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
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
❤16
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
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
❤21🔥4💯1
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.
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.
❤10👍7🔥3💯2
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!
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!
❤45👍4🔥1
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!
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!
❤44👍13🔥6💯5
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
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
❤20🔥2👍1
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
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
❤24💯6👍2
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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 prescriptions 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.
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 prescriptions 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.
❤23👍2🔥2
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.
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.
❤25💯3🔥2
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.
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.
OpenAI Academy
Introduction to Prompt Engineering - Video | OpenAI Academy
Unlock the new opportunities of the AI era by equipping yourself with the knowledge and skills to harness artificial intelligence effectively.
1❤21🔥6👍3💯3
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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!
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!
❤30🔥4👍3💯2
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
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
arXiv.org
Efficient Estimation of Word Representations in Vector Space
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity...
❤27💯4
Synthetic Image Detection using Gradient Fields 💡
A simple luminance-gradient PCA analysis reveals a consistent separation between real photographs and diffusion-generated images.
Real images produce coherent gradient fields tied to physical lighting and sensor characteristics, while diffusion samples show unstable high-frequency structures from the denoising process.
By converting RGB to luminance, computing spatial gradients, flattening them into a matrix, and evaluating the covariance through PCA, the difference becomes visible in a single projection.
This provides a lightweight and interpretable way to assess image authenticity without relying on metadata or classifier models.
A simple luminance-gradient PCA analysis reveals a consistent separation between real photographs and diffusion-generated images.
Real images produce coherent gradient fields tied to physical lighting and sensor characteristics, while diffusion samples show unstable high-frequency structures from the denoising process.
By converting RGB to luminance, computing spatial gradients, flattening them into a matrix, and evaluating the covariance through PCA, the difference becomes visible in a single projection.
This provides a lightweight and interpretable way to assess image authenticity without relying on metadata or classifier models.
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🚗 If ML Algorithms Were Cars…
🚙 Linear Regression — Maruti 800
Simple, reliable, gets you from A to B.
Struggles on curves, but hey… classic.
🚕 Logistic Regression — Auto-rickshaw
Only two states: yes/no, 0/1, go/stop.
Efficient, but not built for complex roads.
🚐 Decision Tree — Old School Jeep
Takes sharp turns at every split.
Fun, but flips easily. 😅
🚜 Random Forest — Tractor Convoy
A lot of vehicles working together.
Slow individually, powerful as a group.
🏎 SVM — Ferrari
Elegant, fast, and only useful when the road (data) is perfectly separated.
Otherwise… good luck.
🚘 KNN — School Bus
Just follows the nearest kids and stops where they stop.
Zero intelligence, full blind faith.
🚛 Naive Bayes — Delivery Van
Simple, fast, predictable.
Surprisingly efficient despite assumptions that make no sense.
🚗💨 Neural Network — Tesla
Lots of hidden features, runs on massive power.
Even mechanics (developers) can't fully explain how it works.
🚀 Deep Learning — SpaceX Rocket
Needs crazy fuel, insane computing power, and one wrong parameter = explosion.
But when it works… mind-blowing.
🏎💥 Gradient Boosting — Formula 1 Car
Tiny improvements stacked until it becomes a monster.
Warning: overheats (overfits) if not tuned properly.
🤖 Reinforcement Learning — Self-Driving Car
Learns by trial and error.
Sometimes brilliant… sometimes crashes into a wall.
🚙 Linear Regression — Maruti 800
Simple, reliable, gets you from A to B.
Struggles on curves, but hey… classic.
🚕 Logistic Regression — Auto-rickshaw
Only two states: yes/no, 0/1, go/stop.
Efficient, but not built for complex roads.
🚐 Decision Tree — Old School Jeep
Takes sharp turns at every split.
Fun, but flips easily. 😅
🚜 Random Forest — Tractor Convoy
A lot of vehicles working together.
Slow individually, powerful as a group.
🏎 SVM — Ferrari
Elegant, fast, and only useful when the road (data) is perfectly separated.
Otherwise… good luck.
