Faizan Ali

Mia San Mia ~ Science and Technology enthusiast ~ Football Fan ~ World can be made better with science...

[Roan @RohOnChain]

My friend applied to 200 tech jobs in two years. No MIT. No Stanford.

Last month Anthropic offered him $750,000.

I asked him how he broke in from zero.

He sent me the exact video that got him in. A 4-hour course on mastering Claude Code.

I watched it last night.

Halfway through, I realized I've been using Claude Code completely wrong for a year.

Bookmark this and read the article below.

• 00:00 - Claude Code setup
• 49:34 - building apps with Claude Code
• 2:07:52 - prompting Claude Code
• 2:46:16 - Claude Code for production

[Imam Reza Shrine @ImamRezaEN]

A world from which Imam Hussain (as) departs thirsty is a worthless world.

[Param @ParamSiddh]

AI-ML roadmap from scratch

https://t.co/NKYRoxAs94

[ESPN FC @ESPNFC]

A departure from this World Cup that may be overlooked: Granit Xhaka, one of the last remaining members of a Switzerland golden generation that once featured Xherdan Shaqiri, Yann Sommer, Stephan Lichtsteiner, Fabian Schär and more.

In his fourth World Cup, Xhaka reached the furthest stage of his career. He’ll be 38 by 2030, so there’s a chance we may have seen him on this stage for the final time.

Salute to a Swiss football icon 🫡

[Alok Kumar @Alokkumarzz]

Bill Ackman literally gave a 44-minute masterclass that explains money better than any business school.

[Dan Kornas @DanKornas]

Awesome Artificial Intelligence

A curated list of Artificial Intelligence courses, books, video lectures, competitions, AI newsletters, Free books, and papers.

[Praveen Kumar Verma @Alacritic_Super]

MIT's Future of AI course is one of the best free, non-technical introductions to modern AI, covering the evolution from classical machine learning to foundation models and self-supervised learning.

📚 Course
https://t.co/7DtMhrfjst

What you will learn:
🧠 History of AI
📊 Supervised Learning
🎯 Reinforcement Learning
🔍 Self-Supervised Learning
🏗️ Foundation Models
🤖 Generative AI
💬 Large Language Models (LLMs)
🎨 Diffusion Models
🖼️ Image Generation
⚖️ AI in Science & Business
🚀 Future Directions of AI

📺 Lecture Videos
Foundation Models & Generative AI — https://t.co/LFchrSyH6d

Self-Supervised Learning & Foundation Models — https://t.co/7oN02c28zA

[freeCodeCamp.org @freeCodeCamp]

AI agents can do more than send one prompt to one model.

In this course, Mumshad teaches you the basics of LLMs, workflows, tools, and agent loops.

You'll build agent personalities, use structured JSON outputs, add guardrails and human approvals, and more.

https://t.co/6b7BvWMzch

[freeCodeCamp.org @freeCodeCamp]

There are many fields in which AI can streamline processes and automate tedious tasks.

And one area with many applications for AI tools and technologies is agriculture.

In this book, Vahe details how AI can help increase crop yields, use water more efficiently, predict and react to stormy weather, and more.

https://t.co/0HswPOr3iW

[Praveen Kumar Verma @Alacritic_Super]

Want to master Natural Language Processing (NLP)?

Start with Stanford CS224d/CS224N, one of the most influential NLP courses ever created.

📚 Course
• CS224N: Natural Language Processing with Deep Learning
https://t.co/uB1DbrM5MS

• CS224d (Archived Syllabus)
https://t.co/zCH6KN7iRj

🎥 Lecture Videos
• Stanford CS224N (YouTube Playlist)
https://t.co/DgtIgukLje

📖 You will learn
• Word Embeddings (Word2Vec, GloVe)
• Neural Networks & Backpropagation
• Dependency Parsing
• RNNs, LSTMs & GRUs
• Language Models
• Seq2Seq & Machine Translation
• Attention Mechanisms
• Transformers
• BERT & GPT foundations
• Question Answering
• Coreference Resolution
• Large Language Models (LLMs)

📚 Prerequisites
• Linear Algebra
• Probability & Statistics
• Calculus
• Python
• PyTorch
• Machine Learning Basics

[Krishna Agrawal @Krishnasagrawal]

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.

