Rajendra Sendhalkar

Apple fired Steve Jobs. He walked into MIT and gave the most honest 60-minute business lecture ever recorded. No PR filter. No image to protect. Just raw thinking from the guy who built Apple once — and was about to do it again. Watch this instead of Netflix tonight. Bookmark it for later

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🎯Top 25 Free Google AI Tools Boost Your Workflow 3x Faster With These AI Tools Whether it’s writing, coding, research, or automation there’s something here for everyone. If you want to work smarter, create faster, and stay ahead this list is all you need.

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Learn AI for free directly from top companies 𝟭 - 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰: anthropic.skilljar.com 𝟮 - 𝗚𝗼𝗼𝗴𝗹𝗲: grow.google/ai 𝟯 - 𝗠𝗲𝘁𝗮: ai.meta.com/resources/ 𝟰 - 𝗡𝗩𝗜𝗗𝗜𝗔: developer.nvidia.com/cuda 𝟱 - 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁: learn.microsoft.com/en-us/training/ 𝟲 - 𝗢𝗽𝗲𝗻𝗔𝗜: academy.openai.com 𝟳 - 𝗜𝗕𝗠: skillsbuild.org 𝟴 - 𝗔𝗪𝗦: skillbuilder.aws 𝟵 - 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝗔𝗜: deeplearning.ai 𝟭𝟬 - 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲: huggingface.co/learn Follow for more @Artikashyap1996

How to build your first Claude Skill in under 60 minutes. I used to repeat the same prompts every single day. Same instructions. Same formatting. Same corrections. Over and over. Then I discovered Claude Skills. Now Claude already knows what I want before I even finish typing. Here's the 6-step system to build yours today: 𝗦𝘁𝗲𝗽 𝟭: 𝗢𝗽𝗲𝗻 𝗦𝗸𝗶𝗹𝗹-𝗰𝗿𝗲𝗮𝘁𝗼𝗿 (𝟱 𝗺𝗶𝗻) ↳ Open Claude Cowork. ↳ Select your project folder. ↳ Choose Opus 4.6 + Extended Thinking. ↳ Type: "Use the skill-creator to help me build a skill for [your most repeated task]." This starts the entire process. 𝗦𝘁𝗲𝗽 𝟮: 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹 (𝟭𝟱 𝗺𝗶𝗻) ↳ Skill-creator will ask questions. ↳ Answer with extreme detail. ↳ It generates a folder with a SKILL. md file inside. ↳ Contains your trigger, description, and instructions. Be specific here or pay for it later. 𝗦𝘁𝗲𝗽 𝟯: 𝗥𝘂𝗻 𝘁𝗵𝗲 𝗲𝘃𝗮𝗹 (𝟭𝟬 𝗺𝗶𝗻) ↳ Claude auto-creates an eval. ↳ Click "View the eval results." ↳ This tests your Skill before you install it. Most people skip this step. That's why their Skills break. 𝗦𝘁𝗲𝗽 𝟰: 𝗜𝗻𝘀𝘁𝗮𝗹𝗹 & 𝘁𝗲𝘀𝘁 (𝟭𝟬 𝗺𝗶𝗻) ↳ Save the Skill folder. ↳ Settings → Capabilities → Skills → Upload. ↳ Open a brand new chat. ↳ Type something that should trigger your Skill. ↳ Did it activate? You're live. 𝗦𝘁𝗲𝗽 𝟱: 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 & 𝗱𝗲𝗯𝘂𝗴 (𝟭𝟬 𝗺𝗶𝗻) ↳ Try 2-3 unrelated requests. ↳ Your Skill should stay quiet. ↳ If it activates on wrong tasks, your description is too vague. ↳ Ask: "When would you use this skill?" ↳ Claude reads the description back. ↳ Now you see exactly what to fix. 𝗦𝘁𝗲𝗽 𝟲: 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗺𝗼𝗿𝗲 (𝟭𝟬 𝗺𝗶𝗻) ↳ Desktop app → Customize → Personal plugins → Browse plugins. ↳ Download community Skills. ↳ Or use makemyskill. com to build even faster. Total time: under 60 minutes. You go from repeating prompts daily to a custom AI system that just works. That's the difference between using Claude and mastering Claude. Save this for later. I hope this post helped you today. Follow Muhammad Ayan ♻️ Repost to help others

