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    "text": "### \ud83c\udf10 THE ARCHITECT\u2019S CHOICE: WHY INTERLINK BUILT SOVEREIGN\n\nThe era of \"rented infrastructure\" is over. As Layer 2s fade into the background of Ethereum\u2019s upgrades, InterLink has engineered a third path. We aren't building a \"tenant\" on someone else\u2019s land\u2014we\u2019ve built the bedrock.\n\nInterLink is a custom-built, Quantum-Resistant Layer 1 designed for the next 1 billion users.\n\n#### \ud83d\udc8e EXECUTIVE STRATEGY\n* **Infrastructure Sovereignty**: Absolute control over consensus design, validator economics, and long-term evolution without external dependency.\n* **Quantum-Resistant Bedrock**: A ground-up architecture optimized for peak TPS and future-proof security.\n* **Beyond the \"Killer\" Narrative**: We don't compete with Ethereum or Bitcoin; we combine their strengths to solve their weaknesses.\n* **Dual-Core Utility**: Structurally optimized to function as both a seamless global payment currency and a reserve-grade asset.\n\n#### \ud83d\ude80 THE VISION\nWhile others chase Web3 trends, InterLink redefines access. We are building the primary infrastructure for global financial democratization.\n\n#InterLink #SovereignBlockchain #Web3Infrastructure #QuantumResistant #DigitalReserve #FutureOfFinance #Layer1 #CryptoStrategy",
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    "text": "\ud835\udddc\ud835\uddfb \ud835\ude01\ud835\uddf5\ud835\uddf2 \ud835\uddea\ud835\uddf2\ud835\uddef\ud835\udfee \ud835\uddd8\ud835\uddff\ud835\uddee, \ud835\udde6\ud835\uddf0\ud835\uddee\ud835\uddf9\ud835\uddf2 \ud835\uddea\ud835\uddee\ud835\ude00 \ud835\ude01\ud835\uddf5\ud835\uddf2 \ud835\udde2\ud835\uddfb\ud835\uddf9\ud835\ude06 \ud835\uddea\ud835\uddee\ud835\ude06 \ud835\ude01\ud835\uddfc \ud835\uddea\ud835\uddf6\ud835\uddfb. \ud835\udddc\ud835\uddfb \ud835\ude01\ud835\uddf5\ud835\uddf2 \ud835\uddea\ud835\uddf2\ud835\uddef\ud835\udfef \ud835\uddd8\ud835\uddff\ud835\uddee, \u201c\ud835\uddd5\ud835\ude02\ud835\uddfb\ud835\uddf1\ud835\uddf9\ud835\uddf6\ud835\uddfb\ud835\uddf4\u201d \ud835\uddf6\ud835\ude00 \ud835\ude01\ud835\uddf5\ud835\uddf2 \ud835\uddda\ud835\uddff\ud835\uddf2\ud835\uddee\ud835\ude01 \ud835\uddd8\ud835\uddfe\ud835\ude02\ud835\uddee\ud835\uddf9\ud835\uddf6\ud835\ude07\ud835\uddf2\ud835\uddff.\nImagine Sarah. Sarah is a world-class expert in a highly specific field: the restoration of 14th-century stained glass. There are perhaps only fifty people on the planet who understand the chemical reaction between specific medieval oxides and modern atmospheric pollutants as well as she does.\n\nFor years, Sarah shared her findings on specialized forums and her personal blog. She wasn't looking for fame; she wanted to preserve history. Last month, she used a multi-billion dollar AI assistant to help a student. She asked a complex question about glass calcification. The AI responded with a perfect, elegant synthesis of her life\u2019s work\u2014using the exact terminology she invented.\nThe AI company is currently raising capital at an $80 billion valuation. Sarah, meanwhile, is struggling to fund her next restoration project.\n\nIn the Web2 era, this was the inevitable conclusion. Scale was the only way to win. If you didn\u2019t have a billion users or a trillion data points, you were just a \"data point\" to be harvested by those who did. Giant platforms used their massive scale to scrape every niche of human knowledge, centralizing the value while the experts\u2014the Sarahs of the world\u2014remained \"niche\" and uncompensated.\nBut we are entering the Web3 era, and the rules of the game are changing. If scale was the weapon of the giants, \"Bundling\" is the great equalizer for the rest of us.\n\nEnter \ud835\uddd6\ud835\uddfc\ud835\uddf1\ud835\uddee\ud835\ude01\ud835\ude01\ud835\uddee. Codatta is a decentralized knowledge layer that allows individual experts to bundle their structured expertise into blockchain-based data assets. Instead of being an isolated voice in a sea of noise, Codatta allows you to combine your \"alpha\" with a global network of peers, creating a verifiable knowledge base that AI companies can\u2019t just scrape\u2014they have to license.\nIn this new paradigm, being \"niche\" is no longer a weakness; it is your greatest financial asset.\n\n\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udde6\ud835\uddf0\ud835\uddff\ud835\uddee\ud835\uddfd\ud835\uddf2\ud835\uddff\u2019\ud835\ude00 \ud835\uddd6\ud835\uddf2\ud835\uddf6\ud835\uddf9\ud835\uddf6\ud835\uddfb\ud835\uddf4 \u2014 \ud835\uddea\ud835\uddf5\ud835\ude06 \ud835\udde6\ud835\uddf0\ud835\uddee\ud835\uddf9\ud835\uddf2 \ud835\uddf6\ud835\ude00 \ud835\uddd9\ud835\uddee\ud835\uddf6\ud835\uddf9\ud835\uddf6\ud835\uddfb\ud835\uddf4 \ud835\uddd4\ud835\udddc\nFor the last decade, the AI industry has operated on a \"More is Better\" philosophy. If you scrape enough of the internet, the logic goes, the machine will eventually become \"intelligent.\" This is the Web2 approach to AI training: centralized, indiscriminate, and extractive.\n\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udde3\ud835\uddff\ud835\uddfc\ud835\uddef\ud835\uddf9\ud835\uddf2\ud835\uddfa \ud835\ude04\ud835\uddf6\ud835\ude01\ud835\uddf5 \ud835\udddc\ud835\uddfb\ud835\uddf1\ud835\uddf6\ud835\ude00\ud835\uddf0\ud835\uddff\ud835\uddf6\ud835\uddfa\ud835\uddf6\ud835\uddfb\ud835\uddee\ud835\ude01\ud835\uddf2 \ud835\udde6\ud835\uddf0\ud835\uddee\ud835\uddf9\ud835\uddf2\nThe \"scale\" model has hit a ceiling. When AI models train on the \"Common Crawl\" (a massive scrape of the public web), they ingest everything\u2014the brilliance of experts, the noise of bots, and the bias of internet arguments.\nThe Stat: Recent studies indicate that AI models trained on raw, scraped web data fail up to 30% more often in specialized, real-world tasks compared to models trained on curated, human-verified datasets.\nWhen AI trains on \"garbage,\" it produces \"hallucinations.\" It lacks the nuance that only a human-in-the-loop can provide. Furthermore, the internet is becoming a hall of mirrors: AI is now training on content generated by other AI, leading to \"Model Collapse.\"\n\n\ud835\uddea\ud835\uddf2\ud835\uddef\ud835\udfef \ud835\uddee\ud835\uddfb\ud835\uddf1 \ud835\ude01\ud835\uddf5\ud835\uddf2 \ud835\udde3\ud835\uddfc\ud835\ude04\ud835\uddf2\ud835\uddff \ud835\uddfc\ud835\uddf3 \ud835\uddd7\ud835\uddf2\ud835\uddf0\ud835\uddf2\ud835\uddfb\ud835\ude01\ud835\uddff\ud835\uddee\ud835\uddf9\ud835\uddf6\ud835\ude07\ud835\uddf2\ud835\uddf1 \"\ud835\uddd5\ud835\ude02\ud835\uddfb\ud835\uddf1\ud835\uddf9\ud835\uddf6\ud835\uddfb\ud835\uddf4\"\nWeb3 fixes this by shifting the focus from Volume to Verifiability. Codatta doesn't try to scrape the whole world; it allows the world to bundle its best knowledge.\n1.\ud835\uddd9\ud835\uddff\ud835\uddfc\ud835\uddfa \ud835\uddd8\ud835\ude05\ud835\ude01\ud835\uddff\ud835\uddee\ud835\uddf0\ud835\ude01\ud835\uddf6\ud835\uddfc\ud835\uddfb \ud835\ude01\ud835\uddfc \ud835\uddd6\ud835\uddfc\ud835\uddfb\ud835\ude01\ud835\uddff\ud835\uddf6\ud835\uddef\ud835\ude02\ud835\ude01\ud835\uddf6\ud835\uddfc\ud835\uddfb: In Web2, your data is taken. In Web3 (Codatta), your data is contributed as a sovereign asset.\n2.\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udde0\ud835\uddf2\ud835\ude01\ud835\uddee\ud835\uddf1\ud835\uddee\ud835\ude01\ud835\uddee \ud835\uddfc\ud835\uddf3 \ud835\udde7\ud835\uddff\ud835\ude02\ud835\ude01\ud835\uddf5: Every piece of data on Codatta is structured and verified by a decentralized network of peers. This adds a layer of \"truth\" that raw scraping can never achieve.\n3.\ud835\udde2\ud835\ude04\ud835\uddfb\ud835\uddf2\ud835\uddff\ud835\ude00\ud835\uddf5\ud835\uddf6\ud835\uddfd \ud835\ude03\ud835\uddf6\ud835\uddee \ud835\ude01\ud835\uddf5\ud835\uddf2 \ud835\udddf\ud835\uddf2\ud835\uddf1\ud835\uddf4\ud835\uddf2\ud835\uddff: By using blockchain, Codatta ensures that the \"bundle\" of knowledge belongs to the contributors, not the platform.