Rakesh

Let's code(lab)! Go from raw data to forecasting in seconds with AI agents. What you'll do in this codelab from #GoogleCloudNext: - Unstructured discovery - Unified metadata - Cross-engine analytics - Semantic insights - Autonomous governance Start → goo.gle/4vQHOff

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow @codewithimanshu for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow @codewithimanshu. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on @codewithimanshu. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. @codewithimanshu. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on @codewithimanshu. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. @codewithimanshu. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. @codewithimanshu. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow @codewithimanshu for more high-signal content that actually moves your AI engineering career forward.

FYI I just shipped a huge improvement to /improve-codebase-architecture It now ships with a glossary of terminology to describe good/bad codebases Essential reading for anyone wanting to improve their codebases: github.com/mattpocock/ski…

A talk I gave a few weeks ago. Software fundamentals matter more than ever. Here's why: youtube.com/watch?v=v4F1gF…

Great in depth interview with Thomas and Ben on GCP, Gemini Enterprise, Agents, TPUs etc

Cybercab has started production

Introducing workspace agents in ChatGPT—shared agents that can handle complex tasks and long-running workflows across tools and teams.

Big update for AI workflows in VS Code. Bring your own language model/API key is now available in GitHub Copilot. More flexibility. More control. More experimentation. 👇 aka.ms/BYOK-Business

We're bringing the advisor strategy to the Claude Platform. Pair Opus as an advisor with Sonnet or Haiku as an executor, and get near Opus-level intelligence in your agents at a fraction of the cost.

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform.

Monolithic vs Microservices vs Serverless

Announcing Amazon S3 Files. The first and only cloud object store with fully-featured, high-performance file system access. Learn more here. go.aws/4tw17Zg

Spec Driven Development tells you WHAT to build. Agent Skills tell you HOW to build it. Two different things — but powerful when combined. 🔧 #AI #SoftwareDevelopment #AgentSkills

CLI vs MCP AI agents need to talk to external tools, but should they use CLI or MCP? Here goes.

All 7 ByteByteGo ebooks, FREE for a limited time. Link at the end. - System Design Interview Vol. 1 - System Design Interview Vol. 2 - Machine Learning System Design Interview - Coding Interview Patterns - Object-Oriented Design Interview - Generative AI System Design Interview - Mobile System Design Interview Whether you’re preparing for interviews or looking to deepen your architecture knowledge, this is a great opportunity. Offer ends May 1. Please help spread the word. Check it out now at: bytebytego.com

Accounts, Users, Groups, Permission Sets: How do they work together at AWS Organizations & Identity Center? Here's an analogy: Think of it like a company with multiple offices (accounts) where people need different levels of access: 🏢 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝘀 (Offices)

Just curious while everyone is talking about #mcp #MPC what happened to #A2A

@narendramodi Jai Shri Ram 🙏🪔🙏

देशभर के मेरे परिवारजनों को रामनवमी की असीम शुभकामनाएं। त्याग, तप और संयम से भरे मर्यादा पुरुषोत्तम के जीवन से हमें हर परिस्थिति का पूरे सामर्थ्य से सामना करने की प्रेरणा मिलती है। उनके आदर्श अनंतकाल तक भारतवासियों के साथ-साथ संपूर्ण मानवता के पथ-प्रदर्शक बने रहेंगे। मेरी कामना है कि भगवान राम की कृपा से सबका कल्याण हो, जिससे विकसित और आत्मनिर्भर भारत के संकल्प की सिद्धि का मार्ग प्रशस्त हो।

SQS defaults to short polling and nobody tells you this will cost you way more than it should 💸 Here's what actually happens with both polling types. 🔸 Long Polling: