Large language models (LLMs) are becoming an integral tool for businesses to improve their operations, customer interactions, and decision-making processes. However, off-the-shelf LLMs often fall…

Check out the latest (and most visual) video on this topic! The Celestial Mechanics of Attention Mechanisms: https://www.youtube.com/watch?v=RFdb2rKAqFwAtten...

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

When Nvidia announced its Generative AI certification tracks at GTC in March 2024 — the LLM-focused NCA-GENL, as well as the multimodal NCA-GENM — it was clear to me that I wanted to give it a shot, for various reasons: My Google Cloud-certified knowledge from 2020 and 2021 felt outdated (see summar

In this article, we will understand the architecture of the multi-agent research system that Anthropic built.

How to test agent ideas in hours using Claude Code SDK without building orchestration infrastructure - proven methodology from consulting engagements

Agentic Design Patterns A Hands-On Guide to Building Intelligent Systems, Antonio Gulli Table of Contents - total 424 pages = 1+2+1+1+4+9+103+61+34+114+74+5+4 11 Dedication, 1 page Acknowledgment, 2 pages [final, last read done] Foreword, 1 page [final, last read done] A Thought Leader's ...

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Deploy More Resilient Apps. Hatchet is a platform for building distributed web apps that solves scaling problems like concurrency, fairness, and rate limiting. Instead of managing your own task queue or pub/sub system, you can use Hatchet to distribute your functions between a set of workers with minimal configuration or infrastructure.

Inngest's durable functions replace queues, state management, and scheduling to enable any developer to write reliable, multi-step code faster without touching infrastructure.

Applications of reinforcement learning (RL) in AI model building has been a growing topic over the past few months. From Deepseek models incorporating RL mechanics into their training processes to...

Understanding GRPO and New Insights from Reasoning Model Papers

Practical lessons from building production agentic systems

Learn why agent infrastructure is essential to handling stateful, long-running tasks — and how LangGraph Platform provides the runtime support needed to build and scale reliable agents.

This post shares the local optima Manus arrived at through our own "SGD". If you're building your own AI agent, we hope these principles help you converge faster.

Discover the key differences between RAG and fine-tuning, what each approach can bring, and how to choose the right AI approach for your business goals.

Learn all about Reinforcement Learning (RL) and how to train your own DeepSeek-R1 reasoning model with Unsloth using GRPO. A complete guide from beginner to advanced.

➤ Check out our updated Reinforcement Learning guide!

Models for zero-shot text classification that are up to 50 times faster than Cross-Encoders and show the same or higher accuracy.

Postman for MCPs - A tool for testing and debugging MCPs - GitHub - MCPJam/inspector: Postman for MCPs - A tool for testing and debugging MCPs

AI Engineer at ANZ Bank working on intelligent systems, LLM optimization, and scalable ML platforms.

A Qdrant Star shares her hardwon lessons from her extensive opensource building

Contribute to a2a-community/a2a-ui development by creating an account on GitHub.

Repo migrated to a2a-community ogranization https://github.com/a2a-community/a2a-ui - egor-baranov/a2a-ui

Key Contributors: Lily Sierra, Nour Alkhatib, Steven Gross, Jacquelene Obeid, Kyle Swint, Monta Shen, Gary Song, Riddhima Sejpal, Jatin…

Jupyter notebooks testing different OCR models for document parsing (Dolphin, MonkeyOCR, Marker, Nanonets, ...) - AdemBoukhris457/Docs_Parsing_Techniques

A2A (Agent2Agent Protocol) and ACP (Agent Communication Protocol) represent two mainstream technical approaches in AI multi-agent system communication: 'cross-platform interoperability' and 'local/edge autonomy' respectively. A2A, with its powerful cross-vendor interconnection capabilities and rich task collaboration mechanisms, has become the preferred choice for cloud-based and distributed multi-agent scenarios; while ACP, with its low-latency, local-first, cloud-independent characteristics, is suitable for privacy-sensitive, bandwidth-constrained, or edge computing environments. Both protocols have their own focus in protocol design, ecosystem construction, and standardization governance, and are expected to further converge in openness in the future. Developers are advised to choose the most suitable protocol stack based on actual business needs.

Learn to build applications with Claude's API. Find detailed documentation, integration guides, code examples, and best practices for developing with our AI capabilities.