Dhananjay

AI/ML | Java | Python | JS | SaaS | Startup | Engineering Leader | MCA | MBA - NMIMS

[Dhananjay @CCodeCrafters]

A Website Blocker Chrome Extension You Must Have : https://t.co/QiGXNH71ah

[Uncle Bob Martin @unclebobmartin]

Morning bathrobe rant: disengage from the syntax.

[Martin Fowler @martinfowler]

NEW POST

Thoughtworks internal IT use a workflow for agentic programming called Structured-Prompt-Driven Development (SPDD). @WeiZhang595190 and Jessie Jie Xia describe how this works with a simple example plus details in a github project.

https://t.co/6cHnSPWr6L

[Elon Musk @elonmusk]

This is why OpenAI was created

[Kent Beck 🌻 @KentBeck]

Dario demonstrating that he doesn't understand software engineering. The human side of what we do has always been the heart. I can empathize with *wanting* to never talk to an engineer ever again, but engineering becomes more important with better tools.

[Dhananjay @CCodeCrafters]

Sidecar pattern in microservices 👇

Think of it as a “companion” container that runs alongside your app and handles things your service shouldn’t worry about:

🔹 logging
🔹 security
🔹 networking (proxies like Envoy)
🔹 monitoring

[Dhananjay @CCodeCrafters]

@NitinthisSide_

[Dhananjay @CCodeCrafters]

@system_monarch Deeper design concepts...

[Dhananjay @CCodeCrafters]

@0xPrajwal_ A well designed microservices should address all these 10 points. But, if I need to address the monolith issues, I will start addressing them from the top. Things will start getting better as we move down the list...

[Dhananjay @CCodeCrafters]

Strategy to decompose a system into microservices:

- By Business Capability
- By Subdomain (DDD)
- By Business Process / Workflow
- By Bounded Context
- By UI / Frontend (Vertical Slicing)
- By Technical Function (Layer-based)
- By Data / Entity

[Dhananjay @CCodeCrafters]

10 Core Principles of Microservices 🧵

1. Single Responsibility
2. Loose Coupling
3. Independent Deployment
4. Decentralized Data
5. Autonomy
6. Scalability
7. Fault Isolation
8. API-First
9. Observability
10. Continuous Delivery

#Microservices #SoftwareArchitecture #Tech

[rvivek @rvivek]

The hottest job for the next five years is going to be the agent operator.

They don't need to be an engineer. They can walk into marketing, legal, or life sciences research and actually make agents work for that function.

Required skills:
> MCPs
> CLIs
> Writing skills (the file kind)
> agents.md fluency
> Business acumen

None of this is in any CS curriculum today.

Soon, enterprises will be pressured to redesign their workflows for agents, not for people. And when that happens, agent operators will be in massive demand.

[Dhananjay @CCodeCrafters]

@rvivek Great stuff.

[Himanshu Kumar @codewithimanshu]

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.

[Rahul @sairahul1]

Karpathy didn't make a course.

He made THE course.

3 hours. Free.

Tokenization. Attention. Hallucinations. Tool use. RLHF. DeepSeek. AlphaGo.

Every behavior you've ever wondered about in an LLM - where it comes from, why it exists, how it was engineered.

The gap between engineers who understand this and engineers who don't isn't technical depth.

It's the ability to conceive of entirely different things.

[Dhananjay @CCodeCrafters]

@jahirsheikh8

[Dhananjay @CCodeCrafters]

@Samaytwt

[Dhananjay @CCodeCrafters]

@rambuilds_

[Dhananjay @CCodeCrafters]

@jahirsheikh8 Really a good compilation.

[Dhananjay @CCodeCrafters]

@CodeEdison Mostly true. But they had to write the code from scratch. No Codex/Claude.. :)