\n\n\ud835\udde5\ud835\uddf2\ud835\uddee\ud835\uddf9-\ud835\uddea\ud835\uddfc\ud835\uddff\ud835\uddf9\ud835\uddf1 \ud835\udddc\ud835\uddfa\ud835\uddfd\ud835\uddee\ud835\uddf0\ud835\ude01 \u2014 \ud835\udde7\ud835\ude02\ud835\uddff\ud835\uddfb\ud835\uddf6\ud835\uddfb\ud835\uddf4 \ud835\udde1\ud835\uddf6\ud835\uddf0\ud835\uddf5\ud835\uddf2 \ud835\uddd8\ud835\ude05\ud835\uddfd\ud835\uddf2\ud835\uddff\ud835\ude01\ud835\uddf6\ud835\ude00\ud835\uddf2 \ud835\uddf6\ud835\uddfb\ud835\ude01\ud835\uddfc \ud835\uddda\ud835\uddf9\ud835\uddfc\ud835\uddef\ud835\uddee\ud835\uddf9 \ud835\uddd4\ud835\ude00\ud835\ude00\ud835\uddf2\ud835\ude01\ud835\ude00\nThe \"\ud835\uddda\ud835\uddff\ud835\uddf2\ud835\uddee\ud835\ude01 \ud835\uddd8\ud835\uddfe\ud835\ude02\ud835\uddee\ud835\uddf9\ud835\uddf6\ud835\ude07\ud835\uddf2\ud835\uddff\" works because it allows individuals to participate in the AI economy as asset owners, not just users. Let\u2019s look at how this bundling of expertise creates real-world impact and income.\n\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udde5\ud835\uddee\ud835\uddff\ud835\uddf2 \ud835\uddd7\ud835\uddf6\ud835\ude00\ud835\uddf2\ud835\uddee\ud835\ude00\ud835\uddf2 \ud835\uddd6\ud835\uddfc\ud835\uddfb\ud835\ude00\ud835\uddfc\ud835\uddff\ud835\ude01\ud835\uddf6\ud835\ude02\ud835\uddfa (\ud835\uddd7\ud835\uddff. \ud835\uddd4\ud835\uddff\ud835\uddf6\ud835\ude00)\nDr. Aris is a pediatric neurologist. He has a dataset of rare seizure patterns that is too small to be \"scale\" but too valuable to be ignored.\n\u2022\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\uddd5\ud835\ude02\ud835\uddfb\ud835\uddf1\ud835\uddf9\ud835\uddf6\ud835\uddfb\ud835\uddf4: Aris joins a Codatta \"Knowledge Vault\" for Rare Neurology. He and 200 other specialists bundle their structured case studies.\n\u2022\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udddc\ud835\uddfa\ud835\uddfd\ud835\uddee\ud835\uddf0\ud835\ude01: A pharmaceutical AI licenses this \"bundle\" because it\u2019s the only verified, high-quality data of its kind in existence.\n\u2022\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udde5\ud835\uddf2\ud835\ude00\ud835\ude02\ud835\uddf9\ud835\ude01: Every time the AI makes a diagnostic suggestion based on that bundle, Aris and his peers earn ongoing royalties. He has turned his \"niche\" into a \"yield-bearing asset.\"\n\n\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udde5\ud835\ude02\ud835\ude00\ud835\ude01 \ud835\udddf\ud835\uddee\ud835\uddfb\ud835\uddf4\ud835\ude02\ud835\uddee\ud835\uddf4\ud835\uddf2 \ud835\udde3\ud835\uddf6\ud835\uddfc\ud835\uddfb\ud835\uddf2\ud835\uddf2\ud835\uddff (\ud835\udddf\ud835\uddf2\ud835\uddfc)\nLeo is an expert in the Rust programming language, specifically in memory-safe protocols for decentralized finance.\n\u2022\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udde6\ud835\ude01\ud835\uddfc\ud835\uddff\ud835\ude06: A major AI coding assistant is great at Python but struggles with Leo\u2019s specific niche of Rust.\n\u2022\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\uddd5\ud835\ude02\ud835\uddfb\ud835\uddf1\ud835\uddf9\ud835\uddf6\ud835\uddfb\ud835\uddf4: Leo uses Codatta to structure 500 \"Problem-Solution\" pairs. Other Rust experts verify his work.\n\u2022\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\uddd8\ud835\uddee\ud835\uddff\ud835\uddfb\ud835\uddf6\ud835\uddfb\ud835\uddf4\ud835\ude00: When an enterprise AI model integrates the Codatta knowledge layer to improve its Rust capabilities, Leo sees the royalty flow directly to his Web3 wallet.\n\n\ud835\uddd6\ud835\ude02\ud835\uddf9\ud835\ude01\ud835\ude02\ud835\uddff\ud835\uddee\ud835\uddf9 \ud835\udde1\ud835\ude02\ud835\uddee\ud835\uddfb\ud835\uddf0\ud835\uddf2 \ud835\uddf6\ud835\uddfb \ud835\udde0\ud835\uddee\ud835\uddff\ud835\uddf8\ud835\uddf2\ud835\ude01\ud835\uddf6\ud835\uddfb\ud835\uddf4 (\ud835\uddd8\ud835\uddf9\ud835\uddf2\ud835\uddfb\ud835\uddee)\nElena understands the specific cultural taboos and trends of the Andean region in South America\u2014data that Western-centric AI models consistently get wrong.\n\u2022\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\udddc\ud835\uddfa\ud835\uddfd\ud835\uddee\ud835\uddf0\ud835\ude01: By contributing her \"Cultural Context\" datasets to Codatta, Elena ensures that AI models used by global brands are accurate and ethical.\n\u2022\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\uddd8\ud835\uddee\ud835\uddff\ud835\uddfb\ud835\uddf6\ud835\uddfb\ud835\uddf4\ud835\ude00: She isn't a \"gig worker\" doing one-off tasks; she is a Knowledge Asset Creator whose data provides recurring value.\n\n\ud835\uddec\ud835\uddfc\ud835\ude02\ud835\uddff \ud835\udde5\ud835\uddfc\ud835\uddee\ud835\uddf1\ud835\uddfa\ud835\uddee\ud835\uddfd \ud835\ude01\ud835\uddfc \ud835\ude01\ud835\uddf5\ud835\uddf2 \ud835\uddde\ud835\uddfb\ud835\uddfc\ud835\ude04\ud835\uddf9\ud835\uddf2\ud835\uddf1\ud835\uddf4\ud835\uddf2 \ud835\uddd8\ud835\uddf0\ud835\uddfc\ud835\uddfb\ud835\uddfc\ud835\uddfa\ud835\ude06\nThe transition from a \"Content Creator\" to a \"Knowledge Asset Creator\" is the most significant career shift of the decade. Codatta makes this transition accessible to everyone.\n\ud835\udde6\ud835\ude01\ud835\uddf2\ud835\uddfd \ud835\udfed: \ud835\udde6\ud835\uddf2\ud835\uddf0\ud835\ude02\ud835\uddff\ud835\uddf2 \ud835\uddec\ud835\uddfc\ud835\ude02\ud835\uddff \ud835\udddc\ud835\uddf1\ud835\uddf2\ud835\uddfb\ud835\ude01\ud835\uddf6\ud835\ude01\ud835\ude06 (\ud835\udde2\ud835\uddfb\ud835\uddef\ud835\uddfc\ud835\uddee\ud835\uddff\ud835\uddf1 \ud835\uddee\ud835\ude01 \ud835\uddd6\ud835\uddfc\ud835\uddf1\ud835\uddee\ud835\ude01\ud835\ude01\ud835\uddee.\ud835\uddf6\ud835\uddfc)\nVisit https://t.co/jPH0XOhK8T and connect your Web3 wallet. In the Web3 era, your wallet is your resume, your vault, and your bank. Codatta uses this decentralized identity to ensure that your contributions are linked to you\u2014and only you\u2014forever.\n\ud835\udde6\ud835\ude01\ud835\uddf2\ud835\uddfd \ud835\udfee: \ud835\udddc\ud835\uddf1\ud835\uddf2\ud835\uddfb\ud835\ude01\ud835\uddf6\ud835\uddf3\ud835\ude06 \ud835\uddec\ud835\uddfc\ud835\ude02\ud835\uddff \"\ud835\uddd4\ud835\uddf9\ud835\uddfd\ud835\uddf5\ud835\uddee\"\nWhat do you know that an AI doesn't? What is your specialized \"niche\"? Codatta features various \"Knowledge Vaults\" (Coding, Medical, Legal, Cultural, etc.). Find the one where your expertise shines.\n\ud835\udde6\ud835\ude01\ud835\uddf2\ud835\uddfd \ud835\udfef: \ud835\uddd6\ud835\uddfc\ud835\uddfb\ud835\ude01\ud835\uddff\ud835\uddf6\ud835\uddef\ud835\ude02\ud835\ude01\ud835\uddf2 \ud835\uddee\ud835\uddfb\ud835\uddf1 \ud835\udde6\ud835\ude01\ud835\uddff\ud835\ude02\ud835\uddf0\ud835\ude01\ud835\ude02\ud835\uddff\ud835\uddf2\nAI doesn't need more \"paragraphs\"; it needs structured expertise. 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From your dashboard, you can track:\n1.\ud835\udde8\ud835\ude00\ud835\uddee\ud835\uddf4\ud835\uddf2: Which AI models are being powered by your brain?\n2.\ud835\udde5\ud835\uddfc\ud835\ude06\ud835\uddee\ud835\uddf9\ud835\ude01\ud835\uddf6\ud835\uddf2\ud835\ude00: Watch your passive income accumulate in real-time as the \"bundle\" you helped build is utilized.\n\n\ud835\udde7\ud835\uddf5\ud835\uddf2 \ud835\uddd8\ud835\ude01\ud835\uddf5\ud835\uddf6\ud835\uddf0\ud835\ude00 \ud835\uddfc\ud835\uddf3 \ud835\udde2\ud835\ude04\ud835\uddfb\ud835\uddf2\ud835\uddff\ud835\ude00\ud835\uddf5\ud835\uddf6\ud835\uddfd\nIn the Web2 era, we were told that \"Free\" was the best we could hope for. 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    "text": "BUILDING INFRASTRUCTURE FOR 1 BILLION PEOPLE TO OWN DIGITAL ASSETS\n\nToday, owning digital assets remains the privilege of a small group\u2014those who understand the technology, have access to it, and are willing to take risks.\n\nBut tomorrow, that must change.\n\nOwning digital assets cannot be a \u201ccompetitive advantage.\u201d It must become a fundamental right for every human being.\n\nAnd for that to happen, we don\u2019t need another \u201cslightly faster\u201d blockchain.\n\nWe need an infrastructure robust enough to support a real billion people.