erl

software engineer 💻 | Love drawing 🎨 & reading 📚

[Three.js @threejs]

🗒️ https://t.co/EIPfcJnp9m

[rari @0xwhrrari]

SpaceXAI engineer Lauren Tan:

"99% of people are using GrokBot wrong"

"At SpaceXAI, one engineer can now run an entire team of agents"

GrokBot → Managers → Autonomous Agents → Full Agent Teams

GrokBot is turning SpaceXAI engineers into operators of autonomous systems

In a 1-hour talk, Lauren reveals how to unlock 100% of the agentic tools you already use

Prompt → Delegate → Execute → Review → Improve

Worth more than most $1000 agentic courses online

Bookmark and watch it today

Then read how to build your own GrokBot agent team from scratch in the article below

[Lunar @LunarResearcher]

Google just released a free 2-hour course on full Graph Engineering.

How to go from one prompt to an agent graph that can build itself:

0% → 10:16 - build your first AI agent
25% → 41:05 - master prompt engineering
50% → 54:45 - turn agents into graphs
75% → 1:20:10 - run loops inside agent graphs
100% → 1:43:33 - build a graph that builds itself

Most people build one agent and stop there.

Google is teaching everything that comes after:

Prompt → Agents → Graphs → Loops → Self-Building Systems

Single agents are the old workflow.

Graphs that evolve themselves are the next one.

This 2-hour course is worth more than most paid agent engineering courses.

Bookmark it and watch today

Then read how to run 1,000 agents from one prompt below ↓

[CyrilXBT @cyrilXBT]

https://t.co/Kxzl3eI0mZ

[Ronin @DeRonin_]

$4,660 worth of paid robotics courses, according to Opus 5.1

i gave it away for FREE

121 verified links, 27 pet projects, zero theory you won't use

six months of two hours a day between you and hireable

enjoy below:

[Codez @0xCodez]

SpaceXAI engineer, Lauren Tan:

"GrokBot is the most powerful agentic tool we have ever shipped, but only 1% of users use it correctly|

at SpaceXAI, I'm running a team of 15+ GrokBot agents. I have a Chief of Staff agent, 3 managers and 11 workers - that's the new engineering setup in 2026"

In a 1-hour talk, a SpaceXAI engineer reveals how to get 100% of every agentic tool you are using

worth more than a $500 agentic course on the internet

skip Netflix and watch today, it will change the way you use GrokBot forever, then read the article below

[Fight Club @FightClub753]

Only 30 min - MUAY THAI

[Dev Narware @devnarware]

Good Morning fam

[Alpha Beta Gamer @gameralphabeta]

This indie game turns Chess into a soulslike, as a hardcore pawn attempts to conquer the board!

Pawned Souls is free/pwyw on Itch: https://t.co/sAMkj7Mf7p

by indie dev @mikadev_YT

[Harshit Khosla @Harry_The_Nerd]

Insane recommendation by YT
Husband & wife are working on their custom C++ game engine since 2019..peak nerdy stuff🥰

[khas。 @khasmuyk]

Dokumen arsip negara 😅🤣

[Fight Club @FightClub753]

Choose your move wisely

[MuscleMindzzz @BackToBasiczzz]

Beginner Boxing Footwork

[SCR @SCR01111]

Goldmine for AI Engineers! 📌

If you're learning AI, ML, LLMs, or AI agents, don't waste hours jumping between random tutorials.

These are 10 repositories I'd actually keep bookmarked - from Python fundamentals to ML, LLMs, agents, and production AI.

1. Python - 100 Days

jackfrued/Python-100-Days

A 100-day Python learning path covering fundamentals, data analysis, web development, and more.

GitHub:
https://t.co/NMCNWjp4GT

2. Generative AI for Beginners

microsoft/generative-ai-for-beginners

A practical introduction to building Generative AI applications.

Covers:

• LLM fundamentals
• Prompt engineering
• RAG
• AI agents
• Fine-tuning
• AI application development

GitHub:
https://t.co/5QXAC1rsUZ

3. LLMs From Scratch

rasbt/LLMs-from-scratch

Want to understand what's actually happening inside an LLM?

Build one step by step.

