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🔥🔥 𝗪𝗵𝘆 𝙤𝙥𝙚𝙣-𝙨𝙤𝙪𝙧𝙘𝙚 𝙖𝙡𝙬𝙖𝙮𝙨 𝙬𝙞𝙣𝙨? Let’s go back to 1991 A Finnish student named Linus Torvalds had 𝘯𝘰𝘵𝘩𝘪𝘯𝘨: no lab, no team, no funding. Just a dream & a bit of code He released 𝗟𝗶𝗻𝘂𝘅 — open. Free. For everyone. Today? Linux powers 90% of the cloud. Your phone. Your server. Your future Why? Because 𝘄𝗵𝗲𝗻 𝗰𝗼𝗱𝗲 𝗶𝘀 𝗼𝗽𝗲𝗻, 𝙖𝙣𝙮𝙤𝙣𝙚 𝗰𝗮𝗻 𝗳𝗶𝘅 𝗶𝘁. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗶𝘁. 𝗧𝗿𝘂𝘀𝘁 𝗶𝘁 No gatekeepers. No secrets. Just 𝘳𝘦𝘢𝘭 𝘱𝘳𝘰𝘨𝘳𝘦𝘴𝘴. Now, fast forward: 𝗔𝗜’𝘀 𝗺𝗼𝗺𝗲𝗻𝘁 𝗵𝗮𝘀 𝗮𝗿𝗿𝗶𝘃𝗲𝗱 And guess what? > ✅ 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘄𝗶𝗻𝗻𝗶𝗻𝗴 Not “maybe.” Not “one day.” 𝙉𝙊𝙒 Here’s the proof — from @SentientAGI - 𝗢𝗽𝗲𝗻 𝗗𝗲𝗲𝗽 𝗦𝗲𝗮𝗿𝗰𝗵 (𝗢𝗗𝗦) → Beats Perplexity in accuracy, speed & depth. Free. Open. Live. - 𝗥𝗢𝗠𝗔 → #1 on GitHub. A multi-agent system built 𝘋𝘰𝘣𝘣𝘣𝘺 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘵𝘺, for community. Not hype. Just trust. - 𝗙𝗶𝗻𝗴𝗲𝗿𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴 → Detects AI-generated content 𝘣𝘦𝘵𝘵𝘦𝘳 𝘵𝘩𝘢𝘯 𝘱𝘢𝘪𝘥 𝘵𝘰𝘰𝘭𝘴. Built by open minds. - 𝗗𝗼𝗯𝗯𝘆 → Audits AI usage in real time. Finds bias. Exposes leaks. 𝗡𝗼 𝗰𝗹𝗼𝘀𝗲𝗱 𝗹𝗮𝗯 𝗵𝗮𝘀 𝘁𝗵𝗶𝘀. > 🚨 Closed AI says: “Wait, we’ll fix it.” > ✅ Sentient says: “It’s fixed. It’s live. It’s yours.” This isn’t a race. It’s 𝗮 𝗿𝗲𝗽𝗹𝗮𝘆 𝗼𝗳 𝗵𝗶𝘀𝘁𝗼𝗿𝘆 — with the winner already clear. Because𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗹𝗲𝘁 𝟰,𝟱𝟬𝟬+ 𝗽𝗲𝗼𝗽𝗹𝗲 𝗶𝗻 𝟲𝟬 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀 𝗯𝘂𝗶𝗹𝗱, 𝘁𝗲𝘀𝘁, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗮 𝘁𝗼𝗼𝗹… 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? 𝗜𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝗲 The future isn’t locked behind a vault. It’s open. It’s real. It’s 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘩𝘢𝘯𝘥𝘴. 👉 So stop waiting. 👉 Start using. 👉 Start contributing. > 🌍 Open-source isn’t just 𝘣𝘦𝘵𝘵𝘦𝘳. > It’s the only way 𝘵𝘳𝘶𝘵𝘩 and 𝘱𝘰𝘸𝘦𝘳 stay in the hands of people — not profit. @SentientAGI isn’t just a tool. It’s the 𝗻𝗲𝘅𝘁 𝗟𝗶𝗻𝘂𝘅. The future. And it’s already here. #OpenSourceWin #Sentient #LinuxOfAI #ODS #ROMA #OpenFuture #TrustTheCode

Here’s the architecture you’re not seeing in most AI: @SentientAGI's 𝗥𝗢𝗠𝗔-𝗚𝗥𝗜𝗗 𝘀𝘁𝗮𝗰𝗸 is built on 𝘵𝘸𝘰 𝘬𝘦𝘺 𝘱𝘳𝘪𝘯𝘤𝘪𝘱𝘭𝘦𝘴: 1. 𝗠𝗶𝗻𝗶𝗺𝗮𝗹 𝗱𝗮𝘁𝗮 𝗲𝘅𝗽𝗼𝘀𝘂𝗿𝗲 — no PII leaves the device 2. 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 — only 𝘶𝘴𝘦𝘧𝘶𝘭, 𝘷𝘢𝘭𝘪𝘥 work gets rewarded Each ROMA runs a 𝘀𝗲𝗹𝗳-𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗲𝗻𝗴𝗶𝗻𝗲e. It can query others, but only with encrypted, anonymized requests. And when a shared task is completed (like solving a complex problem), the solution is 𝘀𝗶𝗴𝗻𝗲𝗱, 𝗯𝗿𝗼𝗮𝗱𝗰𝗮𝘀𝘁, 𝗮𝗻𝗱 𝘃𝗲𝗿𝗶𝗳𝗶𝗲𝗱 by the network. No central authority. No training data leaks. 𝘑𝘶𝘴𝘵 𝘥𝘦𝘤𝘦𝘯𝘵𝘳𝘢𝘭𝘪𝘻𝘦𝘥 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 with real accountability. This is what open AI should look like.

🔓𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗣𝗮𝗿𝘁 𝗔𝗯𝗼𝘂𝘁 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁’𝘀 𝗕𝗶𝗴 𝗡𝗲𝘂𝗿𝗜𝗣𝗦 𝗪𝗶𝗻? 𝗜𝘁’𝘀 𝗢𝗽𝗲𝗻 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 Most of the time, when you hear about a breakthrough in AI, it comes with one catch: you can’t actually see it. It’s locked down, Proprietary, Hidden behind walls. But not this one. @SentientAGI just dropped 𝘧𝘰𝘶𝘳 major research papers at NeurIPS 2025 on self-improving AI, code intelligence, model fingerprinting, and secure open models and here’s the kicker: 𝗮𝗹𝗹 𝗼𝗳 𝗶𝘁 𝗶𝘀 𝗼𝗽𝗲𝗻. No secret sauce. No paywall. No “trust us, we’re smart.” You can go check the code. Run the experiments. Build on it yourself. And that’s the whole point. Because real progress doesn’t happen in closed labs. It happens in the wild when anyone, anywhere can take an idea and make it better. Whether it’s proving who owns an AI model, testing if it can 𝘳𝘦𝘢𝘭𝘭𝘺 code, or letting agents learn from playing social games, Sentient’s research isn’t just advanced it’s built to be shared. In a world full of AI hype, that’s rare. In a world starting to worry about who controls the future of intelligence, it’s essential. So yeah the tech is impressive. But the 𝘰𝘱𝘦𝘯𝘯𝘦𝘴𝘴? That’s what changes everything.

