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Do you really need a Vector Search Database?

Elasticsearchpulled a Houdini, besting the buzzed-about vector databases. It slashed costs by3xand effortlessly juggled a whopping600M embeddingslike it was born for the job... read more  

Do you really need a Vector Search Database?
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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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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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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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Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads

Perplexity'snew browser,Comet, prowls beyond its app, sniffing out user data for targeted ads. It mirrors Google's relentless data quests. In a plot twist, they're joining forces withMotorolato sneak their app onto every Razr straight from the factory... read more  

Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads
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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
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Agents in your software factory: Introducing the LLM primitive in Dagger

Daggerjust cranked its engine into overdrive with nativeLLMintegration. Now, AI agents can rev through your CI/CD workflows, automating tasks like code reviews with impressive flair. The new configuration lets LLMs jive with programmable building blocks in your code, all securely sandboxed. Consider.. read more  

Agents in your software factory: Introducing the LLM primitive in Dagger
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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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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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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
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.