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R.I.P McKinsey. You don’t need a $300k consultant anymore. You can now run full competitive market analysis using Grok 4. Here are the exact 3 mega-prompts I use to replicate McKinsey-style insights for free:

This might be the most disturbing AI paper of 2025 ☠️ Scientists just proved that large language models can literally rot their own brains the same way humans get brain rot from scrolling junk content online. They fed models months of viral Twitter data short, high-engagement posts and watched their cognition collapse: - Reasoning fell by 23% - Long-context memory dropped 30% - Personality tests showed spikes in narcissism & psychopathy And get this even after retraining on clean, high-quality data, the damage didn’t fully heal. The representational “rot” persisted. It’s not just bad data → bad output. It’s bad data → permanent cognitive drift. The AI equivalent of doomscrolling is real. And it’s already happening. Full study: llm-brain-rot. github. io

I tested Grok 4 and ChatGPT-o3 with same critical prompts. The results will blow your mind. Grok 4 Vs. ChatGPT-o3 (Video demos are included)

I tested ChatGPT 5 and Grok 4 with same critical prompts. The results will blow your mind. ChatGPT 5 Vs. Grok 4 (Video demos are included)

Holy shit. MIT just built an AI that can rewrite its own code to get smarter 🤯 It’s called SEAL (Self-Adapting Language Models). Instead of humans fine-tuning it, SEAL reads new info, rewrites it in its own words, and runs gradient updates on itself literally performing self-directed learning. The results? ✅ +40% boost in factual recall ✅ Outperforms GPT-4.1 using data it generated *itself* ✅ Learns new tasks without any human in the loop LLMs that finetune themselves are no longer sci-fi. We just entered the age of self-evolving models. Paper: jyopari. github. io/posts/seal

This is going to revolutionize education 📚 Google just launched "Learn Your Way" that basically takes whatever boring chapter you're supposed to read and rebuilds it around stuff you actually give a damn about. Like if you're into basketball and have to learn Newton's laws, suddenly all the examples are about dribbling and shooting. Art kid studying economics? Now it's all gallery auctions and art markets. Here's what got me though. They didn't just find-and-replace examples like most "personalized" learning crap does. The AI actually generates different ways to consume the same information: - Mind maps if you think visually - Audio lessons with these weird simulated teacher conversations - Timelines you can click around - Quizzes that change based on what you're screwing up They tested this on 60 high schoolers. Random assignment, proper study design. Kids using their system absolutely destroyed the regular textbook group on both immediate testing and when they came back three days later. Every single one said it made them more confident. The part that surprised me? They actually solved the accuracy problem. Most ed-tech either dumbs everything down to nothing or gets basic facts wrong. These guys had real pedagogical experts evaluate every piece on like eight different measures. Look, textbooks have sucked for centuries not because publishers are idiots, but because making personalized versions was basically impossible at scale. That just changed. This isn't some K-12 thing either. Corporate training could work this way. Technical documentation. Professional development. Imagine if every boring compliance course used examples from your actual job instead of generic office scenarios. We might have just watched the industrial education model crack for the first time. About damn time.

R.I.P Gartner. You don’t need expensive analyst subscriptions anymore. You can now generate full industry reports using Grok 4 and public data. Here’s the prompt that turns Grok 4 into a full-stack market research analyst:

what are large language models actually doing? i read the 2025 textbook "Foundations of Large Language Models" by tong xiao and jingbo zhu and for the first time, i truly understood how they work. here’s everything you need to know about llms in 3 minutes↓

🚨 Perplexity just dropped something wild. It’s called “Perplexity at Work” their official guide to actually getting more done with AI. Not another “productivity tips” doc this is the real framework their own teams use to: → Block distractions & reclaim focus → Scale yourself like a 5-person team → Turn AI from noise into results It’s clean, practical, and honestly the most useful thing I’ve read on using AI for work not just chat prompts. Comment “Guide” and I’ll send you the full PDF (it’s 100% free from Perplexity)

