Learn AI

Get started with the Model Context Protocol (MCP)

Weights & Biases, developer tools for machine learning

Everything you need to know about building production-ready RAG systems.

Use code YOUTUBE to get an extra 20% off my AI courses here:https://www.jointakeoff.com/This is the RAG course from Takeoff. We're making the full videos fro...

People hop on stream all the time and ask me, what is the fastest way to learn about the lowest level? How do I learn about how computers work. Check out this video to find out. Code: https://pastebin.com/raw/TpHbB91G 🏫 COURSES 🏫 Learn to code in C at https://lowlevel.academy 📰 NEWSLETTER 📰 Sign up for our newsletter at https://mailchi.mp/lowlevel/the-low-down 🛒 GREAT BOOKS FOR THE LOWEST LEVEL🛒 Blue Fox: Arm Assembly Internals and Reverse Engineering: https://amzn.to/4394t87 Practical Reverse Engineering: x86, x64, ARM, Windows Kernel, Reversing Tools, and Obfuscation : https://amzn.to/3C1z4sk Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software : https://amzn.to/3C1daFy The Ghidra Book: The Definitive Guide: https://amzn.to/3WC2Vkg 🔥🔥🔥 SOCIALS 🔥🔥🔥 Low Level Merch!: https://lowlevel.store/ Follow me on Twitter: https://twitter.com/LowLevelTweets Follow me on Twitch: https://twitch.tv/lowlevellearning Join me on Discord!: https://discord.gg/gZhRXDdBYY

This post discusses integrating the Stripe agent toolkit with large language models (LLMs) to enhance automation workflows, enabling financial services access, metered billing, and streamlined operations across agent frameworks.

The first Web AI Summit, hosted by Google on October 18, 2024, brought together experts in machine learning models for web browsers.

Build skills, get certified, and learn from NVIDIA experts through hands-on self-paced courses and instructor-led workshops.

Unlock the secrets to mastering Artificial Intelligence (AI) quickly with this self-study roadmap, based on the prestigious Stanford AI Graduate Certificate ...

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Prompt, run, edit, and deploy full-stack web applications - stackblitz/bolt.new

Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team. - openai/swarm

122 votes, 11 comments. I've been working on on workflow for creating high-quality transcripts using primarily open-source tools. Recently, I shared…

— Sebastian Raschka (@rasbt)

This deep dive covers: ➡️ Implementing semantic caching in agent systems to improve speed and efficiency ➡️ Advanced agent techniques like query routing, query decomposition,… — LlamaIndex 🦙 (@llama_index)

Nexa AI On-Device Model Hub: LLaMA, Stable Diffusion, Whisper & more. Pre-trained AI models for NLP, vision, multimodality.

Large Language Models are pushing the boundaries of artificial intelligence, but their immense size poses significant computational challenges. As these models grow, so does the need for smart optimization techniques to keep them running efficiently on modern hardware. In this post, we’ll explore key optimization strategies that are making LLMs faster and more memory-efficient. We’ll start with a brief look at GPU memory hierarchy, which forms the foundation for many of these techniques. Then, we’ll explore algorithms that allow LLMs to process information more quickly and handle longer contexts. Understanding these techniques offers valuable insights helping to unlock the full potential of Large Language Models.

[caption align=

In this post, we demonstrate how to seamlessly automate the deployment of an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and the AWS Cloud Development Kit (AWS CDK), enabling organizations to quickly set up a powerful question answering system.

[caption align=

In this post, we explain how to use Anthropic Claude on Amazon Bedrock to generate synthetic data for evaluating your RAG system.

In this post, we show you how to create accurate and reliable agents. Agents helps you accelerate generative AI application development by orchestrating multistep tasks. Agents use the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps.

In this post, we discuss scaling up generative AI for different lines of businesses (LOBs) and address the challenges that come around legal, compliance, operational complexities, data privacy and security.

Explore AI's impact on SEO strategies and content creation. Find out how to balance AI tools with human expertise for better search rankings.

More and more, people are building products as thin wrappers over other models, especially LLMs. This can cause major issues, and there is a better way.

Enjoy the videos and music that you love, upload original content and share it all with friends, family and the world on YouTube.

📌 Github with all Code, Slides, Resources ⇒ https://tsdb.co/busy-dev-resources ​​PostgreSQL is catching fire as the default database choice for AI applications. But how can you best leverage all that PostgreSQL has to offer for AI applications? What are best practices to follow, common pitfalls to avoid, and tools that will accelerate your development? ​​​ Avthar Sewrathan, PM AI and Vector @ Timescale shares his learning from helping developers build AI applications with PostgreSQL over the past 18 months. Avthar covers the state of the union of developing AI applications in 2024, and why PostgreSQL can save you time and headaches now and in the future. 🛠 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 📌 Free Trial of Timescale ⇒ https://tsdb.co/busy-dev-signup 📌 pgai ⇒ https://tsdb.co/pgai 📌 pgvectorscale ⇒ https://tsdb.co/pgvectorscale 🐯 𝗔𝗯𝗼𝘂𝘁 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲 At Timescale, we see a world made better via innovative technologies, and we are dedicated to serving software developers and businesses worldwide, enabling them to build the next wave of computing. Timescale is a remote-first company with a global workforce backed by top-tier investors with a track record of success in the industry. 💻 𝗙𝗶𝗻𝗱 𝗨𝘀 𝗢𝗻𝗹𝗶𝗻𝗲! 🔍 Website ⇒ https://tsdb.co/homepage 🔍 Slack ⇒ https://slack.timescale.com 🔍 GitHub ⇒ https://github.com/timescale 🔍 Twitter ⇒ https://twitter.com/timescaledb 🔍 Twitch ⇒ https://www.twitch.tv/timescaledb 🔍 LinkedIn ⇒ https://www.linkedin.com/company/timescaledb 🔍 Timescale Blog ⇒ https://tsdb.co/blog 🔍 Timescale Documentation ⇒ https://tsdb.co/docs 📚 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀 00:00 What You’ll Learn Today 02:03 Foundations of AI with Postgres 03:58 Understanding Vector Data and Databases 06:25 Types of AI Applications with Postgres 08:53 Postgres Extensions for AI 14:25 Demo: Using pgVector and pgAI 21:42 Vector Search Indexes in Postgres 25:14 Vector Compression and Indexing Options 26:13 Introducing Streaming Disk Index 28:12 Creating Vector Search Indices in Postgres 30:22 Advanced Topics in AI Systems 32:14 Evaluation Driven Development 35:51 Filtered Vector Search 40:32 Hybrid Search Techniques 42:31 Multi-Tenancy in RAG Applications 44:41 Text to SQL in AI Applications 45:33 Conclusion and Resources

Explore Retrieval Augmented Generation (RAG) with Postgres Vector Store for sophisticated search functionalities in Django applications, leveraging the power of open-source models.