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Edgen’s multi-agent brain shows up in different interfaces: 🔍 Search: plain-language answers 📊 Themes: narratives with assets side by side 📰 News: filtered for impact 🛒 Store: pro modules (360° Reports, Pivot Alerts, Trading Mindshare)

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Edgen

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52,236 görüntüleme • 9 ay önce •via X (Twitter)

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Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!
1:33

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!

Sharpe AI

263,278 görüntüleme • 1 yıl önce

Can Fenbendazole Cure Cancer? According to a case series published in an oncology journal, the answer could be a resounding yes. The case report highlights three cancer patients who were in pretty bad shape. But after taking fenbendazole, they all experienced a complete remission. What Is Fenbendazole, and How Does it Work? Fenbendazole (FBZ) is a medicine originally designed to treat worms and parasites in animals. Its sister drugs, Mebendazole and Albendazole, have had remarkable success treating similar ailments in humans with few side effects. Recently, anecdotal reports have praised fenbendazole as a potentially miraculous anti-cancer drug. It works by destabilizing microtubules, the structures that help cancer cells divide and grow. By disrupting this process, fenbendazole effectively halts cancer cell division and slows or stops tumor growth. Miraculous Recoveries After Taking Fenbendazole Case series #1 features a 63-year-old man with advanced kidney cancer (clear cell renal carcinoma) who experienced tumor recurrence and severe side effects from multiple cancer therapies, including surgery and two different medications. With no effective options left, he turned to fenbendazole (FBZ), taking 1 gram three times a week at a friend’s suggestion. Over the next 10 months, his tumors—including those in his pancreas and spine—showed near-complete resolution on imaging. Remarkably, he experienced no side effects from FBZ, and follow-up scans have shown no signs of recurrence. Case series #2 follows a 72-year-old man with metastatic urethral cancer that had spread to his lungs, lymph nodes, and brain. Despite undergoing multiple rounds of chemotherapy and radiation, one lymph node continued to grow, resisting all treatments. Seeking alternatives, he decided to try fenbendazole (FBZ), taking 1 gram three times a week, along with vitamin E, curcumin, and CBD oil, while postponing further conventional therapies. Over the next nine months, imaging revealed a dramatic response, with the lymph node shrinking significantly until it completely resolved. Remarkably, he reported no side effects during this period. Case series #3 focuses on a 63-year-old woman diagnosed with a large, invasive bladder tumor. Facing a challenging prognosis, she underwent chemotherapy while also taking fenbendazole (FBZ) at 1 gram three times a week. After completing six cycles of treatment, follow-up scans showed a complete resolution of the tumor, with only minimal thickening remaining in the bladder wall. Confident in her recovery, she chose to decline further surgery and remains disease-free under regular surveillance. The abstract concluded, “FBZ appears to be a potentially safe and effective antineoplastic agent that can be repurposed for human use in treating genitourinary malignancies.” Reflecting on these remarkable case reports, Dr. John Campbell (John Campbell) urged drug regulators to “start looking at this as a matter of some urgency because people are dying from cancer now.” “So if something is safe and effective, surely it can be accredited for human use by our national authorizing agencies pretty quickly if they want to,” Dr. Campbell added with a hint of sarcasm. Of course, the key words here are “if they want to.” “Three patients, basically… cured of their cancers. Read the paper for yourself. That’s what they seem to be saying to me.”
3:35

Can Fenbendazole Cure Cancer? According to a case series published in an oncology journal, the answer could be a resounding yes. The case report highlights three cancer patients who were in pretty bad shape. But after taking fenbendazole, they all experienced a complete remission. What Is Fenbendazole, and How Does it Work? Fenbendazole (FBZ) is a medicine originally designed to treat worms and parasites in animals. Its sister drugs, Mebendazole and Albendazole, have had remarkable success treating similar ailments in humans with few side effects. Recently, anecdotal reports have praised fenbendazole as a potentially miraculous anti-cancer drug. It works by destabilizing microtubules, the structures that help cancer cells divide and grow. By disrupting this process, fenbendazole effectively halts cancer cell division and slows or stops tumor growth. Miraculous Recoveries After Taking Fenbendazole Case series #1 features a 63-year-old man with advanced kidney cancer (clear cell renal carcinoma) who experienced tumor recurrence and severe side effects from multiple cancer therapies, including surgery and two different medications. With no effective options left, he turned to fenbendazole (FBZ), taking 1 gram three times a week at a friend’s suggestion. Over the next 10 months, his tumors—including those in his pancreas and spine—showed near-complete resolution on imaging. Remarkably, he experienced no side effects from FBZ, and follow-up scans have shown no signs of recurrence. Case series #2 follows a 72-year-old man with metastatic urethral cancer that had spread to his lungs, lymph nodes, and brain. Despite undergoing multiple rounds of chemotherapy and radiation, one lymph node continued to grow, resisting all treatments. Seeking alternatives, he decided to try fenbendazole (FBZ), taking 1 gram three times a week, along with vitamin E, curcumin, and CBD oil, while postponing further conventional therapies. Over the next nine months, imaging revealed a dramatic response, with the lymph node shrinking significantly until it completely resolved. Remarkably, he reported no side effects during this period. Case series #3 focuses on a 63-year-old woman diagnosed with a large, invasive bladder tumor. Facing a challenging prognosis, she underwent chemotherapy while also taking fenbendazole (FBZ) at 1 gram three times a week. After completing six cycles of treatment, follow-up scans showed a complete resolution of the tumor, with only minimal thickening remaining in the bladder wall. Confident in her recovery, she chose to decline further surgery and remains disease-free under regular surveillance. The abstract concluded, “FBZ appears to be a potentially safe and effective antineoplastic agent that can be repurposed for human use in treating genitourinary malignancies.” Reflecting on these remarkable case reports, Dr. John Campbell (John Campbell) urged drug regulators to “start looking at this as a matter of some urgency because people are dying from cancer now.” “So if something is safe and effective, surely it can be accredited for human use by our national authorizing agencies pretty quickly if they want to,” Dr. Campbell added with a hint of sarcasm. Of course, the key words here are “if they want to.” “Three patients, basically… cured of their cancers. Read the paper for yourself. That’s what they seem to be saying to me.”

