AI Engineering has levels to it:

– Level 1: Using AI
Start by mastering the fundamentals:
-- Prompt engineering (zero-shot, few-shot, chain-of-thought)
-- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face)
-- Understanding tokens, context windows, and parameters (temperature, top-p)

With just these basics, you can already solve real problems.

– Level 2: Integrating AI
Move from using AI to building with it:
-- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus)
-- Embeddings and similarity search (cosine, Euclidean, dot product)
-- Caching and batching for cost and latency improvements
-- Agents and tool use (safe function calling, API orchestration)

This is the foundation of most modern AI products.

– Level 3: Engineering AI Systems
Level up from prototypes to production-ready systems:
-- Fine-tuning vs instruction-tuning vs RLHF (know when each applies)
-- Guardrails for safety and compliance (filters, validators, adversarial testing)
-- Multi-model architectures (LLMs + smaller specialized models)
-- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals)

Here’s where you shift from “it works” to “it works reliably.”

– Level 4: Optimizing AI at Scale
Finally, learn how to run AI systems efficiently and responsibly:
-- Distributed inference (vLLM, Ray Serve, Hugging Face TGI)
-- Managing context length and memory (chunking, summarization, attention strategies)
-- Balancing cost vs performance (open-source vs proprietary tradeoffs)
-- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR)

At this stage, you’re not just building AI—you’re designing systems that scale in the real world.

If you’re serious about learning Generative AI, stop chasing frameworks.

Start here instead....

Also, scrolling YouTube playlists or jumping into random courses doesn’t work.

You need a Ai learning roadmap with layers of learning that compound.

𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗚𝗲𝗻𝗔𝗜 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆:

𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗕𝗹𝗼𝗰𝗸𝘀
• Python (requests, APIs, JSON, environments)
• Git + Docker + Linux basics
• Databases (Postgres, SQLite)

𝟮. 𝗟𝗲𝗮𝗿𝗻 𝗛𝗼𝘄 𝗠𝗼𝗱𝗲𝗹𝘀 𝗧𝗵𝗶𝗻𝗸
• Vectors & embeddings
• Probability & tokenization
• Transformers at a high level

𝟯. 𝗣𝗹𝗮𝘆 𝘄𝗶𝘁𝗵 𝗠𝗼𝗱𝗲𝗹𝘀 𝗘𝗮𝗿𝗹𝘆 (𝗯𝘂𝘁 𝘀𝗺𝗮𝗹𝗹 𝘀𝗰𝗮𝗹𝗲)
• Hugging Face inference APIs
• OpenAI / Anthropic playgrounds
• Local models with Ollama

𝟰. 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝗥𝗔𝗚 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄
• Ingest → chunk → embed → store → retrieve → re-rank → generate
• Build this manually first (no frameworks)
• Add logging, retries, caching

𝟱. 𝗚𝗲𝘁 𝗦𝗲𝗿𝗶𝗼𝘂𝘀 𝗔𝗯𝗼𝘂𝘁 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
• Compare outputs with ground truth
• Track accuracy, latency, and cost
• Learn prompt evaluation patterns

𝟲. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀
• Handle hallucinations & toxicity
• Add redaction for PII
• Experiment with content filters

𝟳. 𝗕𝘂𝗶𝗹𝗱 𝗠𝗶𝗻𝗶-𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
• Document Q&A bot
• Structured extraction (tables/JSON)
• Summarizer with benchmarks

𝟴. 𝗠𝗼𝘃𝗲 𝗧𝗼𝘄𝗮𝗿𝗱 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗠𝗟𝗢𝗽𝘀
• CI/CD for prompts/configs
• Tracing and observability
• Cost dashboards

𝟵. 𝗢𝗻𝗹𝘆 𝗧𝗵𝗲𝗻: 𝗟𝗲𝗮𝗿𝗻 𝗔𝗴𝗲𝗻𝘁𝘀
• Start with one-tool agents
• Add memory/planning when metrics prove value

𝟭𝟬. 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 → 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
• Use LangGraph, ADK, CrewAI or LlamaIndex as orchestration layers
• Keep your core logic framework-agnostic

👉 The order matters.
👉 Learn why before how.
👉 Projects > tutorials.