If you learn just these 9 AI skills… You’ll be ahead of 95% of people in 2026 ⚡👇 1️⃣ Prompt Engineering → get precise, high-quality outputs 2️⃣ Workflow Automation → eliminate repetitive work 3️⃣ AI Image Generation → create visuals in seconds 4️⃣ Vibe Coding → ship fast without overthinking 5️⃣ Custom GPTs → build your own AI assistants 6️⃣ AI Video → turn ideas into content instantly 7️⃣ AI-Assisted Dev → code faster with AI copilots 8️⃣ Agentic Coding → let AI plan + execute tasks 9️⃣ RAG Systems → connect AI to real data Most people dabble. Few people stack skills like this. That’s the difference between: using AI ❌ vs building leverage with AI ✅ Start with 1 skill. Practice daily. Then layer the next. This is how you go from beginner → builder → operator. 📚 If you want to go deeper, here are some insane free resources: → Free AI GTM Kit (credits, workflows, templates) thevccorner.com/p/free-ai-gtm-… → Claude workflows, prompts & templates the-ai-corner.com/t/claude-and-a… → AI Decks (Eleven Labs, Artisan & more) the-ai-corner.com/p/ai-startup-p… → Prompting & Context Engineering the-ai-corner.com/t/prompting-an… → OpenAI's PMF Playbook for AI Startups thevccorner.com/p/ai-product-m… → 2,500+ Angel Investors backing AI thevccorner.com/p/angel-invest… → AI Agents Guides the-ai-corner.com/t/ai-agents → Humanizer Prompt the-ai-corner.com/p/ai-humaniser… → 30 Claude in Excel Prompts the-ai-corner.com/p/claude-excel… Bookmark this. Come back weekly. Execute. Because the future won’t reward people who know AI… It’ll reward people who use AI to build unfair advantages. 🚀

🔴 CADA IA TIENE SU SUPERPODER 🪄 Aprende cuál es la mejor para cada caso de uso: - ChatGPT - Grok - Gemini - Claude - Perplexity (🔖 Guárdalo para luego)

Holy shit… someone turned Claude into a real technical interviewer. Not random questions. Not LeetCode dumps. Not shallow practice. Actual mock interviews — inside AI. It’s called InterviewMentor. And it gives Claude full interviewer personas: SWE System design Data engineer ML Debugging Senior/staff Each one runs like a real interview: Warm-up → Deep dive → Problem → Feedback Here’s why this is different: Instead of asking for questions, you ask the AI to interview you. It adapts difficulty, gives hints, pushes tradeoffs, and scores performance. You get: • role-specific mock interviews • 4-level hint system • system design walkthroughs • debugging scenarios • behavioral practice • evaluation rubric Just: clone → load skill → start interview No scheduling. No $200 mock interviews. No courses. Just infinite interview practice. AI interview prep just changed.

120 AI Tools powering the next generation of work. 1. Ideas - YOU - Claude - ChatGPT - Perplexity - Bing Chat 2. Presentation - Prezi - Pitch - PopAi - Slides AI - Slidebean 3. Website - Dora - Wegic - 10Web - Framer - Durable 4. Writing - Rytr - Jasper - Copy AI - Textblaze - Writesonic 5. AI Models - RenderNet - Glambase App - Luma AI - Sora (OpenAI) - Leonardo AI 6. Meeting - Tldv - Krisp - Otter - Avoma - Fireflies 7. Chatbots - Poe - Claude - Gemini - ChatGPT - HuggingChat 7. Automation - ClickUp - Drift - Outreach - Emplifi - Phrasee 8. UI/UX - Uizard - Visily - Khroma - Galileo AI - VisualEyes 9. Image - Stylar - Freepik - Phygital+ - StockIMG - Bing Create 10. Video - Pictory - HeyGen - Nullface - Decohere - Synthesia 11. Design - Looka - Clipdrop - Autodraw - Vance AI - Designs AI 12. Marketing - AdCopy - Predis AI - Howler AI - Bardeen AI - AdCreative 13. Twitter - Typefully - Postwise - Metricool - Tribescaler - TweetHunter Follow @Tech5353 for curated AI content you’ll actually use.

