the world of Generative AI and have had the opportunity to work with some amazing tools like OpenAI, HuggingFace, and Llama 3.2. I have built a few projects using these technologies and have been impressed with the results. I am excited to see what the future holds for generative AI and how it can be used to solve real-world problems. Llama 3.2: Llama 3.2 is a large language model developed by Meta AI. It is capable of generating Human-quality text, translating languages, writing different kinds of creative content, and answering your questions in a informative way. Gemini AI: Gemini AI is a multimedia AI model developed by DeepMind. It is capable of understanding and generating text, code, images, and audio. Gemini AI is a powerful tool for variety of applications, such as content creation, research, and education. Hugging Face 🤗: Hugging Face is a leading platform for building and sharing machine learning models. It provides a vast library of pre-trained models for various tasks, such as natural language processing, computer vision, and audio processing. Hugging Face also offers tools and resources to help you train your own models and collaborate with other researchers and developers. OpenAI: An OpenAI API Key is a unique identifier that allows you to access and use OpenAI's powerful AI models and tools. With an API key, you can build your AI applications, experiment with different models, and integrate AI capabilities into your existing projects. LangChain: LangChain is a powerful framework that simplifies the process of building applications powered by large language models (LLMs). It provides a modular and flexible approach to integrating LLMs into your projects, allowing you to focus on the care logic of your application while handing the complexities of LLM interactions. links: https://lnkd.in/gRe6zhXC https://lnkd.in/gmRAcWC6 #GenerativeAI #AI #MachineLearning #DeepLearning #OpenAi #HuggingFace #LLM #LangChain #GeminiAI #Llama3.2
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Startup life or research? Por qué no los dos. Together with our friends at Morph Labs, we made a dataset of 1,000 Transformers-related questions to help us climb the ladder towards the best chat-with-your-codebase system in open-source land. See our initial findings 👇
Tired of building AI coding agents based on vibes? Together with our friends from Morph Labs, we made a real-world dataset that gives us a ladder to climb: 1,000 questions about the Transformers library. Here are our initial learnings about proprietary APIs: 1. For embeddings, OpenAI's text-embedding-3-small performed best. 2. For reranking, NVIDIA takes the lead. 3. Sparse retrieval (BM25) actively hurts performance when retrieving *code*. The reason: it prioritizes documents in natural language, so Markdown files tend to undeservedly bubble to the top. 4. Chunk sizes: 200-800 tokens per chunk works well, as suggested by the CodeRag paper. We recommend 800, since it makes codebase indexing faster. 5. Top-K for retrieval: We see diminishing returns beyond 25 documents (but don't forget to rerank them!) Read the full report here: https://lnkd.in/e2EQ77nr
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