That’s how you go from “copy-pasting prompts” → “engineering production-ready GenAI systems.” Show ❤️ if you find this post valuable.

Learn n8n with me:
https://whatsapp.com/channel/0029VbAeZ2SFXUuWxNVqJj22

10 AI courses every founder should take (all free):

1. AI Essentials - Harvard Introduction

2. ChatGPT Mastery - Advanced Prompting

3. Google AI Magic - Business Applications

4. Microsoft AI Basics - Enterprise Perspective

5. Prompt Engineering Pro - Technical Deep Dive.

6. Machine Learning by Harvard - Strategic Foundation

7. Language Models by LangChain - Development Framework

8. Generative AI by Microsoft - Creative Applications

9. AWS AI Foundations - Infrastructure Understanding

10. AI for Everyone - Strategic Overview

- Creadit : Matt Gray

Concisely written:
https://whatsapp.com/channel/0029Va8iIT7KbYMOIWdNVu2Q/392

The “CEOs chasing AI” meme is everywhere right now. It is usually meant to mock leaders blindly chasing hype. But the joke misses the point.

CEOs should want AI, and they should want it now. 𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝘄𝗿𝗼𝗻𝗴 𝘄𝗶𝘁𝗵 𝗻𝗼𝘁 𝘆𝗲𝘁 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗵𝗼𝘄 𝗶𝘁 𝗮𝗽𝗽𝗹𝗶𝗲𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗼𝗿 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆.

Everyone is feeling the shift:
🏃 Competitors are getting more efficient and moving faster
🤖 New players are entering with your service, just AI-powered
🌟 Opportunities once out of reach now feel possible

But knowing what AI is truly good at and what’s just empty promises is not straightforward. Our industry has not done anyone any favors. We pitch super intelligence, but fail to deliver value past flashy demos.

That is why, instead of making fun, I choose to focus on helping business leaders cut through the noise and uncover where AI truly delivers value.

🚨 100+ AI Productivity tools

AI tool teams are actually running in production.

Here’s the signal (not the noise):

1️⃣ Chatbots — It’s no longer just GPT. DeepSeek 🛑 has the dev crowd. Claude 🛑 rules long-form. Perplexity 🛑 quietly killed Google Search for researchers.

2️⃣ Coding Assistants — This category exploded. Cursor is eating share fast. GitHub Copilot is now table stakes. Niche players like Qodo and Tabnine finding loyal users.

3️⃣ Meeting Notes — The silent productivity win. Otter, Fireflies, Fathom save 5+ hours/week per person. Nobody brags about it — but everyone uses them.

4️⃣ Workflow Automation — The surprise ROI machine. Zapier just embedded AI. N8n went AI-native. Make is wiring everything. This is the real multiplier.

Biggest gap? Knowledge Management. Only Notion, Mem, Tettra in the race. Feels like India’s UPI moment waiting to happen here.

Unpopular opinion: You don’t need 100 tools. The best teams run 5–7 max — per core workflow — and win on adoption, not options.

🚀 AI Tools Every Coder Should Know in 2025

The future of coding isn’t just about writing code—it’s about augmenting human creativity with AI.

Here are some of the Ai tools you should explore 👇

💡 GitHub Copilot – Real-time AI pair programmer.
💡 Cursor – AI-powered fork of VS Code.
💡 Tabnine – Secure, private AI code completions.
💡 Amazon Q Developer – Deep AWS ecosystem integration.
💡 Claude & ChatGPT – Conversational AI coding partners.
💡 Replit Ghostwriter – AI inside the Replit IDE.
💡 Google Gemini CLI – AI help directly in your terminal.
💡 JetBrains AI Assistant – Context-aware refactoring and suggestions.
💡 Windsurf (formerly Codeium) – AI-native IDE for flow.
💡 Devin by Cognition AI – Fully autonomous AI software engineer.
💡 Codespell – AI across the entire SDLC.

AI is no longer a “good-to-have” for coders—it’s becoming the new standard toolkit. Those who adopt early will move faster, ship smarter, and stay ahead.

