Discover how Meta AI's Chain of Continuous Thought (Coconut) empowers large language models (LLMs) to reason in their own language.

Keep your users updated with real-time task progress. Now with LLM streaming support and increased limits.

Turn websites into LLM-ready data. Build better RAG systems and train AI models with clean, structured web data. DataFuel handles the complex parts of web scraping, so you can focus on your AI innovations.

In recent years, the spotlight in AI has primarily been on large language models (LLMs) and emerging large multi-modal models (LMMs). Now, building on these ...

Identify which essay was written by a large language model

Everyone's saying 2025 is the year of Agents but there aren't a lot of baseline on what is an AI Agents! This video is exactly to explain AI Agents for Begin...

Want to really understand how large language models work? Here’s a gentle primer.

Everything that went wrong trying to make our LLMs say less.

Structured input/output, prefilling, n-shots prompting, chain-of-thought, reducing hallucinations, etc.

The Stackblitz and Qodo CEOs dish on building production coding agents, from going viral as the hottest new consumer/low-code agent, to the gnarliest enterprise deployments for code/test agents.

How did Bolt.new get to $8M of ARR in 2 months by being a Claude Wrapper? Today's episode with Eric Simons from Bolt.new / Stackblitz and Itamar Friedman fro...

This video introduces CerebrasCoder which is an online free tool to generate apps with text in less than a second.🔥 Get 50% Discount on any A6000 or A5000 G...

In the dynamic landscape of commerce, Shopify merchants rely on our platform's ability to seamlessly and reliably deliver highly relevant products to potential customers. Therefore, a rich and intuitive search experience is an essential part of our offering. Over the past year, Shopify has been integrating AI-powered search capabilities into our merchants’ storefronts. Shopify Storefront Search has transformed the way consumers can shop online. With Semantic Search, we went beyond keyword matching. We improved our understanding of the intent behind a consumer’s search, so that we could match a search with the most relevant products. The net result is helping our merchants boost their sales while offering positive interactive experiences for their consumers. It’s a win-win! Building ML assets with real-time Embeddings Around the same time, Shopify also started investing in creating foundational machine learning (ML) assets. These assets are built as a shared repository of ML primitives which are used as reusable building blocks for more sophisticated AI systems. Shopify Storefront Search is the perfect use case for these ML assets. Complex systems like this need primitives that can transform both text and images into data formats it can process. How do we do that? Enter embeddings, which translate textual and visual content into numerical vectors in a high-dimensional space. This transformation allows us to measure the similarity between different pieces of content, whether text or images, enabling more accurate and context-aware search results. Simplified example of using embeddings to compare search text to result corpus Now that we have a clear idea of the ML primitives that Shopify Storefront Search relies on, let’s take a look at how we send these embedding updates to their systems. Designing ML inference streaming pipelines Currently, Shopify processes roughly 2,500 embeddings per second (or roughly 216 million per day) across our image and text pipelines in near real time. These embeddings include net-new or updated content from across our merchants. Since most of our data platform infrastructure is built on BigQuery,

There has been a lot of new research on the multimodal LLM front, including the latest Llama 3.2 vision models, which employ diverse architectural strategies...

A behind the scenes look at the technical problems we faced and how we solved them.

Vector similarity search has revolutionised data retrieval, particularly in the context of Retrieval-Augmented Generation in conjunction with advanced Large Language Models (LLMs). However, it sometimes falls short when dealing with complex or nuanced queries. In this post, we explore our experimentation with a simple yet effective approach to mitigate this shortcoming by combining the efficiency of vector similarity search with the contextual understanding of LLMs.

Vinesh Gudla, Prakash Putta, Ankit Mittal, Andrew Tanner, Tejaswi Tenneti

All Machine Learning Beginner Mistakes explained in 17 MinDon’t make the same mistakes I made! Here is a list of things to avoid when starting Machine Learni...

Ai is the most booming field in 2024 and the job market for Artificial Intelligence (AI) and Machine Learning (ML) is only starting to skyrocket. I have a B....

This article explores building a VS Code extension to automate code commenting using the Ollama.js library and a local LLM.

A post for developers with advice and workflows for building effective AI agents

Machine Learning School. Contribute to svpino/ml.school development by creating an account on GitHub.

Understanding the AI agents stack landscape.

Learn why the AI Enablement Stack is essential for modern AI development. 5 critical layers to build better, more capable intelligent agents.

In this video, I share my 6 secret tips on how to get an ML job in 2025 because doing so feels almost impossible... At least, if you don't know what options ...

I follow the journey that led to the explosion of Large Language Models. From Jordan's pioneering work in 1986 to today's GPT-4, this documentary traces how ...

We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections t...

Visualizing machine learning one concept at a time.

Code release for Best-of-N Jailbreaking. Contribute to jplhughes/bon-jailbreaking development by creating an account on GitHub.