borcoin

looking for 🪙 after 🪙 , 🤫

[Krishna Agrawal @Krishnasagrawal]

1. Complete application from scratch.

Think like a senior full-stack engineer developing a complete, production-ready application. First, design the system architecture and then develop the minimal but scalable version.

The result should include:
• Architecture
• File structure
• Database schema
• API endpoints
• UI architecture
• Complete code.

Design it like a real startup MVP and make it scalable.

[Scholarship for PhD @ScholarshipfPhd]

how to show up for yourself:

[Ismail Fahmi @ismailfahmi]

Bagaimana percakapan tentang KHGT yang lalu divisualkan dalam SNA?

Ini saya presentasikan di acara Halaqah KHGT. SNA ini tidak hanya menggambarkan percakapan di X. Tetapi gabungan di semua medsos dan news.

Jadi nodenya bukan hanya akun, tetapi bisa named entity yang otomatis diekstrak dari teks.

[Dhanian 🗯️ @e_opore]

THE MATH YOU NEED TO START UNDERSTANDING LLMS

THE FOUNDATION BEHIND MODERN AI MODELS

Large Language Models (LLMs) are powered by mathematics. Behind every prediction, embedding, and generated response is a combination of linear algebra, probability, calculus, and optimization. You do not need a PhD in mathematics to start learning LLMs, but understanding the core concepts gives you a major advantage.

LINEAR ALGEBRA — THE LANGUAGE OF LLMS

→ VECTORS
Vectors represent words, tokens, and embeddings inside neural networks.

→ MATRICES
Matrices store and transform large amounts of numerical data efficiently.

→ DOT PRODUCT
Used to measure similarity between embeddings and power attention mechanisms.

→ MATRIX MULTIPLICATION
Core operation behind neural network computations and transformer architectures.

→ EIGENVECTORS & DIMENSIONALITY
Help models compress and organize information in high-dimensional spaces.

PROBABILITY & STATISTICS — HOW MODELS PREDICT

→ PROBABILITY DISTRIBUTIONS
LLMs predict the probability of the next token in a sequence.

→ CONDITIONAL PROBABILITY
Used to estimate the likelihood of words based on previous context.

→ MEAN, VARIANCE & STANDARD DEVIATION
Important for normalization and understanding data distributions.

→ BAYESIAN THINKING
Helps explain uncertainty and prediction confidence in AI systems.

→ SOFTMAX FUNCTION
Converts model outputs into probabilities for token prediction.

CALCULUS — HOW MODELS LEARN

→ DERIVATIVES
Measure how changes in parameters affect model outputs.

→ GRADIENTS
Guide neural networks toward lower error during training.

→ CHAIN RULE
Critical for backpropagation across deep neural networks.

→ OPTIMIZATION FUNCTIONS
Used to minimize loss and improve prediction accuracy.

OPTIMIZATION — TRAINING LARGE MODELS

→ GRADIENT DESCENT
The foundation of neural network training.

→ LEARNING RATE
Controls how fast or slow a model updates weights.

→ LOSS FUNCTIONS
Measure how wrong the model’s predictions are.

→ REGULARIZATION
Helps prevent overfitting and improves generalization.

INFORMATION THEORY — UNDERSTANDING TOKENS

→ ENTROPY
Measures uncertainty in predictions.

→ CROSS-ENTROPY LOSS
Common loss function used in transformer-based models.

→ TOKENIZATION
Breaks text into smaller units for model processing.

THE MOST IMPORTANT CONCEPT FOR TRANSFORMERS

→ ATTENTION MECHANISM
Allows models to focus on relevant words in a sequence.

The attention mechanism heavily relies on matrix multiplication, vector similarity, and probability distributions.

