Virendra (Viren)

Architected Tech solutions for global Fortune 100 companies. Founder and Speaker.

#Entrepreneur | #CodeWriter | #microservices | #cloudcomputing

[Virendra (Viren) @talk2viren]

I am a technologist and entrepreneur by hart & soul. You join me for discussions related to next-gen technology, Innovations in technology and startups, cloud based solutions. Technology solutions for Enterprises and Off-course lot of fun. 😀😀

#technology #startups #innovation

[Virendra (Viren) @talk2viren]

@RAYSITI @BeanieBlossom @RyanSpitFire252 @PCasino73066 @NotJustMeAgain2 @KittyTellesWolf Love it

[CG @cgtwts]

This is insane from Google

- google just dropped a full stack
vibecoding system

- builder that creates full apps with db, auth, multiplayer and firebase

- it auto sets up what you need, remembers your project and installs libraries

- you can leave and return and it continues where you stopped

- one click deploy, now going after claude code and codex.

[Aastha @aastha_mhaske]

Types of Databases 📚📘

[Gaurav Sen @gkcs_]

A very interesting guide to AI Agents.

https://t.co/NpyIeAfcBL

[Alex Xu @alexxubyte]

What Are Agent Skills Really About?

How Do They Work?

We made a video to explain it clearly.

[Alex Xu @alexxubyte]

RabbitMQ vs Kafka vs Pulsar

[Virendra (Viren) @talk2viren]

AI → predicts.
GenAI → creates.
Agentic AI → plans and acts.

I made a simple 3‑card infographic with a little 🤖✨ “AI teammate” to explain:
AI & ML: classify, score, detect patterns

GenAI: chat, draft code, generate ideas

Agentic AI: plan tasks, call tools/APIs, retry

#AgenticAI #AIAgents #AI

[Priyanka Vergadia @pvergadia]

🚂 All Aboard the 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 Express!

AI Agent-based retrieval systems are the backbone of modern enterprise AI. But an agent is only as powerful as the infrastructure supporting it.

We mapped out the perfect "Journey" to building a robust Agentic RAG ecosystem. Think of it as a train line where every station adds a critical layer of intelligence to your application.

Here is your CloudGirl Guide to the 9 Stations of the Stack:

1️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟭: 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 (𝗟𝗲𝘃𝗲𝗹 𝟬)
The foundation. Where agents live and scale.
🛠️ Tech: Groq, AWS, Google Cloud, https://t.co/GXPjdlukRI

2️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟮: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
If you can't measure it, you can't improve it. Continuous scoring of retrieved data.
🛠️ Tech: LangSmith, Phoenix, DeepEval, Ragas

3️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟯: 𝗟𝗟𝗠𝘀
The "Thinking Core." Reasoning, planning, and decision-making.
🛠️ Tech: Llama 3, Claude 3.5, Gemini, GPT-4o

4️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟰: 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
The orchestration layer managing tool usage and routing.
🛠️ Tech: LangChain, Haystack, DSPy

5️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟱: 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
The retrieval engine for similarity search.
🛠️ Tech: Pinecone, Chroma, Milvus

6️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟲: 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀
Grounding concepts by converting text to dense representations.
🛠️ Tech: Nomic, Ollama, Voyage AI, OpenAI

7️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟳: 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻
Pulling structured data from the wild (Web/PDFs).
🛠️ Tech: Firecrawl, Scrapy, Docling, LlamaParse

8️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟴: 𝗠𝗲𝗺𝗼𝗿𝘆
Giving agents persistence across sessions.
🛠️ Tech: Zep, Mem0, Cognee, Letta

9️⃣ 𝗦𝘁𝗮𝘁𝗶𝗼𝗻 𝟵: 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆
Ensuring safety, tracking costs, and monitoring behavior.
🛠️ Tech: Guardrails AI, Arize, Helicone, Langfuse

Building an Agentic system isn't just about the LLM—it's about the ecosystem. Which station are you currently building at?

#AgenticRAG #AI #MachineLearning #TechStack #CloudComputing #DevOps #LLMs

[Virendra (Viren) @talk2viren]

Great info #Ai

[Shraddha Bharuka @BharukaShraddha]

Google isn’t trying to win the AI race.

They’re trying to own the entire AI Agent ecosystem.

While everyone argues ChatGPT vs Claude, Google quietly built:

Models → Gemini Pro, Flash, Deep Think, Gemma

Design → Stitch, Whisk, Imagen

Research → NotebookLM, AI Mode

Video → Veo, Flow, Google Vids

Coding → Antigravity IDE, Gemini CLI, Jules

Agents → A2A, ADK, FileSearch API

The scary part?

All of these tools talk to each other.

That means:
10x faster prototypes
End-to-end AI workflows
Production-ready agents on GCP

The next AI war won’t be model vs model.
It’ll be ecosystem vs ecosystem.