The Vigilant Fox 🦊

405,175 görüntüleme • 1 yıl önce

Can Fenbendazole Cure Cancer? According to a case series published in an oncology journal, the answer could be a resounding yes. The case report highlights three cancer patients who were in pretty bad shape. But after taking fenbendazole, they all experienced a complete remission. What Is Fenbendazole, and How Does it Work? Fenbendazole (FBZ) is a medicine originally designed to treat worms and parasites in animals. Its sister drugs, Mebendazole and Albendazole, have had remarkable success treating similar ailments in humans with few side effects. Recently, anecdotal reports have praised fenbendazole as a potentially miraculous anti-cancer drug. It works by destabilizing microtubules, the structures that help cancer cells divide and grow. By disrupting this process, fenbendazole effectively halts cancer cell division and slows or stops tumor growth. Miraculous Recoveries After Taking Fenbendazole Case series #1 features a 63-year-old man with advanced kidney cancer (clear cell renal carcinoma) who experienced tumor recurrence and severe side effects from multiple cancer therapies, including surgery and two different medications. With no effective options left, he turned to fenbendazole (FBZ), taking 1 gram three times a week at a friend’s suggestion. Over the next 10 months, his tumors—including those in his pancreas and spine—showed near-complete resolution on imaging. Remarkably, he experienced no side effects from FBZ, and follow-up scans have shown no signs of recurrence. Case series #2 follows a 72-year-old man with metastatic urethral cancer that had spread to his lungs, lymph nodes, and brain. Despite undergoing multiple rounds of chemotherapy and radiation, one lymph node continued to grow, resisting all treatments. Seeking alternatives, he decided to try fenbendazole (FBZ), taking 1 gram three times a week, along with vitamin E, curcumin, and CBD oil, while postponing further conventional therapies. Over the next nine months, imaging revealed a dramatic response, with the lymph node shrinking significantly until it completely resolved. Remarkably, he reported no side effects during this period. Case series #3 focuses on a 63-year-old woman diagnosed with a large, invasive bladder tumor. Facing a challenging prognosis, she underwent chemotherapy while also taking fenbendazole (FBZ) at 1 gram three times a week. After completing six cycles of treatment, follow-up scans showed a complete resolution of the tumor, with only minimal thickening remaining in the bladder wall. Confident in her recovery, she chose to decline further surgery and remains disease-free under regular surveillance. The abstract concluded, “FBZ appears to be a potentially safe and effective antineoplastic agent that can be repurposed for human use in treating genitourinary malignancies.” Reflecting on these remarkable case reports, Dr. John Campbell (John Campbell) urged drug regulators to “start looking at this as a matter of some urgency because people are dying from cancer now.” “So if something is safe and effective, surely it can be accredited for human use by our national authorizing agencies pretty quickly if they want to,” Dr. Campbell added with a hint of sarcasm. Of course, the key words here are “if they want to.” “Three patients, basically… cured of their cancers. Read the paper for yourself. That’s what they seem to be saying to me.”
3:34

Can Fenbendazole Cure Cancer? According to a case series published in an oncology journal, the answer could be a resounding yes. The case report highlights three cancer patients who were in pretty bad shape. But after taking fenbendazole, they all experienced a complete remission. What Is Fenbendazole, and How Does it Work? Fenbendazole (FBZ) is a medicine originally designed to treat worms and parasites in animals. Its sister drugs, Mebendazole and Albendazole, have had remarkable success treating similar ailments in humans with few side effects. Recently, anecdotal reports have praised fenbendazole as a potentially miraculous anti-cancer drug. It works by destabilizing microtubules, the structures that help cancer cells divide and grow. By disrupting this process, fenbendazole effectively halts cancer cell division and slows or stops tumor growth. Miraculous Recoveries After Taking Fenbendazole Case series #1 features a 63-year-old man with advanced kidney cancer (clear cell renal carcinoma) who experienced tumor recurrence and severe side effects from multiple cancer therapies, including surgery and two different medications. With no effective options left, he turned to fenbendazole (FBZ), taking 1 gram three times a week at a friend’s suggestion. Over the next 10 months, his tumors—including those in his pancreas and spine—showed near-complete resolution on imaging. Remarkably, he experienced no side effects from FBZ, and follow-up scans have shown no signs of recurrence. Case series #2 follows a 72-year-old man with metastatic urethral cancer that had spread to his lungs, lymph nodes, and brain. Despite undergoing multiple rounds of chemotherapy and radiation, one lymph node continued to grow, resisting all treatments. Seeking alternatives, he decided to try fenbendazole (FBZ), taking 1 gram three times a week, along with vitamin E, curcumin, and CBD oil, while postponing further conventional therapies. Over the next nine months, imaging revealed a dramatic response, with the lymph node shrinking significantly until it completely resolved. Remarkably, he reported no side effects during this period. Case series #3 focuses on a 63-year-old woman diagnosed with a large, invasive bladder tumor. Facing a challenging prognosis, she underwent chemotherapy while also taking fenbendazole (FBZ) at 1 gram three times a week. After completing six cycles of treatment, follow-up scans showed a complete resolution of the tumor, with only minimal thickening remaining in the bladder wall. Confident in her recovery, she chose to decline further surgery and remains disease-free under regular surveillance. The abstract concluded, “FBZ appears to be a potentially safe and effective antineoplastic agent that can be repurposed for human use in treating genitourinary malignancies.” Reflecting on these remarkable case reports, Dr. John Campbell (John Campbell) urged drug regulators to “start looking at this as a matter of some urgency because people are dying from cancer now.” “So if something is safe and effective, surely it can be accredited for human use by our national authorizing agencies pretty quickly if they want to,” Dr. Campbell added with a hint of sarcasm. Of course, the key words here are “if they want to.” “Three patients, basically… cured of their cancers. Read the paper for yourself. That’s what they seem to be saying to me.”

