I've been wanting to understand what happens beyond writing code how applications actually run and get deployed. Starting my Docker & Kubernetes journey with this course by @Hiteshdotcom sir and @techie_piyush sir. Excited to learn and build along the way. @nirudhuuu

One failed database operation can leave an order half-created. I tested this with PostgreSQL + Prisma today. Created an order. Created its items. Updated stock. Then I deliberately threw an error. What happened next? 🧵

Grateful to have attended the AI Agent Masterclass by @surajtwt_ Happy to finish Top 2 out of 263 participants in the quiz Learned more about LangGraph, agent workflows tools and state Thankful for the opportunity to learn and test my understanding🙌 @Hiteshdotcom @nirudhuuu

I started looking beyond the LLM call. How do we design the backend? Handle tools, workflows, memory, and failures? Still a lot to learn, but my direction is clearer. Grateful to @Hiteshdotcom @piyushgarg_dev @nirudhuuu . Certificate earned. The real building starts now. 🎓

To Tejas: Scan → Authenticate → Pay → ✓ To the systems underneath: Routing → Authorization → Debit → Credit → Confirmation → Failure handling → Reconciliation 24.51 billion transactions look simple from the outside. The engineering underneath is anything but.

Now break it. Tejas's account gets debited. But the confirmation never reaches his app. He sees: "Something went wrong." Did it fail? Should he retry? What if it actually succeeded and a retry double-charges him? This is where it gets interesting.

At a simplified level, UPI involves: Tejas's App → Payer PSP Kunal's App → Payee PSP Tejas's Bank ↔ NPCI / UPI ↔ Kunal's Bank NPCI operates the UPI switch/network. The banks handle the actual debit and credit. Let's follow Tejas's ₹500. 👇

Today we built a Claude Code-style CLI using the Claude SDK. The interesting part wasn't just connecting the model. We focused on building the experience around it tool execution, readable output, transparency, and different modes. @Hiteshdotcom @piyushgarg_dev @nirudhuuu

Today’s GenAI class was about building an AI Pitch Deck generator. The interesting part wasn’t just generating images. We focused on how to make the AI output structured, validated, and usable by the application. @Hiteshdotcom @surajtwt_ @nirudhuuu @ChaiCodeHQ

Your API request doesn’t travel alone. It gets wrapped, addressed, forwarded, unwrapped… before your server ever sees it. Here’s what’s actually happening behind your GET /profile. @wjshubham

Today's GenAI class was about building an AutoWiki for Git repositories a RAG system for asking questions about a GitHub codebase. But my main takeaway wasn't the UI. It was understanding the backend architecture behind repository-aware AI. 🧵👇 @Hiteshdotcom @piyushgarg_dev

Today's GenAI class was about building a Voice Agent from scratch with @piyushgarg_dev sir. The interesting part wasn't just making it talk it was understanding the architecture behind it. Here's the flow 🧵👇 @Hiteshdotcom @nirudhuuu @ChaiCodeHQ

Streaming isn't just about getting tokens onto the screen. The goal is a runtime where provider-specific complexity stays isolated while applications get a predictable streaming API. Agni Agent SDK v1.2.0 🔥 Building the runtime one primitive at a time.

The complete pipeline: Provider SDK → Provider Adapter → ModelStreamChunk → Runner → RunStreamEvent → Application Each layer has a clear responsibility.

From the application side, streaming stays simple: for await (const event of runner.stream(...)) Developers don't need Gemini's streaming objects or provider-specific parsing. Just consume Agni's normalized events.

The Runner consumes the provider stream with for await...of. Each ModelStreamChunk is mapped into a public RunStreamEvent. The important boundary: Provider-specific data stops at the adapter.