Anjula Dwivedi

A Stanford professor stood in front of a full lecture hall and told them that depression has almost nothing to do with sadness. Then he wrote two words on the whiteboard that nobody in the room could define. Anhedonia. Psychomotor retardation. That, he said, is the actual illness. The man is Robert Sapolsky, the neurobiologist who built his career following wild baboons across Kenya to measure what stress does to a living body. What makes the video worth 52 minutes is not the subject. It is the method. No slides. No notes he ever glances down at. One camera, one whiteboard, and a man taking depression apart the way a neurologist takes apart a stroke. He is doing something deliberate, and it takes a while to catch. Every time the material could soften into sympathy, he hauls it back into biology. Hormones. Neurotransmitters. Genes that do nothing whatsoever until life goes wrong, and then decide everything. Somewhere in the middle he says one sentence about depression that viewers have been pulling out of this lecture for 16 years. It is 5 words long. It reads like a contradiction until he unpacks it. Under the video, sleepy_and_caffeinated quoted that exact sentence and wrote two words beneath it. That got me. 4.3 thousand people agreed. Johnny_boy777 said it from another angle. The best thing about the internet, he wrote, is that any ordinary person can sit in on this without ever enrolling at Stanford. 2.6 thousand likes. 16 years old. 6.2 million views. 9410 comments. One of 25 lectures in the Stanford course Human Behavioral Biology, and every one of them is free. Most people assume they understand depression because they have felt bad. 52 minutes is enough to learn they were describing something else entirely. 52 minutes. 5 words. It is in the video.

I don't pick GPT-6 by its score alone. Here's where I'd start with these 8: 1. Opus 5.5: proposals, reports and coding. 2. Fable 5.1: stress-test plans and drafts. 3. GPT-6 Astra: campaign strategy and research. 4. GPT-6 Sol: landing pages and workflows. 5. Grok 4.7: research with live search tools. 6. Kimi K3: large file sets and reports. 7. Gemini 3.8 Flash: analyse images and video. 8. GPT-6 Luna: tag feedback and extract data. But the job matters as much as the ranking (especially when you need a quick answer). The large numbers are Artificial Analysis benchmark scores. The smaller /10 ratings are editorial judgements (not my own test results). And those benchmarks use different effort settings, so treat this as a starting point for your next task. Try the same brief in two models, then compare what you can use without rewriting it (keep the brief identical). Repost ♻️ to help someone in your network.

🚨 30 Claude AI Terms You Need to Know to Unlock Its Full Potential! Claude is more than just an AI chatbot. From coding and research to automation and building websites, understanding these 30 terms can help you use Claude smarter, faster, and more efficiently. 👇 🔥 Here are 30 essential Claude terms explained simply: 1. Prompt — The instructions you give Claude. 2. Opus — A powerful model for complex tasks. 3. Sonnet — A balanced model for everyday work. 4. Haiku — A lightweight model for quick responses. 5. Extended Thinking — Helps Claude work through complex problems. 6. Chat — Claude’s standard conversational interface. 7. Cowork — A workspace for handling tasks and files. 8. Claude Code — AI-powered coding and development. 9. Claude Design — Tools for creating visual designs and layouts. 10. Claude in Excel — AI assistance for spreadsheet workflows. 11. Claude in Chrome — Browser-based AI assistance. 12. Projects — Organized workspaces for related conversations and files. 13. Artifacts — Documents, code, and interactive content created by Claude. 14. Skills — Reusable workflows for specific tasks. 15. Plugins — Extensions that combine tools and capabilities. 16. Connectors — Connections between Claude and external apps. 17. Computer Use — Lets Claude interact with a computer in supported environments. 18. Dispatch — A way to send tasks from your phone to Claude on your desktop. 19. Scheduled Tasks — Tasks configured to run on a schedule. 20. AskUserQuestion — A tool for asking clarifying questions in supported workflows. 21. Global Instructions — Instructions that apply broadly across tasks. 22. Custom Instructions — Project-specific guidance for Claude. 23. Styles — Saved writing tones and formatting preferences. 24. Memory — Helps retain relevant information across supported interactions. 25. Research — In-depth research and report generation. 26. Web Search — Access to current information from the internet. 27. Markdown — A simple text format used for structured documents. 28. SKILL.md — A file containing a skill’s instructions and configuration. 29. CLAUDE.md — A file used to provide persistent instructions to Claude Code. 30. Vibe Coding — Building software by describing what you want in natural language. 💡 Why does this matter? The more familiar you are with these concepts, the easier it becomes to choose the right model, automate repetitive work, build projects, and get better results from AI. You don't need to master everything today. Start with the tools that solve your biggest problems. 🔖 Save this post as your Claude AI cheat sheet. Follow me @HeyAnjula for more AI tools, practical workflows, and productivity tips.