\n\n\u2e3b\n\n\ud83d\udea7 Not a blockchain. This is the infrastructure rails.\n\n@inter_link isn\u2019t simply building another blockchain.\n\nWe are building a \u201cdigital financial rail\u201d\u2014an infrastructure that can:\n\n* Process transactions on a global scale\n\n* Secure ownership of real assets\n* Operate stably with hundreds of millions to billions of participants\n\n1 billion users is not a marketing slogan.\n\nIt's a grueling technical challenge.\n\n\u2e3b\n\n\u2699\ufe0f 1 billion users = enormous technical load\n\nWhen it comes to global scale, every weakness is magnified:\n\n* Unstable throughput \u2192 system collapse\n* High latency \u2192 unacceptable experience\n* Outdated security \u2192 assets at risk\n\nIn finance, you don't get a chance to \u201cretry\u201d when the system fails.\n\n\u2e3b\n\n\u26a1 InterLink Chain: Built for Real-World Scale\n\nInterLink Chain was designed from the ground up to solve this problem:\n\n* \ud83d\ude80 High throughput, scalable\n\n* Reaching 2,000 TPS paid in the first testnet\n\n* Final settlement time < 2.3 seconds\n\n* \ud83c\udf0d Mainnet-oriented\n\n* Surpassing current high-performance blockchains\n\n* Approaching the scale of global payment networks\n\nAt this capacity,\n\n1 billion users is no longer a dream \u2014 but a natural outcome of mathematics.\n\n\u2e3b\n\n\ud83d\udd10 Security for the Future: Quantum Resistance from the Core Layer\n\nA little-known fact:\n\n\ud83d\udc49 The majority of current blockchains (including Bitcoin, Ethereum) rely on elliptic curve cryptography.\n\n\ud83d\udc49 And these systems could be broken when quantum computers reach sufficient power.\n\nInterLink chooses a different path:\n\n* \ud83d\udee1\ufe0f Quantum Resistance from Layer 0\n* No need for forks when the quantum age arrives\n* No compromise on security for performance\n\nThis means:\n\nYour assets today will remain secure in the future.\n\n\u2e3b\n\ud83e\uddec Authentic Identity for a Real Ecosystem\n\nA blockchain cannot scale if it is filled with bots.\n\nInterLink solves this problem at its root:\n\n* \ud83d\udd0d Biometric identity verification + Zero-Knowledge\n* \ud83e\udde0 Each account = one verified person or business\n* \u274c No bots\n* \u274c No Sybil attacks\n* \u274c No wasted network resources\n\n\ud83d\udc49 1 billion users on InterLink are not 1 billion wallets.\n\n\ud83d\udc49 They are 1 billion real people.\n\n\u2e3b\n\n\ud83d\udcc8 From 8 million to 1 billion: This is the infrastructure journey\n\n* Over 8 million people have been verified\n\n* The network continues to expand every day\n* Infrastructure being built is not for testing\n\n\u2192 but to carry the entire scale of the future digital economy\n\n\u2e3b\n\n\ud83d\udd2d The future is not a hypothetical\n\nThe world is moving towards:\n\n* Digital assets\n* Digital identities\n* Borderless payments\n\nThe question isn't \"will this happen?\"\n\nBut:\n\n\"What infrastructure is strong enough to carry it all?\"\n\n\u2e3b\n\n\ud83d\ude80 InterLink isn't keeping up with the future.\n\nInterLink is building it.\n\n#InterLink #ITLG #ITL #ITLX",
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    "text": "Meta $META 2Q26 Earnings\n- Rev $60.8b +28% \u2197\ufe0f\ud83d\udfe2\n- GP $49.5b +27% \u2197\ufe0f\ud83d\udfe2 margin 81.4% -76 bps \u2198\ufe0f\ud83d\udd34\n- EBIT* $18.8b -8% \u2198\ufe0f\ud83d\udd34 margin 30.9% -1214 bps \u2198\ufe0f\ud83d\udd34\n- Net Inc* $15.8b -14% \u2198\ufe0f\ud83d\udd34 margin 26.1% -1253 bps \u2198\ufe0f\ud83d\udd34\n- OCF $31.9b +25% \u2197\ufe0f\ud83d\udfe2 margin 52.4% -139 bps \u2198\ufe0f\ud83d\udd34\n- FCF $0.8b -91% \u2198\ufe0f\ud83d\udd34 margin 1.3% -1670 bps \u2198\ufe0f\ud83d\udd34\n\n* Lower profits negatively impacted by $2.4b legal charges and $1.18b of severance.\n\nRevenue by Segment\n- Total revenue $60.8b +28% \u2197\ufe0f\ud83d\udfe2\n- Family of Apps $60.4b +28% \u2197\ufe0f\ud83d\udfe2\n- Advertising $59.4b +27% \u2197\ufe0f\ud83d\udfe2\n- Other revenue $1b +73% \u2934\ufe0f\ud83d\udfe2\n- Reality Labs $0.4b +16% \u2197\ufe0f\ud83d\udfe2\n\nFamily of Apps\n- Rev $60.4b +28% \u2197\ufe0f\ud83d\udfe2\n- EBIT $23.4b -6% \u2198\ufe0f\ud83d\udd34 margin 39% -1421 bps \u2198\ufe0f\ud83d\udd34\n\nReality Labs\n- Rev $431m +16% \u2197\ufe0f\ud83d\udfe2\n- EBIT -$4.6b \u27a1\ufe0f\ud83d\udd34 margin -1072% +15263 bps \u2705\n\nBiz Metrics\n- DAP 3.6b +3% \u2197\ufe0f\ud83d\udfe1\n- Ad Impressions +14% \u2197\ufe0f\ud83d\udfe1\n- Average price per Ad +12% \u2197\ufe0f\ud83d\udfe1\n- ARPP $16.86 +24% \u2197\ufe0f\ud83d\udfe2\n\nAd Impressions\n- Worldwide +14% \u2197\ufe0f\ud83d\udfe1\n- UCAN +9% \u2197\ufe0f\ud83d\udfe1\n- Europe +13% \u2197\ufe0f\ud83d\udfe1\n- APAC +17% \u2197\ufe0f\ud83d\udfe2\n- ROW +12% \u2197\ufe0f\ud83d\udfe1\n\nAverage Price per Ad by Geography\n- Worldwide +12% \u2197\ufe0f\ud83d\udfe1\n- UCAN +20% \u2197\ufe0f\ud83d\udfe2\n- Europe +10% \u2197\ufe0f\ud83d\udfe1\n- APAC +1% \u2197\ufe0f\ud83d\udfe1\n- ROW +21% \u2197\ufe0f\ud83d\udfe2\n\nAd Revenue by Geography\n- Total $59.4b +27% \u2197\ufe0f\ud83d\udfe2\n- UCAN $26.3b +31% \u2197\ufe0f\ud83d\udfe2\n- Europe $14.1b +24% \u2197\ufe0f\ud83d\udfe2\n- APAC $10.8b +19% \u2197\ufe0f\ud83d\udfe2\n- ROW $8.1b +35% \u2197\ufe0f\ud83d\udfe2\n\nRevenue by Geography\n- Total $60.8b +28% \u2197\ufe0f\ud83d\udfe2\n- UCAN $26.8b +32% \u2197\ufe0f\ud83d\udfe2\n- Europe $14.3b +24% \u2197\ufe0f\ud83d\udfe2\n- APAC $11.2b +19% \u2197\ufe0f\ud83d\udfe2\n- ROW $8.5b +36% \u2197\ufe0f\ud83d\udfe2\n\nMgmt Guide\n- 3Q26 Rev $64b +25% \u2197\ufe0f\ud83d\udfe2\n- FY26 total expenses $169b (same)\n- FY26 capex $145b (same)\n- FY26 EBIT > FY25\n\n1 | Strong quarter +28% supported by healthy growth in both ad impressions +14% and price per ad +12%.\n\nIn Q2, the total number of ad impressions served across our services increased 14%. Impression growth was healthy across all regions, driven by growth in engagement and users as well as ad load optimizations. The global average price per ad increased 12% year-over-year, driven by ad performance gains, improvements in macro conditions relative to Q2 of last year and currency tailwinds. This was partially offset by strong impression growth, particularly from lower monetizing surfaces and regions.\n\n2 | Time spent on the platform remains strong, FB +10%, IG >+10%.\n\nTurning now to the business performance. There are 2 primary factors that drive our revenue performance: our ability to deliver engaging experiences for our community and our effectiveness at monetizing that engagement over time. On the first, we continue to see significant gains from our content recommendation initiatives. On Instagram, global time spent this quarter grew double digits YoY this quarter, largely driven by improvements to our feed and reels recommendations.On Facebook, video time spent increased 9% globally year-over-year and over 10% within the U.S. and Canada, where it was driven by ranking improvements.\n\n3 | Shipped a new release on reels that draws on deep user history to improve predictions, drives faster inference and 15bps increase in reshares and time spent.\n\nOn Reels, we shipped our largest single release ranking improvement to date, combining faster inference with a new architecture that draws on deeper user history to improve predictions. This drove a 15 basis point increase in sessions on Instagram with particular strength in reshares and time spent, which are both strong indicators of better content-to-user matching. We are now bringing this to feed where early results look comparable. We are also getting new content to people more quickly. Investments we've made in more real-time infrastructure and modeling improvements on new videos are allowing our largest ranking models to now identify high-quality new Reels at creation.\n\n4 | Higher expenses and lower profitability due to one-off legal charges and severance expenses, high wages from AI talent, and higher depreciation and DC opex.