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		<title>Google AI Studio vs Gemini App: What’s the Difference?</title>
		<link>https://www.analyticsvidhya.com/blog/2026/06/google-ai-studio-vs-gemini-app/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/06/google-ai-studio-vs-gemini-app/#respond</comments>
		
		<dc:creator><![CDATA[Vasu Deo Sankrityayan]]></dc:creator>
		<pubDate>Mon, 01 Jun 2026 11:53:16 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[LLMs]]></category>
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					<description><![CDATA[<p>Google has made the Gemini ecosystem confusing as hell. You have the Gemini App, which looks like a normal AI chatbot. Then you have Google AI Studio, which also looks like&#8230; a chatbot! But on steroids. So the obvious question is: why do both of these coexist? Here’s the clean answer: Gemini App is for [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/06/google-ai-studio-vs-gemini-app/">Google AI Studio vs Gemini App: What’s the Difference?</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
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		<title>AI Workflows for Sales Teams: Prospect Research, Lead Qualification, and CRM Updates on Autopilot Using LangGraph  </title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/ai-workflows-for-sales-teams-using-langgraph/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/ai-workflows-for-sales-teams-using-langgraph/#respond</comments>
		
		<dc:creator><![CDATA[Vipin Vashisth]]></dc:creator>
		<pubDate>Sun, 31 May 2026 08:10:35 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Intermediate]]></category>
		<category><![CDATA[Project-based Article]]></category>
		<guid isPermaLink="false">https://www.analyticsvidhya.com/?p=255135</guid>

					<description><![CDATA[<p>Sales teams spend hours every day on tasks that should never see a human. Research a prospect, score them against their fit, and put it all into a CRM. These are repeatable, rule based processes AI workflows driven by multi-agent systems can do all three, with speed and consistency that no human team can match.&#160; [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/ai-workflows-for-sales-teams-using-langgraph/">AI Workflows for Sales Teams: Prospect Research, Lead Qualification, and CRM Updates on Autopilot Using LangGraph  </a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
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		<title>25 Most Influential AI Pioneers to Meet at DataHack Summit 2026</title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/25-most-influential-ai-pioneers-to-meet-at-datahack-summit-2026/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/25-most-influential-ai-pioneers-to-meet-at-datahack-summit-2026/#respond</comments>
		
		<dc:creator><![CDATA[Sarthak Dogra]]></dc:creator>
		<pubDate>Fri, 29 May 2026 14:03:52 +0000</pubDate>
				<category><![CDATA[Analytics Vidhya]]></category>
		<guid isPermaLink="false">https://www.analyticsvidhya.com/?p=255206</guid>

					<description><![CDATA[<p>The strongest AI voices are not just people with impressive job titles. They are researchers pushing the technical boundaries of AI. Founders building AI communities. Practitioners turning models into products. Even leaders, helping businesses understand what this technology can actually do. This becomes even more important when we look at India’s growing role in the [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/25-most-influential-ai-pioneers-to-meet-at-datahack-summit-2026/">25 Most Influential AI Pioneers to Meet at DataHack Summit 2026</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
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		<title>Claude Opus 4.8: A Smarter Model in the Right Direction</title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/claude-opus-4-8-pricing-and-features/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/claude-opus-4-8-pricing-and-features/#respond</comments>
		
		<dc:creator><![CDATA[Vasu Deo Sankrityayan]]></dc:creator>
		<pubDate>Fri, 29 May 2026 13:20:29 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[LLMs]]></category>
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					<description><![CDATA[<p>The AI industry has matured to the point where raw intelligence is no longer the only thing that matters. A year ago, every model release was a race to publish bigger benchmark numbers. More parameters, features and everything in between.&#160;&#160; Today, the conversation is shifting. Developers care about reliability. Enterprises care about cost, scalability, and [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/claude-opus-4-8-pricing-and-features/">Claude Opus 4.8: A Smarter Model in the Right Direction</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
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		<title>PySpark Optimization: 12 Proven Techniques to Speed Up Your Spark Jobs</title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/proven-techniques-to-speed-up-your-spark-jobs/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/proven-techniques-to-speed-up-your-spark-jobs/#respond</comments>
		
		<dc:creator><![CDATA[Vipin Vashisth]]></dc:creator>
		<pubDate>Wed, 27 May 2026 13:03:34 +0000</pubDate>
				<category><![CDATA[Beginner]]></category>
		<category><![CDATA[Big data]]></category>
		<category><![CDATA[Database]]></category>
		<category><![CDATA[Spark]]></category>
		<guid isPermaLink="false">https://www.analyticsvidhya.com/?p=255221</guid>

					<description><![CDATA[<p>Modern data pipelines handle massive volumes of structured and unstructured data every day. As datasets grow, poorly optimized Spark jobs become slower, more expensive, and harder to scale. Common issues include long execution times, excessive shuffling, memory bottlenecks, and inefficient joins. Effective PySpark optimization can significantly improve performance, reduce infrastructure costs, and enhance cluster efficiency. [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/proven-techniques-to-speed-up-your-spark-jobs/">PySpark Optimization: 12 Proven Techniques to Speed Up Your Spark Jobs</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
]]></description>
		
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		<title>10 Everyday Tasks You Can Automate with AI Today (With n8n Templates)</title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/everyday-ai-automation-using-n8n/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/everyday-ai-automation-using-n8n/#respond</comments>
		
