ShaktI

Data Engineer | Learning AI, ML | Traveller

[codila @0xCodila]

Google just released free 1-hour course on building agentic knowledge Graphs from 0% to 100%:

10% → 4:01 - how to build a GraphRAG agent
30% → 15:00 - Graph Engineering explanation
55% → 30:00 - Agentic search Engineering
80% → 35:48 - Graph Engineering practice
100% → 47:06 - self-improving agents in graphs

this free Google course mass replaces a $500 graph engineering bootcamp - learn it in 60 min to 100%

watch it today - then read the full graph playbook in the article below ↓

[Khairallah AL-Awady @eng_khairallah1]

Andrew Ng just released a 1-hour course on building multi-agent systems from scratch:

• 00:00 - Introduction to agentic AI
• 03:07 - Building your first graph from scratch
• 14:00 - Connecting tools, memory, and context
• 23:00 - Building with Google ADK
• 01:06:03 - Why graphs are the future

In 1-hour he shows exactly how to build an agentic knowledge Graphs that improves itself.

Most people are still doing all of this by hand.

Watch the session, then save the guide below.

[ShaktI @AIShakti]

Viva Spain

[Movez @0xMovez]

Andrew Ng just dropped a 3-hour course on how to become an AI Engineer in 2026:

• 00:00 - How to build agentic AI systems
• 04:25 - Future of AI engineering
• 23:38 - AI Prompting full course
• 2:52:17 - Creating an app with AI in 30 minutes

This 3-hour watch could replace 10 AI engineering courses on the internet.

Watch it today, then read how to run a self-improving system in the article below.

[K2S @k2sbhai]

nvidia is casually giving you access to 5 frontier chinese AI models for free 😳

no credit card
no subscriptions
just one API key that unlocks everything

what you get for $0:

- DeepSeek V4 Flash for ultra-fast inference
- MiniMax M3 as a drop-in coding assistant
- Qwen3.5-397B for advanced reasoning tasks
- Kimi K2.6 for agentic workflows and long chains
- GLM 5.1 as a reliable everyday model

why this is huge:

> no paying separate subscriptions for different models
> no changing your existing workflows or tools
> no vendor lock-in since everything is OpenAI-compatible

getting started takes less than 2 minutes:

1. go to https://t.co/q50rSatNbb
2. sign up and verify your account
3. generate your nvapi key
4. set your base URL to https://t.co/92kkbFSf8D
5. pick any model and start building

supported models:

> minimaxai/minimax-m3
> qwen/qwen3.5-397b-a17b
> moonshotai/kimi-k2.6
> zhipuai/glm-5.1
> deepseek/deepseek-v4-flash

pro tip:

use DeepSeek V4 Flash for speed, Qwen for hard reasoning, Kimi for agents, and MiniMax as your daily coding companion

the best part?

one free key gives you access to 100+ models across NVIDIA's catalog

~40 requests per minute is more than enough for most developers and personal projects

5 frontier models that compete with GPT and Claude, all without spending a dollar

bookmark this and claim your free API key before the limits change 👀

[Khairallah AL-Awady @eng_khairallah1]

this is f*cking gold

How to build your first AI agent (Full guide)

if I had this a year ago, I would've shipped my first agent in a day instead of 2 weeks

in the right hands, this changes everything:

[Neo Kim @systemdesignone]

If you’re looking to master Docker & Rust in 2026, this account is worth following.

[Anatoli Kopadze @AnatoliKopadze]

A Google Cloud engineer just showed how to build a full app with Claude from scratch

he spent 26 minutes live on stage doing what most teams take weeks to do

worth more than any $500 vibe-coding course

here's what he covers:
> zero to deployed app in a single session
> handling five engineering roles alone with Claude
> the exact workflow Google uses internally
> no team, no setup, just Claude and a goal

the people who figure out what Claude can actually do are building things everyone else thinks requires a team

that's exactly why I wrote a step by step guide on how to build your first AI agent

the guide is in the article below

[robert. @robrtcode]

Un nerd de 33 años acaba de convertir $1.000 en $946.207 operando Bitcoin, con un truco que robó de los pronósticos de huracanes.

Sin título en finanzas. Sin mesa de trading. Solo un truco que todo meteorólogo aplica y todo trader olvida.