𝗪𝗵𝗮𝘁 𝗜𝗳 𝗔𝗜 𝗖𝗼𝘂𝗹𝗱 𝗕𝗲 𝗖𝗼𝗽𝘆𝗿𝗶𝗴𝗵𝘁𝗲𝗱? 𝗧𝗵𝗶𝘀 𝗡𝗲𝘄 𝗧𝗲𝗰𝗵 𝗚𝗶𝘃𝗲𝘀 𝗟𝗟𝗠𝘀 𝗮 𝗣𝗲𝗿𝗺𝗮𝗻𝗲𝗻𝘁 𝗧𝗮𝘁𝘁𝗼𝗼 Let’s say you write a song. You post it online. Next thing you know, someone else is singing it claiming it as their own, changing the lyrics, even selling it. And there’s nothing you can do to prove it was originally yours. That’s 𝘦𝘹𝘢𝘤𝘵𝘭𝘺 what’s been happening with open-source AI. Anyone can train a model, release it to the public, and… boom. It gets copied, tweaked, rebranded, or exploited with zero credit or control for the original creator. But now, @SentientAGI is changing the game with something called 𝗢𝗠𝗟 𝟭.𝟬 a quiet but powerful breakthrough in 𝘓𝘓𝘔 𝘧𝘪𝘯𝘨𝘦𝘳𝘱𝘳𝘪𝘯𝘵𝘪𝘯𝘨. Think of it like a 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗮𝘁𝘁𝗼𝗼 for AI models. This isn’t a watermark that washes off. It’s a deep, invisible signature embedded right into the model, one that can’t be removed, even if someone tries to retrain or modify it. And here’s the wild part: 𝗶𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁 𝘀𝗹𝗼𝘄 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗱𝗼𝘄𝗻. 𝗡𝗼 𝗹𝗮𝗴. 𝗡𝗼 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗵𝗶𝘁. 𝗜𝘁 𝗷𝘂𝘀𝘁… 𝘄𝗼𝗿𝗸𝘀. OML 1.0 can embed 𝗼𝘃𝗲𝗿 𝟮𝟰,𝟬𝟬𝟬 𝘂𝗻𝗶𝗾𝘂𝗲 𝗳𝗶𝗻𝗴𝗲𝗿𝗽𝗿𝗶𝗻𝘁𝘀, each one persistent, verifiable, and undetectable during normal use. Why does this matter? Because now, creators can: - ✅ Prove ownership (no more “that’s my model!” fights) - ✅ Track misuse - ✅ Build trust in open AI - ✅ Even explore fair monetization without locking everything down This isn’t about control for control’s sake. It’s about fairness, Security, Integrity. It’s about making open-source AI 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘴𝘶𝘴𝘵𝘢𝘪𝘯𝘢𝘣𝘭𝘦, not just free for anyone to take and twist. So yeah, it’s not flashy like a new chatbot interface. But if you care about who 𝘳𝘦𝘢𝘭𝘭𝘺 builds AI… And who gets to decide how it’s used… Then this tiny, invisible tattoo? It might just be the most important thing in AI all year.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽 𝘀𝘁𝗮𝘆𝗲𝗱 𝘀𝗲𝗰𝘂𝗿𝗲… 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘆𝗼𝘂 𝗵𝗮𝘃𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁? No extra layers. No “add this plugin, then this config, then this permission.” Just write your app normally but use encrypted types like `euint256` instead of regular numbers. That’s it. @zama_fhe's system handles the rest. The code runs on encrypted data no decryption needed. You’re not learning a new language. You’re not switching environments. You’re just coding 𝘢𝘯𝘥 𝘪𝘵’𝘴 𝘱𝘳𝘪𝘷𝘢𝘵𝘦 𝘣𝘺 𝘥𝘦𝘧𝘢𝘶𝘭𝘵. Think of it like this: Your app asks, “𝘐𝘴 𝘵𝘩𝘪𝘴 𝘶𝘴𝘦𝘳’𝘴 𝘣𝘢𝘭𝘢𝘯𝘤𝘦 𝘢𝘣𝘰𝘷𝘦 𝘻𝘦𝘳𝘰?” And it checks 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘦𝘷𝘦𝘳 𝘴𝘦𝘦𝘪𝘯𝘨 𝘵𝘩𝘦 𝘢𝘤𝘵𝘶𝘢𝘭 𝘣𝘢𝘭𝘢𝘯𝘤𝘦. No data leaks. No forgotten files. No “we’ll fix it later.” It just works securely. Naturally. Privacy isn’t a feature you turn on. It’s how you build today. And thanks to @zama_fhe , it’s finally 𝘴𝘪𝘮𝘱𝘭𝘦. #ZamaCreatorProgram

𝗧𝗵𝗲 𝗜𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗪𝗼𝗿𝗸𝗵𝗼𝗿𝘀𝗲: 𝗛𝗼𝘄 𝗭𝗮𝗺𝗮’𝘀 𝗧𝗲𝗰𝗵 𝗟𝗲𝘁𝘀 𝗗𝗮𝘁𝗮 𝗪𝗼𝗿𝗸 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗕𝗲𝗶𝗻𝗴 𝗦𝗲𝗲𝗻 Imagine you’re a detective. You’re handed a file full of clues about a case. But everything in it is in code. Not just scrambled 𝘦𝘯𝘤𝘳𝘺𝘱𝘵𝘦𝘥. And you're not allowed to open it. You can't read names. You can't see dates. But the case still needs solving. So how do you get answers? That’s where 𝗙𝘂𝗹𝗹𝘆 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗙𝗛𝗘) comes in the quiet revolution behind Zama’s work. FHE isn’t about hiding data. It’s about 𝗹𝗲𝘁𝘁𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝙙𝙤 𝘁𝗵𝗶𝗻𝗴𝘀 𝘄𝗵𝗶𝗹𝗲 𝘀𝘁𝗶𝗹𝗹 𝗵𝗶𝗱𝗱𝗲𝗻 Think of it like this: You send your encrypted file the coded clue sheet to a lab. The lab doesn’t open it. They don’t know what’s inside. But they 𝘤𝘢𝘯 perform calculations: - Check if two dates match - Add up financial figures - Even run a machine learning model All 𝘰𝘯 𝘵𝘩𝘦 𝘦𝘯𝘤𝘳𝘺𝘱𝘵𝘦𝘥 𝘷𝘦𝘳𝘴𝘪𝘰𝘯. And when the output comes back? It’s the same as if you’d done it yourself with the original data. But 𝘯𝘰 𝘰𝘯𝘦 𝘩𝘢𝘴 𝘦𝘷𝘦𝘳 𝘴𝘦𝘦𝘯 𝘪𝘵. That’s not magic. It’s math so advanced, it took over 30 years to make it practical. And now, thanks to @zama_fhe , it’s not just possible it’s 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀* today. Used in: - 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲: Hospitals analyze patient data without exposing records. - 𝗕𝗮𝗻𝗸𝗶𝗻𝗴: Risk checks happen on encrypted financial data — no leaks. - 𝗔𝗜: Models learn from private data, even if they’ve never seen the raw input. Zama’s mission? To make this tech𝘢𝘤𝘤𝘦𝘴𝘴𝘪𝘣𝘭𝘦, 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭, 𝘢𝘯𝘥 𝘣𝘢𝘵𝘵𝘭𝘦-𝘵𝘦𝘴𝘵𝘦𝘥. Their tools like the 𝗧𝗶𝗻𝗸 library and 𝗭𝗮𝗺𝗮 𝗙𝗛𝗘 𝗦𝗗𝗞 let developers integrate FHE into apps, APIs, and models just like any other tool. No PhD required. Just plug and play. And here’s the real game-changer: 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗲𝗮𝗻 𝘂𝘀𝗲𝗹𝗲𝘀𝘀𝗻𝗲𝘀𝘀 𝗮𝗻𝘆𝗺𝗼𝗿𝗲. You don’t have to choose between security and insight. With Zama, you can have both. It’s like giving a key to a locked safe but the key only lets you 𝘶𝘴𝘦 what’s inside, not 𝘴𝘦𝘦 it. The door stays shut. But the work gets done. This isn’t a “maybe someday” idea. It’s live. It’s real. It’s already protecting data in critical systems around the world. In a world where every breach makes headlines, Zama is building a future where data doesn’t just stay safe — it 𝘴𝘵𝘪𝘭𝘭 𝘮𝘢𝘵𝘵𝘦𝘳𝘴. No compromise. No trade-offs. Just privacy, power, and progress all in one. #ZamaCreatorProgram