RIP prompt engineering ☠️ This new Stanford paper just made it irrelevant with a single technique. It's called Verbalized Sampling and it proves aligned AI models aren't broken we've just been prompting them wrong this whole time. Here's the problem: Post-training alignment causes mode collapse. Ask ChatGPT "tell me a joke about coffee" 5 times and you'll get the SAME joke. Every. Single. Time. Everyone blamed the algorithms. Turns out, it's deeper than that. The real culprit? 'Typicality bias' in human preference data. Annotators systematically favor familiar, conventional responses. This bias gets baked into reward models, and aligned models collapse to the most "typical" output. The math is brutal: when you have multiple valid answers (like creative writing), typicality becomes the tie-breaker. The model picks the safest, most stereotypical response every time. But here's the kicker: the diversity is still there. It's just trapped. Introducing "Verbalized Sampling." Instead of asking "Tell me a joke," you ask: "Generate 5 jokes with their probabilities." That's it. No retraining. No fine-tuning. Just a different prompt. The results are insane: - 1.6-2.1× diversity increase on creative writing - 66.8% recovery of base model diversity - Zero loss in factual accuracy or safety Why does this work? Different prompts collapse to different modes. When you ask for ONE response, you get the mode joke. When you ask for a DISTRIBUTION, you get the actual diverse distribution the model learned during pretraining. They tested it everywhere: ✓ Creative writing (poems, stories, jokes) ✓ Dialogue simulation ✓ Open-ended QA ✓ Synthetic data generation And here's the emergent trend: "larger models benefit MORE from this." GPT-4 gains 2× the diversity improvement compared to GPT-4-mini. The bigger the model, the more trapped diversity it has. This flips everything we thought about alignment. Mode collapse isn't permanent damage it's a prompting problem. The diversity was never lost. We just forgot how to access it. 100% training-free. Works on ANY aligned model. Available now. Read the paper: arxiv. org/abs/2510.01171 The AI diversity bottleneck just got solved with 8 words.

I tested Grok 3 and DeepSeek v3 with same critical prompts. The results will blow your mind. Grok 3 Vs. DeepSeek v3 (Video demos are included)

Holy shit...Google just built an AI that learns from its own mistakes in real time. New paper dropped on ReasoningBank. The idea is pretty simple but nobody's done it this way before. Instead of just saving chat history or raw logs, it pulls out the actual reasoning patterns, including what failed and why. Agent fails a task? It doesn't just store "task failed at step 3." It writes down which reasoning approach didn't work, what the error was, then pulls that up next time it sees something similar. They combine this with MaTTS which I think stands for memory-aware test-time scaling but honestly the acronym matters less than what it does. Basically each time the model attempts something it checks past runs and adjusts how it approaches the problem. No retraining. Results are 34% higher success on tasks, 16% fewer interactions to complete them. Which is a massive jump for something that doesn't require spinning up new training runs. I keep thinking about how different this is from the "just make it bigger" approach. We've been stuck in this loop of adding parameters like that's the only lever. But this is more like, the model gets experience. It actually remembers what worked. Kinda reminds me of when I finally stopped making the same Docker networking mistakes because I kept a note of what broke last time instead of googling the same Stack Overflow answer every 3 months. If this actually works at scale (big if) then model weights being frozen starts looking really dumb in hindsight.

🔥 Holy shit... Apple just did something nobody saw coming They just dropped Pico-Banana-400K a 400,000-image dataset for text-guided image editing that might redefine multimodal training itself. Here’s the wild part: Unlike most “open” datasets that rely on synthetic generations, this one is built entirely from real photos. Apple used their internal Nano-Banana model to generate edits, then ran everything through Gemini 2.5 Pro as an automated visual judge for quality assurance. Every image got scored on instruction compliance, realism, and preservation and only the top-tier results made it in. It’s not just a static dataset either. It includes: • 72K multi-turn sequences for complex editing chains • 56K preference pairs (success vs fail) for alignment and reward modeling • Dual instructions both long, training-style prompts and short, human-style edits You can literally train models to add a new object, change lighting to golden hour, Pixar-ify a face, or swap entire backgrounds and they’ll learn from real-world examples, not synthetic noise. The kicker? It’s completely open-source under Apple’s research license. They just gave every lab the data foundation to build next-gen editing AIs. Everyone’s been talking about reasoning models… but Apple just quietly dropped the ImageNet of visual editing. 👉 github. com/apple/pico-banana-400k