The Vigilant Fox 🦊

1,475,318 görüntüleme • 1 yıl önce

Google Search Console gives you numbers. GSC Wizard gives you answers. Decades of doing data driven SEO. Every week I'd export GSC data, wrangle it in spreadsheets, try to find the story in the numbers. Then do it again for the next client. And again. So I built the tool I always wanted: GSC Wizard turns raw Search Console data into actionable intelligence. No spreadsheet gymnastics required. Here's every feature and what it actually solves: ◆ SITE RESTRUCTURING & TOPICAL MAPPING BERTopic clustering with multilingual-e5-large-instruct embeddings maps your entire site into topic clusters. Visual drag-and-drop tree hierarchy lets you redesign site architecture. Auto-generates redirect maps, internal linking plans, and content briefs for gaps. Works across languages so you can see coverage gaps per topic per market at a glance. ◆ CANNIBALIZATION DETECTION Detects when pages compete for the same keywords. Not just keyword overlap, but intent-level conflicts. Shows which URL should win and which should merge or redirect. ◆ CONTENT DECAY MONITORING Visualizes which pages are losing traffic over time with an intuitive heatmap. Spot declining content before it's too late. ◆ FORECASTING Predict future organic traffic based on historical GSC trends. Model scenarios for content investments, seasonal patterns, and growth targets. ◆ ANOMALY DETECTION Automatically flags unusual spikes or drops in clicks, impressions, CTR, and position. No more finding out a month later that something broke. ◆ MIGRATION DASHBOARDS Track performance before and after domain, folder or URL migrations across multiple GSC properties. Monitor traffic recovery, catch URL mapping gaps, and compare old vs. new property data side by side. The cross-property view is critical for enterprise migrations that nobody else handles properly. ◆ EXPERIMENT MONITORING Run A/B tests on title tags, meta descriptions, and content changes. Measure impact with statistical significance testing and group comparisons. Prove that your SEO changes actually worked. ◆ INTERNATIONAL ANALYSIS Analyze performance across countries and languages. Detect country-level cannibalization, and compare properties across markets. Cross-property analysis shows you which markets are underserved. ◆ INDEXING MONITOR Track which pages Google is picking up and which ones are quietly disappearing from the index. ◆ PAGE POACHING OPPORTUNITIES Find keywords ranking at positions 4-20 that are ripe for pushing into the top 3 with small optimizations. ◆ ON-PAGE CHECKS Check if top queries appear in titles, meta descriptions, and H1 headings. Simple but surprisingly powerful. ◆ KEYWORD CLUSTERING Group related keywords into clusters and track aggregate performance. See which topics drive the most traffic and where clusters are thin. Every report surfaces specific opportunities. Sign up for the waiting list now.
0:40

Google Search Console gives you numbers. GSC Wizard gives you answers. Decades of doing data driven SEO. Every week I'd export GSC data, wrangle it in spreadsheets, try to find the story in the numbers. Then do it again for the next client. And again. So I built the tool I always wanted: GSC Wizard turns raw Search Console data into actionable intelligence. No spreadsheet gymnastics required. Here's every feature and what it actually solves: ◆ SITE RESTRUCTURING & TOPICAL MAPPING BERTopic clustering with multilingual-e5-large-instruct embeddings maps your entire site into topic clusters. Visual drag-and-drop tree hierarchy lets you redesign site architecture. Auto-generates redirect maps, internal linking plans, and content briefs for gaps. Works across languages so you can see coverage gaps per topic per market at a glance. ◆ CANNIBALIZATION DETECTION Detects when pages compete for the same keywords. Not just keyword overlap, but intent-level conflicts. Shows which URL should win and which should merge or redirect. ◆ CONTENT DECAY MONITORING Visualizes which pages are losing traffic over time with an intuitive heatmap. Spot declining content before it's too late. ◆ FORECASTING Predict future organic traffic based on historical GSC trends. Model scenarios for content investments, seasonal patterns, and growth targets. ◆ ANOMALY DETECTION Automatically flags unusual spikes or drops in clicks, impressions, CTR, and position. No more finding out a month later that something broke. ◆ MIGRATION DASHBOARDS Track performance before and after domain, folder or URL migrations across multiple GSC properties. Monitor traffic recovery, catch URL mapping gaps, and compare old vs. new property data side by side. The cross-property view is critical for enterprise migrations that nobody else handles properly. ◆ EXPERIMENT MONITORING Run A/B tests on title tags, meta descriptions, and content changes. Measure impact with statistical significance testing and group comparisons. Prove that your SEO changes actually worked. ◆ INTERNATIONAL ANALYSIS Analyze performance across countries and languages. Detect country-level cannibalization, and compare properties across markets. Cross-property analysis shows you which markets are underserved. ◆ INDEXING MONITOR Track which pages Google is picking up and which ones are quietly disappearing from the index. ◆ PAGE POACHING OPPORTUNITIES Find keywords ranking at positions 4-20 that are ripe for pushing into the top 3 with small optimizations. ◆ ON-PAGE CHECKS Check if top queries appear in titles, meta descriptions, and H1 headings. Simple but surprisingly powerful. ◆ KEYWORD CLUSTERING Group related keywords into clusters and track aggregate performance. See which topics drive the most traffic and where clusters are thin. Every report surfaces specific opportunities. Sign up for the waiting list now.