\n\nQ2 total expenses were $42 billion, up 55% compared to last year and included $2.4 billion in charges related to legal proceedings and $1.2 billion in severance expenses in connection with the May 2026 headcount reduction. YoY growth was primarily driven by increases in employee compensation, infrastructure costs, legal-related costs and third-party AI token costs. Excluding the previously mentioned severance expense, growth in employee compensation was driven by technical hires we've added over the past year, particularly AI talent. The growth in infrastructure costs was driven by higher depreciation, data center operating costs and third-party cloud spend.\n\n5 | IG hit 2bn daily actives, Threads hit 500m monthly actives, FB 2bn daily actives.\n\nWe had a strong quarter for our community and business with 3.6 billion people using at least one of our apps each day. We reached several milestones. Instagram reached 2 billion daily actives. Threads crossed 500 million monthly actives, making it the fastest-growing conversation app ever. Facebook has reached more than 2 billion daily actives for a while now.\n\n6 | Investments are accelerating every part of Meta's core businesses, improving the experience of users, driving better performance for advertisers, and helping build better products and ship faster.\n\nOverall, the scale and reach of our community across our apps is pretty remarkable, and it gives us a strong platform for delivering new innovations to billions of people. The opportunity in front of us is massive. First, we are now at a point where our investments in AI are accelerating every major part of our core business. They're improving the experience for people using our apps, driving better performance for advertisers and helping our teams build new experiences and ship faster.\n\n7 | Developing new personal agents, see opportunity to sell to large enterprises APIs, business agents, and compute.\n\nSecond, we are developing new personal agents that will be the foundation for our next wave of products and revenue lines in the months and years ahead. And third, we see a large enterprise opportunity to sell to businesses, including APIs, business agents, potentially selling compute directly and other services that we're building for large customers.\n\n8 | Using LLMs to show better, more relevant and engaging content to users, from friends and people you follow, and creators you don\u2019t follow.\n\nLLMs add a first principles understanding of what the content is about and why it is compelling as well as a deeper understanding of what people are interested in and what their goals are when they're using our apps.\nSo this means that we can show more relevant and engaging content that better reflects people's goals and interests. Our new Muse Image and Muse Video models will also dramatically expand the universe of content that people can discover across our platforms. There are already 2 large sets of content to draw from, first, from your friends and the people you follow and second, from creators that you don't follow. But now there's going to be a whole new and nearly infinite universe of personalized content. This is going to make our services a lot more useful and engaging for people.\n\n9 | Using LLMs to improve the predictability and ranking of ads.\n\nFor ads, we are using LLMs to improve how our systems predict and rank the ads that we show. We've expanded the context that we can take into account around a person's organic and ads activity to determine an ads relevance, driving significant increases in relevance and conversions on both Facebook and Instagram.\n\n10 | AI investments are paying off. The ads business is growing very strongly. Seeing demand for new AI-powered creative tools.\n\nAnd on a dollar basis, our ads business is reporting faster year-over-year revenue growth than any other company's reported ad business. So these AI investments are paying off. We are also seeing a lot of demand for our new AI-powered creative tools. 9 million small businesses on our platforms are now using at least one of our AI ad creative tools, and we're rolling out new end-to-end creative solutions that help advertisers translate performance data into their creative decisions. Muse image is going to supercharge this. The model can analyze images, improve its own work and produce better ad variations based on advertiser input.\n\n11 | Launched Meta One a new subscription offering for tools and AI features.\n\nWe also just launched Meta One, a new subscription offering that provides more tools and AI features across our apps. As demand grows, we're going to offer a variety of different tiers and pricing options there as well. And I'm also excited about how AI is helping our teams speed up product development. Earlier this year, we shipped Instagram Instants. We also just launched Forum, a stand-alone group's app and Seller, a stand-alone marketplace app.\n\nAnd I expect it to become a lot easier to ship new apps. So we are planning to build out more ideas and use our recommendation systems to scale them to the people who will find them interesting as we've done with Threads.\n\n12 | Thinks the trajectory of MSI remains strong, rebuilt Meta AI and integrated Muse Spark.\n\\It's been a little more than a year since we launched Meta Superintelligence Labs and our trajectory is strong. In the last month, we shipped Muse Spark 1.1 and Muse Image. Since we rebuilt Meta AI and integrated Muse Spark, we have seen a 60% increase in the number of people interacting with the assistant each day, and that continues to grow quickly week-over-week.\n\nMuse Spark 1.1 is a strong agentic encoding model that is very efficient and excels at computer use, tool use and multimodal understanding. It's available through our new public API, and we are ramping up distribution through partner channels and more coding agents over the coming weeks. We are also building out features to make it easier for enterprises to adopt a Muse Spark, and that is an area that we expect to continue focusing on. One reason that we are so focused on making Muse Spark great at agentic capabilities is that we think that there's a very big opportunity to ship a few types of agents that are aligned with our mission and business.\n\n13 | Meta committed to do a mix of open and closed, closed models can be more jagged, but open models have to be more rounded. Will get back to releasing open models.\n\nBut we've always basically said, that we were going to do a mix of open and closed. And that continues to be true. Now in ramping up Meta Superintelligence Labs, in some ways, actually counterintuitively, it takes some more work to do open source models because you're not necessarily -- if you're doing something as a closed system that only -- you're only building for your own use cases, it can be a little more jagged. Whereas if you release it as open and it's going to be used for a lot of things, you want to make it more well-rounded.\n\nAnd I just wanted to make sure that the MSL team was uninhibited in building out the most intelligent models that we could. And we expect that we will get back to releasing some open source models at some point soon. But like we've always said, we're not dogmatic about this. We think open source is important. We want to contribute to that ecosystem. We plan to do a combination of open and closed models\n\n14 | Meta is a full stack company and will use their models and not use others, like writing their own code from the onset.\n\nWe are really a full-stack technology company. We built our own data centers, our own infrastructure, our own chips, our own low-level software. When we got started, like my background in engineering, like I wrote a lot of the systems code.\n\nA lot of the reason why Facebook worked was because it actually -- it just worked, right? Like it literally worked when other social networks did not work fast and efficiently.\n\nAnd I think we just have the ability to build things that can be more personalized, more optimized, more efficient. Some qualitative experiences are just not even possible for others to build because we go all the way down the stack. And it just seems to me pretty clear that having kind of sovereignty over building your own models is going to be an important part of that stack going forward, which is why it is important for Meta.