🚘 KNN — School Bus
Just follows the nearest kids and stops where they stop.
Zero intelligence, full blind faith.
🚛 Naive Bayes — Delivery Van
Simple, fast, predictable.
Surprisingly efficient despite assumptions that make no sense.
🚗💨 Neural Network — Tesla
Lots of hidden features, runs on massive power.
Even mechanics (developers) can't fully explain how it works.
🚀 Deep Learning — SpaceX Rocket
Needs crazy fuel, insane computing power, and one wrong parameter = explosion.
But when it works… mind-blowing.
🏎💥 Gradient Boosting — Formula 1 Car
Tiny improvements stacked until it becomes a monster.
Warning: overheats (overfits) if not tuned properly.
🤖 Reinforcement Learning — Self-Driving Car
Learns by trial and error.
Sometimes brilliant… sometimes crashes into a wall.
❤31🔥4👍3
The best fine-tuning guide you'll find on arXiv this year.
Covers:
> NLP basics
> PEFT/LoRA/QLoRA techniques
> Mixture of Experts
> Seven-stage fine-tuning pipeline
Source: https://arxiv.org/pdf/2408.13296v1
Covers:
> NLP basics
> PEFT/LoRA/QLoRA techniques
> Mixture of Experts
> Seven-stage fine-tuning pipeline
Source: https://arxiv.org/pdf/2408.13296v1
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Prototype to Production.pdf
7.7 MB
From AI Agent Prototype to Production — One PDF covers everything.
If you’re building *AI agents* and wondering how to take them from demo to real-world deployment, this is gold.
It explains, in simple terms:
• How to deploy AI agents safely
• How to scale them for enterprise use
• CI/CD, observability & trust in production
• Real challenges of moving from prototype → production
• Agent-to-Agent (A2A) interoperability
Perfect for AI/ML engineers, DevOps teams and architects working on serious AI systems.
📄 Read here: https://www.kaggle.com/whitepaper-prototype-to-production
Sharing this because production-ready AI is where real value is created 💡
If you’re building *AI agents* and wondering how to take them from demo to real-world deployment, this is gold.
It explains, in simple terms:
• How to deploy AI agents safely
• How to scale them for enterprise use
• CI/CD, observability & trust in production
• Real challenges of moving from prototype → production
• Agent-to-Agent (A2A) interoperability
Perfect for AI/ML engineers, DevOps teams and architects working on serious AI systems.
📄 Read here: https://www.kaggle.com/whitepaper-prototype-to-production
Sharing this because production-ready AI is where real value is created 💡
❤6🔥2
🚀 If you’re entering an AI career right now, here’s the truth:
It’s not about learning “everything.”
It’s about learning the right technical foundations — the ones the industry actually uses.
These are the core skills that will matter for the next 5–10 years, no matter how fast AI evolves 👇
1️⃣ Learn how modern LLMs actually work
You don’t need to know the math behind transformers,
but you must understand:
• tokens & embeddings
• context windows
• attention
• prompting vs reasoning
• fine-tuning vs RAG
• when models hallucinate (and why)
If you don’t know how the engine works, you can’t drive it well.
2️⃣ Learn Retrieval — the real backbone of enterprise AI
Most AI applications in companies rely on RAG, not fine-tuning.
Focus on:
• chunking strategies
• embedding models
• hybrid retrieval (dense + sparse)
• vector databases
• knowledge graphs
• context filtering
• evaluation of retrieved docs
If you master retrieval, you instantly become valuable.
3️⃣ Learn how to evaluate AI systems, not just build them
Engineers build models.
Professionals who can evaluate them are the ones who get promoted.
Learn to measure:
• grounding accuracy
• relevance
• completeness
• tool-use correctness
• consistency across runs
• latency
• safety
This is where the real skill gap is.
4️⃣ Learn prompting as an engineering discipline
Not “try random prompts.”
But systematic methods like:
• template prompts
• tool-calling prompts
• guardrail prompts
• chain-of-thought
• reflection prompts
• constraint-based prompting
Prompting is becoming the new API design.