		<dc:creator><![CDATA[Vasu Deo Sankrityayan]]></dc:creator>
		<pubDate>Tue, 26 May 2026 09:15:56 +0000</pubDate>
				<category><![CDATA[ai automation]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<guid isPermaLink="false">https://www.analyticsvidhya.com/?p=255361</guid>

					<description><![CDATA[<p>Most AI automation content sounds useful, but then leaves you with one big question: where to start? Instead of only talking about automation, you probably want to create real-world automation workflows with minimal coding. That&#8217;s where the power of low-code platforms like n8n comes into play. Here are 10 everyday tasks you can AI automate [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/everyday-ai-automation-using-n8n/">10 Everyday Tasks You Can Automate with AI Today (With n8n Templates)</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
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		<title>Google Antigravity 2.0: The Full Developer Guide (I/O 2026) </title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/google-antigravity-2-0/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/google-antigravity-2-0/#respond</comments>
		
		<dc:creator><![CDATA[Riya Bansal]]></dc:creator>
		<pubDate>Mon, 25 May 2026 08:31:29 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Beginner]]></category>
		<category><![CDATA[Generative AI]]></category>
		<guid isPermaLink="false">https://www.analyticsvidhya.com/?p=255341</guid>

					<description><![CDATA[<p>Google didn&#8217;t just ship an update at I/O 2026. They redrew the map.&#160; Google Antigravity 2.0 dropped on May 19th and it&#8217;s not an IDE refresh. It’s a full platform pivot from AI assisted coding, to multi agent orchestration as the core development model. If you’ve been keeping an eye on the Agentic coding race [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/google-antigravity-2-0/">Google Antigravity 2.0: The Full Developer Guide (I/O 2026) </a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
]]></description>
		
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			<media:description type="html">A developer&#039;s guide to Google Antigravity 2.0. Explore the shift from an IDE to a multi-agent orchestration platform using Antigravity 2.0.</media:description>
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		<title>Build a Claude Cowork-Like Browser Agent Using Playwright MCP and Claude Desktop </title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/build-a-claude-cowork-like-browser-agent-using-playwright-mcp/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/build-a-claude-cowork-like-browser-agent-using-playwright-mcp/#respond</comments>
		
		<dc:creator><![CDATA[Harsh Mishra]]></dc:creator>
		<pubDate>Sun, 24 May 2026 16:24:37 +0000</pubDate>
				<category><![CDATA[AI Agents]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Intermediate]]></category>
		<guid isPermaLink="false">https://www.analyticsvidhya.com/?p=255208</guid>

					<description><![CDATA[<p>Claude Cowork shifts AI from chat-based assistance to task delegation. Instead of giving users instructions, it performs actions directly on the user’s computer, files, applications, and browser workflows. Combined with Playwright MCP, Claude Desktop can open pages, click buttons, fill forms, extract data, and debug interfaces in a far more structured way than screenshot-based automation. [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/build-a-claude-cowork-like-browser-agent-using-playwright-mcp/">Build a Claude Cowork-Like Browser Agent Using Playwright MCP and Claude Desktop </a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
]]></description>
		
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		<item>
		<title>Pandas vs Polars vs DuckDB: Which Library Should You Choose?</title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/#respond</comments>
		
		<dc:creator><![CDATA[Janvi Kumari]]></dc:creator>
		<pubDate>Sat, 23 May 2026 18:00:11 +0000</pubDate>
				<category><![CDATA[Beginner]]></category>
		<category><![CDATA[Database]]></category>
		<category><![CDATA[Python]]></category>
		<guid isPermaLink="false">https://www.analyticsvidhya.com/?p=255303</guid>

					<description><![CDATA[<p>pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows. Polars focus on fast, memory-efficient DataFrame processing, while DuckDB brings a SQL-first approach for querying local files and embedded analytics. Each tool fits a different kind of local data workflow. In this article, we compare pandas, Polars, and DuckDB across performance, [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/">Pandas vs Polars vs DuckDB: Which Library Should You Choose?</a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
]]></description>
		
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		<title>Qwen3.7-Max: Alibaba’s New Agent-First LLM for Coding, Reasoning, and Long-Horizon AI Workflows </title>
		<link>https://www.analyticsvidhya.com/blog/2026/05/qwen3-7-max/</link>
					<comments>https://www.analyticsvidhya.com/blog/2026/05/qwen3-7-max/#respond</comments>
		
		<dc:creator><![CDATA[Harsh Mishra]]></dc:creator>
		<pubDate>Fri, 22 May 2026 16:12:50 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[LLMs]]></category>
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					<description><![CDATA[<p>Alibaba’s Qwen team has unveiled Qwen3.7-Max, a flagship model built for the agent era. Unlike conventional chatbot-focused LLMs, it is designed as a foundation for autonomous AI agents that can code, debug, use tools, manage workflows, and execute long-running enterprise tasks. Alibaba claims the model can operate autonomously for up to 35 hours without performance [&#8230;]</p>
<p>The post <a href="https://www.analyticsvidhya.com/blog/2026/05/qwen3-7-max/">Qwen3.7-Max: Alibaba’s New Agent-First LLM for Coding, Reasoning, and Long-Horizon AI Workflows </a> appeared first on <a href="https://www.analyticsvidhya.com">Analytics Vidhya</a>.</p>
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