Anmol D B

3 yoe in venting
Data Scientist
strategy , pattern recognition
suffering from ADHD

[Movez @0xMovez]

Anthropic just dropped 5 workshops on building self-improving agentic systems from scratch:

00:00 - Ship your first Claude agent
36:44 - Build memory for Claude agents
1:05:06 - Make your agent autonomous
1:26:46 - Set up a proactive agent
2:03:35 - self-improving agents (tools,skills)

These 3-hours of free Claude workshops will replace 10 paid agentic courses.

Watch today, then read article below on how to build a self-improving agentic system with Fable 5.

[Vidit Gujrathi @viditchess]

Chess engines tell you the best move.

But grandmasters are human, they don’t always play it.

So I built "Kibitz": a human move predictor for chess broadcasts. I trained this model on my Nvidia RTX 5080.

Then I made it run as a business by itself.

A channel buys the overlay, Hermes onboards them, charges via @stripe test mode, runs the broadcast, narrates with @NVIDIAAI Nemotron, tracks inference cost, and books its own P&L.

I build. Hermes operates.

This is my demo and entry for the @NousResearch × @NVIDIAAI × @stripe Hermes Agent Accelerated Business Hackathon.

[Oxygen 💨 @WhateverVishal]

When you are suffocating inside at the JEE coaching centre kids in West are living the time of their lives

Being born at correct place defines 95% of your life

[Anmol D B @ANMOLDBHANDARE2]

@nitesh_singh5 Interested

[Anmol D B @ANMOLDBHANDARE2]

Proved yet again "The greatest coaches can be the worst player"

[Anmol D B @ANMOLDBHANDARE2]

@Priyannkaaaa Let's connect

[Anmol D B @ANMOLDBHANDARE2]

It's USD the price goes up if NVD
21,

[Elon Musk @elonmusk]

Grok Imagine tutorial made with Grok Imagine.

These is all AI-generated!

[Nikki Siapno @NikkiSiapno]

AI concepts developers should know:

1. RAG (Retrieval-Augmented Generation)
↳ Retrieves relevant external data to ground model responses.

2. MCP (Model Context Protocol)
↳ A standard for connecting models to external tools, data, and context.

3. Model Routing
↳ Dynamically selecting the best model for a given task.

4. Embeddings
↳ Turning data into vectors so models can search and compare meaning.

5. Context Windows
↳ The amount of information a model can process at once.

6. Evals
↳ Measuring the quality, reliability, and behavior of AI systems.

7. Multi-Agent Systems
↳ Multiple agents collaborating to solve complex tasks.

8. A2A (Agent-to-Agent)
↳ How agents communicate and coordinate with each other.

9. Memory & State Management
↳ Persisting and retrieving context across interactions.

Understanding these concepts is one thing. Seeing them work together is where it clicks.

Oracle has a great guide that walks you through building a scalable multi-agent RAG system → https://t.co/icEylLlR45

Their free DeepLearning course on building memory-aware agents also puts these concepts into action: https://t.co/DBrwzmvStK

What else should be on the list?

——

♻️ Repost to help others learn AI.
🙏 Thanks to @Oracle for sponsoring this post.
➕ Follow me ( Nikki Siapno ) to improve at AI engineering.

[Anmol D B @ANMOLDBHANDARE2]

YouTube down let's go😭

[Anmol D B @ANMOLDBHANDARE2]

@MakinHisWay Study

[Elon Musk @elonmusk]

Think in probabilities

[Anmol D B @ANMOLDBHANDARE2]

@boxmining Ai vibe coding

[Anmol D B @ANMOLDBHANDARE2]

@JobFound5 Interested

[Anmol D B @ANMOLDBHANDARE2]

@feelzyou Myself

[Anmol D B @ANMOLDBHANDARE2]

@Tech_Rose1 Steal

[Anmol D B @ANMOLDBHANDARE2]

@dranthoniaeddo ❤️

[Param @Param_eth]

Whoever built this deserves a million dollars in their pocket.

[Anmol D B @ANMOLDBHANDARE2]

@baeincrypto LFG

[Anmol D B @ANMOLDBHANDARE2]

Pytorch >> Tensor flow
#DeepLearning #machinelearning
#AI