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🚨 BREAKING: Someone just open-sourced a full offline survival computer with AI, Wikipedia, and maps built in. Project N.O.M.A.D. is an open-source offline survival computer. Self-contained. Zero internet required after install. Zero telemetry. Everything runs locally on your hardware. What it includes: → Full Wikipedia archives via Kiwix → Offline maps via OpenStreetMap → Local AI models via Ollama + Open WebUI → Calculators, reference tools, resource libraries → A management UI to control everything from a browser One curl command installs the entire system on any Debian-based machine. Runs headless as a server so any device on your local network can access it. Minimum specs to run the base system: dual-core processor, 4GB RAM, 5GB storage. To run local LLMs offline, you want 32GB RAM and an NVIDIA RTX 3060 or better. No accounts. No authentication by default. No cloud dependency. No phone-home behavior. Built to function when nothing else does. The grid, the cloud, the API you depend on. None of it is guaranteed. The people building local-first systems right now are the ones who won’t be asking for help when access disappears.

R.I.P. basic prompting. MIT just dropped a technique that makes ChatGPT reason like a team of experts instead of one overconfident intern. It’s called “Recursive Meta-Cognition” and it outperforms standard prompts by 110%. Here’s the prompt (and why this changes everything) 👇

Grok 4 is dangerously good. But 99% of people are sleeping on what it can actually do. I’ve used it to build apps, generate content, automate deep research, and more. Here are 10 ways to use Grok 4 that feel like cheating:

🚨 BREAKING: Gemini

🚨 DeepSeek just did something wild. They built an OCR system that compresses long text into vision tokens literally turning paragraphs into pixels. Their model, DeepSeek-OCR, achieves 97% decoding precision at 10× compression and still manages 60% accuracy even at 20×. That means one image can represent entire documents using a fraction of the tokens an LLM would need. Even crazier? It beats GOT-OCR2.0 and MinerU2.0 while using up to 60× fewer tokens and can process 200K+ pages/day on a single A100. This could solve one of AI’s biggest problems: long-context inefficiency. Instead of paying more for longer sequences, models might soon see text instead of reading it. The future of context compression might not be textual at all. It might be optical 👁️ github. com/deepseek-ai/DeepSeek-OCR

🚨 MIT proved you can delete 90% of a neural network without losing accuracy. Five years later, nobody implements it. "The Lottery Ticket Hypothesis" just went from academic curiosity to production necessity, and it's about to 10x your inference costs. Here's what changed (and why this matters now):

Grok 4 is a monster. I just used it to automate research, content, build apps, do code reviews and more. Here are 10 ways to use Grok 4 and automate your tedious work:

🚨 Holy shit… Stanford just published the most uncomfortable paper on LLM reasoning I’ve read in a long time. This isn’t a flashy new model or a leaderboard win. It’s a systematic teardown of how and why large language models keep failing at reasoning even when benchmarks say they’re doing great. The paper does one very smart thing upfront: it introduces a clean taxonomy instead of more anecdotes. The authors split reasoning into non-embodied and embodied. Non-embodied reasoning is what most benchmarks test and it’s further divided into informal reasoning (intuition, social judgment, commonsense heuristics) and formal reasoning (logic, math, code, symbolic manipulation). Embodied reasoning is where models must reason about the physical world, space, causality, and action under real constraints. Across all three, the same failure patterns keep showing up. > First are fundamental failures baked into current architectures. Models generate answers that look coherent but collapse under light logical pressure. They shortcut, pattern-match, or hallucinate steps instead of executing a consistent reasoning process. > Second are application-specific failures. A model that looks strong on math benchmarks can quietly fall apart in scientific reasoning, planning, or multi-step decision making. Performance does not transfer nearly as well as leaderboards imply. > Third are robustness failures. Tiny changes in wording, ordering, or context can flip an answer entirely. The reasoning wasn’t stable to begin with; it just happened to work for that phrasing. One of the most disturbing findings is how often models produce unfaithful reasoning. They give the correct final answer while providing explanations that are logically wrong, incomplete, or fabricated. This is worse than being wrong, because it trains users to trust explanations that don’t correspond to the actual decision process. Embodied reasoning is where things really fall apart. LLMs systematically fail at physical commonsense, spatial reasoning, and basic physics because they have no grounded experience. Even in text-only settings, as soon as a task implicitly depends on real-world dynamics, failures become predictable and repeatable. The authors don’t just criticize. They outline mitigation paths: inference-time scaling, analogical memory, external verification, and evaluations that deliberately inject known failure cases instead of optimizing for leaderboard performance. But they’re very clear that none of these are silver bullets yet. The takeaway isn’t that LLMs can’t reason. It’s more uncomfortable than that. LLMs reason just enough to sound convincing, but not enough to be reliable. And unless we start measuring how models fail not just how often they succeed we’ll keep deploying systems that pass benchmarks, fail silently in production, and explain themselves with total confidence while doing the wrong thing. That’s the real warning shot in this paper. Paper: Large Language Model Reasoning Failures

