GPT-5 is here. This is the moment when AI stops being a shiny toy and becomes infrastructure.

The Essentials of GPT-5:

🔷 Unified system with smart router:
No more choosing a model. GPT-5 evaluates your request itself.

🔷 Drastic leap in reliability: reduction in hallucinations of between 45% and 80% compared to previous models.

🔷 Cutting-edge performance in key tasks:

💻 Code: Reaches 74.9% on SWE-bench (SOTA). Top-notch software engineering tool, not just an assistant.

🔬 Reasoning and Science: Notable improvement in benchmarks in mathematics, science, multimodal perception.

✍️ Writing: It goes beyond grammar. The examples show a superior handling of structure, rhythm & metaphor.

🔷 Access and Economy:
Available from today in ChatGPT (replacing GPT-4o). The API tops out at $10/M for output tokens and $1.25/M for input. This is VERY competitive with Gemini 2.5.

The real change is not just power, it is confidence. This is designed for the job.

MIT. Stanford. DeepMind. Berkeley. UMich.
8 playlists that teach AI better than most of MS degrees

🎓 Here's the AI Engineering Mix:

1️⃣ MIT 6.S191 - Intro to Deep Learning: ↳ Fast-track tour of modern Deep Learning. Master everything from the basics of DL to the latest applications
https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI&si

2️⃣Neural Networks: Zero to Hero: ↳ Learn from Karpathy as he walks you through backpropping to building your own GPT from scratch https://m.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ

3️⃣ Stanford CS336 - Language Models: ↳ Deeply understand data preproc, LLM building blocks, evals, scaling, and reasoning https://youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_&si=lPGRF-ugOVd0LT2l

4️⃣ UMich Deep Learning for CV: ↳ Learn SOTA computer vision from CNNs to modern applications
https://youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r&si=bqQqKP_Bse4Njn_0

5️⃣ Stanford CS236 - Generative AI: ↳ Build intuition on modern gen-AI, including diffusion models, VAEs & flows, and image synthesis https://youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8&si=2IXRT2TeVVM0JWZQ

6️⃣ DeepMind RL Course: ↳ RL is the next frontier. Learn everything from basics to policy optimization, value learning, and planning https://youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ&si=cghmy4MvqXuqjJZQ

7️⃣ Berkeley LLM Agents: ↳ Learn how agents work, including planning engines, tool usage, and reasoning systems from real-world practitioners https://youtube.com/playlist?list=PLS01nW3RtgopsNLeM936V4TNSsvvVglLc&si=eAKkkSKAsKQXg_RX

8️⃣ Stanford MLSys: ↳ Learn the foundations of production-ready ML, including system architecture, productionization, and performance tuning https://youtube.com/playlist?list=PLSrTvUm384I9PV10koj_cqit9OfbJXEkq&si=-tXicYQWZ4RuTMH2

🚀 Why This Stack Works:
• Covers theory + practice
• Taught by world-class instructors
• Focused on shipping code
• Built for real-world AI

💡These 8 playlists will move you from guesswork to building real-world systems.

ChatGPT creates plan between Free and Plus in India.

ChatGPT Go. 399/- a month

OpenAI has launched a brand-new tier of ChatGPT and it is called ChatGPT Go. It is a lower-cost version of the more premium version, ChatGPT Plus, and brings some of the most popular ChatGPT features at a low price point for Indian users.

At large, it has multiple differences compared to the free tier of the service, including extended access to GPT-5, which is the company's latest large language model family, as well as higher image generation limits.

ChatGPT Go Vs ChatGPT Free

1. ChatGPT Go comes with more expanded to the flagship GPT-5 model.

2. More file uploads: with Go, you will have the ability to upload more files, analyse them, create spreadsheets and more.

3. A longer context window: GPT will remember your details for longer, allowing you more personalised responses.
https://help.openai.com/en/articles/11989085-what-is-chatgpt-go

🚨 GPT-5: Breakthrough or Blunder?

The AI world is buzzing—and not all of it is positive.

Users are reporting:
⚡️ Slower responses
🤖 Less personality than GPT-4
💡 Basic mistakes that shouldn’t happen

So is GPT-5 truly a step back… or is OpenAI playing a bigger game—optimizing for cost, scale, and enterprise use?

🔗 Watch here: https://youtu.be/IKbsdKO9aNQ

🚀 This 277-page PDF unlocks the secrets of Large Language Models (LLMs).

If you’ve ever wondered how LLMs are built, optimized, and applied at scale, this paper is a goldmine.

Here’s what’s inside: 🧵
✅ Foundations of LLMs
✅ Training strategies & architectures
✅ Scaling laws & efficiency
✅ Applications across industries
✅ Open challenges & future directions

A must-read for researchers, developers, and AI leaders.

📖 Get the full 277-page PDF here: arxiv.org/abs/2501.09223

🚀 PyTorch vs TensorFlow – Which Should YOU Choose?

If you’re starting in AI or planning to build real-world apps, this is the big question.

👉 PyTorch – simple, feels like Python, runs instantly. Perfect for learning, experiments, and research.
👉 TensorFlow – built by Google, comes with a full production toolkit (mobile, web, cloud). Perfect for apps at scale.

✨ Developer Experience: PyTorch is beginner-friendly. TensorFlow has improved with Keras but still leans towards production use.
📊 Research vs Production: 75% of research papers use PyTorch, but TensorFlow powers large-scale deployments.

💡 Think of it like this:
PyTorch = Notebook for experiments ✍️
TensorFlow = Office suite for real apps 🏢

So the choice is simple:

Learning & Research → PyTorch

Scaling & Deployment → TensorFlow

Both are amazing. Pick what matches your goals.
🎥 Full breakdown here: https://youtu.be/x1vsxJKnAxw

Google’s “Nano Banana” (Gemini 2.5 Flash Image) — Making Photo Editing as Easy as Typing a Message 🍌

What if you could edit photos with the simplicity of sending a text?

That’s now a reality with Nano Banana, Google’s playful codename for its newest AI-powered image editing model, integrated into the Gemini app as Gemini 2.5 Flash Image.

Nano Banana (Gemini 2.5 Flash Image) is setting a new bar for AI-powered visual editing—clever, intuitive, and creative.

This is a defining moment in shifting photo editing from a skill-intensive task to an idea you can express with words. And that could reshape how professionals and creators everywhere work with visuals.

Checkout blog: https://blog.google/intl/en-mena/product-updates/explore-get-answers/nano-banana-image-editing-in-gemini-just-got-a-major-upgrade/

Programming with AI is insanely fun. Process is:
1. generate code
2. read & understand code that was generated
3. make small changes "manually" (still with great autocomplete)
4. test & debug
5. make big changes with new prompt
6. go back to step 1

Pure vibe coding skips step 2 & 3. And I think we'll need human expertise & experience for steps 2, 3 (and 4) for quite a while.

But holy shit, I'm learning much faster, being way more productive, and having more fun.

Not sure we're close to "AGI/ASI", but the software engineering world is definitely getting transformed very rapidly. It feels surreal to be experiencing it directly on a daily basis. Of course, there are both scary (jobs, security) & exciting (productivity, access) consequences to this transformation, as with all powerful technology.

This post was fully written by human in one-shot without spellcheck, it's 100% organic human writing 🤣

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!