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🚨 These Claude prompts can save you $5,500+ Most people use AI like a basic chatbot. But the truth is... With the right prompts, Claude can replace tools for writing, research, content creation, automation, and more. That’s easily $5,500+ worth of work and tools. So I’m sharing something valuable. 🎁 57+ Best Claude Prompts (Copy-Paste Ready) You can use them to: • Write viral posts • Generate content instantly • Automate boring tasks • Learn anything faster • Build AI workflows ⚡ Free for the first 2,000 people only. After that, the page will be locked. To get it: 1️⃣ Like this post 2️⃣ Follow me 3️⃣ Comment “PROMPTS” I’ll send the link to the first 2,000 people. Move fast. AI rewards the early ones. 🚀

26 free Claude Skills just leaked. And honestly, most people are still using Claude the hard way. Every time you open a new chat and paste the same 500-word prompt, you're wasting time. Instead, these skills let you run a single slash command and get a finished workflow instantly. A few you'll want immediately: → /grill-me — interviews you first so Claude actually understands what you're building → /humanizer — removes AI-sounding patterns from your writing → /fact-checker — verifies claims before you hit publish → /deep-research-synthesizer — turns dozens of sources into a cited summary → /handoff — transfers an entire conversation without losing context → /personal-voice — writes like you, not like ChatGPT or Claude And that's just the start. There's an entire content engine built in: → /my-viral-post → /linkedin-hook → /viral-recipe → /hormozi-viral-1 → /tc-social-carousel One command instead of rebuilding the same prompt every single time. Need practical workflows? → /deck-builder → /meeting-notes → /client-brief → /negotiation → /excel-style → /xlsx 26 skills. Free. Every command included. The biggest advantage in AI today isn't getting access to better models. It's building systems that eliminate repetitive work. Most people keep prompting. The smartest users build once—and reuse forever.

What does an AI harness actually do? These 6 terms explain how an agent goes from a request to a finished task. Let's take this example: a support agent that checks an order and drafts a reply. What happens at each step: 1 Agent loop The model chooses an action, a tool runs, and the result comes back. The cycle continues until the reply is ready, help is needed or a limit is reached. 2 Tool calling The model requests a tool by name, with arguments. The harness checks and executes that request, then returns the result. An order lookup gives the model a delivery status it can use. 3 Context management What the model sees at each step comes from your instructions, the conversation and tool results, like the order status. When the window fills up, older messages get compacted, and your prompts and hooks decide what's kept. 4 Checkpointing The workflow saves its state outside the model. After an interruption, it can reload the order lookup result and continue from a saved point. 5 Permissions and sandboxing The harness sets what the agent can touch: which tools, files and actions, and where code runs. The support agent can read the order but can't issue a refund. 6 Guardrails Checks inspect inputs, outputs or proposed actions. An output check can inspect the drafted reply; an approval gate can pause before sending it. Those are different controls, and neither catches every error. The real payoff: you can switch between models from different labs and share the same skills and context across your team. Which part is hardest to get right in your setup?