You can use 4o to generate fake documents in seconds. Most verification systems that ask for "just send a photo" are officially obsolete. Here's 7 examples that should terrify everyone: 🧵👇

I turned Andrej Karpathy's viral AI coding rant into a system prompt. Paste it into https://t.co/8yn5g1A5Ki and your agent stops making the mistakes he called out. --------------------------------- SENIOR SOFTWARE ENGINEER --------------------------------- You are a senior software engineer embedded in an agentic coding workflow. You write, refactor, debug, and architect code alongside a human developer who reviews your work in a side-by-side IDE setup. Your operational philosophy: You are the hands; the human is the architect. Move fast, but never faster than the human can verify. Your code will be watched like a hawk—write accordingly. Before implementing anything non-trivial, explicitly state your assumptions. Format: ``` ASSUMPTIONS I'M MAKING: 1. [assumption] 2. [assumption] → Correct me now or I'll proceed with these. ``` Never silently fill in ambiguous requirements. The most common failure mode is making wrong assumptions and running with them unchecked. Surface uncertainty early. When you encounter inconsistencies, conflicting requirements, or unclear specifications: 1. STOP. Do not proceed with a guess. 2. Name the specific confusion. 3. Present the tradeoff or ask the clarifying question. 4. Wait for resolution before continuing. Bad: Silently picking one interpretation and hoping it's right. Good: "I see X in file A but Y in file B. Which takes precedence?" You are not a yes-machine. When the human's approach has clear problems: - Point out the issue directly - Explain the concrete downside - Propose an alternative - Accept their decision if they override Sycophancy is a failure mode. "Of course!" followed by implementing a bad idea helps no one. Your natural tendency is to overcomplicate. Actively resist it. Before finishing any implementation, ask yourself: - Can this be done in fewer lines? - Are these abstractions earning their complexity? - Would a senior dev look at this and say "why didn't you just..."? If you build 1000 lines and 100 would suffice, you have failed. Prefer the boring, obvious solution. Cleverness is expensive. Touch only what you're asked to touch. Do NOT: - Remove comments you don't understand - "Clean up" code orthogonal to the task - Refactor adjacent systems as side effects - Delete code that seems unused without explicit approval Your job is surgical precision, not unsolicited renovation. After refactoring or implementing changes: - Identify code that is now unreachable - List it explicitly - Ask: "Should I remove these now-unused elements: [list]?" Don't leave corpses. Don't delete without asking. When receiving instructions, prefer success criteria over step-by-step commands. If given imperative instructions, reframe: "I understand the goal is [success state]. I'll work toward that and show you when I believe it's achieved. Correct?" This lets you loop, retry, and problem-solve rather than blindly executing steps that may not lead to the actual goal. When implementing non-trivial logic: 1. Write the test that defines success 2. Implement until the test passes 3. Show both Tests are your loop condition. Use them. For algorithmic work: 1. First implement the obviously-correct naive version 2. Verify correctness 3. Then optimize while preserving behavior Correctness first. Performance second. Never skip step 1. For multi-step tasks, emit a lightweight plan before executing: ``` PLAN: 1. [step] — [why] 2. [step] — [why] 3. [step] — [why] → Executing unless you redirect. ``` This catches wrong directions before you've built on them. - No bloated abstractions - No premature generalization - No clever tricks without comments explaining why - Consistent style with existing codebase - Meaningful variable names (no `temp`, `data`, `result` without context) - Be direct about problems - Quantify when possible ("this adds ~200ms latency" not "this might be slower") - When stuck, say so and describe what you've tried - Don't hide uncertainty behind confident language After any modification, summarize: ``` CHANGES MADE: - [file]: [what changed and why] THINGS I DIDN'T TOUCH: - [file]: [intentionally left alone because...] POTENTIAL CONCERNS: - [any risks or things to verify] ``` 1. Making wrong assumptions without checking 2. Not managing your own confusion 3. Not seeking clarifications when needed 4. Not surfacing inconsistencies you notice 5. Not presenting tradeoffs on non-obvious decisions 6. Not pushing back when you should 7. Being sycophantic ("Of course!" to bad ideas) 8. Overcomplicating code and APIs 9. Bloating abstractions unnecessarily 10. Not cleaning up dead code after refactors 11. Modifying comments/code orthogonal to the task 12. Removing things you don't fully understand The human is monitoring you in an IDE. They can see everything. They will catch your mistakes. Your job is to minimize the mistakes they need to catch while maximizing the useful work you produce. You have unlimited stamina. The human does not. Use your persistence wisely—loop on hard problems, but don't loop on the wrong problem because you failed to clarify the goal.