Most AI coders are cheating. Not on purpose they just memorize answers like students with leaked exams. You give them a coding problem, and they don’t 𝘴𝘰𝘭𝘷𝘦 it they recognize it from somewhere online and spit back a copy. Looks impressive… until you realize it’s not real understanding. But can they 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 code when faced with something new? Something messy? Something no one’s posted on GitHub yet? That’s the big question @SentientAGI's 𝗟𝗶𝘃𝗲𝗖𝗼𝗱𝗲𝗕𝗲𝗻𝗰𝗵𝗣𝗿𝗼 was built to answer. It’s not just another coding test. It’s a reality check. Instead of recycling old problems, it uses 𝘭𝘪𝘷𝘦, evolving challenges real-world scenarios where the model has to think, debug, adapt. No shortcuts. No cheating. Just problem-solving, like a real developer on the job. And here’s the kicker: Some of the 𝘀𝗺𝗮𝗹𝗹𝗲𝘀𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 scored just as high as the billion-parameter giants. That’s not just surprising it’s revolutionary. It means you don’t need massive compute, endless data, or a corporate lab to build capable AI. What you 𝘥𝘰 need? Better benchmarks. Smarter training. Real evaluation. LiveCodeBenchPro isn’t just measuring progress it’s redefining what “progress” even means. And in a world full of hype, that’s exactly what open AI needs. Smaller. Smarter. Honest. Now 𝘵𝘩𝘢𝘵’𝘴 an AI we can trust.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲𝗼𝗻𝗲’𝘀 𝗱𝗮𝘁𝗮… 𝗯𝘂𝘁 𝗻𝗲𝘃𝗲𝗿 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗲𝗲 𝗶𝘁? Sounds like sci-fi, right? But it’s not. This is 𝗙𝗛𝗘 (Fully Homomorphic Encryption) and it’s already powering real apps today. With @zama_fhe's tools, you can write code like: `if (user_score > 100)` …even if `user_score` is encrypted. The logic runs. The answer comes back. But the actual number? Still private. It’s like checking if a sealed envelope has a red card without opening it. You don’t know the card, but you 𝘥𝘰 know whether it’s red. And now? This isn’t theory or research. It’s real. It’s usable. And it’s designed for 𝘳𝘦𝘢𝘭 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴 not just crypto nerds. No special degree needed. Just write code the way you always do but make it private by default. Health apps that track your vitals without ever knowing what they are. Voting dApps where no one sees your vote. Games where your rank stays yours. All possible. No compromises. The future of apps isn’t just secure. It’s private by design. #ZamaCreatorProgram

Here’s the real game-changer: You prove your identity once. But the proof is always valid forever. And it’s shared only via cryptographic proof, not actual data. @idOS_network Network doesn’t just let you skip re-verifying. It makes it impossible to lose your identity. Your trust. Your control. All on a decentralized network. #idOS #Web3 #SelfSovereignty #Privacy #FutureOfIdentity

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗰𝗼𝘂𝗹𝗱 𝘀𝘁𝗮𝘆 𝗹𝗼𝗰𝗸𝗲𝗱… 𝗯𝘂𝘁 𝘀𝘁𝗶𝗹𝗹 𝙙𝙤 𝙬𝙤𝙧𝙠? Yeah, we’ve all heard “encrypt your data” cool, right? But here’s the catch: once it’s encrypted, it’s basically useless. Like a locked briefcase. Great for security. Terrible for business. @zama_fhe flipped that. They built a machine that lets you 𝘥𝘰 𝘴𝘵𝘶𝘧𝘧 𝘰𝘯 𝘵𝘩𝘦 𝘦𝘯𝘤𝘳𝘺𝘱𝘵𝘦𝘥 𝘥𝘢𝘵𝘢, without unlocking it. Think of it like this: You’ve got a magical safe. It’s locked. No key. No one can see the documents inside. But you 𝘤𝘢𝘯 still: - Count the pages - Check if it’s heavy - Even run a scan for handwriting All without ever opening it. That’s what Zama’s Fully Homomorphic Encryption (FHE) is 𝘮𝘢𝘵𝘩 𝘮𝘢𝘨𝘪𝘤. It lets systems process encrypted data and give real answers while keeping 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 private. No data leaks. No risky transfers. Just computing in full secrecy. This isn’t the future. It’s running now. And your data? Finally, truly safe 𝘸𝘩𝘪𝘭𝘦 𝘴𝘵𝘪𝘭𝘭 𝘣𝘦𝘪𝘯𝘨 𝘶𝘴𝘦𝘧𝘶𝘭. #ZamaCreatorProgram

🔒 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗡𝗼𝘄 𝗟𝗼𝗰𝗸 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝗘𝘃𝗲𝗻 𝗜𝗳 𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗛𝗮𝘀 𝗙𝘂𝗹𝗹 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗜𝘁 Imagine giving someone a powerful AI like handing over the keys to a sports car but only letting them drive it on certain roads, at certain speeds, and never letting them pop the hood to mess with the engine. Sounds impossible? Not anymore. This is what @SentientAGI's 𝗟𝗼𝗰𝗸-𝗟𝗟𝗠𝘀 research pulls off and it’s a total game-changer for open AI. Right now, when a developer releases an open-source AI model, it’s like setting a robot free. Anyone can download it, tweak it, and use it for anything even things the original creator never intended, Spam, Scams, Deepfakes. You name it. There’s been no way to 𝘦𝘯𝘧𝘰𝘳𝘤𝘦 𝘳𝘶𝘭𝘦𝘴. Until now. 𝗟𝗼𝗰𝗸-𝗟𝗟𝗠𝘀 𝘂𝘀𝗲𝘀 𝗰𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆 the same kind that secures your bank app to build 𝘶𝘯𝘣𝘳𝘦𝘢𝘬𝘢𝘣𝘭𝘦 𝘶𝘴𝘢𝘨𝘦 𝘳𝘶𝘭𝘦𝘴 directly into the model. Want your AI to only answer medical questions? Lock it. Only allow use in certain countries? Lock it. Block commercial use? Yep lock that too. And here’s the magic: 𝗲𝘃𝗲𝗻 𝗶𝗳 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗵𝗮𝘀 𝗳𝘂𝗹𝗹 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹’𝘀 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝘄𝗲𝗶𝗴𝗵𝘁𝘀, 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻’𝘁 𝗯𝘆𝗽𝗮𝘀𝘀 𝘁𝗵𝗲 𝗹𝗼𝗰𝗸. No hacks. No loopholes. The rules stay enforced, forever. Think of it like a smart app license except it works even offline, on the model itself. Why does this matter? Because for the first time, developers can 𝘀𝗵𝗮𝗿𝗲 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗳𝗲𝗮𝗿. No need to keep everything closed-source. No need to over-restrict access. You can 𝘰𝘱𝘦𝘯 your AI and still 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 it. This isn’t just about control. It’s about 𝘁𝗿𝘂𝘀𝘁, 𝘀𝗮𝗳𝗲𝘁𝘆, 𝗮𝗻𝗱 𝗸𝗲𝗲𝗽𝗶𝗻𝗴 𝗼𝗽𝗲𝗻 𝗔𝗜 𝘁𝗿𝘂𝗹𝘆 𝗼𝗽𝗲𝗻 without inviting chaos. And honestly? If open AI is going to survive, stuff like this isn’t optional. It’s essential.