LLMs are AI… but not all AI is LLMs. A new wave of specialized models is redefining what AI can do ⚡ Here are 8 architectures you should know: 1️⃣ LLMs (Large Language Models) Process text token-by-token → Power chatbots, coding, reasoning 2️⃣ LCMs (Large Concept Models) Encode full ideas as “concepts” → Move beyond word-level thinking 3️⃣ VLMs (Vision-Language Models) See + read → Understand images and describe them 4️⃣ SLMs (Small Language Models) Lightweight + fast → Built for edge devices 5️⃣ MoE (Mixture of Experts) Activate only relevant parts → Smarter + more efficient 6️⃣ MLMs (Masked Language Models) Bidirectional context → Strong understanding of meaning 7️⃣ LAMs (Large Action Models) Think → Act → Execute real-world/system tasks 8️⃣ SAMs (Segment Anything Models) Pixel-level vision → Precise object segmentation Old AI vs New AI Traditional AI: • One model → many tasks • Limited flexibility • Heavy compute Specialized AI: • Purpose-built models • Optimized (speed, size, accuracy) • Unlock new capabilities Why this matters: ✅ Better performance per task ✅ Faster + more efficient ✅ More useful real-world outputs The future isn’t one model. It’s the right model for the right job. Which architecture would help your work most? 🤔 #AI #MachineLearning #LLM #TechTrends

15 blogs that will help you become a better engineer: 1. Uber Engineering: uber.com/blog/engineeri… 2. Airbnb Tech Blog: airbnb.tech/blog 3. OpenAI Engineering: openai.com/news/engineeri… 4. AWS Architecture: aws.amazon.com/blogs/architec… 5. Netflix TechBlog: netflixtechblog.com 6. Discord Engineering: discord.com/category/engin… 7. Anthropic Engineering: anthropic.com/engineering 8. NVIDIA Developer: developer.nvidia.com/blog 9. Slack Engineering: slack.engineering 10. Cloudflare Blog: blog.cloudflare.com/tag/engineering 11. Figma Tech Blog: figma.com/blog/engineeri… 12. Shopify Engineering: shopify.engineering 13. Stripe Engineering: stripe.com/blog/engineeri… 14. Microsoft Engineering: devblogs.microsoft.com/engineering-at… 15. GitHub Engineering: github.blog/engineering Bonus (system design deep dives) Level Up Coding: lucode.co/luc-system-des… What other blogs should be on this list? 🔖 Save for later. 🤝Follow me @AIandTechh87 for more resources 📷 Repost to help other engineers learn and grow.

Day 2 of Learning Backend Engineering. Setting Up Your Local Development Environment for Backend Work. Grab the Backend Engineering Ebook: codewithdhanian.gumroad.com/l/ungqng

10 AI websites you’ll wish you knew earlier 🤯 1️⃣ ChatGPT — Your all-in-one AI brain. 👉 chat.openai.com⁠ 2️⃣ Gemini — Google’s smartest AI yet. 👉 gemini.google.com⁠ 3️⃣ Perplexity — Search the internet in seconds. 👉 perplexity.ai⁠ 4️⃣ Midjourney — Turn ideas into stunning art. 👉 midjourney.com⁠ 5️⃣ Canva AI — Design anything, instantly. 👉 canva.com/ai⁠ 6️⃣ HeyGen — AI videos with avatars. 👉 heygen.com⁠ 7️⃣ Replit — Build apps with AI help. 👉 replit.com⁠ 8️⃣ Jasper — AI content that sells. 👉 jasper.ai⁠ 9️⃣ Durable — Website in 30 seconds. 👉 durable.co⁠ 🔟 Framer AI — Text → live website. 👉 framer.com/ai⁠ 🔖Save this before it disappears.