[CS50 @cs50]

Join us today for Chapter 6 of Introduction to Artificial Intelligence with Brian Yu. Learn how AI can generate text, images, video, and more.

https://t.co/OOmhc7BRib

[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.

[freeCodeCamp.org @freeCodeCamp]

Calculus is a powerful tool for understanding change, motion, and growth - and it's good to know as a dev.

In this College Calculus course, Ed teaches you the key concepts through the lens of Python.

You'll learn about limits, derivative rules, slope interpretation, various theorems, and how to apply symbolic math libraries like SymPy for graphing & computation.

https://t.co/KGvwu3EQwO

[freeCodeCamp.org @freeCodeCamp]

A lot of professional work still gets lost to repetitive tasks like searching docs, writing slides, and so on.

In this article, @manishmshiva shares some AI productivity tools that can help.

You'll learn how Notion AI, Glean, QuillBot, and others (plus some open-source alternatives) fit into modern workflows.

https://t.co/1WWM5e5wDt

[freeCodeCamp.org @freeCodeCamp]

If you want to learn how to build & deploy ML models, this course is for you.

It covers Exploratory Data Analysis, Feature Engineering, Model Validation, & MLOps integration.

By the end you'll be able to manage complex data projects & deploy your models.

https://t.co/ljX0ZUwvMc

[Dan Kornas @DanKornas]

Stop learning applied AI from random tutorial tabs.

AI Engineering Academy is an open-source learning platform for applied AI builders who want a structured path instead of scattered resources.

It helps you move from fundamentals to implementation by organizing the material into tracks for prompt engineering, RAG, LLM fine-tuning, deployment, agents, and projects.

Key features:

• Structured learning paths – starts from fundamentals and moves into advanced applied AI topics
• Prompt engineering track – includes basic prompting, advanced prompting, function calling, and hands-on notebooks
• Deep RAG section – covers RAG from scratch, evaluation, observability, hybrid search, Graph RAG, Vision RAG, and CAG
• LLM + deployment material – includes fine-tuning notebooks, quantization, and LLM-to-production notes
• Agent and project tracks – includes agent patterns, MCP basics, multi-document agents, and YouTube clone projects

It’s open-source (MIT license).

Link in the reply 👇

[CS50 @cs50]

Join us today for Chapters 2 and 3 of Introduction to Artificial Intelligence with Brian Yu. Learn how AI can make predictions and analyze data for you.

https://t.co/StfDP47sOj | https://t.co/GvIMdSNotB

[freeCodeCamp.org @freeCodeCamp]

If you want to improve your programming skills, try comparing and contrasting similar code.

In this in-depth guide, Evaristo walks you through five different versions of a Rock, Paper, Scissors game in JavaScript.

You'll use System Block Diagrams to analyze each project, dissect their methodologies, and learn how to build mental models.

https://t.co/J7Ii0pLqs6

[Dan Kornas @DanKornas]

RAG apps get messy when retrieval is split across vectors, graphs, text, and agents.

ApeRAG is an open-source RAG platform for builders creating knowledge-base apps with GraphRAG, vector search, full-text search, multimodal indexing, and AI agents.

It helps you turn documents into a queryable AI system by combining multiple index types with MCP access, a web/API interface, and Docker/Kubernetes deployment paths.

Key features:

• Hybrid retrieval – combines Graph RAG, vector, full-text, summary, and vision search
• Multimodal indexing – supports document processing plus images, charts, and visual content
• Agent + MCP support – lets assistants browse collections, run hybrid search, and query a knowledge base
• MinerU parsing option – adds parsing for complex documents, tables, formulas, and scientific content
• Deployment paths – Docker Compose quick start plus Helm/Kubernetes deployment docs

It’s open-source under the Apache License 2.0.

Link in the reply 👇