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Building with AI? Here's our engineering ins and outs for 2024: 👍 In: Running a pipeline by calling an API. 👎 Out: Manually implementing an AI pipeline with code. 👍 In: Step-level observability of pipelines. 👎 Out: Reading a wall of code that only engineers can understand. 👍 In: A visual WSYIWIG overview of the pipeline that *is* the pipeline. 👎 Out: Manually creating and maintaining a pipeline diagram to match with the code. 👍 In: Knowing that you won’t break a pipeline when making changes. 👎 Out: Breaking a pipeline every time you iterate on it. 👍 In: Building a pipeline at the same time as you’re designing it. 👎 Out: Designing a pipeline, then coding it separately.

#Day114ofDeepLearning: 🎯Started creating loss function for training optimization for YOLOv1 architecture. Spent sometime making helper function on utils where it helps to get IOU and box attributes. Today, did implementation for loss optimization, the loss function combines various components, such as coordinate loss for localization, confidence loss for object detection, and penalties for incorrect/no object predictions. Similarly, created responsible: The model needs to assign responsibility for detecting the object to the bounding box with the highest IoU. Github: https://lnkd.in/dn67Vuz3 #machinelearning #ai #deeplearning

Developers!, in an AI-driven era, tools like Copilot are transforming the development process. But is AI enhancing the quality of your software? Are you leveraging generative AI to refine your code and workflow? Would you consider daily use of an AI tool to enhance code testing? Or perhaps you’re already doing so? Dedicate less than 1 minute, click here to answer 5 quick questions and share your insights on AI’s role in software quality!

📣 Excited to kick off a new series: “Top 5 Everything in LLM”! 🚀 Today, we’re diving into the Top 5 Local LLM UIs and comparing their features. Let’s get started! 🧐 1. LM Studio Local API: Yes Docker: Yes Ease of Use: Easy, User Friendly Document Chat: No Chat with Custom Characters: No Features: Discover, download, and run local LLMs Additional Features: Quick setup 2. Jan Local API: Yes Docker: Yes Ease of Use: User-friendly Document Chat: No Chat with Custom Characters: No Features: Clean and elegant UI, model card viewing, model downloading, system compatibility checks Additional Features: Active community support 3. Oobabooga Local API: Yes Docker: No Ease of Use: Moderate (extensions are a pain to install and not really user friendly) Document Chat: No Chat with Custom Characters: Yes Features: Based on Gradio web UI, 3 interface modes: default (two columns), notebook, and chat. Train LoRA adapters from UI Additional Features: Extensive customization options 4. Anything LLM Local API: Yes Docker: Yes Ease of Use: Easy Document Chat: Yes Chat with Custom Characters: Yes Features: Local vector DB, multiple document support, agents like web search, run code etc. Additional Features: Full-stack application 5. GPT4All Local API: Yes Docker: Yes Ease of Use: Easy Document Chat: Yes Chat with Custom Characters: No Features: Mirrors ChatGPT’s interface, runs local LLMs for common tasks and RAG Additional Features: Extensive documentation There are many more such UIs, if you are using any UI #comment below to help the community. Course Link: https://lnkd.in/dv_DmGhB 50% off using Coupon Code: LLM50 (Limited time) Start your journey towards mastering LLM today! Stay tuned for more in this series! 🎉 #Top5LLM #LLMUI #AI #MachineLearning #LocalLLM #LLM #genai #generativeai #largelanguagemodels #huggingface #lmstudio #LLMS

Delving into the frontier of web development with an intriguing paper: "How Far Are We From Automating Front-End Engineering?" 🚀🤖 This research breaks ground in Generative AI, introducing a Design2Code task and benchmarking 484 real-world webpages. 🎨💡 GPT-4V steals the spotlight, showcasing its prowess in generating visually stunning and content-rich webpages. 🌟 The future is here—explore the possibilities! #FrontEndInnovation #AIRevolution #TechBreakthrough 🚀🌐

An efficient engineering lifecycle can be a real game changer. Ascendion’s generative AI-powered engineering companion, Digital Ascender, helps us automate coding, generate test cases, fix bugs, optimize code, and so much more. For our clients, this means improved products and smoother platform launches.   Explore how we can use Digital Ascender to deliver the best of generative AI to your business: https://lnkd.in/e3icn4wc   #ExponentialImpact #Engineering #PoweredByAI

Appwrite ❤️ AI You can use your favorite AI tools with Appwrite: • LangChain • Hugging Face • ElevenLabs • Pinecone • OpenAI • Replicate • Perplexity Visit our docs to learn more 👉 https://lnkd.in/ecw_jSXP

AI for Web Devs: Prompt Engineering https://bit.ly/48KNfAx Welcome back to this series where we are building web applications that incorporate AI tooling. The previous post covered what AI is, how it works, and some related terminology. Intro & Setup Your First AI Prompt Streaming Responses How Does AI Work Prompt Engineering AI-Generated Images Security & Reliability Deploying In this post, we will cover prompt engineering, which is a way to modify your application’s behavior without changing the code. Since it’s challenging to explain without seeing the code, let’s get to it. via DZone AI/ML Zone https://bit.ly/41qvfZp January 23, 2024 at 08:04AM

Traditional code reviews, while essential, can be time-consuming and prone to human bias. AI-powered code review tools are revolutionizing the process by automating tasks and providing valuable insights.

If you've ever struggled to understand what AI does and how it works, I highly recommend reading this fantastic article by my brilliant colleague, Paz. She has a unique ability to break down complex concepts in a clear and concise way. While I know how to use a prompt, the inner workings of the architecture that answers it have always been a bit of a mystery to me. Parez's article unlockes the structure in a simple way, speaks to the mental model and shift that we need to make as designers, as well as gives you a quick understanding of what a context window is. And of course gives you a guide to prompt better.