Jan-Willem Bobbink

24,221 görüntüleme • 4 ay önce

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:
1:39

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

124,625 görüntüleme • 1 yıl önce

there's now a formal proof that your agent's vector memory forgets what you stored and fabricates things you never did. scaling it up makes both worse, not better. "the price of meaning" (arxiv 2603.27116) proves it for any memory that retrieves by similarity in an embedding space. the same geometry that lets embeddings generalize creates competitor mass in every neighborhood. add data and the crowding grows: retention decays toward zero, and false recall can't be tuned out without throwing away true hits. not a bug in your pipeline. the shape of the math. i learned this the expensive way. 500 stored facts, two weeks into a build, a user asks what i know about their job. retrieval hands back four fragments from different weeks: "i love my job," "thinking of quitting," "my manager is supportive," "my manager micromanages." the agent invents a clean synthesis of all four. the user had switched jobs in between. embeddings measure similarity, not truth. the topology angle says stop storing meaning as geometry, store it as structure. navigate an edge in an AST or a graph instead of searching a neighborhood. no crowding, no decay, no false recall. FORGE backs it: plain AST checks catch structural hallucinations at 100% precision (arxiv 2601.19106). here's what the structural pitch skips. the same proof shows pure structure escapes the geometry only by surrendering the connections embeddings find. you trade fabrication for blindness. so i stopped picking a side. what actually ships across thousands of sessions: – extract facts, not transcripts – resolve conflicts on write: archive the old job, mark the new one active – hybrid retrieval: vectors for discovery, graph for precision – decay plus nightly consolidation, so memory keeps what matters and lets the rest go memory is infrastructure, not a feature. that reframe is the whole game. and it's being measured now. WorldMemArena (may 28, arxiv 2605.29341) scores these paradigms head to head, embedding memory against retrieval-augmented against terminal-agent harnesses, across multimodal action-world tasks. the question moved from "does it remember" to "what kind of memory survives scale." full architecture, with the code, here:
0:42

there's now a formal proof that your agent's vector memory forgets what you stored and fabricates things you never did. scaling it up makes both worse, not better. "the price of meaning" (arxiv 2603.27116) proves it for any memory that retrieves by similarity in an embedding space. the same geometry that lets embeddings generalize creates competitor mass in every neighborhood. add data and the crowding grows: retention decays toward zero, and false recall can't be tuned out without throwing away true hits. not a bug in your pipeline. the shape of the math. i learned this the expensive way. 500 stored facts, two weeks into a build, a user asks what i know about their job. retrieval hands back four fragments from different weeks: "i love my job," "thinking of quitting," "my manager is supportive," "my manager micromanages." the agent invents a clean synthesis of all four. the user had switched jobs in between. embeddings measure similarity, not truth. the topology angle says stop storing meaning as geometry, store it as structure. navigate an edge in an AST or a graph instead of searching a neighborhood. no crowding, no decay, no false recall. FORGE backs it: plain AST checks catch structural hallucinations at 100% precision (arxiv 2601.19106). here's what the structural pitch skips. the same proof shows pure structure escapes the geometry only by surrendering the connections embeddings find. you trade fabrication for blindness. so i stopped picking a side. what actually ships across thousands of sessions: – extract facts, not transcripts – resolve conflicts on write: archive the old job, mark the new one active – hybrid retrieval: vectors for discovery, graph for precision – decay plus nightly consolidation, so memory keeps what matters and lets the rest go memory is infrastructure, not a feature. that reframe is the whole game. and it's being measured now. WorldMemArena (may 28, arxiv 2605.29341) scores these paradigms head to head, embedding memory against retrieval-augmented against terminal-agent harnesses, across multimodal action-world tasks. the question moved from "does it remember" to "what kind of memory survives scale." full architecture, with the code, here:

Rohit

15,966 görüntüleme • 1 ay önce

Scaling campaigns overseas sounds like a creative problem. Honestly, it’s not. The real bottleneck is localization. As a product lead, I’ve lost too many weeks waiting for native voice actors, rebuilding region-specific edits, and manually fixing lip-sync issues that still looked slightly off in the final export. The worst part is that every new market turns into another production branch to maintain. That simply does not scale. So over the last few weeks, I started testing a few different AI localization workflows with our own ecommerce video ads to see which ones could actually survive real production conditions. Wizstar_official ended up being the one we kept coming back to. Not because it generated the flashiest demo. Because it was the first one that consistently held together once we pushed it into actual multi-market production. The Video Translation workflow supports 12 major languages, which already covers most global consumer markets we care about. But what stood out during testing was how natural the localization sounded. Not “translated”. Actually localized. The tone, pacing, and delivery felt native enough that most people on our team genuinely stopped noticing it was AI-generated after a few runs. More importantly, the video itself stays intact. Audio and visual timing remain aligned after translation, lip-sync holds even during side angles and faster speech, and multi-character scenes stay surprisingly stable instead of collapsing into mismatched cuts. That matters a lot more in production than benchmark style demos. We also tested it against a few other tools internally, and Wizstar consistently handled complex scenes better, especially when multiple speakers, product close-ups, and fast pacing were involved. The output needed significantly less cleanup before going live. Video Reference was another reason we kept using it. Being able to reuse existing high-performing ecommerce structures instead of rebuilding creative logic market by market saves an unreasonable amount of time. Seedance 2.0 also supports face input and multi-model orchestration, which noticeably improves character consistency and scene stability across longer sequences. After a few projects, Wizstar quietly became part of our workflow. We can now produce localized ecommerce creatives in minutes instead of rebuilding entire pipelines around every market. If you want to test it yourself: New users get free credits on signup. First subscription is $19 and includes a complimentary 30-second Ecommerce Agent workflow to test features like Product to Video. Let the tools handle the production overhead. The side-by-side comparison below shows one of our English masters translated into Spanish while keeping almost the exact same pacing and vibe intact. #Wizstar #AIVideo #GrowthHacking
0:43