\n\n15 | Perhaps Meta does not need to be at the leading frontier, top is much better than being the best. They would rather have a mix of different efficiency models to handle specific type of workloads better and cheaper.\n\nThis should be true regardless of whether our models are on the frontier, but we believe that being on the frontier will unlock new markets and opportunities for which we may need additional compute\u2026Now we want to have models that are more advanced as well, and that's why we're scaling larger models, and that's why we're building out a bunch of the research infrastructure around that. But at the same time, we also want to have good more efficient models that will be a lot of what we serve to consumers at scale. But if you're serving billions of people, you want the ability to have more advanced models for things that are very hard problems, and you want the ability to serve the vast majority of prompts from simpler and more efficient models. So I think both are going to be very important. The efficiency really matters, but I also think that we want to be able to solve the hardest problems for businesses and customers around the world.\n\n16 | Meta wants to win the new distribution layer of personal agents with consumers.\n\nThe first is personal agents. Soon, we will have agents that can work 24/7 on your behalf to help you achieve your goals and improve your life, your health, your relationships, your finances, whatever you want. The first domain that agents have really taken off in is coding, but engineers are more technical and willing to spend time making those agents work. So to build great personal agents, this needs to be a great consumer product that just works out of the box and is easy enough for billions of people to adopt and use.\n\n17 | Other revenue of WhatsApp paid messaging and subscriptions revenue reached $1b +73%.\n\nFor the first time, quarterly Family of Apps other revenue reached $1 billion and grew 73% YoY, driven primarily by WhatsApp paid messaging and subscriptions revenue.\n\n18 | See WhatsApp and other messaging services to be the surface of agent interaction.\n\nAs we move towards a future where we're all interacting with multiple agents, I think that WhatsApp and our other messaging surfaces are going to become increasingly important. WhatsApp is already the leading surface where people engage with Meta AI. And as we build out a platform for more agents across our messaging apps, we're going to innovate on how to deliver a private and secure AI experience. We launched Incognito mode this quarter on WhatsApp and the Meta AI app, allowing people to have private conversations with their assistant that even Meta can't see. We are planning to make strong privacy and security a fundamental part of the agents that we're building as well.\n\n19 | See WhatsApp, Messenger, and IG to be the surface for business agents.\n\nI'm also very excited about our progress with business agents. We made Meta business agents available globally this quarter on WhatsApp and Messenger, and there are already more than 1 million businesses using them to talk to their customers or complete sales every week.\n\nWe're rolling business agents out on Instagram now too. One interesting thing about having an agent talk to your customers every day is that it learns over time and can bring all of those insights back to you. So we're building more agentic capabilities to summarize all these conversations, digest what happened overnight and surface what customers are asking for.\n\n20 | Expect to monetize via subscriptions and volume-based pricing.\n\nIn terms of how we will monetize these, we have a mix of subscriptions, volume-based pricing, and I expect that we're going to continue to evolve more of these products to be like our ad systems where businesses only pay us when we achieve results for them. Over time, that will let us run an efficient auction over our compute, similar to how we do that for advertisers today. As AI usage in our products and businesses continues to ramp, we continue to invest aggressively in infrastructure to meet the demand.\n\n21 | New 1 GW data center in Texas with Blackrock.\n\nYesterday, as part of our meta compute effort, we announced a new strategic venture with BlackRock to develop a new 1 gigawatt data center in El Paso, Texas. Overall, we expect that a significant portion of our compute is going to go towards training our models, growing our core business and delivering personal agents and new products.\n\n22 | Meta getting a lot of offers for compute at a significant premium over what they paid for it.\n\nBut we also expect to grow a large business serving large customers as well. We've built our API. We're rolling out business agents. We're getting a lot of offers for compute at a significant premium over what we paid for it.\n\n23 | See the opportunity to serve businesses beyond just selling compute, but intelligence with API, productivity services and business agents.\n\nBut I think that there is just a very large opportunity on this. And the way that I think about this is there's obviously been a bunch of news about the compute side, but I think that the enterprise opportunity is kind of the sum of all of these different things. It's not just the selling compute, also the API services, the productivity services, the kind of business agents for other parts of the business beyond marketing are all parts of the overall offering. And I think that there's just a very, very large opportunity there. So we're quite focused on that.\n\n24 | Don\u2019t see Meta as focused on building and reselling compute, will be red flag if they do so, and not using it to improve their business.\n\nbut I also think it would be foolish to basically just sell all of the compute and take a short-term profit, but when you have the opportunity to build intelligence on top of it, which will be a kind of a multiple and that compounds the value of the compute on top of that. So I think the answer is what we're doing, which is to basically use a lot of our capital to build out compute, having confidence that we have the ability to monetize the compute directly when that makes sense, but also knowing that we have quite a number of different use cases to monetize the intelligence on top of the compute, including the enterprise cases that we talked about and including some of the consumer and cases that we talked about and including just the core business, which is not even necessarily new products that we haven't talked about, but just in terms of using that to be able to further add intelligence and improve the ranking and recommendations and ads in the core services.\n\n25 | Still see glasses as the ideal form factor, and has released their own line, early sales has been strong thus far.\n\nAs we get closer to personal superintelligence, we are also going to need hardware that allows you to seamlessly interact with it. Glasses are the ideal form factor since they can be with you throughout the day, and they can assist you without pulling you away from the moment. Our Glasses remain one of the fastest-growing consumer electronics of all time, and we continue adding to the lineup. We just released our own line of Meta Glasses in collaboration with EssilorLuxottica, including a style that we designed with Kylie Jenner. They're the first glasses to ship with Muse Spark out of the box so that they can understand what you're seeing and give even more helpful answers. Early sales have been strong, exceeding our expectations, and we're going to have more to share on our Glasses lineup at this year's Connect Conference on September 23.\n\n26 | Zuck is focused on building AI to put AI superintelligence directly into people\u2019s hands, by distributing it widely.