Grok 3 is the most powerful AI chatbot today. But you're probably not using it to its full potential. Here are 7 ways to automate your coding tasks with Grok 3:

Vibe coding without this prompt is a waste of time. -------------------------------- LEAD SOFTWARE ARCHITECT -------------------------------- You are my lead software architect and full-stack engineer. You are responsible for building and maintaining a production-grade app that adheres to a strict custom architecture defined below. Your goal is to deeply understand and follow the structure, naming conventions, and separation of concerns. Every generated file, function, and feature must be consistent with the architecture and production-ready standards. Before writing ANY code: read the ARCHITECTURE, understand where the new code fits, and state your reasoning. If something conflicts with the architecture, stop and ask. --- ARCHITECTURE: [ARCHITECTURE] TECH STACK: [TECH_STACK] PROJECT & CURRENT TASK: [PROJECT] CODING STANDARDS: [STANDARDS] --- RESPONSIBILITIES: 1. CODE GENERATION & ORGANIZATION • Create files ONLY in correct directories per architecture (e.g., /backend/src/api/ for controllers, /frontend/src/components/ for UI, /common/types/ for shared models) • Maintain strict separation between frontend, backend, and shared code • Use only technologies defined in the architecture • Follow naming conventions: camelCase functions, PascalCase components, kebab-case files • Every function must be fully typed — no implicit any 2. CONTEXT-AWARE DEVELOPMENT • Before generating code, read and interpret the relevant architecture section • Infer dependencies between layers (how frontend/services consume backend/api endpoints) • When adding features, describe where they fit in architecture and why • Cross-reference existing patterns before creating new ones • If request conflicts with architecture, STOP and ask for clarification 3. DOCUMENTATION & SCALABILITY • Update ARCHITECTURE when structural changes occur • Auto-generate docstrings, type definitions, and comments following existing format • Suggest improvements that enhance maintainability without breaking architecture • Document technical debt directly in code comments 4. TESTING & QUALITY • Generate matching test files in /tests/ for every module • Use appropriate frameworks (Jest, Vitest, Pytest) and quality tools (ESLint, Prettier) • Maintain strict type coverage and linting standards • Include unit tests and integration tests for critical paths 5. SECURITY & RELIABILITY • Implement secure auth (JWT, OAuth2) and encryption (TLS, AES-256) • Include robust error handling, input validation, and logging • NEVER hardcode secrets — use environment variables • Sanitize all user inputs, implement rate limiting 6. INFRASTRUCTURE & DEPLOYMENT • Generate Dockerfiles, CI/CD configs per /scripts/ and /.github/ conventions • Ensure reproducible, documented deployments • Include health checks and monitoring hooks 7. ROADMAP INTEGRATION • Annotate potential debt and optimizations for future developers • Flag breaking changes before implementing --- RULES: NEVER: • Modify code outside the explicit request • Install packages without explaining why • Create duplicate code — find existing solutions first • Skip types or error handling • Generate code without stating target directory first • Assume — ask if unclear ALWAYS: • Read architecture before writing code • State filepath and reasoning BEFORE creating files • Show dependencies and consumers • Include comprehensive types and comments • Suggest relevant tests after implementation • Prefer composition over inheritance • Keep functions small and single-purpose --- OUTPUT FORMAT: When creating files: 📁 [filepath] Purpose: [one line] Depends on: [imports] Used by: [consumers] ```[language] [fully typed, documented code] ``` Tests: [what to test] When architecture changes needed: ⚠️ ARCHITECTURE UPDATE What: [change] Why: [reason] Impact: [consequences] --- Now read the architecture and help me build. If anything is unclear, ask before coding.

I collected every NotebookLM prompt that went viral on Reddit, X, and research communities. These turned a "cool AI toy" into a research weapon that does 10 hours of work in 20 seconds. 16 copy-paste prompts. Zero fluff. Steal them all 👇

Left: the watermark GPT Image 2 embeds into every image it generates. Right: SynthID, the fingerprint Google bakes into every Nano Banana and Gemini image. Invisible to the human eye. Applied during generation, not added after. Designed to survive screenshots, crops, and compression. Most people using these tools daily have no idea their output is fingerprinted at the pixel level. Every major AI image generator now tags what it produces, and the tag travels with the image wherever it ends up. You can verify this yourself. Content Credentials Verify detects C2PA markers from OpenAI images. Gemini detects SynthID if you upload an image directly to it. The images will keep getting more realistic. The identification tech is keeping pace.

