You don't need raise $2M to launch a startup anymore.

This $120/month AI stack will let you ship ANY idea in 2 weeks👇
- Perplexity Deep Research for market research/ ChatGPT / Grok.
- v0 for beautiful frontend
- Cursor for core logic and documentation
- Google DeepMind Veo 3 for launch videos
- Supabase for database
- Vercel for hosting
- n8n for automating workflows
- Stripe for payments
- Bonus : HeygenAI

Use AI Browser - Comet by Perplexity AI 🚀

I’ve been exploring Comet — the AI-powered browser released last month by Perplexity and I can confidently say this might be the smartest upgrade to your daily browsing experience.

Built from the ground up for knowledge workers, researchers, and tech-savvy professionals, Comet isn’t just a browser, it’s your AI co-pilot for the internet.

🔍What makes Comet different?
✅ Ask anything, get structured answers — fast
✅ Built-in Perplexity AI that searches and summarizes from reliable sources
✅ Real-time web summaries of articles, PDFs, even long pages
✅ Context-aware browsing: it remembers what you’re working on
✅ Transparent citations: always shows sources behind the answers
✅ Distraction-free interface for focused research sessions

🔍 Key Features:
1. AI-Powered Search – No more 10-tab chaos. Ask a question and get instant, well-researched answers from reliable sources.
2. Real-Time Summaries – Visiting a long webpage or article? Comet provides instant, accurate summaries without missing context.
3. Memory + Recall – It remembers your queries, builds context, and improves with every interaction.
4. Built-in Perplexity Assistant – Like ChatGPT, but trained to search the web live. Perfect for market research, competitive analysis, and quick learning.
5. Source Citations – Transparency first — every answer comes with links to original sources. No black box.
6. Fast, Clean, Distraction-Free UI – Beautifully designed for productivity.

💡Why Should You Use It?
✅ Save time on research
✅ Get accurate, AI-curated knowledge
✅ Perfect tool for professionals, researchers, content creators, and students
✅ Reduces context-switching between browser + AI chat tools
✅ Your workflow, now accelerated by real-time intelligence

Checkout: https://youtube.com/shorts/cSwCTYWtI5c?si=NY5SsUuEVN5vFeQc

🧠 Is AI Going to Take Your Job?

Microsoft’s latest research, based on 200K+ real-world Copilot use cases says it isn’t just a list of roles at risk. It’s a strategic lens into how work is fundamentally evolving.


The study introduced the AI Applicability Score highlighting the degree to which current roles can be augmented or automated by AI. The takeaway isn’t panic. It’s a wake-up call: the era of static job roles is over.

🔍 What the data says:
High AI exposure: Writers, translators, customer support reps, data analysts, sales associates — roles built on repeatable, structured output.
Low AI exposure: Roles requiring spatial dexterity, physical presence, or real-time human judgment — like roofers, surgical assistants, dredge operators.

But here’s the nuance:
- AI doesn’t eliminate Jobs.
- It eliminates inefficiencies.
- And in doing so, it reshapes the value chain of every role it touches.

🧭 What this means for the future of work:
✅ Task-level automation will redefine job descriptions faster than org charts can adapt.
✅ Mid-skill white collar roles will see the greatest turbulence — not blue collar, as many assume.
✅ Roles will bifurcate: those who use AI as leverage will rise; those who don’t, won’t stay relevant.

Employers won't just hire for degrees or titles. They'll hire for AI fluency, systems thinking, and human differentiation.

This is not about "AI vs jobs." : It’s about you + AI vs a faster-moving world.

Checkout: https://www.youtube.com/@AiIndia/shorts

Source: https://arxiv.org/pdf/2507.07935

🗣 Curious where your role stands?
Drop your job title in the comments of this Instagram post. I will DM you a customized AI Exposure Insight — based on this research and real industry application trends.: https://www.instagram.com/p/DNA4S-rBpah/

By 2030, 40% of today’s skills will be outdated, and 59% of workers will need reskilling - though 1 in 5 may not get it.

According to the new Future of Jobs 2025 report from the World Economic Forum, AI and information-processing technologies are expected to transform 86% of businesses, more than any other technology.

We’re living in a time of rapid transformation.
There’s no “future of work” moment anymore, we’re already in it, and AI is at the center.

𝐓𝐡𝐞 𝐀𝐈-𝐧𝐚𝐭𝐢𝐯𝐞 𝐰𝐨𝐫𝐤𝐟𝐨𝐫𝐜𝐞 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥.
Yes, AI and big data top the list of fastest-growing skills.
But so do:

🔹 Resilience & agility
🔹 Creative thinking
🔹 Curiosity & lifelong learning
🔹 Leadership & influence
🔹 Systems thinking

The world doesn’t just need more AI experts.
It needs more 𝐚𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 who can think with AI, strategically, creatively, and ethically.

Employer response strategies
- 77% plan large-scale upskilling and reskilling initiatives
- 41% expect workforce reductions due to AI
- Nearly half aim to transition vulnerable roles into higher-skilled areas internally

The AI era is as much about human reinvention as it is about technological change.

The most future-proof professionals will be those who can blend human strengths,
𝐣𝐮𝐝𝐠𝐦𝐞𝐧𝐭, 𝐞𝐦𝐩𝐚𝐭𝐡𝐲, 𝐞𝐭𝐡𝐢𝐜𝐬 + 𝐰𝐢𝐭𝐡 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬

And for organizations, the winners won’t just be the fastest adopters of AI. They’ll be the ones who:

- Invest early in people’s growth
- Build cultures of continuous learning
- Encourage employees to co-design solutions with AI rather than compete against it

The AI-native workforce will be built, not bought.
And it starts now.

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!