Wallet pública:
https://t.co/J65WvxHpdC

El truco: los meteorólogos nunca pronostican el mañana con un solo modelo. Corren 31 y cuentan los votos. Él apuntó exactamente ese mismo truco hacia Bitcoin.

Un agente de Claude lee cada mercado de BTC de 5 minutos y lo mete en MiroFish, una simulación que corre 31 rutas de modelo y solo dispara cuando 28 de ellas coinciden. Por debajo de 26 votos, mata la operación.

La velocidad de cobertura del sistema de agentes es muchísimo mayor que la de cualquier equipo de trading de élite.

Recopilan datos 24/7 y corren simulaciones con esos datos en el motor de MiroFish, de forma completamente autónoma.

Cada operación es un ciclo perfecto. Cada dólar ganado es pura explotación de la ineficiencia del mercado.

Esa es toda la ventaja. No una predicción. Un quórum.

Dimensiona con Kelly y aprieta un botón. La mayoría de las señales nunca pasan la votación, así que la mayoría de los días se queda quieto.

Pasó años aprendiendo que la certeza es una estafa y el consenso es la ventaja.

[angel @angeldot_]

Anthropic ha lanzado una guía de 37 minutos para construir Agentes de IA que automatizan una empresa entera.

Gratis. De los ingenieros que construyeron Claude.

Agentes que trabajan, se reparten tareas y ejecutan todo solos.

Subtitulado al español.

Guárdate este post. 🔖

[Tom Dörr @tom_doerr]

Converts documents and media into structured JSON for LLMs

https://t.co/Mt55t9Uuvn

[Tech with Mak @techNmak]

The full AI engineering curriculum is now free.

It's called AI Engineering from Scratch. 20 phases, 428 lessons, roughly 320 hours end to end. Free. MIT license. Runs on your own laptop.

The design principle that makes it different from everything else => every algorithm gets built from raw math before a single framework loads. Backprop by hand. Tokenizer by hand. Attention by hand. Agent loop by hand. Then you implement the same thing in PyTorch or sklearn. By the time the production library appears, you already know what it's doing underneath.

Every lesson ends with something you keep:
→ Prompt templates for any AI assistant
→ Skill files for Claude, Cursor, Codex, OpenClaw, Hermes 
→ Agent definitions you wrote the loop for yourself 
→ MCP servers built from scratch in Phase 13

428 lessons means 428 artifacts by the end. Tools you built and actually understand.

The full 20 phases:
→ Phase 0 - Setup & Tooling (12 lessons) 
→ Phase 1 - Math Foundations (22 lessons) 
→ Phase 2 - ML Fundamentals (18 lessons) 
→ Phase 3 - Deep Learning Core (13 lessons) 
→ Phase 4 - Computer Vision (28 lessons) 
→ Phase 5 - NLP (29 lessons) 
→ Phase 6 - Speech & Audio (17 lessons) 
→ Phase 7 - Transformers Deep Dive (14 lessons) 
→ Phase 8 - Generative AI (14 lessons) 
→ Phase 9 - Reinforcement Learning (12 lessons) 
→ Phase 10 - LLMs from Scratch (22 lessons) 
→ Phase 11 - LLM Engineering (15 lessons) 
→ Phase 12 - Multimodal AI (25 lessons) 
→ Phase 13 - Tools & Protocols (23 lessons) 
→ Phase 14 - Agent Engineering (42 lessons) 
→ Phase 15 - Autonomous Systems (22 lessons) 
→ Phase 16 - Multi-Agent & Swarms (25 lessons) 
→ Phase 17 - Infrastructure & Production (28 lessons) 
→ Phase 18 - Ethics, Safety & Alignment (30 lessons) 
→ Phase 19 - Capstone Projects (17 projects, 20-40 hours each)

Python, TypeScript, Rust, Julia throughout.