Covers:

• Tokenization
• Embeddings
• Attention
• Transformers
• Training
• Fine-tuning

GitHub:
https://t.co/iEtPYVPpXV

4. Machine Learning for Beginners

microsoft/ML-For-Beginners

A structured 12-week, 26-lesson curriculum covering classical machine learning.

A good starting point if you want ML fundamentals before jumping into LLMs.

GitHub:
https://t.co/sLijf46NKB

5. OpenAI Cookbook

openai/openai-cookbook

A collection of practical examples and guides for building applications with OpenAI models.

Useful when you want to move from:

Learning → Building

GitHub:
https://t.co/H3ZiMmpMAq

6. Stable Diffusion

CompVis/stable-diffusion

Interested in generative image models?

This repository contains the original Stable Diffusion implementation and research code.

GitHub:
https://t.co/DFwSLqOlyn

7. AI Agents for Beginners

microsoft/ai-agents-for-beginners

A practical course for understanding and building AI agents.

Covers:

• Agentic AI
• RAG
• Agent frameworks
• Tool use
• Multi-agent systems

GitHub:
https://t.co/zNeYlZRQqa

8. AI for Beginners

microsoft/AI-For-Beginners

A structured 12-week, 24-lesson introduction to AI.

Covers:

• Neural networks
• Computer vision
• NLP
• Deep learning
• Classical AI

GitHub:
https://t.co/V7Oilwt7nA

9. LLM App

pathwaycom/llm-app

Focused on building practical LLM applications.

Explore:

• RAG
• AI pipelines
• Enterprise search
• Real-time data
• Vector search

GitHub:
https://t.co/2FdeXjZfQ8

10. Segment Anything

facebookresearch/segment-anything

A foundation model for promptable image segmentation.

Worth exploring if you're interested in computer vision and multimodal AI.

GitHub:
https://t.co/yCSrZH8b5w

Don't bookmark all 10 and forget about them.

Pick based on where you are:

Python → Python-100-Days

ML → ML-For-Beginners

AI Fundamentals → AI-For-Beginners

LLMs → LLMs-from-scratch

Generative AI → Generative-AI-for-Beginners

Agents → AI-Agents-for-Beginners

Building → OpenAI Cookbook / LLM App

Computer Vision → Segment Anything

Pick one.

Build something.

Then move to the next.

[Morlex @0xMorlex]

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture:

• 00:00 - understanding the different layers of AI memory
• 30:00 - moving from simple LLM calls to AI agents
• 50:00 - why agent systems are becoming a graph engineering problem
• 1:12:00 - connecting context, tools and agent state
• 1:24:00 - building a minimal agent harness from scratch
• 1:46:00 - breaking down the architecture behind Hermes Agent
• 2:08:00 - turning agent concepts into working systems

167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts

The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness

Watch it today, then read the full roadmap below

[Caspar @caspar_br]

anatomy of a managed deep agent. https://t.co/pnmD06la9i

[Survival Instinct @SurvivalInstint]

Time to Upgrade your Boxing Skills

[Dhairya Karekar @dkare1009]

Most people use LLMs.
Very few actually understand how they work under the hood.

If you want to go from prompt user → real AI engineer, study these 9 concepts in order:

1️⃣ Transformers — attention, tokens, self-attention basics
https://t.co/65pkMfS4og

2️⃣ Transformer tricks — what makes them stable & scalable
https://t.co/mnUQYpQ9q2

3️⃣ From Transformers → LLMs — how scale changes behavior
https://t.co/uXOVnCsmGA

4️⃣ LLM training — where “intelligence” actually emerges
https://t.co/RFkBljQ8Pi

5️⃣ Instruction tuning & alignment — why fine-tuning matters
https://t.co/nmZB6JE0NK

6️⃣ LLM reasoning — why models fail + what improves them
https://t.co/UY8V9bZjAU

7️⃣ Agentic LLMs — models that plan, call tools, and act
https://t.co/EZbxuxBimR

8️⃣ LLM evaluation — measure beyond demos & vibes
https://t.co/yZ4Uig7uGw

9️⃣ What’s next — trends that actually matter

Bookmark this. Study step-by-step. Your prompts will level up — and so will your builds.

CC: Author