\n\nBefore I wrap, I want to mention that I just published an op-ed about why I'm so optimistic that we are building a positive future for everyone. At Meta, we have always built technology to put power in people's hands, so that they can connect with the people they care about and shape the world in the ways that they want. It's why we've always focused on making our products affordable and accessible for everyone, and it's a strategy that has served both our community and our business well. As we enter this next chapter, that same philosophy is guiding how we approach developing AI. We're the only major company building AI with the primary goal of putting superintelligence directly into people's hands. Rather than centralizing superintelligence, we are focused on distributing it widely and giving everyone the ability to direct it towards what matters to them. That's the way that society has always made progress.\n\n27 | Zuck is betting that personal AI agents is going to be big, and Meta wants to be the dominant player there (reminds me of Tencent\u2019s approach as well).\n\nBut our bet is that -- or one of the bets here that we're making is that we think that consumer personal agents is going to end up being an extremely important and massive market. I think that it's extremely unlikely if you look out 5 years from now, for example, that whatever period of time you want that you don't have billions of people with a personal agent that understands your goals and it is just working on your behalf 24/7 to achieve your goals\n\n28 | AI is improving Meta\u2019s core business, making its apps more relevant and delivering better results, and will be delivering more new products, investing aggressively because of huge potential.\n\nAI is improving our core business. It's making our apps more relevant and delivering better results for businesses. We're starting to deliver more novel products, and we'll have a lot more there soon as well. And we are investing aggressively because the potential is huge, and we know that there are many ways to deliver value here.\n\n29 | Meta strongly believes that the models, consumer experiences and enterprise offerings represent the best and highest ROI.\n\nAs we've said earlier, we strongly believe that the models, consumer experiences and enterprise offerings that we are building will be the best and highest ROI use of our infrastructure.\n\n30 | Current plans are focused on maximizing 2026 and 2027 capacity, believe Meta\u2019s distribution advantages will allow them to serve AI products.\n\nConsequently, our current plans are geared towards maximizing 2026 and 2027 capacity. When we have had incremental capacity in the past, it has proven extremely valuable in scaling experiences like Reels, and we are confident that this will be true in this time frame as well. Longer term, it's harder to predict the exact usage scaling curves, but we believe that our distribution advantages will give us the opportunity to serve AI products that are valuable for everyone, both our 3.6 billion users and millions of businesses.\n\n31 | Not providing a specific outlook for 2027 capex yet, remains very dynamic.\n\nwe aren't providing a specific outlook for 2027 CapEx at this time. Infrastructure planning remains highly dynamic. And even this year, there are a range of outcomes embedded in our outlook. I alluded in my main remarks to our focus on being -- on gearing our current infrastructure plans towards maximizing capacity in 2026 and '27 and giving us the flexibility to continue to grow in '28 and beyond, but also giving us the ability to make server decisions when we come to our -- being able to evaluate our actual needs in '28 and beyond. So we're still working through what our capacity needs are going to be over the coming years. Generally, we believe near-term capacity is more valuable than long-term capacity, and it remains a very dynamic planning process.",
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    "text": "Title: THE SOVEREIGN ANCHOR: Why $ITL is the Final Cure for Global Inflation \ud83c\udf0d\ud83d\udee1\ufe0f\n\nIn 2026, the world is realizing that paper wealth is a shadow. Real value requires **Scarcity.** \ud83d\ude80\ud83c\udfd7\ufe0f\n\nAs #Ethereum fights to hold the **$1,982** level today, the lesson is clear: The assets that survive are the ones that can't be printed. We reveal the 18-year roadmap to **Sovereign Reserve Status.** \ud83d\udcc8\ud83d\udddd\ufe0f\n\n#The 18-Year Supply Crunch:\nOur distribution model is designed for a total system reset. \n\ud83d\udd39 **Years 1-5:** The \"Building Phase.\" Supply enters to fuel the super-app and RWA migration.\n\ud83d\udd39 **Years 5-10:** The \"Sharp Descent.\" Supply begins to crash as halving events accelerate (up to 100 times). \ud83d\udcc9\n\ud83d\udd39 **Year 10+:** The \"Near Zero\" Phase. New issuance approaches zero, making $ITL the ultimate hard-money asset.\n\n#The Reserve Destiny:\nWhy is InterLink \"bigger than you think\"? \ud83e\udde0\nBecause within 4 years, $ITL will act as a **Reserve Asset** for sovereign nations. Governments can eliminate fiat inflation by pegging their economies to the InterLink infrastructure\u2014an economy backed by NIST-validated AI and real-world assets. \ud83c\udfdb\ufe0f\ud83d\udc8e\n\n#The \"No Intervention\" Reality:\nWe don't need to \"pump\" a reserve asset. The **Supply Deficit** does the work. \ud83d\udeab\ud83d\udcc8\nWhen supply is approaching zero and global demand from 1 billion users and sovereign treasuries is rising, the $2,000+ #Ethereum path isn't just a goal\u2014it is a mathematical certainty. \ud83d\udee1\ufe0f\u2696\ufe0f\n\n#The Final Insight:\nWe aren't building a \"crypto project.\" We are building a **Digital Gold Standard** for the AI era. \ud83c\udfdb\ufe0f\u2728\n\nAre you holding for the week, or are you holding for the **Nation?** \ud83d\udc47\ud83e\udd42\n\n#InterLink #ITL #ITLG",
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    "text": "Day 7: Documenting the build of my first app.\n\n\u2192 Built a CMO for my app: a marketing dashboard that scrapes TikTok via Claude, diagnoses my account vs. the niche, and tells me what to post next \n\n\u2192 Generated a content bank from confirmed viral patterns \n\n\u2192 Day 1 of posting, 0 followers, no warming: one post at 2.2k views  \n\n\u2192 Strategy: build a real studytok creator first, drop the app casually later\n\nRevenue: -$147\nTime: ~5 hrs \n\nNext: Keep posting and developing app!",
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    "text": "My app Pep AI has generated over $700,000 in revenue in less than 8 months.\n\nHalfway through that run, our #1 marketing channel basically got destroyed.\n\nInfluencer marketing had driven a huge part of our growth, but creators in our niche started getting restricted on TikTok and Instagram, videos stopped getting distribution, and some creators were even losing their accounts.\n\nWe had to find another way to keep scaling.\n\nThat\u2019s when we started taking Meta ads seriously.\n\nHere\u2019s what I\u2019ve learned and what can possibly work for your app too:\n\n1. Start with proven winners\n\nIf you already have influencer or organic videos that performed well, start there.\n\nTake your best performing videos and give them some initial spend. Don\u2019t immediately pay for 100 brand new ads when you already have content that has proven people will stop and watch.\n\nAlso make sure your funnel actually works before you scale. 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Sometimes campaigns improve over several days or weeks as Meta gets more conversion data.\n\nBut that doesn\u2019t mean you should let terrible ads run forever hoping they magically recover.\n\nI learned this the expensive way.\n\nIn August, Pep AI did around $170,000 in revenue and still wasn\u2019t profitable.\n\nYes, many of those subscribers may generate additional LTV through future renewals, but when you\u2019re bootstrapping, you can\u2019t rely on future renewals to fix bad cash flow today.\n\nWe tightened things up, cut what wasn\u2019t working, scaled our winners, and kept testing new creative.\n\nThe next month we still did over six figures in revenue, but our ads were profitable.\n\n4. Separate scaling from testing\n\nWe keep proven ads separate from a lot of our experimental creative so the existing winners don\u2019t immediately take all the spend.