Heads up, creators! the @zama_fhe 𝗖𝗿𝗲𝗮𝘁𝗼𝗿 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗦𝗲𝗮𝘀𝗼𝗻 𝟯 ends in just a few days. This isn’t just another contest. It’s your chance to join a growing wave of AI artists, meme lords, fashion tinkerers, and pet trait magicians all shaping the future of AI creativity. 💰$𝟱𝟲𝗞 𝗺𝗼𝗻𝘁𝗵𝗹𝘆 𝗽𝗿𝗶𝘇𝗲 𝗽𝗼𝗼𝗹 🎁 And *everyone* who finishes strong gets the 𝗭𝗮𝗺𝗮 𝗢𝗚 𝗖𝗿𝗲𝗮𝘁𝗼𝗿 𝗡𝗙𝗧 Now, this NFT? It’s more than art. It’s your 𝗹𝗶𝗳𝗲𝘁𝗶𝗺𝗲 𝗸𝗲𝘆 to: ✅ Early alpha access to new Zama tools ✅ Future airdrops & exclusive rewards ✅ Governance seats your voice in the ecosystem ✅ Priority for collabs, events, and real-world perks Only a limited number are minted each season. That means scarcity. That means legacy. #ZamaCreatorProgram

𝗛𝗼𝘄 𝗰𝗮𝗻 𝗮 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 𝗼𝗻 𝗲𝗻𝗰𝗿𝘆𝗽𝘁𝗲𝗱 𝗱𝗮𝘁𝗮? (𝗬𝗲𝘀, 𝗿𝗲𝗮𝗹𝗹𝘆.) You know how your phone encrypts your messages? Good. That means nobody can read them. But here’s the catch: once it’s encrypted,𝘯𝘰 𝘰𝘯𝘦 can use it not even the system. It’s like wrapping your math homework in bubble wrap. You can’t solve it. You can’t check it. It’s just… stuck. @zama_fhe's tech 𝗙𝘂𝗹𝗹𝘆 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗙𝗛𝗘) is like this: You wrap your data in 𝘮𝘢𝘵𝘩 𝘢𝘳𝘮𝘰𝘳. Then, even while it’s still wrapped, someone else can: 👉 Add two numbers 👉 Run a risk check 👉 Even run an AI model All on the encrypted version. No unboxing. No peeking. Just answers that come out 𝘤𝘰𝘳𝘳𝘦𝘤𝘵, even though the calculation happened in a locked box. It’s not a trick. It’s advanced math that’s 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 running in real systems today. And it’s changing how we handle sensitive data safely, smartly, powerfully. No compromise. Just privacy with power. #ZamaCreatorProgram

Imagine a world where your identity isn’t tied to a password, an email, or a centralized database. Where proving you’re you doesn’t mean surrendering your data. And where one proof cryptographically secure works across apps, chains, and services, forever. That’s not sci-fi. It’s @idOS_network . Built on 𝗭𝗲𝗿𝗼-𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗣𝗿𝗼𝗼𝗳𝘀 (𝗭𝗞𝗣𝘀), idOS verifies identity without ever seeing your personal information. No face scans. No ID numbers. No birthdates. Just a cryptographic guarantee: “This is the real person.” Your proof lives in your wallet. Not on a server. Not in the cloud. Not anywhere else. You own it. You control it. And you decide when and to whom you share it. #idOS #Web3 #ZeroKnowledge #PrivacyFirst #SelfSovereignIdentity

🔥 Imagine this: You send your bank details to a website… but instead of seeing them, the computer just 𝘤𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘦𝘴 your balance without ever unlocking the code. No peeking. No leaks. Just math magic working silently in the background. That’s what 𝗙𝘂𝗹𝗹𝘆 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗙𝗛𝗘) does, it lets you run calculations on encrypted data 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 ever decrypting it first. And here’s the wild part: @zama_fhe is making this real for the blockchain world. With tools like their 𝗦𝗼𝗹𝗶𝗱𝗶𝘁𝘆 𝗴𝘂𝗶𝗱𝗲𝘀, you can now write smart contracts that 𝘤𝘰𝘮𝘱𝘶𝘵𝘦 on private data like medical records, financial info, or even secret votes all while keeping everything locked down. You don’t need to trust anyone. The code does the math 𝘰𝘯 the encrypted data, and the result is encrypted too. Only the intended person can decrypt it. Think of it like a locked suitcase you can 𝘰𝘱𝘦𝘯 𝘴𝘵𝘶𝘧𝘧 𝘢𝘥𝘥 𝘴𝘵𝘶𝘧𝘧 𝘪𝘯𝘴𝘪𝘥𝘦, 𝘢𝘯𝘥 𝘭𝘰𝘤𝘬 𝘢𝘨𝘢𝘪𝘯 all while staying sealed. Nobody sees what’s inside, but you can still do things with it. @zama_fhe gives devs the tools to actually 𝘣𝘶𝘪𝘭𝘥 this today from quick-start tutorials to real code examples. It’s not sci-fi. It’s built with open-source libraries like 𝗖𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝗠𝗟 (for AI) and 𝗧𝗙𝗛𝗘-𝗿𝘀 (for Rust). So if you’re tired of “privacy” being just a buzzword… This might be the real deal. Because in the future, privacy isn’t about hiding data it’s about letting it 𝘥𝘰 𝘸𝘰𝘳𝘬 while staying safe. And Zama’s already writing that future. #ZamaCreatorProgram

𝗭𝗮𝗺𝗮 𝘄𝗮𝘀 𝗯𝗼𝗿𝗻 𝗳𝗿𝗼𝗺 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗶𝗱𝗲𝗮: 𝘞𝘩𝘢𝘵 𝘪𝘧 𝘱𝘳𝘪𝘷𝘢𝘵𝘦 𝘥𝘢𝘵𝘢 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘶𝘴𝘦𝘥 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘦𝘷𝘦𝘳 𝘣𝘦𝘪𝘯𝘨 𝘴𝘦𝘦𝘯? In 2018, a team of cryptographers realized FHE (Fully Homomorphic Encryption) a 2009 breakthrough was too slow for real use. So they set out to fix it. Now? FHE is fast enough to run on the cloud. Zama’s open-source tools (Tink, FHE SDK) let developers: - Run AI on encrypted health records - Detect fraud without seeing data - Train models on private datasets All without decryption. No leaks. No trade-offs. Just privacy and power. @zama_fhe : Where math meets real-world impact. #ZamaCreatorProgram

Here’s the architecture you’re not seeing in most AI: @SentientAGI's 𝗥𝗢𝗠𝗔-𝗚𝗥𝗜𝗗 𝘀𝘁𝗮𝗰𝗸 is built on 𝘵𝘸𝘰 𝘬𝘦𝘺 𝘱𝘳𝘪𝘯𝘤𝘪𝘱𝘭𝘦𝘴: 1. 𝗠𝗶𝗻𝗶𝗺𝗮𝗹 𝗱𝗮𝘁𝗮 𝗲𝘅𝗽𝗼𝘀𝘂𝗿𝗲 — no PII leaves the device 2. 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 — only 𝘶𝘴𝘦𝘧𝘶𝘭, 𝘷𝘢𝘭𝘪𝘥 work gets rewarded Each ROMA runs a 𝘀𝗲𝗹𝗳-𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗲𝗻𝗴𝗶𝗻𝗲e. It can query others, but only with encrypted, anonymized requests. And when a shared task is completed (like solving a complex problem), the solution is 𝘀𝗶𝗴𝗻𝗲𝗱, 𝗯𝗿𝗼𝗮𝗱𝗰𝗮𝘀𝘁, 𝗮𝗻𝗱 𝘃𝗲𝗿𝗶𝗳𝗶𝗲𝗱 by the network. No central authority. No training data leaks. 𝘑𝘶𝘴𝘵 𝘥𝘦𝘤𝘦𝘯𝘵𝘳𝘢𝘭𝘪𝘻𝘦𝘥 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 with real accountability. This is what open AI should look like.

𝗪𝗵𝗮𝘁 𝗜𝗳 𝗔𝗜 𝗖𝗼𝘂𝗹𝗱 𝗕𝗲 𝗖𝗼𝗽𝘆𝗿𝗶𝗴𝗵𝘁𝗲𝗱? 𝗧𝗵𝗶𝘀 𝗡𝗲𝘄 𝗧𝗲𝗰𝗵 𝗚𝗶𝘃𝗲𝘀 𝗟𝗟𝗠𝘀 𝗮 𝗣𝗲𝗿𝗺𝗮𝗻𝗲𝗻𝘁 𝗧𝗮𝘁𝘁𝗼𝗼 Let’s say you write a song. You post it online. Next thing you know, someone else is singing it claiming it as their own, changing the lyrics, even selling it. And there’s nothing you can do to prove it was originally yours. That’s 𝘦𝘹𝘢𝘤𝘵𝘭𝘺 what’s been happening with open-source AI. Anyone can train a model, release it to the public, and… boom. It gets copied, tweaked, rebranded, or exploited with zero credit or control for the original creator. But now, @SentientAGI is changing the game with something called 𝗢𝗠𝗟 𝟭.𝟬 a quiet but powerful breakthrough in 𝘓𝘓𝘔 𝘧𝘪𝘯𝘨𝘦𝘳𝘱𝘳𝘪𝘯𝘵𝘪𝘯𝘨. Think of it like a 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗮𝘁𝘁𝗼𝗼 for AI models. This isn’t a watermark that washes off. It’s a deep, invisible signature embedded right into the model, one that can’t be removed, even if someone tries to retrain or modify it. And here’s the wild part: 𝗶𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁 𝘀𝗹𝗼𝘄 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗱𝗼𝘄𝗻. 𝗡𝗼 𝗹𝗮𝗴. 𝗡𝗼 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗵𝗶𝘁. 𝗜𝘁 𝗷𝘂𝘀𝘁… 𝘄𝗼𝗿𝗸𝘀. OML 1.0 can embed 𝗼𝘃𝗲𝗿 𝟮𝟰,𝟬𝟬𝟬 𝘂𝗻𝗶𝗾𝘂𝗲 𝗳𝗶𝗻𝗴𝗲𝗿𝗽𝗿𝗶𝗻𝘁𝘀, each one persistent, verifiable, and undetectable during normal use. Why does this matter? Because now, creators can: - ✅ Prove ownership (no more “that’s my model!” fights) - ✅ Track misuse - ✅ Build trust in open AI - ✅ Even explore fair monetization without locking everything down This isn’t about control for control’s sake. It’s about fairness, Security, Integrity. It’s about making open-source AI 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘴𝘶𝘴𝘵𝘢𝘪𝘯𝘢𝘣𝘭𝘦, not just free for anyone to take and twist. So yeah, it’s not flashy like a new chatbot interface. But if you care about who 𝘳𝘦𝘢𝘭𝘭𝘺 builds AI… And who gets to decide how it’s used… Then this tiny, invisible tattoo? It might just be the most important thing in AI all year.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽 𝘀𝘁𝗮𝘆𝗲𝗱 𝘀𝗲𝗰𝘂𝗿𝗲… 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘆𝗼𝘂 𝗵𝗮𝘃𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁? No extra layers. No “add this plugin, then this config, then this permission.” Just write your app normally but use encrypted types like `euint256` instead of regular numbers. That’s it. @zama_fhe's system handles the rest. The code runs on encrypted data no decryption needed. You’re not learning a new language. You’re not switching environments. You’re just coding 𝘢𝘯𝘥 𝘪𝘵’𝘴 𝘱𝘳𝘪𝘷𝘢𝘵𝘦 𝘣𝘺 𝘥𝘦𝘧𝘢𝘶𝘭𝘵. Think of it like this: Your app asks, “𝘐𝘴 𝘵𝘩𝘪𝘴 𝘶𝘴𝘦𝘳’𝘴 𝘣𝘢𝘭𝘢𝘯𝘤𝘦 𝘢𝘣𝘰𝘷𝘦 𝘻𝘦𝘳𝘰?” And it checks 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘦𝘷𝘦𝘳 𝘴𝘦𝘦𝘪𝘯𝘨 𝘵𝘩𝘦 𝘢𝘤𝘵𝘶𝘢𝘭 𝘣𝘢𝘭𝘢𝘯𝘤𝘦. No data leaks. No forgotten files. No “we’ll fix it later.” It just works securely. Naturally. Privacy isn’t a feature you turn on. It’s how you build today. And thanks to @zama_fhe , it’s finally 𝘴𝘪𝘮𝘱𝘭𝘦. #ZamaCreatorProgram

🔥 Imagine this: You send your bank details to a website… but instead of seeing them, the computer just 𝘤𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘦𝘴 your balance without ever unlocking the code. No peeking. No leaks. Just math magic working silently in the background. That’s what 𝗙𝘂𝗹𝗹𝘆 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗙𝗛𝗘) does, it lets you run calculations on encrypted data 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 ever decrypting it first. And here’s the wild part: @zama_fhe is making this real for the blockchain world. With tools like their 𝗦𝗼𝗹𝗶𝗱𝗶𝘁𝘆 𝗴𝘂𝗶𝗱𝗲𝘀, you can now write smart contracts that 𝘤𝘰𝘮𝘱𝘶𝘵𝘦 on private data like medical records, financial info, or even secret votes all while keeping everything locked down. You don’t need to trust anyone. The code does the math 𝘰𝘯 the encrypted data, and the result is encrypted too. Only the intended person can decrypt it. Think of it like a locked suitcase you can 𝘰𝘱𝘦𝘯 𝘴𝘵𝘶𝘧𝘧 𝘢𝘥𝘥 𝘴𝘵𝘶𝘧𝘧 𝘪𝘯𝘴𝘪𝘥𝘦, 𝘢𝘯𝘥 𝘭𝘰𝘤𝘬 𝘢𝘨𝘢𝘪𝘯 all while staying sealed. Nobody sees what’s inside, but you can still do things with it. @zama_fhe gives devs the tools to actually 𝘣𝘶𝘪𝘭𝘥 this today from quick-start tutorials to real code examples. It’s not sci-fi. It’s built with open-source libraries like 𝗖𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝗠𝗟 (for AI) and 𝗧𝗙𝗛𝗘-𝗿𝘀 (for Rust). So if you’re tired of “privacy” being just a buzzword… This might be the real deal. Because in the future, privacy isn’t about hiding data it’s about letting it 𝘥𝘰 𝘸𝘰𝘳𝘬 while staying safe. And Zama’s already writing that future. #ZamaCreatorProgram

🔓𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗣𝗮𝗿𝘁 𝗔𝗯𝗼𝘂𝘁 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁’𝘀 𝗕𝗶𝗴 𝗡𝗲𝘂𝗿𝗜𝗣𝗦 𝗪𝗶𝗻? 𝗜𝘁’𝘀 𝗢𝗽𝗲𝗻 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 Most of the time, when you hear about a breakthrough in AI, it comes with one catch: you can’t actually see it. It’s locked down, Proprietary, Hidden behind walls. But not this one. @SentientAGI just dropped 𝘧𝘰𝘶𝘳 major research papers at NeurIPS 2025 on self-improving AI, code intelligence, model fingerprinting, and secure open models and here’s the kicker: 𝗮𝗹𝗹 𝗼𝗳 𝗶𝘁 𝗶𝘀 𝗼𝗽𝗲𝗻. No secret sauce. No paywall. No “trust us, we’re smart.” You can go check the code. Run the experiments. Build on it yourself. And that’s the whole point. Because real progress doesn’t happen in closed labs. It happens in the wild when anyone, anywhere can take an idea and make it better. Whether it’s proving who owns an AI model, testing if it can 𝘳𝘦𝘢𝘭𝘭𝘺 code, or letting agents learn from playing social games, Sentient’s research isn’t just advanced it’s built to be shared. In a world full of AI hype, that’s rare. In a world starting to worry about who controls the future of intelligence, it’s essential. So yeah the tech is impressive. But the 𝘰𝘱𝘦𝘯𝘯𝘦𝘴𝘴? That’s what changes everything.

𝗧𝗵𝗲 𝗜𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗪𝗼𝗿𝗸𝗵𝗼𝗿𝘀𝗲: 𝗛𝗼𝘄 𝗭𝗮𝗺𝗮’𝘀 𝗧𝗲𝗰𝗵 𝗟𝗲𝘁𝘀 𝗗𝗮𝘁𝗮 𝗪𝗼𝗿𝗸 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗕𝗲𝗶𝗻𝗴 𝗦𝗲𝗲𝗻 Imagine you’re a detective. You’re handed a file full of clues about a case. But everything in it is in code. Not just scrambled 𝘦𝘯𝘤𝘳𝘺𝘱𝘵𝘦𝘥. And you're not allowed to open it. You can't read names. You can't see dates. But the case still needs solving. So how do you get answers? That’s where 𝗙𝘂𝗹𝗹𝘆 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗙𝗛𝗘) comes in the quiet revolution behind Zama’s work. FHE isn’t about hiding data. It’s about 𝗹𝗲𝘁𝘁𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝙙𝙤 𝘁𝗵𝗶𝗻𝗴𝘀 𝘄𝗵𝗶𝗹𝗲 𝘀𝘁𝗶𝗹𝗹 𝗵𝗶𝗱𝗱𝗲𝗻 Think of it like this: You send your encrypted file the coded clue sheet to a lab. The lab doesn’t open it. They don’t know what’s inside. But they 𝘤𝘢𝘯 perform calculations: - Check if two dates match - Add up financial figures - Even run a machine learning model All 𝘰𝘯 𝘵𝘩𝘦 𝘦𝘯𝘤𝘳𝘺𝘱𝘵𝘦𝘥 𝘷𝘦𝘳𝘴𝘪𝘰𝘯. And when the output comes back? It’s the same as if you’d done it yourself with the original data. But 𝘯𝘰 𝘰𝘯𝘦 𝘩𝘢𝘴 𝘦𝘷𝘦𝘳 𝘴𝘦𝘦𝘯 𝘪𝘵. That’s not magic. It’s math so advanced, it took over 30 years to make it practical. And now, thanks to @zama_fhe , it’s not just possible it’s 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀* today. Used in: - 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲: Hospitals analyze patient data without exposing records. - 𝗕𝗮𝗻𝗸𝗶𝗻𝗴: Risk checks happen on encrypted financial data — no leaks. - 𝗔𝗜: Models learn from private data, even if they’ve never seen the raw input. Zama’s mission? To make this tech𝘢𝘤𝘤𝘦𝘴𝘴𝘪𝘣𝘭𝘦, 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭, 𝘢𝘯𝘥 𝘣𝘢𝘵𝘵𝘭𝘦-𝘵𝘦𝘴𝘵𝘦𝘥. Their tools like the 𝗧𝗶𝗻𝗸 library and 𝗭𝗮𝗺𝗮 𝗙𝗛𝗘 𝗦𝗗𝗞 let developers integrate FHE into apps, APIs, and models just like any other tool. No PhD required. Just plug and play. And here’s the real game-changer: 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗲𝗮𝗻 𝘂𝘀𝗲𝗹𝗲𝘀𝘀𝗻𝗲𝘀𝘀 𝗮𝗻𝘆𝗺𝗼𝗿𝗲. You don’t have to choose between security and insight. With Zama, you can have both. It’s like giving a key to a locked safe but the key only lets you 𝘶𝘴𝘦 what’s inside, not 𝘴𝘦𝘦 it. The door stays shut. But the work gets done. This isn’t a “maybe someday” idea. It’s live. It’s real. It’s already protecting data in critical systems around the world. In a world where every breach makes headlines, Zama is building a future where data doesn’t just stay safe — it 𝘴𝘵𝘪𝘭𝘭 𝘮𝘢𝘵𝘵𝘦𝘳𝘴. No compromise. No trade-offs. Just privacy, power, and progress all in one. #ZamaCreatorProgram

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲𝗼𝗻𝗲’𝘀 𝗱𝗮𝘁𝗮… 𝗯𝘂𝘁 𝗻𝗲𝘃𝗲𝗿 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗲𝗲 𝗶𝘁? Sounds like sci-fi, right? But it’s not. This is 𝗙𝗛𝗘 (Fully Homomorphic Encryption) and it’s already powering real apps today. With @zama_fhe's tools, you can write code like: `if (user_score > 100)` …even if `user_score` is encrypted. The logic runs. The answer comes back. But the actual number? Still private. It’s like checking if a sealed envelope has a red card without opening it. You don’t know the card, but you 𝘥𝘰 know whether it’s red. And now? This isn’t theory or research. It’s real. It’s usable. And it’s designed for 𝘳𝘦𝘢𝘭 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴 not just crypto nerds. No special degree needed. Just write code the way you always do but make it private by default. Health apps that track your vitals without ever knowing what they are. Voting dApps where no one sees your vote. Games where your rank stays yours. All possible. No compromises. The future of apps isn’t just secure. It’s private by design. #ZamaCreatorProgram

🔒 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗡𝗼𝘄 𝗟𝗼𝗰𝗸 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝗘𝘃𝗲𝗻 𝗜𝗳 𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗛𝗮𝘀 𝗙𝘂𝗹𝗹 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗜𝘁 Imagine giving someone a powerful AI like handing over the keys to a sports car but only letting them drive it on certain roads, at certain speeds, and never letting them pop the hood to mess with the engine. Sounds impossible? Not anymore. This is what @SentientAGI's 𝗟𝗼𝗰𝗸-𝗟𝗟𝗠𝘀 research pulls off and it’s a total game-changer for open AI. Right now, when a developer releases an open-source AI model, it’s like setting a robot free. Anyone can download it, tweak it, and use it for anything even things the original creator never intended, Spam, Scams, Deepfakes. You name it. There’s been no way to 𝘦𝘯𝘧𝘰𝘳𝘤𝘦 𝘳𝘶𝘭𝘦𝘴. Until now. 𝗟𝗼𝗰𝗸-𝗟𝗟𝗠𝘀 𝘂𝘀𝗲𝘀 𝗰𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆 the same kind that secures your bank app to build 𝘶𝘯𝘣𝘳𝘦𝘢𝘬𝘢𝘣𝘭𝘦 𝘶𝘴𝘢𝘨𝘦 𝘳𝘶𝘭𝘦𝘴 directly into the model. Want your AI to only answer medical questions? Lock it. Only allow use in certain countries? Lock it. Block commercial use? Yep lock that too. And here’s the magic: 𝗲𝘃𝗲𝗻 𝗶𝗳 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗵𝗮𝘀 𝗳𝘂𝗹𝗹 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹’𝘀 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝘄𝗲𝗶𝗴𝗵𝘁𝘀, 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻’𝘁 𝗯𝘆𝗽𝗮𝘀𝘀 𝘁𝗵𝗲 𝗹𝗼𝗰𝗸. No hacks. No loopholes. The rules stay enforced, forever. Think of it like a smart app license except it works even offline, on the model itself. Why does this matter? Because for the first time, developers can 𝘀𝗵𝗮𝗿𝗲 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗳𝗲𝗮𝗿. No need to keep everything closed-source. No need to over-restrict access. You can 𝘰𝘱𝘦𝘯 your AI and still 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 it. This isn’t just about control. It’s about 𝘁𝗿𝘂𝘀𝘁, 𝘀𝗮𝗳𝗲𝘁𝘆, 𝗮𝗻𝗱 𝗸𝗲𝗲𝗽𝗶𝗻𝗴 𝗼𝗽𝗲𝗻 𝗔𝗜 𝘁𝗿𝘂𝗹𝘆 𝗼𝗽𝗲𝗻 without inviting chaos. And honestly? If open AI is going to survive, stuff like this isn’t optional. It’s essential.

Most AI coders are cheating. Not on purpose they just memorize answers like students with leaked exams. You give them a coding problem, and they don’t 𝘴𝘰𝘭𝘷𝘦 it they recognize it from somewhere online and spit back a copy. Looks impressive… until you realize it’s not real understanding. But can they 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 code when faced with something new? Something messy? Something no one’s posted on GitHub yet? That’s the big question @SentientAGI's 𝗟𝗶𝘃𝗲𝗖𝗼𝗱𝗲𝗕𝗲𝗻𝗰𝗵𝗣𝗿𝗼 was built to answer. It’s not just another coding test. It’s a reality check. Instead of recycling old problems, it uses 𝘭𝘪𝘷𝘦, evolving challenges real-world scenarios where the model has to think, debug, adapt. No shortcuts. No cheating. Just problem-solving, like a real developer on the job. And here’s the kicker: Some of the 𝘀𝗺𝗮𝗹𝗹𝗲𝘀𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 scored just as high as the billion-parameter giants. That’s not just surprising it’s revolutionary. It means you don’t need massive compute, endless data, or a corporate lab to build capable AI. What you 𝘥𝘰 need? Better benchmarks. Smarter training. Real evaluation. LiveCodeBenchPro isn’t just measuring progress it’s redefining what “progress” even means. And in a world full of hype, that’s exactly what open AI needs. Smaller. Smarter. Honest. Now 𝘵𝘩𝘢𝘵’𝘴 an AI we can trust.

Heads up, creators! the @zama_fhe 𝗖𝗿𝗲𝗮𝘁𝗼𝗿 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗦𝗲𝗮𝘀𝗼𝗻 𝟯 ends in just a few days. This isn’t just another contest. It’s your chance to join a growing wave of AI artists, meme lords, fashion tinkerers, and pet trait magicians all shaping the future of AI creativity. 💰$𝟱𝟲𝗞 𝗺𝗼𝗻𝘁𝗵𝗹𝘆 𝗽𝗿𝗶𝘇𝗲 𝗽𝗼𝗼𝗹 🎁 And *everyone* who finishes strong gets the 𝗭𝗮𝗺𝗮 𝗢𝗚 𝗖𝗿𝗲𝗮𝘁𝗼𝗿 𝗡𝗙𝗧 Now, this NFT? It’s more than art. It’s your 𝗹𝗶𝗳𝗲𝘁𝗶𝗺𝗲 𝗸𝗲𝘆 to: ✅ Early alpha access to new Zama tools ✅ Future airdrops & exclusive rewards ✅ Governance seats your voice in the ecosystem ✅ Priority for collabs, events, and real-world perks Only a limited number are minted each season. That means scarcity. That means legacy. #ZamaCreatorProgram

Imagine a world where your identity isn’t tied to a password, an email, or a centralized database. Where proving you’re you doesn’t mean surrendering your data. And where one proof cryptographically secure works across apps, chains, and services, forever. That’s not sci-fi. It’s @idOS_network . Built on 𝗭𝗲𝗿𝗼-𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗣𝗿𝗼𝗼𝗳𝘀 (𝗭𝗞𝗣𝘀), idOS verifies identity without ever seeing your personal information. No face scans. No ID numbers. No birthdates. Just a cryptographic guarantee: “This is the real person.” Your proof lives in your wallet. Not on a server. Not in the cloud. Not anywhere else. You own it. You control it. And you decide when and to whom you share it. #idOS #Web3 #ZeroKnowledge #PrivacyFirst #SelfSovereignIdentity

𝗛𝗼𝘄 𝗰𝗮𝗻 𝗮 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 𝗼𝗻 𝗲𝗻𝗰𝗿𝘆𝗽𝘁𝗲𝗱 𝗱𝗮𝘁𝗮? (𝗬𝗲𝘀, 𝗿𝗲𝗮𝗹𝗹𝘆.) You know how your phone encrypts your messages? Good. That means nobody can read them. But here’s the catch: once it’s encrypted,𝘯𝘰 𝘰𝘯𝘦 can use it not even the system. It’s like wrapping your math homework in bubble wrap. You can’t solve it. You can’t check it. It’s just… stuck. @zama_fhe's tech 𝗙𝘂𝗹𝗹𝘆 𝗛𝗼𝗺𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 (𝗙𝗛𝗘) is like this: You wrap your data in 𝘮𝘢𝘵𝘩 𝘢𝘳𝘮𝘰𝘳. Then, even while it’s still wrapped, someone else can: 👉 Add two numbers 👉 Run a risk check 👉 Even run an AI model All on the encrypted version. No unboxing. No peeking. Just answers that come out 𝘤𝘰𝘳𝘳𝘦𝘤𝘵, even though the calculation happened in a locked box. It’s not a trick. It’s advanced math that’s 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 running in real systems today. And it’s changing how we handle sensitive data safely, smartly, powerfully. No compromise. Just privacy with power. #ZamaCreatorProgram