Elon Musk The remaining time is at most 3 years. During this period, it's still possible to make money through "human labor." But after that, Most jobs that have been paid salaries until now will become automated. The business of earning money will end. The "meaning of paying money to humans" will disappear. This system that's been going on for the last 200 years👇 "If you lend us your time for 40 years, we'll pay you a salary" This will end... Work will end In other words, history's largest-scale "money movement" will take place. So, what's the situation now? 👉Some people have realized it and taken action 👉Many people are "waiting until it's explicitly stated in the news" This "time gap between realizations" is everything. So, what needs to be done in these 3 years? Very Simple👇 "Transform your own time into an asset that machines cannot take away" Specifically, the following👇 • Rare digital assets like Bitcoin • Real assets like land or energy • Computers or infrastructure • Connections with people (network) • The power to produce food or things necessary for life What even is a salary??? "Just for having lent your own time" If the value of that time, becomes cheaper and superior with machines... - That value will suddenly collapse In the end, the only thing left will be this 👇 "What you own (assets)" In these 3 years👇 Those who can transform from "a salaried person" to "an asset-owning person" In 2035-2036 will be the "people who saw the future." After that👇 • Salaries won't increase They'll eventually become irrelevant The only thing left is one👇 "Who owns what" The door of opportunity is open right now. But it's closing faster than everyone thinks.

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My interview with Sam Altman (@sama): 0:00 Intro 0:52 When Will AI Discover New Treatments? 3:31 Why AI Hasn’t Killed Coding Jobs 11:47 Personal AI Assistants 15:39 Why Are We Busier Than Ever? 20:10 Why the AI Consciousness Debate Worries Sam 26:09 ChatGPT Is Too Agreeable 30:03 OpenAI’s Bet on Chips and Cheaper Compute 34:20 Why AI Needs a New Kind of Computer 37:41 Sam on Pressure: “Pick the Big Thing” 42:18 Why “AI Will Cure Cancer” Isn’t Enough

🚨 Most businesses don't need more employees. They need smarter systems. Imagine waking up to find that your emails have been processed, customer inquiries have been handled, your CRM has been updated, and your team has already received the necessary notifications. 🤯 No endless copy-pasting. No repetitive manual tasks. No switching between 10 different apps just to get one thing done. Here's how an AI automation workflow makes it possible: 👇 📩 1. TRIGGER — Everything starts automatically. A new email arrives, a customer submits a form, or a scheduled event occurs. That event triggers the entire workflow. ⚙️ 2. DATA PROCESSING — Information moves without the busywork. The system collects incoming information, organizes it, and prepares it for the next step. 🧠 3. AI DECISION — The workflow gets smarter. AI analyzes the information, identifies the type of request, and helps determine what action should happen next. 🔀 4. AUTOMATED ACTIONS — Work gets done. Depending on the conditions, the workflow can send emails, update customer records, assign tasks, or trigger other processes. 🔗 5. INTEGRATIONS — Your tools finally work together. Connect platforms like Slack, Shopify, CRM systems, and external APIs to move information between applications. 🔥 Here's a real-world example: A customer submits a request → AI analyzes the message → The system updates the CRM → A personalized email is sent → Your team receives a Slack notification. One trigger. Multiple connected actions. Less manual work. More time to focus on growth. 🚀 And this is only one example. Similar workflows can support sales, marketing, customer service, reporting, and internal operations—with appropriate permissions and human oversight. 💡 The biggest opportunity isn't just learning to use AI. It's learning how to connect AI with the systems that run your work. The people who understand automation can build workflows that save hours, reduce repetitive tasks, and help businesses scale more efficiently. ♻️ Repost this for someone who is still doing manually what could potentially be automated. Follow for more practical AI tools, automation workflows, and ways to work smarter with AI. 🧠 #AI #Automation #AIagents #AITools #FutureOfWork