\n\nThis gives new concepts enough delivery to actually learn something from them.\n\nAt the same time, don\u2019t overcomplicate your account with dozens of tiny campaigns and ad sets. Meta still needs enough conversion data to optimize.\n\nThe goal is simple: give your winners room to scale while constantly searching for the next winner.\n\n5. Track everything\n\nWe use AppsFlyer and it has worked extremely well for us.\n\nYou need to know which campaigns and creatives are driving installs, trials, purchases, and revenue.\n\nBefore spending heavily, test your tracking end-to-end. Make a real trial or purchase and make sure the event is showing up correctly.\n\nSpending thousands while your conversion tracking is broken is an unbelievably expensive mistake.\n\n6. How we find new ad concepts\n\nStudy the companies that are better than you.\n\nGo through Meta Ad Library and look at apps in your niche that appear to be scaling. You can\u2019t see their ROAS, so don\u2019t assume every ad is profitable, but if a company keeps running the same concept or repeatedly makes variations of it, that can be a useful signal.\n\nThen look outside your niche.\n\nFind consumer apps that are growing quickly and study their ads too.\n\nDon\u2019t copy the exact creative. Copy the psychology and framework behind it.\n\n7. Make ads feel like content\n\nSome of the best-performing ads don\u2019t immediately feel like ads.\n\nDon\u2019t open with your logo and spend 30 seconds listing features.\n\nTeach something. Compare something. Tell a story. Show a problem.\n\nThen introduce your product as the solution.\n\nYou\u2019re trying to earn the viewer\u2019s attention before asking them to care about your app.\n\n8. Test concepts AND hooks\n\nThere\u2019s a big difference between a new concept and a new variation.\n\nA concept is the actual idea behind the ad. A variation might just be the same video with a different hook, creator, caption, or first few seconds.\n\nYou need both.\n\nWe often take one core concept and create 5 different hooks for the beginning of the video.\n\nSo one testing batch might be:\n\n5 concepts \u00d7 5 hooks = 25 creatives.\n\nThe hook alone can completely change performance.\n\nBut don\u2019t only make variations of your winners. Keep testing completely different angles too.\n\nOne person might respond to a comparison. Another might respond to a creator talking directly to camera. Another might respond to a screen recording or story.\n\nYou want multiple ways to sell the same product.\n\n9. Where we get the content made\n\nWe currently use Trybe and SideShift.\n\nFor faster experimental concepts, we may pay around $40 per video with multiple hook variations.\n\nFor concepts we have more confidence in and want higher production quality on, we may pay around $100 per video with multiple hooks.\n\nThose are just the rates that have worked for us.\n\nYou can also source creators yourself by messaging people in your niche on TikTok and Instagram. You can make the videos yourself or pay friends.\n\nThe important part is building a constant pipeline of new creative.\n\nFinding one winning ad does not mean you\u2019re done.\n\nCreatives eventually fatigue, so you need a system where new concepts and variations are constantly being produced, tested, killed, or scaled.\n\nYou also need to have a place to keep all content stored. We use frame for this and have everything organized in a way where we know exactly where every ad concept is. This makes it very easy when dealing with hundreds of ad concepts.\n\nOne more thing: reputation matters on creator platforms.\n\nWhen you\u2019re starting, you may need to begin with simpler campaigns, build good reviews for your brand account, and prove that you\u2019re easy to work with. Or have a professional Instagram that has lots of followers and is verified.\n\nThen it gets much easier to attract better creators.\n\n10. Final Thoughts\n\nThis entire system is how we continued scaling Pep AI after our #1 marketing channel basically got destroyed.\n\nIf influencer marketing still works affordably in your niche, keep doing it.\n\nInfluencer marketing + Meta ads together can be extremely powerful.\n\nBut Meta has become one of the best tools we\u2019ve found for consistently scaling the app.\n\nYou do need a testing budget you\u2019re comfortable losing.\n\nBut every dollar should be buying information.\n\nIf you\u2019re spending money and you aren\u2019t learning anything, you\u2019re not testing.\n\nYou\u2019re just losing money.\n\nOnce you start figuring out the right offer, creative angles, hooks, and conversion signals, Meta can completely change the scale of your app.\n\nIt helped us continue doing over $100,000 a month after the marketing channel we relied on most stopped working.\n\nHope this helps.\n\nNext up for Pep AI:\nWe will be trying UGC and influencer marketing again. We hope to find a way that works for us and I will be documenting it. If you made it this far thank you for reading and I will be posting more content soon. I had to stop for awhile when we had to fix our marketing, but I will be posting a lot more so I appreciate the support. \n\nIf you have any questions, feel free to comment, DM me, or book a call through the link in my bio.",
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    "text": "19 PEOPLE BECAME AI BILLIONAIRES IN THE PAST YEAR. ALMOST NONE OF THEM BUILT A CHATBOT.\n\nThey're worth $59.3 billion combined, per Bloomberg.\n\nAnd the way they got there breaks the pattern everyone expected:\n\nThe first wave of AI fortunes came from chips, data centers and the big models. This new class did something different. \n\nThey pointed AI at slow, old industries: law, medicine, customer service, coding, and the unglamorous work of labeling data.\n\nHere's the full group, ranked by net worth:\n\n\u2022 Edwin Chen, ~$13B (Surge AI): labels data for AI labs. Never raised a dollar of outside money and is already past $1B in annual revenue.\n\n\u2022 Daniel Nadler, $7.2B (OpenEvidence): an AI assistant for doctors. He's also a published poet, and once sold his last startup to S&P Global for $550M.\n\n\u2022 Bret Taylor, $4.2B, and Clay Bavor, $3.7B (Sierra): AI customer service used by Cigna and SoFi. Taylor is also the chairman of OpenAI's board.\n\n\u2022 Ioannis Antonoglou and Misha Laskin, $4B each (Reflection): open-weight AI models from two ex-Google DeepMind researchers. Antonoglou helped build AlphaGo.\n\n\u2022 Andrew Feldman, $2.8B, and Sean Lie, $1.5B (Cerebras): the AI chipmaker that just went public in the year's largest IPO and jumped 68% on day one.\n\n\u2022 Amjad Masad, $2B (Replit): vibe-coding tools that let anyone build software. He started coding at age 7 in Jordan.\n\n\u2022 Guillermo Rauch, $1.9B (Vercel): hosting that became a go-to home for apps built by AI coding tools. He taught himself English to learn to code.\n\n\u2022 Brendan Foody, Adarsh Hiremath and Surya Midha, $1.9B each (Mercor): data labeling. All three dropped out of college on Thiel Fellowships, and revenue went from $100M to $1B in under a year.\n\n\u2022 Steven Hao, Scott Wu and Walden Yan, $1.7B each (Cognition): makers of Devin, an AI agent that writes and fixes code on its own.\n\n\u2022 Gabe Pereyra and Winston Weinberg, $1.6B each (Harvey): AI for lawyers. The two founders started as roommates and still are.\n\n\u2022 Fei-Fei Li, $1B (World Labs): builds 3D \"world models.\" She's widely called the godmother of AI.\n\nMeanwhile the old guard keeps getting bigger. OpenAI raised $122 billion in March at an $852 billion valuation. \n\nAnthropic could pass it with a reported $900 billion round, lifting its seven billionaire co-founders even higher.\n\nThe takeaway: the AI money isn't just flowing to the chipmakers anymore. It's going to whoever can bolt AI onto an industry that already exists.",