🔥🔥 𝗪𝗵𝘆 𝙤𝙥𝙚𝙣-𝙨𝙤𝙪𝙧𝙘𝙚 𝙖𝙡𝙬𝙖𝙮𝙨 𝙬𝙞𝙣𝙨? Let’s go back to 1991 A Finnish student named Linus Torvalds had 𝘯𝘰𝘵𝘩𝘪𝘯𝘨: no lab, no team, no funding. Just a dream & a bit of code He released 𝗟𝗶𝗻𝘂𝘅 — open. Free. For everyone. Today? Linux powers 90% of the cloud. Your phone. Your server. Your future Why? Because 𝘄𝗵𝗲𝗻 𝗰𝗼𝗱𝗲 𝗶𝘀 𝗼𝗽𝗲𝗻, 𝙖𝙣𝙮𝙤𝙣𝙚 𝗰𝗮𝗻 𝗳𝗶𝘅 𝗶𝘁. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗶𝘁. 𝗧𝗿𝘂𝘀𝘁 𝗶𝘁 No gatekeepers. No secrets. Just 𝘳𝘦𝘢𝘭 𝘱𝘳𝘰𝘨𝘳𝘦𝘴𝘴. Now, fast forward: 𝗔𝗜’𝘀 𝗺𝗼𝗺𝗲𝗻𝘁 𝗵𝗮𝘀 𝗮𝗿𝗿𝗶𝘃𝗲𝗱 And guess what? > ✅ 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘄𝗶𝗻𝗻𝗶𝗻𝗴 Not “maybe.” Not “one day.” 𝙉𝙊𝙒 Here’s the proof — from @SentientAGI - 𝗢𝗽𝗲𝗻 𝗗𝗲𝗲𝗽 𝗦𝗲𝗮𝗿𝗰𝗵 (𝗢𝗗𝗦) → Beats Perplexity in accuracy, speed & depth. Free. Open. Live. - 𝗥𝗢𝗠𝗔 → #1 on GitHub. A multi-agent system built 𝘋𝘰𝘣𝘣𝘣𝘺 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘵𝘺, for community. Not hype. Just trust. - 𝗙𝗶𝗻𝗴𝗲𝗿𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴 → Detects AI-generated content 𝘣𝘦𝘵𝘵𝘦𝘳 𝘵𝘩𝘢𝘯 𝘱𝘢𝘪𝘥 𝘵𝘰𝘰𝘭𝘴. Built by open minds. - 𝗗𝗼𝗯𝗯𝘆 → Audits AI usage in real time. Finds bias. Exposes leaks. 𝗡𝗼 𝗰𝗹𝗼𝘀𝗲𝗱 𝗹𝗮𝗯 𝗵𝗮𝘀 𝘁𝗵𝗶𝘀. > 🚨 Closed AI says: “Wait, we’ll fix it.” > ✅ Sentient says: “It’s fixed. It’s live. It’s yours.” This isn’t a race. It’s 𝗮 𝗿𝗲𝗽𝗹𝗮𝘆 𝗼𝗳 𝗵𝗶𝘀𝘁𝗼𝗿𝘆 — with the winner already clear. Because𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗹𝗲𝘁 𝟰,𝟱𝟬𝟬+ 𝗽𝗲𝗼𝗽𝗹𝗲 𝗶𝗻 𝟲𝟬 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀 𝗯𝘂𝗶𝗹𝗱, 𝘁𝗲𝘀𝘁, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗮 𝘁𝗼𝗼𝗹… 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? 𝗜𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝗲 The future isn’t locked behind a vault. It’s open. It’s real. It’s 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘩𝘢𝘯𝘥𝘴. 👉 So stop waiting. 👉 Start using. 👉 Start contributing. > 🌍 Open-source isn’t just 𝘣𝘦𝘵𝘵𝘦𝘳. > It’s the only way 𝘵𝘳𝘶𝘵𝘩 and 𝘱𝘰𝘸𝘦𝘳 stay in the hands of people — not profit. @SentientAGI isn’t just a tool. It’s the 𝗻𝗲𝘅𝘁 𝗟𝗶𝗻𝘂𝘅. The future. And it’s already here. #OpenSourceWin #Sentient #LinuxOfAI #ODS #ROMA #OpenFuture #TrustTheCode

Here’s the real game-changer: You prove your identity once. But the proof is always valid forever. And it’s shared only via cryptographic proof, not actual data. @idOS_network Network doesn’t just let you skip re-verifying. It makes it impossible to lose your identity. Your trust. Your control. All on a decentralized network. #idOS #Web3 #SelfSovereignty #Privacy #FutureOfIdentity

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗰𝗼𝘂𝗹𝗱 𝘀𝘁𝗮𝘆 𝗹𝗼𝗰𝗸𝗲𝗱… 𝗯𝘂𝘁 𝘀𝘁𝗶𝗹𝗹 𝙙𝙤 𝙬𝙤𝙧𝙠? Yeah, we’ve all heard “encrypt your data” cool, right? But here’s the catch: once it’s encrypted, it’s basically useless. Like a locked briefcase. Great for security. Terrible for business. @zama_fhe flipped that. They built a machine that lets you 𝘥𝘰 𝘴𝘵𝘶𝘧𝘧 𝘰𝘯 𝘵𝘩𝘦 𝘦𝘯𝘤𝘳𝘺𝘱𝘵𝘦𝘥 𝘥𝘢𝘵𝘢, without unlocking it. Think of it like this: You’ve got a magical safe. It’s locked. No key. No one can see the documents inside. But you 𝘤𝘢𝘯 still: - Count the pages - Check if it’s heavy - Even run a scan for handwriting All without ever opening it. That’s what Zama’s Fully Homomorphic Encryption (FHE) is 𝘮𝘢𝘵𝘩 𝘮𝘢𝘨𝘪𝘤. It lets systems process encrypted data and give real answers while keeping 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 private. No data leaks. No risky transfers. Just computing in full secrecy. This isn’t the future. It’s running now. And your data? Finally, truly safe 𝘸𝘩𝘪𝘭𝘦 𝘴𝘵𝘪𝘭𝘭 𝘣𝘦𝘪𝘯𝘨 𝘶𝘴𝘦𝘧𝘶𝘭. #ZamaCreatorProgram

🤖 𝗪𝗵𝗮𝘁 𝗜𝗳 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗖𝗼𝘂𝗹𝗱 𝗟𝗲𝗮𝗿𝗻 𝗟𝗶𝗸𝗲 𝗮 𝗛𝘂𝗺𝗮𝗻 𝗙𝗿𝗼𝗺 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗗𝗮𝘁𝗮? Not from endless lectures. Not from memorizing textbooks. But by 𝘥𝘰𝘪𝘯𝘨 trying things, failing, adapting, getting smarter over time. That’s the future @SentientAGI is building with their latest NeurIPS 2025 research. Most AI today learns once during training and then stays frozen. Like a student who crams for an exam and never studies again. But real intelligence doesn’t work like that. Sentient’s new work introduces 𝗔𝗜 𝘁𝗵𝗮𝘁 𝗸𝗲𝗲𝗽𝘀 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, not in isolation, but in dynamic, interactive environments where models play, compete, and cooperate kind of like how humans learn through life. One of their breakthroughs, 𝗠𝗶𝗻𝗱𝗚𝗮𝗺𝗲𝘀 𝗔𝗿𝗲𝗻𝗮, is like a gym for AI minds. Agents aren’t just fed data they 𝘦𝘢𝘳𝘯 knowledge by solving challenges, negotiating with others, even outsmarting rivals in strategy games. Every win, every loss, every interaction becomes a lesson. And the best part? It’s not locked down. The research is open. The code is shareable. Anyone can test it, tweak it, build on it. Because building truly intelligent machines isn’t about secrecy it’s about evolution. So while others train AI to pass tests, Sentient is teaching it to grow. And that’s not just progress. That’s a shift in mindset. The future of AI isn’t static. It learns. It adapts. It evolves. And it’s already here.