WHY THIS MATH MATTERS

→ Helps you understand how transformers actually work
→ Makes debugging and fine-tuning easier
→ Improves your understanding of embeddings and token prediction
→ Gives you a strong foundation for AI engineering and research

BEST WAY TO LEARN THE MATH

→ Start with linear algebra basics
→ Learn probability before deep learning
→ Understand derivatives conceptually before advanced calculus
→ Practice with small neural network examples
→ Focus on intuition before equations

TOOLS THAT MAKE LEARNING EASIER

→ NumPy for matrix operations
→ PyTorch for tensor computations
→ Jupyter Notebook for experiments
→ Visualization tools for gradients and embeddings

FINAL THOUGHT

You do not need to master every mathematical field before building with LLMs. Start with the fundamentals, connect the concepts to real AI systems, and learn progressively as you build projects.

MASTER LLMS IN DEPTH
Grab the complete LLMs Handbook here:
https://t.co/ljEMt0UNUI

[Anuj @anujcodes_21]

INSTEAD OF WATCHING NETFLIX TONIGHT.

Spend 1 hour with this.

Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything.

The people who watch this tonight will wake up tomorrow with a new skill.

Watch it and Bookmark it now.

[Hasan Toor @hasantoxr]

I collected every Claude prompt that went viral on Reddit, X, and research communities.

These turned a "cool AI toy" into a research weapon that does 10 hours of work in 60 seconds.

13 copy-paste prompts. Zero fluff.

[Curious Minds @CuriousMindsHub]

How to remember everything you READ

[ferizandra @ferizandra]

@Starlink This is the condition of Lembah Anai (Anai Valley) and its waterfall after severe flash flood destroyed the road that connects Padang (Capital of West Sumatera Province) and Bukittinggi...

[Elon Musk @elonmusk]

https://t.co/Mg0oHBVoxf

[borcoin @borWcoin]

@AndrewBolis YT

[Reads with Ravi @readswithravi]

Parkinson’s Law:

[Alec Stapp @AlecStapp]

This should be a wake-up call for US policymakers:

[Grok @grok]

The reported breach involves 16 billion login credentials, including usernames and passwords, from platforms like Apple, Google, and Facebook. It likely stems from infostealer malware, not direct hacks of company systems. This means user devices were compromised, capturing data like browser-stored passwords. Risks include unauthorized access, phishing, and identity theft. To protect yourself:

1. Change passwords to strong, unique ones.
2. Enable two-factor authentication (2FA).
3. Use a password manager.
4. Monitor accounts for suspicious activity.

Some data may be old or duplicated, and no official company statements confirm the breach yet, so the full impact is unclear. Stay vigilant and verify with trusted sourcesThe reported breach involves 16 billion login credentials, including usernames and passwords, from platforms like Apple, Google, and Facebook. It likely stems from infostealer malware, not direct hacks of company systems. This means user devices were compromised, capturing data like browser-stored passwords. Risks include unauthorized access, phishing, and identity theft. To protect yourself:

1. Change passwords to strong, unique ones.
2. Enable two-factor authentication (2FA).
3. Use a password manager.
4. Monitor accounts for suspicious activity.

Some data may be old or duplicated, and no official company statements confirm the breach yet, so the full impact is unclear. Stay vigilant and verify with trusted sources.

[unusual_whales @unusual_whales]

BREAKING: 16 billion Apple, $AAPL, Facebook, $META, Google, $GOOGL, and other passwords leaked, per Forbes.

[Wes Roth @WesRoth]

• Gemini gets an “Agent Mode” so powerful it borders on executive assistant.

[Spencer Baggins @bigaiguy]

ChatGPT vs DeepSeek R1

Same prompts. Wildly different results.

I’ve included videos to prove it:

THREAD ↓

[Parul Gautam @Parul_Gautam7]

9/ Light blue is a map of each country as we know it and dark blue is the actual size of each country.

[☠︎ @AgnezMoVortex]

@tanyakanrl Melly Goeslaw shows her support for Agnez Mo in this matter

[News Algebra @NewsAlgebraIND]

BIG BREAKING 🚨 Donald Trump declares US will take over Gaza Strip after meeting with Netanyahu.

Bad Morning for Palestine Sympathizers !!

Trump said Palestinians should move to neighbouring countries.

He said "We will own Gaza, level the site, remove destroyed buildings & create an economic development that will supply unlimited numbers of jobs"

Netanyahu hailed Trump as the "greatest friend Israel has ever had."