Scaling campaigns overseas sounds like a creative problem. Honestly, it’s not. The real bottleneck is localization. As a product lead, I’ve lost too many weeks waiting for native voice actors, rebuilding region-specific edits, and manually fixing lip-sync issues that still looked slightly off in the final export. The worst part is that every new market turns into another production branch to maintain. That simply does not scale. So over the last few weeks, I started testing a few different AI localization workflows with our own ecommerce video ads to see which ones could actually survive real production conditions. Wizstar_official ended up being the one we kept coming back to. Not because it generated the flashiest demo. Because it was the first one that consistently held together once we pushed it into actual multi-market production. The Video Translation workflow supports 12 major languages, which already covers most global consumer markets we care about. But what stood out during testing was how natural the localization sounded. Not “translated”. Actually localized. The tone, pacing, and delivery felt native enough that most people on our team genuinely stopped noticing it was AI-generated after a few runs. More importantly, the video itself stays intact. Audio and visual timing remain aligned after translation, lip-sync holds even during side angles and faster speech, and multi-character scenes stay surprisingly stable instead of collapsing into mismatched cuts. That matters a lot more in production than benchmark style demos. We also tested it against a few other tools internally, and Wizstar consistently handled complex scenes better, especially when multiple speakers, product close-ups, and fast pacing were involved. The output needed significantly less cleanup before going live. Video Reference was another reason we kept using it. Being able to reuse existing high-performing ecommerce structures instead of rebuilding creative logic market by market saves an unreasonable amount of time. Seedance 2.0 also supports face input and multi-model orchestration, which noticeably improves character consistency and scene stability across longer sequences. After a few projects, Wizstar quietly became part of our workflow. We can now produce localized ecommerce creatives in minutes instead of rebuilding entire pipelines around every market. If you want to test it yourself: New users get free credits on signup. First subscription is $19 and includes a complimentary 30-second Ecommerce Agent workflow to test features like Product to Video. Let the tools handle the production overhead. The side-by-side comparison below shows one of our English masters translated into Spanish while keeping almost the exact same pacing and vibe intact. #Wizstar #AIVideo #GrowthHacking

Leo Reed

121,626 görüntüleme • 2 ay önce

Explainer Video of all the Settings of Hiddentrades - Hidden Liquidity Finder It's quite a long one (44 minutes), but wanted to touch on everything important! If you have any questions or suggestions let me know! Giving away 3 Beta access: just Like, RT and Comment! Overview with timestamps: Core Detection & Filtering [00:30] History: Controls how many past candles the script checks (capped at 10,000). Lowering this number significantly speeds up the script's loading time. [01:32] BB Formation Window: Sets how many candles into the future the script will look to see if an order block converts into a breaker block. [02:25] Partial Mitigation: Defines the percentage an order block can be pierced by price action before the setup is considered fully mitigated and invalidated. [04:48] Use ICT OBs: Enforces stricter ICT (Inner Circle Trader) rules. It requires the displacement to have three consecutive confirmation candles moving in the opposite direction. [06:48] Allow Two Candle Impulse: A modifier for the ICT setting that reduces the required displacement confirmation from three candles down to two. [07:39] Only Show Multi-TFs: Cleans up the chart by hiding single breaker blocks, displaying only the stronger multi-timeframe setups. [08:13] Early BB Detection: Allows breaker blocks to confirm and form directly on the next candle after the displacement, helping you spot setups earlier. Order Block & Gap Configuration [09:30] Strict OB Filter: Makes pivot order blocks stricter by validating the height of the specific displacement candles surrounding the block. [11:24] Requires Same Direction Candle: Ensures the candle immediately preceding the order block is moving in the same direction, creating a true pivot point. [12:18] Use Wicks for Pivot Detection: Uses the extremes of the candle wicks to calculate the pivot low/high, rather than relying strictly on the candle bodies. [13:16] OB Candle Color Filter: Adds a strict color requirement to the candles forming the order block to ensure it perfectly aligns with traditional definitions. [14:08] OB Side Mode (Pivot vs. Chain): Allows the script to follow a continuous chain of fair value gaps (FVGs) rather than strictly requiring a perfect pivot setup. [16:13] Displacement Candle Setup: Explains how the script handles displacement candles that open directly inside the order block (common with market gaps). [17:17] Allow Gaps to Create a Breaker Block: Very useful for trading stocks with pre/post-market price jumps, allowing the physical price gap itself to serve as a valid trigger. [18:42] Body Size Filtering: Allows you to set a minimum percentage size for order blocks, with separate inputs for lower, middle, and higher timeframes. Time Frame (TF) Selection Modes [20:07] Auto Mode: Automatically searches up to 20 timeframes above and 20 timeframes below your current chart to find setups. [22:10] Auto From Selected: Uses the auto-search logic, but strictly limits the search to the specific timeframes you have manually checked off. [23:49] Selected Only: Scans exactly the timeframes you select, regardless of the timeframe you are currently viewing on the chart. Visual & Chart Display Settings [25:04] Merge Nearby Breaker Blocks into Clusters: Combines multiple overlapping or nearby zones into one clean, unified cluster zone. [27:27] Show Order Blocks Inside Cluster: Hides isolated single order blocks, only visualizing those that contribute to a larger multi-timeframe cluster. [29:03] Distance Filter: Instantly hides any setups that fall outside a set percentage range (e.g., 10%) from the current market price. [30:01] Show Forming Breaker Blocks: Displays upcoming setups in a faint gray color before the final confirming candle has officially closed. [31:52] Hide Mitigated BB Always: Automatically removes breaker blocks from your screen the moment price action fully mitigates them. [34:10] Show Recently Deleted Breaker Blocks: Keeps failed or mitigated blocks visible in gray for the last 240 candles — an excellent feature for backtesting. [36:03] Text Offsets: Adjusts the padding of the text labels so they sit cleanly away from the chart blocks without overlapping. [37:39] Merge Overlapping Labels: Intelligently combines the text labels of two nearby breaker blocks into a single centered tag to prevent visual clutter. [39:23] Colors and Opacity: Customizes block colors and automatically fades the opacity of lower-timeframe blocks while keeping higher-timeframe blocks solid and prominent. [41:32] Bold Text Label: A simple toggle to bold the chart labels for easier reading.
44:09

Explainer Video of all the Settings of Hiddentrades - Hidden Liquidity Finder It's quite a long one (44 minutes), but wanted to touch on everything important! If you have any questions or suggestions let me know! Giving away 3 Beta access: just Like, RT and Comment! Overview with timestamps: Core Detection & Filtering [00:30] History: Controls how many past candles the script checks (capped at 10,000). Lowering this number significantly speeds up the script's loading time. [01:32] BB Formation Window: Sets how many candles into the future the script will look to see if an order block converts into a breaker block. [02:25] Partial Mitigation: Defines the percentage an order block can be pierced by price action before the setup is considered fully mitigated and invalidated. [04:48] Use ICT OBs: Enforces stricter ICT (Inner Circle Trader) rules. It requires the displacement to have three consecutive confirmation candles moving in the opposite direction. [06:48] Allow Two Candle Impulse: A modifier for the ICT setting that reduces the required displacement confirmation from three candles down to two. [07:39] Only Show Multi-TFs: Cleans up the chart by hiding single breaker blocks, displaying only the stronger multi-timeframe setups. [08:13] Early BB Detection: Allows breaker blocks to confirm and form directly on the next candle after the displacement, helping you spot setups earlier. Order Block & Gap Configuration [09:30] Strict OB Filter: Makes pivot order blocks stricter by validating the height of the specific displacement candles surrounding the block. [11:24] Requires Same Direction Candle: Ensures the candle immediately preceding the order block is moving in the same direction, creating a true pivot point. [12:18] Use Wicks for Pivot Detection: Uses the extremes of the candle wicks to calculate the pivot low/high, rather than relying strictly on the candle bodies. [13:16] OB Candle Color Filter: Adds a strict color requirement to the candles forming the order block to ensure it perfectly aligns with traditional definitions. [14:08] OB Side Mode (Pivot vs. Chain): Allows the script to follow a continuous chain of fair value gaps (FVGs) rather than strictly requiring a perfect pivot setup. [16:13] Displacement Candle Setup: Explains how the script handles displacement candles that open directly inside the order block (common with market gaps). [17:17] Allow Gaps to Create a Breaker Block: Very useful for trading stocks with pre/post-market price jumps, allowing the physical price gap itself to serve as a valid trigger. [18:42] Body Size Filtering: Allows you to set a minimum percentage size for order blocks, with separate inputs for lower, middle, and higher timeframes. Time Frame (TF) Selection Modes [20:07] Auto Mode: Automatically searches up to 20 timeframes above and 20 timeframes below your current chart to find setups. [22:10] Auto From Selected: Uses the auto-search logic, but strictly limits the search to the specific timeframes you have manually checked off. [23:49] Selected Only: Scans exactly the timeframes you select, regardless of the timeframe you are currently viewing on the chart. Visual & Chart Display Settings [25:04] Merge Nearby Breaker Blocks into Clusters: Combines multiple overlapping or nearby zones into one clean, unified cluster zone. [27:27] Show Order Blocks Inside Cluster: Hides isolated single order blocks, only visualizing those that contribute to a larger multi-timeframe cluster. [29:03] Distance Filter: Instantly hides any setups that fall outside a set percentage range (e.g., 10%) from the current market price. [30:01] Show Forming Breaker Blocks: Displays upcoming setups in a faint gray color before the final confirming candle has officially closed. [31:52] Hide Mitigated BB Always: Automatically removes breaker blocks from your screen the moment price action fully mitigates them. [34:10] Show Recently Deleted Breaker Blocks: Keeps failed or mitigated blocks visible in gray for the last 240 candles — an excellent feature for backtesting. [36:03] Text Offsets: Adjusts the padding of the text labels so they sit cleanly away from the chart blocks without overlapping. [37:39] Merge Overlapping Labels: Intelligently combines the text labels of two nearby breaker blocks into a single centered tag to prevent visual clutter. [39:23] Colors and Opacity: Customizes block colors and automatically fades the opacity of lower-timeframe blocks while keeping higher-timeframe blocks solid and prominent. [41:32] Bold Text Label: A simple toggle to bold the chart labels for easier reading.

Marius 👁️⚡🌱

18,201 görüntüleme • 1 ay önce

🚨Breaking🚨Canada Confirms Covid Boosters Linked to Death Surge Iron🍁Wire (formerly Iron Will Report The Iron Wire ) just published the following video. We've posted the transcript below and the video. (As a side note - Once again Ottawa Police Detective Helen Grus is proven correct in her suspicions that the COVID-19 'vaccines' were linked to injuries and deaths. My reporting on the Grus case is here: ) Check out the original IronWire video and the many other excellently-researched major articles and interviews at Iron🍁Wire is well worth your time. I've subscribed to their free daily newsletter. Transcript via Otter dot ai with minimal human checking: CANADA CONFIRMS COVID BOOSTERS LINKED TO DEATH SURGE The Public Health Agency of Canada finally confessed this past week what we've all known for some time, confirming that mRNA COVID booster shots triggered a significant surge in deaths among vaccinated Canadians. The agency's latest report, quietly released after months of pressure from independent researchers and parliamentary inquiries, reveals that boosted individuals experienced mortality rates markedly higher than their unvaccinated peers. According to data obtained last fall through a Freedom of Information request, this trend became evident as early as late 2021 - so Statistics Canada, updates on vaccine related deaths mysteriously ceased by mid 2022. Independent media outlets like slay news and well us, have long tracked whistleblower claims now validated that the boosters heavily promoted by Trudeau Government carried severe risks. Canada's vaccine rollout launched in December 2020 saw over 80% of adults receive at least two doses by mid 2022 - at least, according to Statistics Canada, although this figure was artificially and consciously inflated by sometimes counting the first booster as a first shot. The third and fourth boosters pushed in 2023 targeted the elderly and immunocompromised. Yet the report shows deaths spiked across all age groups as people - either coerced or fooled by the safe and effective government propaganda - continued to line up for yet more shots of a vaccine our government had already confessed did not prevent transmission or infection. Posts on social media since the announcement erupted with outrage pointing to censored doctors like Byram Bridle and many others who warned of mRNA side effects years ago. The agency offers no explanation for the delay in acknowledging these findings, nor does it address the fate of officials who dismissed early concerns as misinformation. This revelation lands as Health Canada grapples with ongoing vaccine injury claims, with over 10,000 reports logged by late last year. The data stands unrefuted. The boosters, touted by Trudeau Government as the key to ending lockdowns, have a lethal legacy - the impact of which we may not know for decades. Meanwhile, Health Canada and many provincial health agencies continue to recommend the COVID vaccines.
2:21

🚨Breaking🚨Canada Confirms Covid Boosters Linked to Death Surge Iron🍁Wire (formerly Iron Will Report The Iron Wire ) just published the following video. We've posted the transcript below and the video. (As a side note - Once again Ottawa Police Detective Helen Grus is proven correct in her suspicions that the COVID-19 'vaccines' were linked to injuries and deaths. My reporting on the Grus case is here: ) Check out the original IronWire video and the many other excellently-researched major articles and interviews at Iron🍁Wire is well worth your time. I've subscribed to their free daily newsletter. Transcript via Otter dot ai with minimal human checking: CANADA CONFIRMS COVID BOOSTERS LINKED TO DEATH SURGE The Public Health Agency of Canada finally confessed this past week what we've all known for some time, confirming that mRNA COVID booster shots triggered a significant surge in deaths among vaccinated Canadians. The agency's latest report, quietly released after months of pressure from independent researchers and parliamentary inquiries, reveals that boosted individuals experienced mortality rates markedly higher than their unvaccinated peers. According to data obtained last fall through a Freedom of Information request, this trend became evident as early as late 2021 - so Statistics Canada, updates on vaccine related deaths mysteriously ceased by mid 2022. Independent media outlets like slay news and well us, have long tracked whistleblower claims now validated that the boosters heavily promoted by Trudeau Government carried severe risks. Canada's vaccine rollout launched in December 2020 saw over 80% of adults receive at least two doses by mid 2022 - at least, according to Statistics Canada, although this figure was artificially and consciously inflated by sometimes counting the first booster as a first shot. The third and fourth boosters pushed in 2023 targeted the elderly and immunocompromised. Yet the report shows deaths spiked across all age groups as people - either coerced or fooled by the safe and effective government propaganda - continued to line up for yet more shots of a vaccine our government had already confessed did not prevent transmission or infection. Posts on social media since the announcement erupted with outrage pointing to censored doctors like Byram Bridle and many others who warned of mRNA side effects years ago. The agency offers no explanation for the delay in acknowledging these findings, nor does it address the fate of officials who dismissed early concerns as misinformation. This revelation lands as Health Canada grapples with ongoing vaccine injury claims, with over 10,000 reports logged by late last year. The data stands unrefuted. The boosters, touted by Trudeau Government as the key to ending lockdowns, have a lethal legacy - the impact of which we may not know for decades. Meanwhile, Health Canada and many provincial health agencies continue to recommend the COVID vaccines.

