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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.

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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.

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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.

When integrating an LLM into your applicaton to extend it and make it smarter, it’s important to be aware of the pitfalls and best practices you need to follow to avoid some common problems and integrate them successfully. This article will guide you through some key best practices that I’ve come across. Understanding the Challenges of Implementing LLMs in Real-World Applications One of the first challenges is that LLMs are constantly being improved.

Check out the FREE non-technical guide for using AI in your business here: https://clickhubspot.com/fuu7 This video imma be yapping about why prompt engineering is unreasonable and everything that is a bit sus about it. The sequel of this video will probably be me roasting o1 (hopefully not) check out my newsletter https://mail.bycloud.ai/ Special thanks to LDJ for helping with this video OpenAI o1 [Announcement] https://openai.com/index/introducing-openai-o1-preview/ [Blogs] https://openai.com/o1/ Claude 3.5 Sonnet [Announcement] https://www.anthropic.com/news/claude-3-5-sonnet [AntThinking] https://gist.github.com/dedlim/6bf6d81f77c19e20cd40594aa09e3ecd Language Models (Mostly) Know What They Know [Paper] https://arxiv.org/abs/2207.05221 Plan-and-Solve Prompting [Paper] https://arxiv.org/abs/2305.04091 Llama-3.1 System Card [Documentation] https://www.llama.com/docs/model-cards-and-prompt-formats/meta-llama-3/ Think Before You Speak [Paper] https://arxiv.org/abs/2310.02226 Future Lens [Paper] https://arxiv.org/abs/2311.04897 This video is supported by the kind Patrons & YouTube Members: 🙏Andrew Lescelius, alex j, Chris LeDoux, Alex Maurice, Miguilim, Deagan, FiFaŁ, Robert Zawiasa, Owen Ingraham, Daddy Wen, Tony Jimenez, Panther Modern, Jake Disco, Demilson Quintao, Penumbraa, Shuhong Chen, Hongbo Men, happi nyuu nyaa, Carol Lo, Mose Sakashita, Miguel, Bandera, Gennaro Schiano, gunwoo, Ravid Freedman, Mert Seftali, Mrityunjay, Richárd Nagyfi, Timo Steiner, Henrik G Sundt, projectAnthony, Brigham Hall, Kyle Hudson, Kalila, Jef Come, Jvari Williams, Tien Tien, BIll Mangrum, owned, Janne Kytölä, SO, Richárd Nagyfi, Hector, Drexon, Claxvii 177th, Inferencer, Michael Brenner, Akkusativ, Oleg Wock, FantomBloth, Thipok Tham, Clayton Ford, Theo, Handenon, Diego Silva, mayssam [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Music 1] Spirit Blossom - RomanBelov [Music 2] Tie Me Down - PremiumMusicOdyssey [Music 3] Dimmed Lights - PremiumMusicOdyssey [Music 4] Wrapped in your Love - PremiumMusicOdyssey [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] @bhargavesque 0:00 Intro 4:21 The prooompting basics 6:36 How companies make prompting works 12:11 Does how you prompt REALLY matter?

What’s the *right* use for AI? Laurie Voss thinks it’s great at doing boring chores, and in this episode we learn what that means and how we can put the robo...

Like Claude Artifacts, but with a backend and database

Welcome to LLM University, your premier learning destination for mastering Enterprise AI technologies. Designed for developers and technical professionals, our hub offers comprehensive resources, expert-led courses, and step-by-step guides to help you start building quickly and stay ahead in the rapidly evolving AI landscape.

Part 1 of the LLM University module on Retrieval-Augmented Generation.

Toy implementation of Strawberry . Contribute to ack-sec/toyberry development by creating an account on GitHub.

Optimizing inference proxy for LLMs.

Code examples and jupyter notebooks for the Cohere Platform - cohere-ai/notebooks

Full parameter fine-tuning is a method that fine-tunes all the parameters of all the layers of the pre-trained model.