I'm sad to see that people are still sleeping on AI agents. Here are all the different types of AI agents you must know: 📂 ai agents ┃ ┣ 📂 fundamentals ┃ ┣ 📂 what is an ai agent ┃ ┣ 📂 environment interaction ┃ ┣ 📂 decision making ┃ ┣ 📂 goal execution ┃ ┗ 📂 autonomy levels ┃ ┣ 📂 core types ┃ ┣ 📂 simple reflex agent ┃ ┃ ┣ 📂 rule-based actions ┃ ┃ ┣ 📂 no memory ┃ ┃ ┗ 📂 instant response ┃ ┃ ┃ ┣ 📂 model-based agent ┃ ┃ ┣ 📂 internal state ┃ ┃ ┣ 📂 environment tracking ┃ ┃ ┗ 📂 memory usage ┃ ┃ ┃ ┣ 📂 goal-based agent ┃ ┃ ┣ 📂 goal definition ┃ ┃ ┣ 📂 path planning ┃ ┃ ┗ 📂 outcome evaluation ┃ ┃ ┃ ┣ 📂 utility-based agent ┃ ┃ ┣ 📂 decision optimization ┃ ┃ ┣ 📂 trade-off analysis ┃ ┃ ┗ 📂 value maximization ┃ ┃ ┃ ┣ 📂 learning agent ┃ ┃ ┣ 📂 experience learning ┃ ┃ ┣ 📂 feedback loop ┃ ┃ ┗ 📂 continuous improvement ┃ ┃ ┃ ┣ 📂 hierarchical agent ┃ ┃ ┣ 📂 task decomposition ┃ ┃ ┣ 📂 multi-level control ┃ ┃ ┗ 📂 delegation systems ┃ ┃ ┃ ┗ 📂 multi-agent system ┃ ┣ 📂 agent collaboration ┃ ┣ 📂 communication protocols ┃ ┗ 📂 distributed tasks ┃ ┣ 📂 modern agents (2025+) ┃ ┣ 📂 coding agents ┃ ┃ ┣ 📂 code generation ┃ ┃ ┣ 📂 debugging ┃ ┃ ┗ 📂 autonomous execution ┃ ┃ ┃ ┣ 📂 agentic browsers ┃ ┃ ┣ 📂 web navigation ┃ ┃ ┣ 📂 task automation ┃ ┃ ┗ 📂 data extraction ┃ ┃ ┃ ┗ 📂 copilot agents ┃ ┣ 📂 real-time assistance ┃ ┣ 📂 workflow integration ┃ ┗ 📂 decision support ┃ ┣ 📂 capabilities ┃ ┣ 📂 perception ┃ ┣ 📂 reasoning ┃ ┣ 📂 planning ┃ ┣ 📂 learning ┃ ┗ 📂 action execution ┃ ┣ 📂 architectures ┃ ┣ 📂 reactive systems ┃ ┣ 📂 deliberative systems ┃ ┣ 📂 hybrid models ┃ ┗ 📂 agent frameworks ┃ ┣ 📂 applications ┃ ┣ 📂 customer support ┃ ┣ 📂 autonomous driving ┃ ┣ 📂 finance automation ┃ ┣ 📂 healthcare systems ┃ ┗ 📂 enterprise workflows ┃ ┣ 📂 benefits ┃ ┣ 📂 efficiency gains ┃ ┣ 📂 automation at scale ┃ ┣ 📂 reduced human effort ┃ ┣ 📂 faster decision making ┃ ┗ 📂 continuous learning ┃ ┣ 📂 limitations ┃ ┣ 📂 data dependency ┃ ┣ 📂 bias risks ┃ ┣ 📂 coordination complexity ┃ ┣ 📂 error propagation ┃ ┗ 📂 oversight challenges ┃ ┣ 📂 evaluation ┃ ┣ 📂 task success rate ┃ ┣ 📂 accuracy metrics ┃ ┣ 📂 response time ┃ ┣ 📂 adaptability ┃ ┗ 📂 reliability ┃ ┣ 📂 deployment ┃ ┣ 📂 apis ┃ ┣ 📂 cloud integration ┃ ┣ 📂 tool usage ┃ ┣ 📂 workflow embedding ┃ ┗ 📂 monitoring systems ┃ ┣ 📂 optimization ┃ ┣ 📂 prompt engineering ┃ ┣ 📂 memory tuning ┃ ┣ 📂 tool selection ┃ ┣ 📂 feedback loops ┃ ┗ 📂 performance scaling ┃ ┣ 📂 security ┃ ┣ 📂 access control ┃ ┣ 📂 data privacy ┃ ┣ 📂 safe execution ┃ ┣ 📂 audit logs ┃ ┗ 📂 risk mitigation ┃ ┣ 📂 future trends ┃ ┣ 📂 autonomous systems ┃ ┣ 📂 multi-agent ecosystems ┃ ┣ 📂 ai-native workflows ┃ ┣ 📂 human-ai collaboration ┃ ┗ 📂 generalized agents ┃ ┗ 📂 summary ┣ 📂 simple → reactive ┣ 📂 advanced → goal + utility driven ┣ 📂 modern → autonomous + integrated ┗ 📂 future → fully collaborative systems