I mapped this stack out here:
https://t.co/G3hahQclKI

Save. Share. Build.

[Shraddha Bharuka @BharukaShraddha]

Google isn’t trying to win the AI race.

They’re trying to own the entire AI Agent ecosystem.

While everyone argues ChatGPT vs Claude, Google quietly built:

Models → Gemini Pro, Flash, Deep Think, Gemma

Design → Stitch, Whisk, Imagen

Research → NotebookLM, AI Mode

Video → Veo, Flow, Google Vids

Coding → Antigravity IDE, Gemini CLI, Jules

Agents → A2A, ADK, FileSearch API

The scary part?

All of these tools talk to each other.

That means:
10x faster prototypes
End-to-end AI workflows
Production-ready agents on GCP

The next AI war won’t be model vs model.
It’ll be ecosystem vs ecosystem.

I mapped this stack out here:
https://t.co/G3hahQclKI

Save. Share. Build.

[Alex Xu @alexxubyte]

How does gRPC work?

[Virendra (Viren) @talk2viren]

Good recaps

[Eyo Eyo, PhD @Eyowhite3]

How to design a system.

[Alex Xu @alexxubyte]

OAP vs REST vs GraphQL vs RPC.

[Priyanka Vergadia @pvergadia]

🚀 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜 𝗗𝗲𝘀𝗶𝗴𝗻 𝗶𝗻 𝟱 𝗠𝗶𝗻𝘂𝘁𝗲𝘀 (𝗦𝗮𝘃𝗲 𝗧𝗵𝗶𝘀!)

After reviewing 1000’s of APIs in production, I’ve seen the same mistakes over and over.

Here’s what separates amateur APIs from professional ones:

𝗧𝗵𝗲 𝗣𝗶𝘇𝘇𝗮 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗔𝗻𝗮𝗹𝗼𝗴𝘆 🍕

REST APIs work like ordering pizza. You (client) make a request to the restaurant (server), they process it, and deliver the pizza (response). Simple, right?

But here’s where most developers mess up:

𝟱 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗔𝗣𝗜 𝗗𝗲𝘀𝗶𝗴𝗻:

1️⃣ Resource-Based URLs (Not action-based!)
❌ GET /getPizza
✅ GET /pizzas/123

Think nouns, not verbs. Your resources ARE the things, HTTP methods ARE the actions.

2️⃣ Standard HTTP Methods
→ GET (Read)
→ POST (Create)
→ PUT (Update)
→ DELETE (Remove)

Stop inventing custom methods. HTTP already solved this.

3️⃣ Stateless Communication
The server shouldn’t remember you between requests. Send everything needed each time. This is what makes APIs scalable.

4️⃣ Proper Data Formats
JSON has won. Use it. Structure your responses consistently. Your future self will thank you.

5️⃣ HATEOAS
Include links to related resources. Guide your API consumers to the next logical steps. Make your API discoverable.

𝗣𝗿𝗼 𝘁𝗶𝗽: Your API is a product. Design it like one. Think about the developer experience, not just functionality.

What’s the worst API design decision you’ve encountered? Drop it in the comments 👇

P.S. If this helped you, repost ♻️ to help others. Follow me @pvergadia for more cloud and AI concepts.

#API #SoftwareDevelopment #WebDevelopment #Programming #SoftwareEngineering #RESTful #BackendDevelopment

[Virendra (Viren) @talk2viren]

Good

[Priyanka Vergadia @pvergadia]

🏗️ The 11 Steps of Agentic Architecture

1. User / Application Layer
The entry point. Whether it’s a Chat UI or an API, this is where the raw intent is captured. In the example, the user wants a full "Robot-themed party plan."

2. AI Gateway
Before the LLM even sees the request, the Gateway handles the "adulting":
• Auth & Rate Limiting: Protecting your infra.
• Policy & PII Filtering: Ensuring no sensitive data leaks out.

3. Model Router / LLM Access
Not every task needs GPT-4o or Claude 3.5 Sonnet. The Router chooses the best model based on cost, latency, or specific capability (e.g., small models for summarization, large models for complex reasoning).

4. Planner / Reasoning Layer
This is the "Brain." Instead of answering immediately, the agent decomposes the big request into smaller tasks:
• Task 1: Search for robot games.
• Task 2: Find cake recipes.
• Task 3: Draft the guest list.

5. Memory & Cache
Agents need context.
• Short-term: What did we just talk about?
• Long-term: User preferences (e.g., "The user hates the color red").

6. RAG Pipeline
The agent reaches into your private data. It uses Vector DBs and Hybrid Search to pull relevant context that wasn't in its original training data.

7. MCP - Tool Access Layer (The Game Changer)
The Model Context Protocol (MCP) allows agents to actually do things. It connects the agent to SQL databases, Slack, Jira, or Google Search.

