Rakshya

daydream daydream

[Vaishnavi @_vmlops]

Algorithms for Artificial Intelligence

https://t.co/f4CgTgpViW

[freeCodeCamp.org @freeCodeCamp]

WhatsApp automation gets more flexible when you self-host the stack.

In this guide, Achiya shows you how to use n8n and WAHA to build bots that respond to messages, trigger workflows, and more.

He covers setup, APIs, automation logic, and deployment workflows along the way.

https://t.co/yMZGwHYkbm

[AilaunchX @Ai_Tech_tool]

Instead of watching an hour of Netflix, watch this 2 hour hour Stanford lecture will teach you more about how LLMs like ChatGPT and Claude are built than most people working at top AI companies learn in their entire careers.

[freeCodeCamp.org @freeCodeCamp]

Good web design really starts with understanding how people think and behave.

In this handbook, Ujah applies academic theories like cognitive load, Gestalt principles, and mental models to real web design decisions.

You’ll learn how psychology and UX research can help you shape more intuitive interfaces.

https://t.co/gHXOnB9p1O

[divyansh tiwari @DivyanshT91162]

🚨 Learn Data Science for FREE (no excuses left).

Most people think you need expensive courses… you don’t.

Start with these beginner-friendly YouTube resources in 2026:

1. Python (Basics + Projects)
https://t.co/JW6qVUnDDa

2. Python Libraries (NumPy, Pandas, Matplotlib)
https://t.co/mhFOU6mdTw

3. SQL for Data Analysis
https://t.co/10qcnjMxn9

4. Excel (Data Analysis + Dashboards)
https://t.co/iOGdRhXJNy

5. Power BI (Data Visualization)
https://t.co/oX2gavWI2O

6. Tableau (Dashboard + Storytelling)
https://t.co/IBocU1Gp3g

7. Statistics for Data Science
https://t.co/b2ucYrb907

8. Data Analysis Projects (Real-world)
https://t.co/p34zcmy0eg

Stop overcomplicating it.

Pick one skill → practice daily → build projects.

Bookmark this so you don’t lose it.
RT to help someone who’s stuck.

Follow @DivyanshT91162 for more AI & Data Science content 🚀

[Ronin @DeRonin_]

This 1-hour Stanford lecture on Agentic AI covers exactly what you need to become an AI automation builder

tool calling, multi-step workflows, planning, reflection — the foundations behind every automation system that actually works

most people learn this by copying Make and n8n tutorials..

Stanford teaches it by showing you WHY agents work the way they do

watch it first. then go through my practical tasks below

9 real projects across 2 weeks: APIs, webhooks, LLM integration, AI workflows

no theory. just build

you'll understand 10x more than everyone grinding tutorials blindly

bookmark this. seriously. do it THIS WEEKEND

P.S. I'm dropping practical tasks for every week if this gets enough feedback

[Learn German Language @Lernen_German]

German Book - Fit fürs Goethe-Zertifikat B2 - PDF + Audio
https://t.co/OZsPRUkZhU

[freeCodeCamp.org @freeCodeCamp]

Not all data pipelines need to run in real time.

Some are better as scheduled batch jobs, others need streaming from the start.

In this tutorial, @balawc27 teaches you the differences between batch and streaming pipelines in Python, and when to use each.

https://t.co/2hDZDcPMe8

[Suryansh Tiwari @Suryanshti777]

Holy shit… someone just made machine learning click.

Not static diagrams.
Not math-heavy PDFs.
Not black-box training.

Real algorithms — training step-by-step — visually.

It’s called Machine Learning Visualized
and it lets you watch models learn in real time.

Here’s why this is different:

Instead of dumping theory first,
it shows optimization happening live:

• gradients moving
• weights updating
• decision boundaries shifting
• loss decreasing
• models converging

You literally see learning happen.

Everything is built from first principles:

• Gradient Descent
• Logistic Regression
• Perceptron
• PCA
• K-Means
• Neural Networks
• Backpropagation

No magic. Just math → code → visualization.

Each chapter is a Jupyter notebook
that derives the math
then implements it
then animates training.

So you can watch:

• neural nets shape decision surfaces
• PCA rotate feature space
• K-means clusters form live
• gradient descent find minima
• sigmoid reshape boundaries
• backprop update weights step-by-step

This solves a huge problem:

Most ML resources teach: math → code → ??? → trained model

This shows: math → code → learning process → result

Which means you finally understand:

• why gradients matter
• how weights evolve
• what loss landscapes look like
• how convergence actually happens
• why deep nets learn non-linear functions

Even better:

You can open any notebook
modify parameters
and watch behavior change instantly.

Learning ML becomes interactive.

Not passive.
Not abstract.
Not confusing.

Just… visible.

Perfect for:

• beginners learning ML
• devs moving into AI
• interview prep
• teaching concepts
• understanding backprop
• visual learners
• building intuition

This is the kind of resource
that makes neural networks finally “click”.

Link: https://t.co/i0k7LzGbJt

We’re moving from:

reading about ML
→ watching ML learn

That’s a big shift.

Because once you can see training,
you stop memorizing… and start understanding.

AI education just got visual.

[Roan @RohOnChain]

This 2 hour Stanford lecture will teach you more about how LLMs like ChatGPT & Claude are built than most people working at top AI companies learn in their entire careers.

Bookmark this & give 2 hours today, no matter what. It'll be the most productive thing you do this week.

[freeCodeCamp.org @freeCodeCamp]

A database is one of the key parts of many software systems.

And over time, database design has changed and evolved, from the early B-Tree structures to more modern LSM Tree options.

In this handbook, Ramesh shows you how to build an LSM Tree storage engine from scratch to help you understand how it works.

https://t.co/JrhNP56haR

[The Scientific Lens @LensScientific]

“Mathematical Thinking”

For People Who Hate Math

by Albert Rutherford

It’s designed for math-averse people, emphasizing how mathematical thinking (not just doing math) helps you see the world more clearly, manage test anxiety, and make better decisions.

A light, accessible read with practical, research-backed examples.

[Math Cafe @Riazi_Cafe_en]

Carnegie Mellon University's "Advanced Algorithms" course notes

PDF: https://t.co/a8rm1cAKXY

[Deep Psychology @DeepPsycho_HQ]

Constantly dope your mind with reading :

Read a lot of

- Human Evolution
- Physics
- Metaphysics
- Mathematics
- Psychology
- Science
- History
- Civilizations
- New Technology
- Geopolitics
- Great Biographies
- Elon Musk urgency
- Robotics & Ai

You can grow your intelligence
You can grow your intelligence
You can grow your intelligence
You can grow your intelligence
You can grow your intelligence

[freeCodeCamp.org @freeCodeCamp]

Math isn't just about numbers - it's about finding complex patterns in our world.

And there are many ways that you can apply mathematical concepts in programming, beyond what you might've learned in school.

In this guide, Tiago discusses the architecture of math – and how you can use it in your code.

https://t.co/mH2DFQBGXy

[freeCodeCamp.org @freeCodeCamp]

Video processing in the browser used to be slow or required servers.

But the WebCodecs API changes that by enabling hardware-accelerated video processing directly in JavaScript.

In this handbook, @sam_bha teaches you all about codecs, frames, muxing/demuxing, and how to build high-performance video apps.

https://t.co/bXUrLvjWqP

[Movez @0xMovez]

This 1 hour lecture on "Probability Theory" from MIT will teach you more about prediction markets than 2 month internship at at a Wall Street Quant firm.

Bookmark this & give it 1 hour today, no matter what. It’s the most productive start you can give your week. Then read post below.

[Jo Kar @i_am_gustakh]

Peak Detailing by me 😍 #Dhurandhar #Dhurandhar2

[Men of Purpose @Men_Of_Purpose]

She literally explained why you must keep going:

[freeCodeCamp.org @freeCodeCamp]

System design addresses how different parts of a system communicate, manage data, & handle requests.

And this course teaches you the key concepts by building a YouTube clone.

Along the way you'll incorporate 3 key services: upload, watch, & transcoder.

https://t.co/Afo0S4o8IB