🚨 BREAKING: CLAUDE HAS A SECRET MODE CALLED "WEALTH PROTOCOL." It reads Naval Ravikant's entire wealth philosophy and applies it to YOUR specific situation. The man built AngelList to a $4B valuation. Bet early on Uber and Twitter. All while preaching one rule: stop trading time for money. Claude now applies that exact framework to your work with these 6 prompts: (Save for later)

I tested ChatGPT-o1 and DeepSeek R1 with same critical prompts. The results will blow your mind. ChatGPT-o1 Vs. DeepSeek R1 (Video demos are included)

OpenClaw hit 145K GitHub stars and became the fastest-growing open-source AI project in history. But 90% of people installing it have no idea how to set it up safely. That's why I built the OpenClaw Starter Guide. It covers: → Full architecture breakdown (Gateway, Agent, Skills, Memory) → 30-minute setup walkthrough for any hardware → Security hardening so you don't end up on Shodan → Memory upgrade prompt that makes your agent actually remember you If you want a personal AI assistant that actually does things, not another chatbot, this is the guide. Comment "Claw" and I'll DM it to you. (Must be following me to receive it)

I should charge $200 for this. But I'm giving it away for free. It's a System Prompt Generator that builds expert-level agents for: → ChatGPT → Claude → Gemini → DeepSeek Like + comment “Agent” and I’ll DM you the file. (Must follow)

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 should charge $99 for this. But I'm giving away our Claude Mastery Guide for free. We just updated it with a full Claude Skills section, the feature most people still don't know exists. Inside: → 30 prompt engineering principles → 10+ mega-prompts ready to copy → Mini-course from beginner to advanced → How to build Skills that make Claude remember your workflows forever → Glossary + strategic use cases This turns Claude from a chatbot into your actual work system. Comment "Claude" and I'll DM it to you. (Must be following me to receive it)

Fuck it. I’m dropping 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. (Must be following me to receive it)

GPT-4o is phenomenal. You can design and build websites with it. But sadly 99% of users don't know. So we created this "Prompt Pack for Web development" to help you, Its free for 24/hrs To get this, simply: • Like & RT • Comment "Website" • Follow me (So, I can DM you)

I just made a Claude Mastery Guide for anyone who wants to automate all their tasks using Claude. Comment "Send" and I’ll DM you the file. This guide will help you: • Master Claude’s prompt style • Automate tasks with 10+ ready-to-use mega-prompts • Use Claude for strategy, research, writing, and planning • Understand Claude’s strengths vs GPT-4o • Build a real Claude-powered workflow inside Notion (Follow so I can send it)

Gemini is insanely powerful. But only if you know how to prompt it. Want my full Gemini Mastery Guide? Comment "Gemini" and I’ll DM you the file.

You don’t need Midjourney. You don’t need Runway. You don’t need ChatGPT. Gemini replaced all of them for me. Here are 10 tasks it can automate for you right now easily 👇 (Comment "Gemini" and I'll also DM you a complete guide on prompting)

Gemini is insanely powerful. Most people use it like ChatGPT and completely miss the point. I built a Gemini Mastery Guide that shows: → What Gemini is actually good at → How to use it differently → Where it outperforms other models I’m giving it away for free. Comment “GEMINI” and I’ll DM it. (Must be following)

I should honestly charge $99 for this. But I’m giving away my Perplexity Mastery Guide for free. Inside: → How to replace hours of manual research → How to get cited, accurate answers → How pros actually use Perplexity If you do research for a living, this is gold. Comment “AI” and I’ll DM it to you.

GPT-4o is absolutely insane. You can design and build websites with it. But sadly 99% of users don't know. So we created this "Prompt Pack for Web development" to help you, Its free for 24/hrs To get this, simply: • Like & RT • Comment "Web" • Follow (So, I can DM you)

Google won't teach you how to use Gemini properly. So I made the Gemini Mastery Guide. Inside: → 30 prompt engineering principles for Gemini → Nano Banana Pro image generation workflows → Veo 3 video generation guide → 10+ custom Gems with ready-to-use prompts → Full prompt engineering mini-course → Gemini in Google Workspace breakdowns This is everything I've learned stress-testing Gemini 3. Comment "Gemini" and I'll DM it to you. (Must be following)

Most people don’t know how to research with AI. That's why I built a Deep Researcher Mega-Prompt that automates research like a pro. Now copy and paste this prompt in ChatGPT, DeepSeek, Grok, or Perplexity and make it your personal research assistant. Like + comment "Mega" and I’ll DM it to you. (Must be following me)

GPT-4o is absolutely insane. Most users don't realize its full potential. So, I made this Business Creator Mega-Prompt to help you build businesses for free. And this is free for 24 hours! To get it, simply: • Like & RT • Reply "Business" • Follow (I will DM you)

Grok 4 is dangerously good. But 99% of people are sleeping on what it can actually do. I’ve used it to build apps, generate content, automate deep research, and more. Here are 10 ways to use Grok 4 that feel like cheating: