<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Google on The Coders Blog</title><link>https://thecodersblog.com/tag/google/</link><description>Recent content in Google on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 06 May 2026 22:26:07 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/google/index.xml" rel="self" type="application/rss+xml"/><item><title>Google Dev: Agents CLI for Production AI Creation</title><link>https://thecodersblog.com/google-agents-cli-for-production-ai-2026/</link><pubDate>Wed, 06 May 2026 22:26:07 +0000</pubDate><guid>https://thecodersblog.com/google-agents-cli-for-production-ai-2026/</guid><description>&lt;p&gt;The AI agent development lifecycle is a fragmented mess of custom scripts, ad-hoc deployments, and manual evaluations. Until now. Google&amp;rsquo;s new Agents CLI promises to bring order to chaos, offering a unified command-line interface for building, testing, and deploying AI agents directly to Google Cloud. This could finally accelerate your time to market, but it&amp;rsquo;s not without its caveats.&lt;/p&gt;
&lt;h3 id="the-deployment-gap-in-ai-agent-development"&gt;The &amp;ldquo;Deployment Gap&amp;rdquo; in AI Agent Development&lt;/h3&gt;
&lt;p&gt;Developing sophisticated AI agents often involves multiple stages: scaffolding, local iteration, rigorous evaluation, and finally, robust production deployment. Each stage typically requires different tools and approaches, leading to a &amp;ldquo;deployment gap.&amp;rdquo; Teams spend valuable time stitching together disparate services, wrestling with environment inconsistencies, and manually verifying agent performance. This friction slows innovation and delays the realization of AI’s true potential. Google&amp;rsquo;s Agents CLI directly targets this pain point, aiming to streamline the entire Agent Development Lifecycle (ADLC) within a single, opinionated framework.&lt;/p&gt;</description></item><item><title>3X Speed Boost: Supercharging LLM Inference on Google TPUs</title><link>https://thecodersblog.com/supercharging-llm-inference-on-google-tpus-2026/</link><pubDate>Wed, 06 May 2026 22:22:01 +0000</pubDate><guid>https://thecodersblog.com/supercharging-llm-inference-on-google-tpus-2026/</guid><description>&lt;p&gt;The cost of generative AI is directly proportional to its latency. If your cutting-edge LLM is taking an eternity to produce a single token, your dreams of real-time conversational agents or rapid code generation are just that – dreams.&lt;/p&gt;
&lt;h3 id="the-bottleneck-sequential-speculative-decoding"&gt;The Bottleneck: Sequential Speculative Decoding&lt;/h3&gt;
&lt;p&gt;Traditional LLM inference, even with optimizations, often resorts to autoregressive generation, token by token. Speculative decoding aims to speed this up by using a smaller, faster &amp;ldquo;draft&amp;rdquo; model to predict multiple tokens ahead, which are then verified by the larger, more accurate &amp;ldquo;target&amp;rdquo; model. However, the drafting phase itself is typically sequential, mirroring the autoregressive nature of the target model. This becomes the Achilles&amp;rsquo; heel, negating much of the potential speedup, especially as models grow larger.&lt;/p&gt;</description></item><item><title>Big Tech's AI Pact: Sharing Models to Accelerate Innovation</title><link>https://thecodersblog.com/major-tech-companies-sharing-early-ai-models-2026/</link><pubDate>Tue, 05 May 2026 15:16:24 +0000</pubDate><guid>https://thecodersblog.com/major-tech-companies-sharing-early-ai-models-2026/</guid><description>&lt;p&gt;The floodgates are opening. What was once a tightly guarded fortress of proprietary algorithms is rapidly transforming into a more open, albeit carefully curated, ecosystem. Major tech giants like Google, Microsoft, and even OpenAI (through its API offerings) are increasingly sharing early-stage AI models, not just as finished products, but as foundational building blocks. This isn&amp;rsquo;t altruism; it&amp;rsquo;s a strategic gamble to outpace innovation and entrench their platforms in the burgeoning AI economy.&lt;/p&gt;</description></item><item><title>AI Overviews Stealing Your Traffic? Complete Guide to Fight Back (2025)</title><link>https://thecodersblog.com/ai-overviews-stealing-your-traffic-complete-guide-to-fight-back-2025/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://thecodersblog.com/ai-overviews-stealing-your-traffic-complete-guide-to-fight-back-2025/</guid><description>&lt;p&gt;If your website traffic has mysteriously plummeted despite maintaining solid rankings, you&amp;rsquo;re likely experiencing the devastating impact of Google&amp;rsquo;s AI Overviews. With &lt;strong&gt;60% of Google searches now ending in zero-clicks&lt;/strong&gt;, traditional SEO strategies are failing to deliver the traffic they once did. Even top-3 rankings are losing clicks as AI-generated summaries provide instant answers without requiring users to visit your site.&lt;/p&gt;
&lt;p&gt;This comprehensive guide reveals proven strategies to fight back against AI Overviews, including Generative Engine Optimization (GEO) tactics, commercial-intent targeting, and advanced schema implementations that can help you reclaim your organic traffic.&lt;/p&gt;</description></item></channel></rss>