Oops, No Victims: The Largest Supply Chain Attack Stole 5 Cents

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The Biggest NPM Supply Chain Attack What is a Supply Chain Attack? A supply chain attack occurs when attackers target trusted third-party components, such as libraries or registries, instead of attacking users directly. By injecting malicious code at the source, they can spread it to all downstream users. These attacks are dangerous because updates happen automatically in build pipelines, making detection harder. A small modification in a common dependency can silently compromise thousands of projects. Defenses require strong authentication, artifact signing, reproducible builds, and active monitoring of supply chain integrity. Introduction On September 8, 2025, the npm ecosystem faced one of its largest compromises. A maintainer’s account was hijacked, and malicious versions of popular packages were published. Since npm packages are used globally in countless projects, the exposure was immediate and severe. Although the financial damage was limited, the operational dis...
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Retrieval Augmented Generation

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Mastering RAG: The Future of Fact-Aware AI 

“An AI that thinks like L, but researches like Sherlock.”
– That’s the power of RAG.

What is RAG?

RAG stands for Retrieval-Augmented Generation — an advanced AI technique that combines a retriever (like a search engine) with a generator (like GPT).

Traditional AI only replies based on training. RAG goes further — it retrieves live facts from external documents before generating an answer.

Why RAG Was Introduced

Most LLMs are like students who read thousands of books last year — but can’t learn new things today. RAG fixes this by:

  • Reducing hallucinations (wrong facts)
  • Adding real-time knowledge to AI responses
  • Combining search + generation into one smart process

What RAG Replaces

Before RAG, you needed to:

  • Manually feed facts into prompts
  • Use Google + ChatGPT separately
  • Retrain your models constantly

RAG solves this with one pipeline that searches, reads, and generates together.

RAG Workflow: Step by Step

Let’s say you ask: “What are the risks of cloud storage?”

  1. Input: Your question is received
  2. Embedding: It is converted into numbers that represent meaning
  3. Search: Those numbers are used to search a vector database
  4. Retrieve: The most relevant documents are selected
  5. Generate: The AI reads those and creates a meaningful answer


Key Concepts Made Simple

🔹 Text Embeddings

A way to convert sentences into numbers. Similar meanings get similar numbers. It’s like mapping ideas into a digital space.

🔹 Vector Database

A searchable storage that uses embeddings. It finds the most relevant documents by meaning, not just keywords.

Popular tools: FAISS, Pinecone, Chroma

🔹 Retriever + Generator

The retriever finds the right data. The generator uses it to write a human-like answer. Combined, they become a super-smart assistant.


Where RAG Shines

  • Company-specific AI assistants
  • Chatbots that answer using your documents
  • Research tools with up-to-date knowledge
  • Educational tutors powered by personal notes

Final Thoughts

RAG is the future of intelligent AI. It combines knowledge, research, and writing into one smooth process.

If you're building next-gen AI, don’t just use memory — teach it to read and respond like RAG does.

🔗 Stay Tuned!
Next, I’ll post a full code walkthrough of RAG using LangChain + FAISS + GPT.
Let’s keep learning like ( Death Note ) L and deducing like Sherlock! 

Location: Kanchipuram, Tamil Nadu 631502, India

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