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While agents can be powerful, they are not perfect. This often makes it important to keep the human “in the loop” when building agents. For example, in our fireside chat we did with Michele Catasta (President of Replit) on their Replit Agent, he speaks several times about the human-in-the-loop component

Everything we learned, and everything we think you need to know, from technical details on 24khz/G.711 audio, RTMP, HLS, WebRTC, to Interruption/VAD, to Cost, Latency, Tool Calls, and Context Mgmt

This post demonstrates how to use Amazon Bedrock Agents, Amazon Knowledge Bases, and the RAGAS evaluation metrics to build a custom hallucination detector and remediate it by using human-in-the-loop. The agentic workflow can be extended to custom use cases through different hallucination remediation techniques and offers the flexibility to detect and mitigate hallucinations using custom actions.

The Prometheus suite, JudgeLM, PandaLM, AutoJ, and more...

In the past few years, Large Language Models (LLMs) have entered our lives and enabled us to perform a wide range of advanced tasks such as…

Discover how Grab's LLM-Kit enhances AI app development by addressing scalability, security, and integration challenges. This article discusses the challenges faced in LLM app building, the solution, the architecture of the LLM-Kit as well as the future plans of the LLM-Kit.

This post introduces the workflow of Amazon Bedrock Model Distillation. We first introduce the general concept of model distillation in Amazon Bedrock, and then focus on the important steps in model distillation, including setting up permissions, selecting the models, providing input dataset, commencing the model distillation jobs, and conducting evaluation and deployment of the student models after model distillation.

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Introducing PDF support, computer use, and an xAI Grok provider

Learn to become an AI Engineer using this roadmap. Community driven, articles, resources, guides, interview questions, quizzes for modern backend development.

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...

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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.

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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.