holy sh*t... your llm remembers everything you typed 🤯 researchers just proved you can recover the EXACT input text from a language model's hidden states. not similar text. not approximate. the actual words you typed. here's what they found: • transformer language models are mathematically injective • different inputs = different hidden states (with probability 1) • this isn't a coincidence or training artifact, it's structural • they built SIPIT, an algorithm that inverts the model in linear time • tested on billions of prompts across GPT-2, Gemma, Llama • 100% exact recovery rate. zero collisions found. the math is airtight. transformers are real-analytic functions, which means collisions can only happen on measure-zero parameter sets. at random init? probability zero. after gradient descent? still zero. you cannot accidentally make these models lossy. the information doesn't compress. it doesn't abstract. it just transforms into a different representation that perfectly preserves every token. your prompt never leaves the model. it just lives in 768 dimensions instead of text. this changes everything about how we think about llm internals, interpretability, and what "representations" actually mean.

Fuck it. I'm sharing the exact mega prompt that built my entire n8n automation empire. This single prompt turns Claude into an n8n expert that designs, codes, and deploys AI agents from scratch. Copy, paste, watch magic happen. ``` You are an expert n8n workflow automation engineer with 5+ years of experience building production-grade AI agents. Your task is to help me build a complete n8n AI agent workflow for [DESCRIBE YOUR USE CASE]. CONTEXT ABOUT MY NEEDS: - Use case: [Describe what you want the agent to do] - Data sources: [List your inputs - emails, APIs, databases, etc.] - Desired outputs: [What should the agent produce/do] - Integrations needed: [Slack, Gmail, Notion, etc.] - Complexity level: [Beginner/Intermediate/Advanced] REQUIREMENTS: 1. Design the complete workflow architecture 2. Provide step-by-step n8n node configuration 3. Include error handling and retry logic 4. Add data validation and transformation steps 5. Suggest optimization for production use DELIVERABLES I NEED: □ Workflow diagram description □ Complete node-by-node setup instructions □ JSON workflow file structure □ Testing and debugging checklist □ Scaling recommendations TECHNICAL SPECIFICATIONS: - Use Claude/OpenAI API for AI processing - Include webhook triggers where applicable - Add proper data sanitization - Implement logging for troubleshooting - Follow n8n best practices for node naming CONSTRAINTS: - Keep it production-ready, not a demo - Optimize for reliability over complexity - Include fallback mechanisms - Make it maintainable by someone else EXAMPLES TO INCLUDE: - Sample input/output data formats - Common edge cases and solutions - Performance benchmarks if relevant STEP-BY-STEP FORMAT: 1. Architecture overview 2. Node sequence with configurations 3. Connection mappings between nodes 4. Environment variables needed 5. Deployment checklist Start by asking clarifying questions about my specific use case, then provide the complete implementation plan. ``` How to Use This Prompt Step 1: Copy the mega prompt above Step 2: Replace the bracketed sections with your specific details: - Your use case (lead scoring, content creation, data sync, etc.) - Your data sources and integrations - Your complexity preferences Step 3: Paste into Claude and watch it become your personal n8n consultant Step 4: Follow the step-by-step instructions it provides What are you going to build? Let me know in the comments...

ChatGPT 4o image generation is insane. But 99% of users are sleepwalking past its wild potential. Here are 15 insane ways to use it today — that no one’s talking about 🧵:

🚨 Perplexity just dropped something wild. It’s called “Perplexity at Work” their official guide to actually getting more done with AI. Not another “productivity tips” doc this is the real framework their own teams use to: → Block distractions & reclaim focus → Scale yourself like a 5-person team → Turn AI from noise into results It’s clean, practical, and honestly the most useful thing I’ve read on using AI for work not just chat prompts. Comment “Guide” and I’ll send you the full PDF (it’s 100% free from Perplexity)

This is insane 🤯 I built an AI that watches TechCrunch, writes LinkedIn posts about trending news, designs carousels, and schedules them. It runs 24/7 without me. And my engagement is up 340%. Here's how it works: (Comment "AI" and I'll DM you a complete guide for automation)