How big is Nvidia!

Anthropic has packed everything you need to know about building AI agents into one playlist.

And this changes how we think about automation.

20 videos.
Zero fluff.
Just builders shipping real automation.

Here’s whats covered:

➜ Building AI agents in Amazon Bedrock and Google Cloud's Vertex AI

➜ Headless browser automation with Claude Code

➜ Claude playing Pokemon (yes, really! - and the lessons from it)

➜ Best practices for production-grade Claude Code workflows

➜ MCP deep dives and Sourcegraph integration

➜ Advanced prompting techniques for agents

Automation gap is only about:
giving AI the right access
to the right information
at the right time.

📌 Bookmark the full playlist here: https://www.youtube.com/playlist?list=PLf2m23nhTg1P5BsOHUOXyQz5RhfUSSVUi

Google has just released Gemini Robotics-ER 1.5 🤖🔥

It is a vision-language model (VLM) that brings Gemini's agentic capabilities to robotics. It's designed for advanced reasoning in the physical world, allowing robots to interpret complex visual data, perform spatial reasoning, and plan actions from natural language commands.

Enhanced autonomy - Robots can reason, adapt, and respond to changes in open-ended environments.

Natural language interaction - Makes robots easier to use by enabling complex task assignments using natural language.

Task orchestration - Deconstructs natural language commands into subtasks and integrates with existing robot controllers and behaviors to complete long-horizon tasks.

Versatile capabilities - Locates and identifies objects, understands object relationships, plans grasps and trajectories, and interprets dynamic scenes.

https://ai.google.dev/gemini-api/docs/robotics-overview

AI is changing faster than ever. Every few months, new frameworks, models, and standards redefine how we build, scale, and reason with intelligence.

In 2025, understanding the language of AI is no longer optional — it’s how you stay relevant.

Here’s a structured breakdown of the terms shaping the next phase of AI systems, products, and research.

𝗖𝗼𝗿𝗲 𝗔𝗜 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀

AI still begins with its fundamentals. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘁𝗲𝗮𝗰𝗵𝗲𝘀 systems to learn from data. Deep Learning enables that learning through neural networks.
Supervised and Unsupervised Learning determine whether AI learns with or without labeled data, while Reinforcement Learning adds feedback through rewards and penalties.
And at the edge of ambition sits AGI — Artificial General Intelligence — where machines start reasoning like humans.

These are not just definitions. They form the mental model for how all intelligence is built.

𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁

Once the foundation is set, development begins. Fine-tuning reshapes pre-trained models for specific domains. Prompt Engineering optimizes inputs for better outcomes.
Concepts like Tokenization, Parameters, Weights, and Embeddings describe how models represent and adjust information.
Quantization makes them smaller and faster, while high-quality Training Data makes them useful and trustworthy.

𝗔𝗜 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲

Modern AI depends on a specialized computing stack. GPUs and TPUs provide the horsepower.
Transformers remain the dominant architecture.
New standards like MCP — the Model Context Protocol — are emerging to help models, agents, and data talk to each other seamlessly.
And APIs continue to make AI accessible from anywhere, turning isolated intelligence into connected ecosystems.

𝗔𝗜 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝗻𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀

How does AI actually think and respond?
Concepts like RAG (Retrieval-Augmented Generation) merge search and reasoning. CoT (Chain of Thought) simulates human-like logical steps.
Inference defines how models generate responses, while Context Window sets the limits of what AI can remember.

𝗔𝗜 𝗘𝘁𝗵𝗶𝗰𝘀 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆

As capabilities grow, so does the need for alignment.
AI Alignment ensures systems reflect human intent. Bias and Privacy protection build trust.
Regulation and governance ensure responsible adoption across industries.
And behind it all, the quality and transparency of Training Data continue to define fairness.

𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀

The boundaries between science fiction and software continue to blur.
Computer Vision and NLP are powering new interfaces.
Chatbots and Generative AI have redefined how we interact and create.
And newer ideas like Vibe Coding and AI Agents hint at a future where AI doesn’t just assist — it autonomously builds, executes, and learns.