This is the only YouTube roadmap you need to go from beginner → AI engineer. Bookmark this 👇

Learn AI for free directly from top companies 1 - Anthropic: anthropic.skilljar.com 2 - Google: grow.google/ai 3 - Meta: ai.meta.com/resources/ 4 - NVIDIA: developer.nvidia.com/cuda 5 - Microsoft: learn.microsoft.com/en-us/training/ 6 - OpenAI: academy.openai.com 7 - IBM: skillsbuild.org 8 - AWS: skillbuilder.aws 9 - DeepLearning.AI: deeplearning.ai 10 - Hugging Face: huggingface.co/learn Comment "AI" for more resources. Like, Retweet & Bookmark for future updates.

Most people think AI “knows everything.” It doesn’t. It retrieves… then thinks. Here’s how modern AI systems actually answer using RAG 👇 → 𝗨𝘀𝗲𝗿 𝗤𝘂𝗲𝗿𝘆 You ask a question. But instead of relying only on training data, the system prepares to fetch external knowledge. → 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗤𝘂𝗲𝗿𝘆 Your query → converted into a vector. Not keywords. Not exact match. → Meaning-based representation. → 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 The system searches a vector DB. It finds the most semantically similar chunks of data. Not pages. → Small, relevant pieces. → 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗧𝗼𝗽-𝗞) Top K relevant chunks are selected. This is critical: Too little → missing context Too much → noise Balance = everything. → 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Retrieved data + your query → combined into a single prompt Now the model has fresh knowledge. → 𝗟𝗟𝗠 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 The model processes this enriched prompt. Important: It doesn’t memorize the data. → It uses it on the fly → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Final answer is generated using: • Pre-trained knowledge • Retrieved context This is why answers feel both smart + up-to-date ⚠️ Where things break: → Bad embeddings = irrelevant retrieval → Poor chunking = lost meaning → Too much context = confusion → Too little context = hallucination RAG is simple in theory. Hard in production. 💡 Why RAG matters: → No retraining needed → Real-time knowledge updates → Lower hallucination risk → Works with private data This is how most serious AI products are built today. AI isn’t just getting smarter. It’s getting better at finding the right information at the right time. What’s been your biggest challenge while working with RAG? 👇

9 AI Tools for Researchers 🧠 📌. Perplexity.ai – research faster 📌. Scite.ai – verify sources 📌. Elicit.org – summarize papers 📌. ChatGPT.com – explain theories 📌. Scholarcy.com – auto summaries 📌. Consensus.app – find evidence 📌. ResearchRabbit.ai – discover related work 📌. Litmaps.com – visualize citations 📌. SemanticScholar.org – deep paper search Here's how you can earn $1599 a month—with no experience required!