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Enabling 1 MW IT racks and liquid cooling at OCP EMEA Summit

Google revamps its AI tech, swapping out the old wiring for+/-400 VDCjuice. Enter the fifth-genProject Deschutesliquid cooling, the latest in their mad scientist lab. The promise? A cool 1 MW per rack and uptime so reliable, you could set your watch by it—99.999%... read more  

Enabling 1 MW IT racks and liquid cooling at OCP EMEA Summit
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Leveraging Chain‑of‑Thought Network Effects To Compete With Open Source Models

Open-source AImodels are hot on the heels of their proprietary cousins, speeding through life cycles that now barely stretch pastsix months. Companies caught in this sprint scramble to scale using reusableChain-of-Thought tokens—a crafty way to slice through redundant computation and chop down infer.. read more  

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Build a gen AI–powered financial assistant with Amazon Bedrock multi-agent collaboration

Amazon Bedrock's multi-agent frameworkacts like a brain transplant for your AI projects. It lets you unleash specialized AI agents on beastly tasks likefinancial analysis. Why rely on a lone LLM when you can have a band of them tackling the complexities of high-stakes operations? This approach zeroe.. read more  

Build a gen AI–powered financial assistant with Amazon Bedrock multi-agent collaboration
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Agentic AI 101: Starting Your Journey Building AI Agents

AI agents are evolving from simple chatbots into powerful, tool-using assistants capable of web search, automation, and even reasoning. This guide walks you through building your first agent using the Agno Python library—from setup and tool integration to memory and RAG features. With just a few lin.. read more  

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From MCP to multi-agents: The top 10 new open source AI projects on GitHub right now and why they matter

Get insights on the latest trends from GitHub experts while catching up on these exciting new projects... read more  

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How To Set Up a Model Context Protocol Server

Model Context Protocol (MCP)is like that cool tool you didn't know you needed. It's a nimble bridge between LLM models and developer tools, though someday it might just become the backbone of future libraries—nothing fancy, just fundamental. EnterFastMCP, the under-the-radar hero. Fire it up, and it.. read more  

How To Set Up a Model Context Protocol Server
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Explainable AI Needs Explainable Infrastructure

AWS S3 choked, and prediction accuracy took a nosedive. Voilà: an uninvited reminder thatexplainable infrastructureis crucial for genuine AI transparency. It’s not just a hunch—47% of AI downtime stems from these scaffolding snafus. Luckily, warriors likeOpenTelemetryandGrafanastep up, offering a wa.. read more  

Explainable AI Needs Explainable Infrastructure
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Prompt chaining reimagined with type inference

Graceusesbidirectional type inferenceto simplify prompt chaining. No more wrestling with schema definitions. Think: less JSON, more wizardry... read more  

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How to Build an Agent

Craft a code-editing agent in under 400 lines. It's just an LLM, a loop, and some enhanced tokens. No rocket science here—just solid, hands-on engineering... read more  

How to Build an Agent
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Open source AI models favor men for hiring, study finds

Open-source AI's at it again. Picks men over women. Shocking, right? EnterLlama-3.1, the rebel. It ignores gender in 6% of cases, which is a small but mighty improvement. Yet, even the upgraded models can't shake the gender wage gap. TakeMinistral, for instance, slapping an 84 log point penalty on w.. read more  

Open source AI models favor men for hiring, study finds
NanoClaw is an open-source personal AI agent designed to run locally on your machine while remaining small enough to fully understand and audit. Built as a lightweight alternative to larger agent frameworks, the system runs as a single Node.js process with roughly 3,900 lines of code spread across about 15 source files.

The agent integrates with messaging platforms such as WhatsApp and Telegram, allowing users to interact with their AI assistant directly through familiar chat applications. Each conversation group operates independently and maintains its own memory and execution environment.

A core design principle of NanoClaw is security through isolation. Every agent session runs inside its own container using Docker or Apple Container, ensuring that the agent can only access files and resources that are explicitly mounted. This approach relies on operating system–level sandboxing rather than application-level permission checks.

The architecture is intentionally simple: a single orchestrator process manages message queues, schedules tasks, launches containerized agents, and stores state in SQLite. Additional functionality can be added through a modular skills system, allowing users to extend capabilities without increasing the complexity of the core codebase.

By combining a minimal architecture with container-based isolation and messaging integration, NanoClaw aims to provide a transparent, customizable personal AI agent that users can run and control entirely on their own infrastructure.