GitHub Repo: https://t.co/E2Rg09gnrR

[Julian Goldie SEO @JulianGoldieSEO]

Google Antigravity FULL COURSE 4 HOURS (Build & Automate Anything)

[Ajit kumar @ajitcodes]

These 9 lectures from Stanford University are the BEST for anyone wanting to learn and understand LLMs in depth

Lecture 1 - Transformer: https://t.co/6wl1VXyQxS

Lecture 2 - Transformer-Based Models & Tricks: https://t.co/rFoGOnsOY2

Lecture 3 - Tranformers & Large Language Models: https://t.co/t8H8UebPg0

Lecture 4 - LLM Training: https://t.co/KZxOEL0ezz

Lecture 5 - LLM tuning: https://t.co/PapIUSlToT

Lecture 6 - LLM Reasoning: https://t.co/dr02iTGXHs

Lecture 7 - Agentic LLMs: https://t.co/10EQm5iCBp

Lecture 8 - LLM Evaluation: https://t.co/eOKwCn3LBo

Lecture 9 - Recap & Current Trends: https://t.co/MQAGVGlqiX

Start understanding LLMs in depth from the experts. Go through each step-by-step video

Start understanding LLMs in depth from the experts. Go through each step-by-step video

[Julian Goldie SEO @JulianGoldieSEO]

FREE AI Agents Course Build & Automate ANYTHING

[Sunusi Danjuma Ali @SunusiMinjibir]

Instead of watching Netflix or football tonight, spend just 1 hour on this.

A full Claude AI course that shows you how to build and automate almost anything.

The people who take this seriously tonight could wake up tomorrow with a skill most people won’t have even after 2 years.

If you’re busy tonight, bookmark this post, so that you can watch it tomorrow or anytime of your convenience.

[Nainsi Dwivedi @NainsiDwiv50980]

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/kJDquHyQuR

2. LLMs from Scratch: https://t.co/0tVKf67LWE

3. Agentic AI Overview (Stanford): https://t.co/F3eMqlyx7o

4. Building and Evaluating Agents: https://t.co/p2wAwQkmc1

5. Building Effective Agents: https://t.co/soZEzoU6eu

6. Building Agents with MCP: https://t.co/7rXLH619p4

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

8. Philo Agents: https://t.co/oALtKeEhg1

🗂️ Repos

1. GenAI Agents: https://t.co/SzAvw64ZA3

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

3. Prompt Engineering Guide: https://t.co/zFZJT6V60r

4. Hands-On Large Language Models: https://t.co/S5E4390RIk

5. AI Agents for Beginners: https://t.co/MYCOwStucr

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

7. Made with ML: https://t.co/mAb4b9Li9o

8. Hands-On AI Engineering:https://t.co/2QvXB3WJhe

9. Awesome Generative AI Guide: https://t.co/dYaAsRgfO6

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

11. Machine Learning for Beginners from Microsoft: https://t.co/6u48FQng1g

12. LLM Course: https://t.co/o0NnbEjH6X

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/cs0P2Tt165

2. Google's Agent Companion: https://t.co/Qnv3PsJZIx

3. Building Effective Agents by Anthropic: https://t.co/5ZfcMllO9N.

4. Claude Code Best Agentic Coding practices: https://t.co/zX9ep8ER0h

5. OpenAI's Practical Guide to Building Agents: https://t.co/uwdBKet060

📚Books:

1. Understanding Deep Learning: https://t.co/Rix5N440Y8

2. Building an LLM from Scratch: https://t.co/V20ES23ZH8

3. The LLM Engineering Handbook: https://t.co/avpqPTA0I8

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/8bgDLtebU0

5. Building Applications with AI Agents - Michael Albada: https://t.co/W70co41CCW

6. AI Agents with MCP - Kyle Stratis: https://t.co/vF8VqTeyfA

7. AI Engineering: https://t.co/eJrAoLMW0Z

📜 Papers

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

2. Generative Agents: https://t.co/q50bu1PPnQ.

3. Toolformer: https://t.co/CFssbdAXvQ

4. Chain-of-Thought Prompting: https://t.co/n84jvdyxWL.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/yhVP0jcs6w

2. MCP with Anthropic: https://t.co/w9LxesXtjx

3. Building Vector Databases with Pinecone: https://t.co/GeI4yarzHH

4. Vector Databases from Embeddings to Apps: https://t.co/eMrFYZaY8d

5. Agent Memory: https://t.co/hjbH72Qwqr

Repost for your network ♻️

[Julian Goldie SEO @JulianGoldieSEO]

N8N FULL COURSE 1 HOUR (Build & Automate Anything)

[ShaktI @AIShakti]

@JulianGoldieSEO N8n

[Motorsport MP4 @MotorsportMP4]

Eau Rouge from Max Verstappen’s helmet cam 🫠

https://t.co/dFVLdaSD6O