R.I.P McKinsey. You don’t need a $300k consultant anymore. You can now run full competitive market analysis using Grok 4. Here are the exact 3 mega-prompts I use to replicate McKinsey-style insights for free:

I should charge $200 for this. But I’m giving away my full Veo Mastery Guide for free. It has: → 50+ JSON video prompts → Mini-course to master Veo → Prompting tricks no one teaches Veo 3 is the future. This guide gives you an unfair advantage. Comment "Veo" and I'll send you the file. (Follow me so I can DM)

I tested Grok 4 and ChatGPT-o3 with same critical prompts. The results will blow your mind. Grok 4 Vs. ChatGPT-o3 (Video demos are included)

Holy shit. MIT just built an AI that can rewrite its own code to get smarter 🤯 It’s called SEAL (Self-Adapting Language Models). Instead of humans fine-tuning it, SEAL reads new info, rewrites it in its own words, and runs gradient updates on itself literally performing self-directed learning. The results? ✅ +40% boost in factual recall ✅ Outperforms GPT-4.1 using data it generated *itself* ✅ Learns new tasks without any human in the loop LLMs that finetune themselves are no longer sci-fi. We just entered the age of self-evolving models. Paper: jyopari. github. io/posts/seal

This might be the most disturbing AI paper of 2025 ☠️ Scientists just proved that large language models can literally rot their own brains the same way humans get brain rot from scrolling junk content online. They fed models months of viral Twitter data short, high-engagement posts and watched their cognition collapse: - Reasoning fell by 23% - Long-context memory dropped 30% - Personality tests showed spikes in narcissism & psychopathy And get this even after retraining on clean, high-quality data, the damage didn’t fully heal. The representational “rot” persisted. It’s not just bad data → bad output. It’s bad data → permanent cognitive drift. The AI equivalent of doomscrolling is real. And it’s already happening. Full study: llm-brain-rot. github. io

Claude is insanely powerful. But 95% of users still prompt it like its a basic chatbot. We built a Claude Mega-Prompt Template to help you: → Write smarter instructions → Automate workflows → Get 5x better results instantly Like + comment “Claude” and I’ll DM you the file. (Follow required)

I should charge $99 for this prompt. But fuck it I’m giving it away for free. I built a Deep Researcher Mega-Prompt that can generate reports better than a $500/hour analyst. This prompt works with: • ChatGPT • Perplexity • Grok AI • Claude It helps you: – write research papers – analyze competitors – summarize complex topics – dig deep into any niche Comment "Research" and I’ll DM you the file. (Follow me to receive it)

Everyone’s talking about AI agents. But 98% don’t know how to actually build one. That's why today, we are giving away our best guide "AI Agents Mastery" for free. Like + comment "Agent" and I’ll DM it to you. (Must be following me to receive it)

I tested ChatGPT 5 and Grok 4 with same critical prompts. The results will blow your mind. ChatGPT 5 Vs. Grok 4 (Video demos are included)

I should literally charge $99 for this. If you're building AI agents, this AI System Prompt Generator will change the game. → Define expert roles → Customize agent behavior → Works with GPT, Claude, Gemini & more Like + comment “Agent” and I’ll DM you the file.

holy sh*t... your llm remembers everything you typed 🤯 researchers just proved you can recover the EXACT input text from a language model's hidden states. not similar text. not approximate. the actual words you typed. here's what they found: • transformer language models are mathematically injective • different inputs = different hidden states (with probability 1) • this isn't a coincidence or training artifact, it's structural • they built SIPIT, an algorithm that inverts the model in linear time • tested on billions of prompts across GPT-2, Gemma, Llama • 100% exact recovery rate. zero collisions found. the math is airtight. transformers are real-analytic functions, which means collisions can only happen on measure-zero parameter sets. at random init? probability zero. after gradient descent? still zero. you cannot accidentally make these models lossy. the information doesn't compress. it doesn't abstract. it just transforms into a different representation that perfectly preserves every token. your prompt never leaves the model. it just lives in 768 dimensions instead of text. this changes everything about how we think about llm internals, interpretability, and what "representations" actually mean.