Understanding them deeply will shape how we design, deploy, and scale the intelligence of tomorrow.

The well-known 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 course from 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 is coming back now for Autumn 2025. It is taught by the legendary Andrew Ng and Kian Katanforoosh, the founder of Workera, an AI agent platform.

This course has been one of the best online classes for AI since the early days of Deep Learning, and it's 𝗳𝗿𝗲𝗲𝗹𝘆 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 on YouTube. The course is updated every year to include the latest developments in AI.

4 lectures have been released as of now:

📕 Lecture 1: Introduction to Deep Learning (by Andrew)
https://www.youtube.com/watch?v=_NLHFoVNlbg

📕 Lecture 2: Supervised, Self-Supervised, & Weakly Supervised Learning (by Kian)
https://www.youtube.com/watch?v=DNCn1BpCAUY

📕 Lecture 3: Full Cycle of a DL project (by Andrew)
https://www.youtube.com/watch?v=MGqQuQEUXhk

📕 Lecture 4: Adversarial Robustness and Generative Models (by Kian)
https://www.youtube.com/watch?v=aWlRtOlacYM

📚📚📚 Happy Learning!

In 1995, people said “Programming is for nerds” and suggested I become a doctor or lawyer.

10 years later, they warned “Someone in India will take my job for $5/hr.”

Then came the “No-code revolution will replace you.”

Fast forward to 2024 and beyond:
Codex. Copilot. ChatGPT. Devin. Grok. 🤖

Every year, someone screams “Programming is dead!”

Yet here we are... and the demand for great engineers has never been higher 💼🚀

Stop listening to midwit people. Learn to build good software, and you'll be okay. 👨‍💻✅

Excellence never goes out of style!

Our WhatsApp channel “Artificial Intelligence” just crossed 1,00,000 followers. 🚀

This community started with a simple mission: democratize AI knowledge, share breakthroughs, and build the future together.

Grateful to everyone learning, experimenting, and pushing boundaries with us.

This is just the beginning.
Bigger initiatives, deeper learning, and global collaborations loading.

Stay plugged in. The future is being built here. 💡✨
Join if you haven’t yet: https://whatsapp.com/channel/0029Va8iIT7KbYMOIWdNVu2Q

Nvidia CEO Jensen Huang said China might soon pass the US in the race for artificial intelligence because it has cheaper energy, faster development, and fewer rules.

At the Financial Times Future of AI Summit, Huang said the US and UK are slowing themselves down with too many restrictions and too much negativity. He believes the West needs more confidence and support for innovation to stay ahead in AI.

He explained that while the US leads in AI chip design and software, China’s ability to build and scale faster could change who leads the global AI race. China’s speed and government support make it a serious competitor.

Huang’s warning shows that the AI race is not just about technology, but also about how nations manage energy, costs, and policies. The outcome could shape the world’s tech future.

Source: Financial Times

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗜𝘀 𝗔𝗿𝗿𝗶𝘃𝗶𝗻𝗴... 𝗖𝗵𝗶𝗻𝗮 𝘂𝗻𝘃𝗲𝗶𝗹𝘀 𝗗𝗼𝗰𝘁𝗼𝗿𝗹𝗲𝘀𝘀 𝗔𝗜 𝗞𝗶𝗼𝘀𝗸𝘀

In China, AI-powered health kiosks are redefining what “accessible healthcare” means. These doctorless, fully automated booths can:
✅ Scan vital signs and perform basic medical tests
✅ Diagnose common illnesses using advanced AI algorithms
✅ Dispense over-the-counter medicines instantly
✅ Refer patients to hospitals when needed

Deployed in metro stations, malls and rural areas, these kiosks bring 24/7 care to millions, especially in regions with limited access to physicians. Each unit includes sensors, cameras and automated dispensers for over-the-counter medicines. Patients step inside, input symptoms and receive instant prenoscriptions or referrals to hospitals if needed.

This is not a futuristic concept — it’s happening now.

I believe AI will be the next great equalizer in healthcare, enabling early intervention, smarter diagnostics and patient-first innovation at scale.