8. A2A & Recovery
• Agent-to-Agent: One agent might be a "Researcher" while another is a "Critic." They talk to each other to refine the output.
• Recovery: If a tool call fails, the system triggers a retry or a fallback strategy rather than just crashing.

9. Execution Runtime
This is the sandbox where the code actually runs. It manages the state and handles parallel calls to speed up the process.

10. Guardrails
The "Safety Referee." Before the plan reaches the user, it’s checked for:
• Factuality: Is the information real or a hallucination?
• Tone: Does it align with the brand voice?

11. Response Generation & Delivery
The final plan is packaged with citations and UI artifacts (like a PDF or a calendar invite) and delivered back to the user.

📊 Why this matters?
Moving to an agentic workflow means shifting from Prompt Engineering to System Engineering. You aren't just writing text; you're managing state, orchestrating microservices, and building feedback loops.

The stack is evolving. Are you?

Which part of this architecture are you currently struggling with most?
• A) Planning & Reasoning
• B) Tool Integration (MCP)
• C) Evaluation & Guardrails

Let’s discuss in the comments! 👇

#AI #GenerativeAI #SoftwareEngineering #LLM #AgenticAI #MachineLearning #SystemDesign

[Alex Xu @alexxubyte]

Containerization Explained: From Build to Runtime

[Melorine Parsy @MelorineParsy]

The key stages and components involved in the AI agent development process. This process is a structured approach for creating AI systems that can perceive their environment, make decisions, and take actions to achieve specific goals autonomously.

#AI

[Vlad Mihalcea @vlad_mihalcea]

The best way to use the Spring Transactional annotation

https://t.co/G4AZecoMHL

[Shraddha Bharuka @BharukaShraddha]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/kyDon6qLrb

2. LLMs from Scratch: https://t.co/2hyMhuKoiI

3. Agentic AI Overview (Stanford): https://t.co/FXu6cAqITC

4. Building and Evaluating Agents: https://t.co/ZigR1tdOFL

5. Building Effective Agents: https://t.co/uYwfwO55mO

6. Building Agents with MCP: https://t.co/4arFTW1b3i

7. Building an Agent from Scratch: https://t.co/eOmveyM9Hz

8. Philo Agents: https://t.co/zLu7x1tx9m

🗂️ Repos

1. GenAI Agents: https://t.co/eXCl2YaRPv

2. Microsoft's AI Agents for Beginners: https://t.co/3CSW4zPAwf

3. Prompt Engineering Guide: https://t.co/GVzvxPYDVO

4. Hands-On Large Language Models: https://t.co/0rgDvhx3pI

5. AI Agents for Beginners: https://t.co/3CSW4zPAwf

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/9z5KHF9DMe

8. Hands-On AI Engineering:https://t.co/dldAj5Xkr6

9. Awesome Generative AI Guide: https://t.co/U2WZhT4ERV

10. Designing Machine Learning Systems: https://t.co/sYAZX34YdQ

11. Machine Learning for Beginners from Microsoft: https://t.co/NjFxHbC9jZ

12. LLM Course: https://t.co/N34YTPu1OK

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/bW3Ov3vMW0

2. Google's Agent Companion: https://t.co/wredwWAbBA

3. Building Effective Agents by Anthropic: https://t.co/fxtE4alVrJ.

4. Claude Code Best Agentic Coding practices: https://t.co/lLSwJ9pG7C

5. OpenAI's Practical Guide to Building Agents: https://t.co/xgkEIogGfh

📚Books:

1. Understanding Deep Learning: https://t.co/CjcKpTemmV

2. Building an LLM from Scratch: https://t.co/DaWBxOx8o3

3. The LLM Engineering Handbook: https://t.co/ZA1n0N41Mf

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/boLkl1VlKb

5. Building Applications with AI Agents - Michael Albada: https://t.co/H1Xf5EkJLL

6. AI Agents with MCP - Kyle Stratis: https://t.co/JI3ELQZE6a

7. AI Engineering: https://t.co/Xk0JzMIf7o

📜 Papers

1. ReAct: https://t.co/QNqE4UU55w

2. Generative Agents: https://t.co/CwEpoJgY1U.

3. Toolformer: https://t.co/5m9xZd5teZ

4. Chain-of-Thought Prompting: https://t.co/KjVlgdWi77.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/7FSUYKxIdG

2. MCP with Anthropic: https://t.co/IkZGiWm2yS

3. Building Vector Databases with Pinecone: https://t.co/2YRoMfLdXd

4. Vector Databases from Embeddings to Apps: https://t.co/23A50ixbHJ

5. Agent Memory: https://t.co/uc3L9BrNF7

Repost for your network ♻️

[Vaishnavi @_vmlops]

7-layers of LLM Stack