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The team behind Artifacts - an innovative new way to interact with Claude - shares how they built this innovative feature in just three months with a distributed team. Exclusive details.

To automatically determine the most important context from your codebase, we've just released v1 of Continue's codebase retrieval feature. You can ask questions like “where do I add a new endpoint to the server?”, “what scripts are used to build this project?”, or “where do I edit the user profile

This week, we discover the different types of bugs that LLM-generated code can provide, and how to mitigate their potential impact.

Visit TensorArt & Get Extra FREE 100 Credits : https://tensor.art/u/761703886053474892?source_id=AICodeKing TensorArt now supports Flux, SD3, hunyuan-dit, and Kolors model online training functions and comfyUI functions. ---- In this video, I'll be telling you that how you can custom train Flux Image models which is a new Text-To-Image generator and beats Midjourney, Stable Diffusion-3 and every other text-to-image generator. You can train it with your custom images including your face. I'll be telling you that how you can train and use it for FREE without GPU and for 100% FREE without any Installation. You can use it as a fully free alternative to Midjourney. ---- Key Takeaways: 🚀 Discover the trending Flux model and how to fine-tune it using your own custom data and images—all for free! 🎨 Learn how to train your own AI model on Tensor Art, a powerful and free platform for generating and training AI models. 📸 Unleash the full potential of Flux by using up to 1000 images and auto-captioning with Vision LLM for better training results. ⚙️ Maximize your model’s performance by fine-tuning with epochs and trigger words, perfect for creating your custom AI with specific styles! 🧠 See how simple it is to create and train your AI with Flux, and why uploading more images leads to better results! 🏆 Get ahead with exclusive tips on using Tensor Art to publish or download your models and win exciting prizes in the Flux training contest! 💰 Don’t miss out on earning extra credits by signing up with the special link, giving you more power to create and train on Tensor Art! ---- Timestamps: 00:00 - Introduction 00:08 - About Flux-1 Schnell / Dev & Training 00:35 - About TensorArt (How to use & Train Flux-1 for Free) 07:33 - Ending

When you’re writing in your favorite editor, the AI-like copilot will instantly guess and complete based on what you’ve written in the…

A Comprehensive Overview of Prompt Engineering

Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.

However, there are RAG challenges associated with the process. In this blog, we will explore the key RAG challenges in building LLM applications.

Solving the core challenges of Retrieval-Augmented Generation

Choosing the best reranking model for your RAG-based QA system can be tricky. This blog post simplifies RAG reranking model selection, helping you pick the right one to optimize your system's performance.

Indexing in terms of RAG is the process of organizing a vast amount of text data in a way that allows the RAG system to quickly find the…

For the runtime performance aware developer

Explaining the Intuition of Word2Vec & Implementing it in Python

Cost of AI in 2024: find out how much does AI cost now depending on your use case learn effective strategies to manage and optimize expenses.

Problem: Coding manually is painstakingly slow and inefficient. Solution: Innovative ways to integrate AI during development.

Here's a thread on how to make a RAG that supports PDF, URL, Text Uploads 👇🏻 — akhlas (@akhlas_hussain)

Authors: PromptArmor

We study the effectiveness of a simple approach to develop a small base language model (LM) starting from an existing large base LM: first inherit a few transformer blocks from the larger LM, and...

Stephen Wolfram explores minimal models and their visualizations, aiming to explain the underneath functionality of neural nets and ultimately machine learning.

Learn about data chunking in RAG and explore fetch surrounding chunking, a pattern in RAG that uses chunking and Elasticsearch to refine LLM responses.

Vector databases are fascinating, and I'm surprised more people aren't talking about what makes them so fast.You can use a vector database to store arrays of...

Vercel AI SDK 3.3 introduces tracing, multi-modal attachments, JSON streaming to clients, and more.

Tutorial on building intuition about LLMs. Slides: https://link.excalidraw.com/p/readonly/aBWlNjEckdUlrszwwo6V or https://github.com/srush/LLM-Talk/blob/main...

Confused by all the jargon? Me, too. So, I did a little deep dive into LLM fine-tuning. Here’s what I understood: — Leonie (@helloiamleonie)

RAG Part1: An overview of how to Do Retrieval Augmented Generation

How to monitor AWS container environments at scale (Sponsored) In this eBook, Datadog and AWS share insights into the changing state of containers in the cloud and explore why orchestration technologies are an essential part of managing ever-changing containerized workloads.

An interactive visualization tool that explains in a visual way how the Transformer architecture works. Tool: https://poloclub.github.io/transformer-explaine...