From Data Science to GenAI: A Roadmap Every Aspiring ML/GenAI Engineer Should Follow
Most freshers jump straight into ChatGPT and LangChain tutorials. That’s the biggest mistake.
If you want to build a real career in AI, start with the core engineering foundations — and climb your way up to Generative AI systematically.

Starting TIP: Don't use sklearn, only use pandas and numpy

Here’s how:

1. Start with Core Programming Concepts
Learn OOPs properly — classes, inheritance, encapsulation, interfaces.
Understand data structures — lists, dicts, heaps, graphs, and when to use each.
Write clean, modular, testable code. Every ML system you build later will rely on this discipline.

2. Master Data Handling with NumPy and pandas
Create data preprocessing pipelines using only these two libraries.
Handle missing values, outliers, and normalization manually — no scikit-learn shortcuts.
Learn vectorization and broadcasting; it’ll make you faster and efficient when data scales.

3. Move to Statistical Thinking & Machine Learning
Learn basic probability, sampling, and hypothesis testing.
Build regression, classification, and clustering models from scratch.
Understand evaluation metrics — accuracy, precision, recall, AUC, RMSE — and when to use each.
Study model bias-variance trade-offs, feature selection, and regularization.
Get comfortable with how training, validation, and test splits affect performance.

4. Advance into Generative AI
Once you can explain why a linear model works, you’re ready to understand how a transformer thinks.
Key areas to study:
Tokenization: Learn Byte Pair Encoding (BPE) — how words are broken into subwords for model efficiency.
Embeddings: How meaning is represented numerically and used for similarity and retrieval.
Attention Mechanism: How models decide which words to focus on when generating text.
Transformer Architecture: Multi-head attention, feed-forward layers, layer normalization, residual connections.
Pretraining & Fine-tuning: Understand masked language modeling, causal modeling, and instruction tuning.
Evaluation of LLMs: Perplexity, factual consistency, hallucination rate, and reasoning accuracy.
Retrieval-Augmented Generation (RAG): How to connect external knowledge to improve contextual accuracy.

You don’t need to “learn everything” — you need to build from fundamentals upward.
When you can connect statistics to systems to semantics, you’re no longer a learner — you’re an engineer who can reason with models.

OpenAI just dropped 11 free prompt courses.

It's for every level (I added the links too):

✦ Introduction to Prompt Engineering
↳ https://academy.openai.com/public/videos/introduction-to-prompt-engineering-2025-02-13

✦ Advanced Prompt Engineering
↳ https://academy.openai.com/public/videos/advanced-prompt-engineering-2025-02-13

✦ ChatGPT 101: A Guide to Your AI Super Assistant
↳ https://academy.openai.com/public/videos/chatgpt-101-a-guide-to-your-ai-superassistant-recording

✦ ChatGPT Projects
↳ https://academy.openai.com/public/videos/chatgpt-projects-2025-02-13

✦ ChatGPT & Reasoning
↳ https://academy.openai.com/public/videos/chatgpt-and-reasoning-2025-02-13

✦ Multimodality Explained
↳ https://academy.openai.com/public/videos/multimodality-explained-2025-02-13

✦ ChatGPT Search
↳ https://academy.openai.com/public/videos/chatgpt-search-2025-02-13

✦ OpenAI, LLMs & ChatGPT
↳ https://academy.openai.com/public/videos/openai-llms-and-chatgpt-2025-02-13

✦ Introduction to GPTs
↳ https://academy.openai.com/public/videos/introduction-to-gpts-2025-02-13

✦ ChatGPT for Data Analysis
↳ https://academy.openai.com/public/videos/chatgpt-for-data-analysis-2025-02-13

✦ Deep Research
↳ https://academy.openai.com/public/videos/deep-research-2025-03-11

ChatGPT went from 0 to 800 million users in 3 years. And I'm convinced less than 1% master it.

It's your opportunity to be ahead, today.

𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐨𝐥𝐚𝐛 𝐦𝐞𝐞𝐭𝐬 𝐕𝐒 𝐂𝐨𝐝𝐞

Google just now released Google Colab extension for VS Code IDE.

