Educloud Academy

@atehrosius 🔥🔥🔥🔥

Gemini 3 flash is an exceptionally good coding model. 🤯🤯

In this workshop, you'll learn how to build a workflow that saves video embeddings into an S3 vector bucket and later lets you search, cut and display snippets of the video. - You'll use AWS Stepfunctions - Lambda Durable functions - S3 Vectors - Appsync - Evenbridge - AWS CDK

- Create hundreds of hyper-personalized video and audio ad variations in minutes. - and the list goes on.

AWS Nova Embeddings Model lets you generate Vector embeddings of videos, text, audios and images. These embeddings can be saved into a vector store and later retrieved for purposes such as

Back in May, we had about a thousand learners . Now we’re at 4k and counting 🚀🚀🚀🚀

Build comprehensive SEO and digital marketing data solutions via API with DataForSEO, Bedrock AgentCore and Serverless Services, in this workshop. Now available on @educloudHQ

Introduction to building Multi-tenant AI workflows

Let's dive a little bit into Amazon Agentcore. An enterprise Grade Service that helps developers build production grade AI Agents in a secure, isolated, scalable and serverless manner. It has a couple of core services such as - AgentCore Runtime: This is a secure, serverless runtime environment purpose-built for deploying and scaling dynamic AI agents and tools. - AgentCore Memory: Designed to eliminate the complexity of memory infrastructure, it allows developers to build context-aware agents. So your agents remember past conversations and user preferences. - AgentCore Gateway: This service provides a secure way for agents to discover and use tools. It automatically converts APIs, Lambda functions, and existing services into Model Context Protocol (MCP)-compatible tools. - AgentCore Identity: It provides secure, scalable agent identity and access management. - AgentCore Code Intepreter: It enables AI agents to securely write and execute code in isolated sandbox environments. - AgentCore Browser tool: This is a cloud-based browser runtime that provides an enterprise-grade capability for AI agents to navigate websites, complete multi-step forms, and perform complex web-based tasks within a fully managed, secure sandbox. - AgentCore Observability: With full support for OTEL, this service helps developers trace, debug, and monitor agent performance in production through unified operational dashboards. Just talking about them doesn't do enough justice to their capabilities. To see these services in action, head over to Educloud academy. We're building a comprehensive workshop that'll cover everything from beginners perspective.

Amazon Agentcore just went GA, here’s a summary of what you neee to know. #aws #agentcore #educloudacademy

@trikcode They’ve been solving problems for longer

The developers you look up to are nothing special. They've just been coding for longer.

What’s a “Prompt”? A prompt is how you talk to an AI model — instructions or examples. Great prompts = great results. Think of them as the AI’s “training wheels.” ✍️

3/ What’s “MCP”? MCP = Model Context Protocol. It’s a standard way for agents and LLMs to connect to tools, databases, or APIs. Think of it like “USB for AI” — plug in tools, and your model can use them safely. 🔌

1/ What’s an “Agent”? An AI agent is a system that acts on behalf of a user. It can reason, plan, and use tools (like APIs or databases) to achieve a goal. Think of it as an AI employee that knows how to get things done. 🤖

1/ Building Agentic apps? Start with the job-to-be-done (one clear outcome). Then add skills (research, plan, call APIs, write, verify). Keep it lean. 🧵👇 2/ Pick a runtime: AWS Bedrock AgentCore (runtime, gateway, memory, identity) or Vertex Agent Builder. Choose 1–2 models. Don’t over-stack. 3/ Pattern: start with Planner → Tools, then evolve to Swarm/Graph if you need parallelism or strict control. 4/ Tools & Memory: wrap APIs via a secure gateway, and define what to remember (DynamoDB/S3 + embeddings). Remember less, better. 5/ Guardrails & Ops: scoped IAM per session, schema-checked tool I/O, CloudWatch logs & traces, eval sets to keep quality stable. 6/ Ship a thin slice this week. Iterate with user feedback. Want hands-on builds? Educloud Academy teaches AgentCore + event-driven patterns by building.

@OpenAI Wowwwww

Sora 2 is here.

@Ujjwal__Baliyan Nice 😊

How to become an AI engineer in just 3 months? A thread, I literally spend 40 hours to find the perfect roadmap for beginners with a step by step weekly guide. (1/n)

@e_opore Nice posts . AWS cloudfront is also a pretty good CDN

CDN, DNS, and Edge Computing in System Design ➤ Content Delivery Network (CDN) → Definition: A distributed network of servers that deliver content to users from the nearest location → Pros: Reduces latency, Improves website speed, Offloads traffic from origin servers → Cons: Additional cost, Cached content may not update instantly → Example: Cloudflare, Akamai, AWS CloudFront serving images, videos, and static assets close to users ➤ Domain Name System (DNS) → Definition: A system that translates human-readable domain names into IP addresses → Pros: Enables easy access to websites, Supports load balancing via DNS routing, Improves availability with DNS redundancy → Cons: DNS propagation delays, Can be targeted by attacks (DNS spoofing, DDoS) → Example: example.com is mapped to server IP addresses using DNS resolvers ➤ Edge Computing → Definition: Processing data closer to the end user or device instead of sending everything to centralized servers → Pros: Reduces latency, Saves bandwidth, Enables real-time processing → Cons: Requires distributed infrastructure, More complex to manage, Limited compute compared to centralized servers → Example: IoT devices processing sensor data locally, gaming platforms reducing lag by computing at the edge ➤ Key Differences → CDN = distributes static/dynamic content closer to users → DNS = translates domain names into IP addresses for routing requests → Edge Computing = executes computation near the user or device → CDN focuses on content delivery | DNS focuses on addressing and routing | Edge focuses on computation and latency reduction ➤ Real-World Example → CDN: Netflix streams movies from edge servers near users to reduce buffering → DNS: Google Public DNS and Cloudflare DNS resolve billions of domain queries daily → Edge Computing: Autonomous cars and IoT systems process data at the edge for real-time decisions 📘 System Design Handbook A practical guide that explains modern system design concepts with clarity. It covers CDN, DNS, edge computing, caching, load balancing, scalability patterns, and real-world architectures for building reliable and efficient systems. Get it here → codewithdhanian.gumroad.com/l/fywkaw

In this workshop, we’ll learn how to build an intelligent, multi-agent system that writes SEO-optimized blog posts automatically. You’ll design a system that thinks like a full-stack content team. A researcher, strategist, and writer working together, powered entirely by agentic workflows. By the end, you’ll have a fully functional swarm of AI agents that: - Understands user interests and generates targeted SEO keywords. - Fetches live search data (search volume, CPC, competition) from DataForSEO. - Performs web research using Tavily to collect reliable facts and examples. - Enriches content using context from a Knowledge Base (e.g., your brand’s tone, past articles, internal notes). - Writes and saves a high-quality draft automatically to S3, ready for review or publishing.