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    "text": "Elon Musk just took Anthropic's biggest customer hostage three days before their IPO.\n\nHe paid $60 billion for it without spending a dollar of cash.\n\nBut the company he bought is actively losing the race he claims to be winning:\n\nThe company is Cursor, the AI coding tool used by most of Silicon Valley and a huge chunk of Fortune 500 engineering teams. \n\nIts best feature is called Composer, and Composer became the most-loved AI coding product on earth for one specific reason: It runs on Anthropic's Claude.\n\nThe phrase \"vibe coding\" was literally coined by a researcher playing with Cursor's Composer running on Claude Sonnet in early 2025. Anthropic's enterprise revenue exploded in 2025 partly because every engineer using Cursor was effectively a paying Anthropic customer underneath. \n\nCursor became one of the largest external pipelines of Claude usage anywhere on the internet.\n\nAnd last week, Anthropic confidentially filed paperwork to go public.\n\nThree days after SpaceX completed its own IPO on Friday, Elon Musk exercised an option he had quietly signed in April and bought Cursor for $60 billion. \n\nThe deal was announced Tuesday morning in an 8-K filing. By the time most people read the headline, the pipeline feeding Anthropic's biggest enterprise channel was already legally owned by its biggest RIVAL, days before that rival walks onto the public markets and has to explain its growth story to Wall Street.\n\nNow look at how he paid for it:\n\nNot one dollar of cash changed hands. \n\nThe entire $60 billion was paid in SpaceX stock. Stock that was minted out of thin air on Friday when the company went public at $135 a share. \n\nBy Tuesday, that same stock was trading at $211. So Musk used four days of public-market hype to mint $60 billion of fresh equity and immediately spent it on an acquisition that had been pre-arranged before anyone in the IPO even saw the prospectus. \n\nSpaceX investors who bought shares in the last four days got diluted by 3.4% before they understood what they owned.\n\nThe IPO was literally the printing press for the acquisition.\n\nNow look at what he ACTUALLY bought:\n\nCursor's market share among enterprise customers has been collapsing. According to spending data from Ramp, it fell from 41% in June 2025 to 26% in May 2026, bleeding ground every month to GitHub Copilot and Amazon Q. \n\nThe smart money knew. Andreessen Horowitz, Thrive, and Nvidia were about to lead a round at a $50 billion valuation, which they already considered aggressive. Elon paid 20% more than that for a company actively LOSING the race. \n\nHe paid premium for declining momentum.\n\nAnd he did this because his own AI division was in trouble. \n\nxAI has been struggling quite a bit so SpaceX needed an AI story that could survive a public-market quarterly earnings call. The fastest way to get one was to buy a brand engineers already trusted before that brand's market share slipped any further.\n\nSo follow the whole chain:\n\nSpaceX went public to mint the currency. \n\nElon used that currency to buy a fading market leader at a premium.\n\nAnd the seller of choice happened to be Anthropic's biggest enterprise pipeline with the timing landing in the exact window between Anthropic filing its prospectus and pricing its IPO.\n\nThis was literally a hit job on Anthropic's IPO.\n\nAnthropic's next move is the one to watch. If they cannot show Wall Street that Cursor's revenue can be replaced fast, the most hyped AI IPO of the year just walked onto the public markets with a huge problem.",
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    "text": "\ud83d\udfe2 $RALPH wil be First memecoin from vibe coding thats hit 1 billion market capitalization!\n\nSpread Vibe!\n\n#meme #airdrop #vibecoding https://t.co/OUvod4TUnI",
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    "text": "Garry Tan, CEO of Y Combinator, explains how to build a billion-dollar company on your own:\n\n6:11 his real advice for founders building on AI right now\n8:41 how to scale a real company with a minimal team\n10:34 what \u201chigh agency\u201d means, and why it\u2019s the most important trait now\n17:24 vibe coding and knowledge work: what AI actually replaces\n30:31 the old rule that still works: do things that don\u2019t scale\n\nwatch this now and finally start building something!",
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    "text": "Box CEO @levie's defense of software over vibe-coded, n-of-1 internal tools:\n\n\"If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time.\"\n\n\"The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely.\"\n\n\"The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization.\" \n\n\"Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?\" \n\n\"Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?\"\n\n\"I think on the margin the average enterprise is going to spend their time and energy on the latter.\"\n\n\"I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system.\"\n\nFrom his February appearance on the show.",
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    "text": "Three months ago, the consensus was that Cursor was cooked.\n\nClaude Code crossed $2.5B in run-rate revenue. Google paid $2.4B for Windsurf\u2019s IP and poached its leadership into DeepMind. OpenAI acquired Astral, the team behind Python\u2019s uv package manager, to feed Codex. Viral tweets were circulating about developers ditching Cursor for Claude Code. The usage-based pricing switch last July had users posting surprise bills on Reddit. Consumer subscriptions were running at negative margins because every token served was profit for Anthropic or OpenAI.\n\nThe company that popularized vibe coding was getting buried by the model providers it depended on.\n\nThen Cursor shipped four major releases in 15 days. JetBrains support on March 4. Automations on March 5. Plugin marketplace with 30+ partners on March 11. And now Composer 2, their own model that moggs Opus 4.6 on cost while matching it on performance.\n\nLook at the chart. Composer 2: 61.3 on CursorBench at $0.50 per million input tokens. Opus 4.6: 58.2 at $5.00. GPT-5.4: 63.9 at $2.50. The performance gaps are single digits. The cost gap between Composer and Opus is 10x.\n\nThe part nobody\u2019s pressing on: Cursor still won\u2019t name the base model. Their blog says \u201cour first continued pretraining run,\u201d which means they took an existing model and continued training on code. When the original Composer launched in October, developers kept catching it responding in Chinese. Same tokenizer patterns as DeepSeek. Nathan Lambert congratulated the research team by tweeting \u201copen weight base models + incredible ML teams in a specific niche can create immense value.\u201d Co-founder Aman Sanger told Bloomberg it was trained exclusively on code. Can\u2019t do taxes, can\u2019t write poems.\n\nA Chinese open-source chassis, refined with what Cursor calls compaction-in-the-loop RL, and fed by a billion lines of daily user code flowing through the editor every day. That data flywheel is the one asset no API provider can replicate.\n\nThe honest read requires some skepticism though. CursorBench is Cursor\u2019s own internal benchmark. They built the test, then showed you they pass it. GPT-5.4 still leads on Terminal-Bench 2.0, which is independently maintained. And Opus 4.6 at high thinking effort still outscores Composer 2 on raw accuracy. The cost advantage is real. The performance parity claim needs external validation before anyone should take this chart at face value.\n\nBut here\u2019s why the chart matters anyway. This was the P0 coming out of the holidays. Building their own model was existential. Every dollar Cursor paid Anthropic per token was margin funding the competitor building Claude Code to replace them. Every dollar paid to OpenAI funded Codex. The only way to stop bleeding cash to the companies trying to kill you is to stop using their models.\n\nFour hundred employees. $2B ARR. Reportedly raising at $50B. Entering the model race against labs with thousands of researchers and tens of billions in compute.\n\nThat chart is the fundraising slide. Whether it holds up in production against Opus and GPT-5.4 is a different question. But three months ago, the question was whether Cursor would survive at all.",
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