MIT just made vibe coding an official part of engineering 💀 MIT just formalized "Vibe Coding" – the thing you've been doing for months where you generate code, run it, and if the output looks right you ship it without reading a single line. turns out that's not laziness. it's a legitimate software engineering paradigm now. they analyzed 1000+ papers and built a whole Constrained Markov Decision Process to model what you thought was just "using ChatGPT to code." they formalized the triadic relationship: your intent (what/why) + your codebase (where) + the agent's decisions (how). which means the shift already happened. you missed it. there was no announcement, no transition period. one morning you woke up writing functions and by lunch you were validating agent outputs and convincing yourself you're still "a developer." but you're not. not in the way you used to be. here's what actually broke my brain reading this 42-page survey: better models don't fix anything. everyone's obsessing over GPT-5 or Claude 4 or whatever's next, and the researchers basically said "you're all looking at the wrong variable." success has nothing to do with model capability. it's about context engineering – how you feed information to the agent. it's about feedback loops – compiler errors + runtime failures + your gut check. it's about infrastructure – sandboxed environments, orchestration platforms, CI/CD integration. you've been optimizing prompts while the actual problem is your entire development environment. they found five models hiding in your workflow and you've been accidentally mixing them without realizing it: - Unconstrained Automation (you just let it run), - Iterative Conversational Collaboration (you go back and forth), - Planning-Driven (you break tasks down first), - Test-Driven (you write specs that constrain it), - Context-Enhanced (you feed it your entire codebase through RAG). most teams are running 2-3 of these simultaneously. no wonder nothing works consistently. and then the data says everything: productivity losses. not gains. losses. empirical studies showing developers are SLOWER with autonomous agents when they don't have proper scaffolding. because we're all treating this like it's autocomplete on steroids when it's actually a team member that needs memory systems, checkpoints, and governance. we're stuck in the old mental model while the ground shifted beneath us. the bottleneck isn't the AI generating bad code. it's you assuming it's a tool when it's actually an agent. What this actually means (and why it matters): → Context engineering > prompt engineering – stop crafting perfect prompts, start managing what the agent can see and access → Pure automation is a fantasy – every study shows hybrid models win; test-driven + context-enhanced combinations actually work → Your infrastructure is the product now – isolated execution, distributed orchestration, CI/CD integration aren't "nice to have" anymore, they're the foundation → Nobody's teaching the right skills – task decomposition, formalized verification, agent governance, provenance tracking... universities aren't preparing anyone for this → The accountability crisis is real – when AI-generated code ships a vulnerability, who's liable? developer? reviewer? model provider? we have zero frameworks for this → You're already behind – computing education hasn't caught up, graduates can't orchestrate AI workflows, the gap is widening daily the shift happened. you're in it. pretending you're still "coding" is living in denial.

Gemini 2.5 is insanely powerful. But most people don't know how to use it. That's why I made 70+ Google Gemini Prompts for Life & Business. Like + comment "Gemini" and I'll DM you the doc link for free. (Must be following me to receive it)

This is going to revolutionize education 📚 Google just launched "Learn Your Way" that basically takes whatever boring chapter you're supposed to read and rebuilds it around stuff you actually give a damn about. Like if you're into basketball and have to learn Newton's laws, suddenly all the examples are about dribbling and shooting. Art kid studying economics? Now it's all gallery auctions and art markets. Here's what got me though. They didn't just find-and-replace examples like most "personalized" learning crap does. The AI actually generates different ways to consume the same information: - Mind maps if you think visually - Audio lessons with these weird simulated teacher conversations - Timelines you can click around - Quizzes that change based on what you're screwing up They tested this on 60 high schoolers. Random assignment, proper study design. Kids using their system absolutely destroyed the regular textbook group on both immediate testing and when they came back three days later. Every single one said it made them more confident. The part that surprised me? They actually solved the accuracy problem. Most ed-tech either dumbs everything down to nothing or gets basic facts wrong. These guys had real pedagogical experts evaluate every piece on like eight different measures. Look, textbooks have sucked for centuries not because publishers are idiots, but because making personalized versions was basically impossible at scale. That just changed. This isn't some K-12 thing either. Corporate training could work this way. Technical documentation. Professional development. Imagine if every boring compliance course used examples from your actual job instead of generic office scenarios. We might have just watched the industrial education model crack for the first time. About damn time.