5️⃣ Learn how to build agentic workflows
AI is moving from answers → decisions → actions.
You should know:
• planner → executor → verifier agent structure
• tool routing
• action space design
• human-in-the-loop workflows
• permissioning
• error recovery loops
This is what separates beginners from real AI engineers.
6️⃣ Learn Python + APIs deeply
You don’t need to be a software engineer,
but you must be comfortable with:
• Python basics
• API calls
• JSON
• LangChain / LlamaIndex / DSPy
• building small scripts
• reading logs
• debugging AI pipelines
This is the “plumbing” behind AI systems.
7️⃣ Build real projects, not toy demos
Instead of “build a chatbot,” build:
• a support email classifier
• a RAG system on company policies
• a customer insights extractor
• an automatic meeting summarizer
• a multimodal analyzer (text + image)
• an internal tool-calling agent
Projects that solve real problems get you hired.
8️⃣ Learn one domain deeply
AI generalists struggle.
AI + domain experts win.
Choose one:
• finance
• healthcare
• retail
• manufacturing
• real estate
• cybersecurity
• operations
• supply chain
• HR tech
AI skill + domain depth = career acceleration.
If you’re entering AI today:
Focus on retrieval, reasoning, evaluation, agents, and real projects.
These are the skills companies are desperate for.
It’s not about learning “everything.”
It’s about learning the right technical foundations — the ones the industry actually uses.
These are the core skills that will matter for the next 5–10 years, no matter how fast AI evolves 👇
1️⃣ Learn how modern LLMs actually work
You don’t need to know the math behind transformers,
but you must understand:
• tokens & embeddings
• context windows
• attention
• prompting vs reasoning
• fine-tuning vs RAG
• when models hallucinate (and why)
If you don’t know how the engine works, you can’t drive it well.
2️⃣ Learn Retrieval — the real backbone of enterprise AI
Most AI applications in companies rely on RAG, not fine-tuning.
Focus on:
• chunking strategies
• embedding models
• hybrid retrieval (dense + sparse)
• vector databases
• knowledge graphs
• context filtering
• evaluation of retrieved docs
If you master retrieval, you instantly become valuable.
3️⃣ Learn how to evaluate AI systems, not just build them
Engineers build models.
Professionals who can evaluate them are the ones who get promoted.
Learn to measure:
• grounding accuracy
• relevance
• completeness
• tool-use correctness
• consistency across runs
• latency
• safety
This is where the real skill gap is.
4️⃣ Learn prompting as an engineering discipline
Not “try random prompts.”
But systematic methods like:
• template prompts
• tool-calling prompts
• guardrail prompts
• chain-of-thought
• reflection prompts
• constraint-based prompting
Prompting is becoming the new API design.
5️⃣ Learn how to build agentic workflows
AI is moving from answers → decisions → actions.
You should know:
• planner → executor → verifier agent structure
• tool routing
• action space design
• human-in-the-loop workflows
• permissioning
• error recovery loops
This is what separates beginners from real AI engineers.
6️⃣ Learn Python + APIs deeply
You don’t need to be a software engineer,
but you must be comfortable with:
• Python basics
• API calls
• JSON
• LangChain / LlamaIndex / DSPy
• building small scripts
• reading logs
• debugging AI pipelines
This is the “plumbing” behind AI systems.
7️⃣ Build real projects, not toy demos
Instead of “build a chatbot,” build:
• a support email classifier
• a RAG system on company policies
• a customer insights extractor
• an automatic meeting summarizer
• a multimodal analyzer (text + image)
• an internal tool-calling agent
Projects that solve real problems get you hired.
8️⃣ Learn one domain deeply
AI generalists struggle.
AI + domain experts win.
Choose one:
• finance
• healthcare
• retail
• manufacturing
• real estate
• cybersecurity
• operations
• supply chain
• HR tech
AI skill + domain depth = career acceleration.
If you’re entering AI today:
Focus on retrieval, reasoning, evaluation, agents, and real projects.
These are the skills companies are desperate for.
❤7💯2