Ganeshwaran Gabilan

| Developer | Biomedical Engineering Graduate | UOM | Hartleyite | From Point-Pedro |

[chuplung @choopyplug1]

https://t.co/LSAMpgaool

[Alex Xu @alexxubyte]

What Is Harness Engineering, Explained Visually.

[codila @0xCodila]

Andrew Ng just dropped 8-page PDF on 4 agentic steps "from Loops to Graphs from scartch"

The twist: agent has amnesia without both: Loops let agents think - Graphs let agents remember

here's 4 workflows, step by step:

step 1 → reflection - agent writes, second prompt critiques, agent rewrites - one self-review loop beats a smarter model with none

step 2 → tool use - give it search, code execution, APIs - thinking without tools is hallucinating

step 3 → planning - break the task into JSON steps before running - Step fails? Agent replans around it

step 4 → multi-agent - stop running one agent - run a team - one codes, one reviews, one tests

how to wire this today:

step 5 → add one critique call after every generation - 10-30% quality lift, one day of work

step 6 → connect all 4 into a graph - agents share memory instead of transcripts - agent forgets, graph doesn't

the result: a weak model with 4 steps destroys a strong model without them - same cost, it's the architecture

this 8-page PDF is what comes after loop engineering

save this - then read the full build workflow in the article below ↓

[Ganeshwaran Gabilan @ganeshwargabil]

If we are not prepared to surrender ourselves entirely, then there is no meaning in worrying about their anger. We must simply continue onward, steadfastly walking our own path.

[Ganeshwaran Gabilan @ganeshwargabil]

Many people in this world may harbor anger toward us. That, in itself, is only natural.

But when someone is angry with you, observe whether they are willing to grant you the space to explain your perspective, to express your side of the story.

[Ganeshwaran Gabilan @ganeshwargabil]

Whether such people are individuals, a community, an ethnicity, or even a nation, there is little we can do to avoid their anger. Nothing short of complete submission will satisfy them.

[Ganeshwaran Gabilan @ganeshwargabil]

They are convinced that their point of view alone is correct, and that no alternative perspective could possibly exist.

[Ganeshwaran Gabilan @ganeshwargabil]

If they are unwilling to offer you even the opportunity to speak your truth, then such people are those who believe that the entire world revolves around them. They see no suffering beyond their own suffering, no problems beyond their own problems.

[Ganeshwaran Gabilan @ganeshwargabil]

They may not ultimately understand your viewpoint. Yet ask yourself this: do they at least acknowledge that you have a viewpoint of your own? Do they make even the slightest effort to understand it?

[Codez @0xCodez]

https://t.co/RSly7N44jl

[Anatoli Kopadze @AnatoliKopadze]

https://t.co/SJ5O5tIrlT

[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.

[Himanshu Kumar @codewithimanshu]

The Head of Claude Code at Anthropic hasn't written code by hand in months.

In 2 days he shipped 49 full features. 100% written by AI.

He just dropped a 30-minute talk on exactly how he does it.

More valuable than any $500 vibe coding course. Bookmark it.

[Avi Chawla @_avichawla]

RAG vs. CAG, clearly explained!

RAG is great, but it has a major problem:

Every query hits the vector DB. Even for static information that hasn't changed in months.

This is expensive, slow, and unnecessary.

Cache-Augmented Generation (CAG) addresses this issue by enabling the model to "remember" static information directly in its key-value (KV) memory.

In fact, you can combine RAG and CAG for the best of both worlds.

Here's how it works:

RAG + CAG splits your knowledge into two layers:

↳ Static data (policies, documentation) gets cached once in the model's KV memory

↳ Dynamic data (recent updates, live documents) gets fetched via retrieval

This gives faster inference, lower costs, and less redundancy.

The trick is being selective about what you cache.

Only cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating "cold" (cacheable) and "hot" (retrievable) data keeps this system reliable.

You can start today. OpenAI and Anthropic already support prompt caching in their APIs.

I have shared my recent article on prompt caching below if you want to dive deeper.

👉 Over to you: Have you tried CAG in production yet?

[Movez @0xMovez]

This 30-min workshop by the creator of Claude Code will teach you more about vibe-coding than 100 YouTube video guides.

Bookmark it & give it 30 minutes today. This video will change the way you use Claude forever.

[Andrew Ng @AndrewYNg]

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with @Oracle and taught by @richmondalake and Nacho Martínez.

Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions.

You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time.

Skills you'll gain:
- Build persistent memory stores for different agent memory types
- Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory
- Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search

Join and learn to build agents that remember and improve over time!

https://t.co/nxNSEHGmr9

[Vasilije @tricalt]

https://t.co/BBIva1uPHn

[Alex Xu @alexxubyte]

How does gRPC work?

[Boris Cherny @bcherny]

I'm Boris and I created Claude Code. Lots of people have asked how I use Claude Code, so I wanted to show off my setup a bit.

My setup might be surprisingly vanilla! Claude Code works great out of the box, so I personally don't customize it much. There is no one correct way to use Claude Code: we intentionally build it in a way that you can use it, customize it, and hack it however you like. Each person on the Claude Code team uses it very differently.

So, here goes.