First, VS Code is one of the world's most popular and beloved code editors. VS Code is fast, lightweight, and infinitely adaptable.

Second, Colab has become the go-to platform for millions of AI/ML developers, students, and researchers, across the world.

The new Colab VS Code extension combines the strengths of both platforms

𝐅𝐨𝐫 𝐂𝐨𝐥𝐚𝐛 𝐔𝐬𝐞𝐫𝐬: This extension bridges the gap between simple to provision Colab runtimes and the prolific VS Code editor.

🚀 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐂𝐨𝐥𝐚𝐛 𝐄𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧

✅ 𝐈𝐧𝐬𝐭𝐚𝐥𝐥 𝐭𝐡𝐞 𝐂𝐨𝐥𝐚𝐛 𝐄𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧 : In VS Code, open the Extensions view from the Activity Bar on the left (or press [Ctrl|Cmd]+Shift+X). Search the marketplace for Google Colab. Click Install on the official Colab extension.

☑️ 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐭𝐨 𝐚 𝐂𝐨𝐥𝐚𝐛 𝐑𝐮𝐧𝐭𝐢𝐦𝐞 : Create or open any .ipynb notebook file in your local workspace and Click Colab and then select your desired runtime, sign in with your Google account, and you're all set!

AI research is exploding 🔥— thousands of new papers every month. But these 9 built the foundation.

Most developers jump straight into LLMs without understanding the foundational breakthroughs.

Here's your reading roadmap ↓

1️⃣ 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐖𝐨𝐫𝐝 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐩𝐚𝐜𝐞 (𝟐𝟎𝟏𝟑)
Where it all began.
Introduced word2vec and semantic word understanding.
→ Made "king - man + woman = queen" math possible
→ 70K+ citations, still used everywhere today
🔗 https://arxiv.org/abs/1301.3781

2️⃣ 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐈𝐬 𝐀𝐥𝐥 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 (𝟐𝟎𝟏𝟕)
Killed RNNs. Created the Transformer architecture.
→ Every major LLM uses this foundation
🔗 https://arxiv.org/pdf/1706.03762

3️⃣ 𝐁𝐄𝐑𝐓 (𝟐𝟎𝟏𝟖)
Stepping stone on Transformer architecture. Introduced bidirectional pretraining for deep language understanding.
→ Looks left AND right to understand meaning
🔗 https://arxiv.org/pdf/1810.04805

4️⃣ 𝐆𝐏𝐓 (𝟐𝟎𝟏𝟖)
Unsupervised pretraining + supervised fine-tuning.
→ Started the entire GPT revolution
🔗 https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf

5️⃣ 𝐂𝐡𝐚𝐢𝐧-𝐨𝐟-𝐓𝐡𝐨𝐮𝐠𝐡𝐭 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 (𝟐𝟎𝟐𝟐)
"Think step by step" = 3x better reasoning
🔗 https://arxiv.org/pdf/2201.11903

6️⃣ 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐋𝐚𝐰𝐬 𝐟𝐨𝐫 𝐍𝐞𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝟐𝟎𝟐𝟎)
Math behind "bigger = better"
→ Predictable power laws guide AI investment
🔗 https://arxiv.org/pdf/2001.08361

7️⃣ 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐭𝐨 𝐒𝐮𝐦𝐦𝐚𝐫𝐢𝐳𝐞 𝐰𝐢𝐭𝐡 𝐇𝐮𝐦𝐚𝐧 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 (𝟐𝟎𝟐𝟎)
Introduced RLHF - the secret behind ChatGPT's helpfulness
🔗 https://arxiv.org/pdf/2009.01325

8️⃣ 𝐋𝐨𝐑𝐀 (𝟐𝟎𝟐𝟏)
Fine-tune 175B models by training 0.01% of weights
→ Made LLM customization affordable for everyone
🔗 https://arxiv.org/pdf/2106.09685

9️⃣ 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝟐𝟎𝟐𝟎)
Original RAG paper - combines retrieval with generation
→ Foundation of every knowledge-grounded AI system
🔗 https://arxiv.org/abs/2005.11401