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⋘Commencing new experiment.. loading data…⋙ ▰═════════ 10% ▰▰▰▰▰═════ 50% ▰▰▰▰▰▰▰═══ 75% ▰▰▰▰▰▰▰▰▰═ 99% //ACCESS GRANTED. #OXT_1206 #THEBOYZ #더보이즈

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THE BOYZ Projects PH

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27,950 Aufrufe • vor 3 Jahren •via X (Twitter)

#OXT_1206#THEBOYZ#더보이즈
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#ZENERATION_MNL 🧬 Experiment 0403 Love C.O.D.E: #OXT_1206 completion date in D-12 🧪 Coding status ……… 10.05% #THEBOYZ #더보이즈

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Under a new agreement with Boeing, we will add 75 wide-body B787-9 and B787-10 aircraft to our fleet between 2029 and 2034, with 50 confirmed and 25 as options. We have also finalized discussions for 150 737-8 and 737-10 MAX aircraft, of which 100 are confirmed and 50 optional. Once negotiations with engine manufacturer CFM International are successfully completed, we will proceed with these orders. This landmark agreement brings us one step closer to our 2033 vision of an 800-aircraft fleet. With every new destination we reach, we bring the dream of a Türkiye soaring in the skies closer to reality. 🇹🇷 #TurkishAirlines

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In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

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To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

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Loading DeFAI infrastructure ▓▓▓▓▓▓▓▓▓░ io.net, a decentralized GPU compute network has expanded to power the future of DeFAI for Injective builders. The integration aims to support the growing trend of developers building AI Agents, DeFAI, and gaming initiatives in web3. io.net provides Web3 builders with tools to train, fine-tune, and deploy ML models using decentralized resources. The collaboration combines Injective's iAgent framework with io.net’s extensive GPU network, setting the stage for more accessible and innovative development across use cases that require compute resources. Key features include: ✅ Access to over 10,000 cluster-ready GPUs and CPUs, ✅ AI-driven blockchain activities using Injective's iAgent SDK, and ✅ Potential for new on-chain financial products leveraging GPU pricing and data feeds. 👀 io.net’s DePIN network deploys on-demand, decentralized GPU resources globally, designed for low latency and high-throughput processing. Benefits include reduced barriers for AI/ML projects, enhanced integration of AI with on-chain activities, and democratized access to high-performance computing resources. Together, this marks a significant milestone in decentralized AI infrastructure, addressing key challenges and paving the way for unprecedented innovation in AI development and on-chain finance.

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“Just vote yes and we’re our own country.” Cool. Here’s the actual to-do list you’d have to negotiate first You’d have to settle: → Share of the national debt → Split of federal assets → Embassies & foreign property → Crown corporations → Federal pensions → Keep the loonie? Make a new currency? → Central bank access → Deposit insurance & banking rules → Foreign reserves → Citizenship & dual nationality → Passports → Free movement across the new border → Exact land borders → Maritime borders → Indigenous treaty rights (these don’t just transfer) → Whether Indigenous nations can stay with Canada → A whole new trade deal → Status under USMCA, CETA, CPTPP (not automatic) → Tariffs, customs, standards → Professional licensing across the border → UN membership (you re-apply) → Hundreds of treaties, renegotiated → Diplomatic recognition → NATO & NORAD membership → Splitting the armed forces → Military bases & equipment → Intelligence sharing (Five Eyes access) → Border security & the RCMP → Coast guard & airspace → CPP & pension portability → Old Age Security → Health transfers → Employment insurance → Pipelines & the power grid → Railways, highways, seaways → Shared rivers & water → Telecom & spectrum → Air traffic control → Tax systems & taxpayer data → Courts & appeals (no more Supreme Court of Canada) → Every federal regulator: food, drugs, aviation, broadcasting → Thousands of federal employees → …and what happens if talks just collapse 50%+1 creates a duty to negotiate — not a guarantee you get what you want, or that a deal even happens. “Independence” is the easy word. That list is the actual job.

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AI will kill Polymarket. $2.2M in 2 months using probability models. This news is going to blow up the internet. Polymarket trader made $2.2M in just 2 months using AI. His account is traded entirely by a bot. I’ve heard plenty of stories about AI trading bots before, and almost all of them turned out to be scams or didn’t work properly. But this case is different and honestly I’m shocked. He uses AI probability models, training machine learning to estimate real odds based on news and social media data. If his model says an outcome has a 60% chance, while the market prices it at 50% (50¢), he buys because the market is mispricing it. According to his profile: > His prediction accuracy is 74%. That’s insane. He runs an ensemble of 10 AI models that retrain themselves every week to stay up to date. What do you think about this? This feels like the new reality.

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The best Polymarket Quant bot for copy-trading with a 99.3% win rate. backtested strategy on 72M Polymarket/Kalshi trades to hit +$805K PnL on 27,000 predictions. bot doesn't gamble - it uses math and statistics in its algo to consistently hit 99% win rate. his algo decoded: 1. Mispricing formula based on 72M trades data, traders constantly overpay for cheap contracts (0.1¢–50¢) most of the edge sits in (80¢-99¢) contracts - that's the range where the bot mostly trades • formula: δ = actual win rate - implied probability bot applies this to every trade to find the edge. // 2. Expected value calculation EV tells you whether a bet is worth taking, regardless of the outcome of any single trade. • formula: EV = (P win × Payout) - (P lose × Cost) bot calculates it to understand if the trade is worth the risk. // 3. Kelly Criterion sizing most powerful position sizing formula ever discovered for gambling, trading and prediction markets it tells the algo what % of your portfolio to size into each bet to win long term. • formula: f* = (p * b - q) / b mispricing found → EV calced → kelly sizing → enter profile: start copy trading the bot with as little as $10 using Ares: 2 more formulas behind its algo revealed in the article below ↓

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🚨12 HOUR NEWS RECAP 1.⁠ Israel’s security cabinet approved Netanyahu’s proposal to take control of Gaza City. Israel also plans to exert “security control” over the entire strip and establish “an alternative civil administration that is neither Hamas nor the Palestinian Authority.” 2.⁠ Iran’s Foreign Minister Seyed Abbas Araghchi called for an extraordinary meeting of the Organization of Islamic Cooperation, warning Gaza faces “systematic annihilation.” 3.⁠ U.S Attorney General Bondi announced that the Justice Department has doubled its reward for information leading to the arrest of Venezuelan President Nicolás Maduro, now set at $50 million. 4.⁠ OpenAI announced the launch of Chat GPT-5: “It will automatically think whenever needed, delivering more comprehensive, accurate, and detailed answers. It’s like having a team of PhDs in your pocket." 5.⁠ Elon’s Grok Imagine cranked out 44 million images in a single day - doubling output while most apps claw for 2% gains. For the next few days, users in the U.S get free access to experiment with it. 6.⁠ Trump unveiled new economic data showing 10 times more income for the average family under Trump compared to Biden. Every income group did better under Trump than Biden, by a wide margin. 7.⁠ Testifying on the Hill, former Biden adviser Anita Dunn told the committee staff that Biden’s inner circle rejected a cognitive test, seeing no political upside even if he passed. 8.⁠ Gina Carano revealed she had settled her lawsuit with Disney after being fired from The Mandalorian: “I want to extend my deepest gratitude to Elon Musk, a man I’ve never met, who did this Good Samaritan deed for me in funding my lawsuit. Thank you Mr. Musk and X for backing my case and asking for nothing in return.” 9.⁠ The Pentagon has begun construction at Fort Bliss, Texas, on what will be the nation’s largest migrant detention facility, 5,000 beds when complete. Initial 1,000-bed capacity is expected by late August, supporting Trump’s executive order to “protect against an invasion.” 10.⁠ France’s largest wildfire this summer, the worst in its Mediterranean region in at least 50 years, has been contained after killing one person, injuring 13 and destroying thousands of hectares and dozens of homes.
0:06

🚨12 HOUR NEWS RECAP 1.⁠ Israel’s security cabinet approved Netanyahu’s proposal to take control of Gaza City. Israel also plans to exert “security control” over the entire strip and establish “an alternative civil administration that is neither Hamas nor the Palestinian Authority.” 2.⁠ Iran’s Foreign Minister Seyed Abbas Araghchi called for an extraordinary meeting of the Organization of Islamic Cooperation, warning Gaza faces “systematic annihilation.” 3.⁠ U.S Attorney General Bondi announced that the Justice Department has doubled its reward for information leading to the arrest of Venezuelan President Nicolás Maduro, now set at $50 million. 4.⁠ OpenAI announced the launch of Chat GPT-5: “It will automatically think whenever needed, delivering more comprehensive, accurate, and detailed answers. It’s like having a team of PhDs in your pocket." 5.⁠ Elon’s Grok Imagine cranked out 44 million images in a single day - doubling output while most apps claw for 2% gains. For the next few days, users in the U.S get free access to experiment with it. 6.⁠ Trump unveiled new economic data showing 10 times more income for the average family under Trump compared to Biden. Every income group did better under Trump than Biden, by a wide margin. 7.⁠ Testifying on the Hill, former Biden adviser Anita Dunn told the committee staff that Biden’s inner circle rejected a cognitive test, seeing no political upside even if he passed. 8.⁠ Gina Carano revealed she had settled her lawsuit with Disney after being fired from The Mandalorian: “I want to extend my deepest gratitude to Elon Musk, a man I’ve never met, who did this Good Samaritan deed for me in funding my lawsuit. Thank you Mr. Musk and X for backing my case and asking for nothing in return.” 9.⁠ The Pentagon has begun construction at Fort Bliss, Texas, on what will be the nation’s largest migrant detention facility, 5,000 beds when complete. Initial 1,000-bed capacity is expected by late August, supporting Trump’s executive order to “protect against an invasion.” 10.⁠ France’s largest wildfire this summer, the worst in its Mediterranean region in at least 50 years, has been contained after killing one person, injuring 13 and destroying thousands of hectares and dozens of homes.

Mario Nawfal

100,154 Aufrufe • vor 1 Jahr

Test Sprint 2 wrap-up + reward decision — please read in full Test Sprint 2 is done, and it ran better than Test Sprint 1. These are still our test sprints — we're using them to stress the system before things go fully live. The new tracking is holding up well overall; the one issue we hit is that publication counts glitch under peak load. We're already fixing it. Now the hard part. A large share of participants this sprint chose to game the system, and the volume of violations is simply too high to score the board fairly. So here's the call: the entire 1st-place reward pool gets split among ranks 11–50. Breakdown: - Ranks 11–25 → $20 USDC each - Ranks 26–30 → $10 USDC each - Ranks 31–50 → $100 in $CLAR each To make that happen, I'm placing my own account at #1 to hold the pool. Within 24 hours I'll push these rewards straight to the wallet linked to your profile. Make sure your linked wallet is correct and that you still have access to it. And to be clear — this is on top of what you can already claim in the leaderboard. Your own leaderboard rewards stay claimable as usual; this redistribution comes as an extra. We'd rather pay the honest contributors than reward farmed numbers. That's the standard from here on. 🫡
0:10

Test Sprint 2 wrap-up + reward decision — please read in full Test Sprint 2 is done, and it ran better than Test Sprint 1. These are still our test sprints — we're using them to stress the system before things go fully live. The new tracking is holding up well overall; the one issue we hit is that publication counts glitch under peak load. We're already fixing it. Now the hard part. A large share of participants this sprint chose to game the system, and the volume of violations is simply too high to score the board fairly. So here's the call: the entire 1st-place reward pool gets split among ranks 11–50. Breakdown: - Ranks 11–25 → $20 USDC each - Ranks 26–30 → $10 USDC each - Ranks 31–50 → $100 in $CLAR each To make that happen, I'm placing my own account at #1 to hold the pool. Within 24 hours I'll push these rewards straight to the wallet linked to your profile. Make sure your linked wallet is correct and that you still have access to it. And to be clear — this is on top of what you can already claim in the leaderboard. Your own leaderboard rewards stay claimable as usual; this redistribution comes as an extra. We'd rather pay the honest contributors than reward farmed numbers. That's the standard from here on. 🫡

Clarnium.io

30,308 Aufrufe • vor 1 Monat

Building a personal knowledge base for my agents is increasingly where I spend my time these days. Like Andrej Karpathy, I also use Obsidian for my MD vaults. What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. You all get to benefit from that with the papers I feature in my timeline and on DAIR.AI. The papers are indexed using tobi lutke qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip. 100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them. Work in progress. More updates soon. Back to building.
0:18

Building a personal knowledge base for my agents is increasingly where I spend my time these days. Like Andrej Karpathy, I also use Obsidian for my MD vaults. What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. You all get to benefit from that with the papers I feature in my timeline and on DAIR.AI. The papers are indexed using tobi lutke qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip. 100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them. Work in progress. More updates soon. Back to building.

elvis

465,132 Aufrufe • vor 4 Monaten

The Milky Way and Andromeda are currently separated by about 2.5 million light-years. Drawn together by gravity, Andromeda approaches us at roughly 110 km/s. Though this speed is immense, the vast cosmic distances mean the process will unfold slowly over billions of years. Recent studies using data from Hubble and Gaia suggest the long-predicted merger is not certain: there's roughly a 50% chance the galaxies will collide and merge within the next 10 billion years, with only a small probability of it beginning in the classic ~4–5 billion-year timeframe. As the galaxies interpenetrate, powerful gravitational tides would eject enormous streams of stars, gas, and dust—forming glowing tidal tails that trail across space like celestial ribbons. New star formation would flare in compressed gas clouds, lighting up the chaos with brilliant nebulae. Despite the dramatic term "collision," individual stars are so sparsely distributed that direct crashes would be exceedingly rare. Instead, gravity would gently reshuffle orbits: some systems flung to the outskirts, others spiraling toward a shared center. At the hearts of both galaxies lie supermassive black holes—ours at ~4 million solar masses, Andromeda's far larger. Over eons, they would inspiral and coalesce in a cataclysmic union, unleashing ripples of gravitational waves across the cosmos. In the end, the spirals we know would dissolve into a single, grand elliptical galaxy—a transformed beacon in the Local Group, born from one of the universe's most patient spectacles. 🎥 skywolf400
0:19

The Milky Way and Andromeda are currently separated by about 2.5 million light-years. Drawn together by gravity, Andromeda approaches us at roughly 110 km/s. Though this speed is immense, the vast cosmic distances mean the process will unfold slowly over billions of years. Recent studies using data from Hubble and Gaia suggest the long-predicted merger is not certain: there's roughly a 50% chance the galaxies will collide and merge within the next 10 billion years, with only a small probability of it beginning in the classic ~4–5 billion-year timeframe. As the galaxies interpenetrate, powerful gravitational tides would eject enormous streams of stars, gas, and dust—forming glowing tidal tails that trail across space like celestial ribbons. New star formation would flare in compressed gas clouds, lighting up the chaos with brilliant nebulae. Despite the dramatic term "collision," individual stars are so sparsely distributed that direct crashes would be exceedingly rare. Instead, gravity would gently reshuffle orbits: some systems flung to the outskirts, others spiraling toward a shared center. At the hearts of both galaxies lie supermassive black holes—ours at ~4 million solar masses, Andromeda's far larger. Over eons, they would inspiral and coalesce in a cataclysmic union, unleashing ripples of gravitational waves across the cosmos. In the end, the spirals we know would dissolve into a single, grand elliptical galaxy—a transformed beacon in the Local Group, born from one of the universe's most patient spectacles. 🎥 skywolf400

Dreams N Science

365,992 Aufrufe • vor 7 Monaten