DonaldBest.CA * DO NOT COMPLY

46,736 görüntüleme • 1 yıl önce

Security footage from the Eastgate Shopping Center in North Carolina shows the unwavering bond between a Soldier and their Service Dog. The veteran walking through the corridor is David. He's 32 years old with 2 deployments. He was diagnosed with PTSD in 2019 and crowds immediately trigger anxiety attacks. The German Shepherd walking beside him is 5 yr old Atlas. David's certified service animal for three years. David told a local news outlet he had driven to the mall to pick up a prescription from the pharmacy near the east entrance. He knew it was a Saturday. He knew it would be crowded. He thought he could manage it. "About halfway down the main corridor I could feel it starting," David explained. "Heart rate going up. Breathing getting shallow. I was scanning every face. It's like my brain just shifts into a different mode and I can't pull it back manually." The security footage shows David walking at a slightly faster pace than normal. His posture tighter. Atlas is right beside him, matching his stride. Then Atlas stops. He sits down directly in David's path. Squarely. Calmly. Like he'd planned it. David takes one more step and nearly stumbles over Atlas. He looks down. Atlas looks up. Direct, steady eye contact. Unwavering. The kind of focus that asks for nothing except full attention in return. David stopped. The crowd parted around them. People kept moving. Nobody stopped. Just two figures standing still in the middle of a busy corridor — a man and his dog looking at each other. David knelt down. Put both hands on either side of Atlas's face. You can hear David talking to Atlas, calming himself by acting like he's calming the dog. He told us: "When Atlas locks eyes with me like that, it forces my whole nervous system to find something to attach to that isn't a threat. He becomes the only thing in the room. And once he's the only thing in the room, I can breathe again." They stayed there for about two minutes. Then Atlas stood up. Resumed walking. Slower this time. And David walked with him. They picked up the prescription. Made it back to the car. David posted the security footage two weeks later with permission slip from the mall. He wanted people to understand what service animals actually do in real time — not in training environments, not in demonstrations, but in the middle of an ordinary Saturday when everything quietly starts to fall apart. "Atlas didn't wait for me to ask for help," David said. "He saw what was coming before I could name it. And he put himself between me and it." Sometimes the bravest thing a guardian does is simply refuse to move. God bless our soldiers and the service dogs who walk beside them...always. 🇺🇸
0:30

Security footage from the Eastgate Shopping Center in North Carolina shows the unwavering bond between a Soldier and their Service Dog. The veteran walking through the corridor is David. He's 32 years old with 2 deployments. He was diagnosed with PTSD in 2019 and crowds immediately trigger anxiety attacks. The German Shepherd walking beside him is 5 yr old Atlas. David's certified service animal for three years. David told a local news outlet he had driven to the mall to pick up a prescription from the pharmacy near the east entrance. He knew it was a Saturday. He knew it would be crowded. He thought he could manage it. "About halfway down the main corridor I could feel it starting," David explained. "Heart rate going up. Breathing getting shallow. I was scanning every face. It's like my brain just shifts into a different mode and I can't pull it back manually." The security footage shows David walking at a slightly faster pace than normal. His posture tighter. Atlas is right beside him, matching his stride. Then Atlas stops. He sits down directly in David's path. Squarely. Calmly. Like he'd planned it. David takes one more step and nearly stumbles over Atlas. He looks down. Atlas looks up. Direct, steady eye contact. Unwavering. The kind of focus that asks for nothing except full attention in return. David stopped. The crowd parted around them. People kept moving. Nobody stopped. Just two figures standing still in the middle of a busy corridor — a man and his dog looking at each other. David knelt down. Put both hands on either side of Atlas's face. You can hear David talking to Atlas, calming himself by acting like he's calming the dog. He told us: "When Atlas locks eyes with me like that, it forces my whole nervous system to find something to attach to that isn't a threat. He becomes the only thing in the room. And once he's the only thing in the room, I can breathe again." They stayed there for about two minutes. Then Atlas stood up. Resumed walking. Slower this time. And David walked with him. They picked up the prescription. Made it back to the car. David posted the security footage two weeks later with permission slip from the mall. He wanted people to understand what service animals actually do in real time — not in training environments, not in demonstrations, but in the middle of an ordinary Saturday when everything quietly starts to fall apart. "Atlas didn't wait for me to ask for help," David said. "He saw what was coming before I could name it. And he put himself between me and it." Sometimes the bravest thing a guardian does is simply refuse to move. God bless our soldiers and the service dogs who walk beside them...always. 🇺🇸

Patriot🇺🇸Newswire

1,106,604 görüntüleme • 3 ay önce

WATCH: Trump ‘wants to get' stock-trading ban done, Hawley insists after president's brutal attack on bill | Peter Pinedo, Fox News President Donald Trump drilled into Sen Josh Hawley in over the bill, calling him a 'pawn' and 'second-tier' senator Despite President Donald Trump's harsh criticism over his bill to ban stock trading among top government officials, Sen. Josh Hawley, R-Mo., insists he has a good rapport with the president, who shares his goal of enacting such a ban. Speaking with Fox News Digital on Thursday, Hawley said that following the criticism, "the president and I had a really good conversation" and "he wants to get it done." Hawley also addressed the controversy on the Jesse Watters Show the night before, when he notes that he and the president are in agreement that stock trading should be banned for members of Congress, saying, "The point of this bill is to ban members of Congress from trading on the information that only they have. I’ve supported this bill for years, Jesse. I think we called it the Pelosi Act. It is named for her because she is the poster child of this kind of behavior. It ought to be illegal and it ought to be prosecutable." Addressing his phone call with Trump, Hawley told Watters, "I think that the president, a number of people who are opposed to banning stock trading, had said to the president that he would be covered by the bill. He’d have to sell Mar-a-Lago and sell assets. Not the case at all. The president and the vice president, all of their assets are totally exempted." Regarding a proposal to investigate Rep. Nancy Pelosi, D-Calif., for allegedly engaging in insider stock trading, Hawley said, "Pelosi shouldn't just be investigated; she should be prosecuted. And we need to make what she is doing, and other members of Congress are doing illegal. I mean, you shouldn’t be able to go up to Congress and get rich by trading on information that only you have and not members of the public. Right now, lots of members of Congress are getting by with it. We need to make the whole thing illegal." Speaking with Fox News Digital on Thursday, Hawley reiterated, "What the White House wanted was that the president, the vice president not be covered. They're not, the offices are, but it'll be the next office holders." The Missouri Republican added that "in fairness, we did the same thing for Joe Biden. We passed this last year, and we set the date out so that it would be the next president who had to comply." "So, Trump and Vance are not covered, but all the members of Congress are. And it's not a perfect bill, but it's pretty tough," said Hawley. Hawley’s measure, originally named the PELOSI Act but switched to the HONEST Act after Senate Democrats agreed to support it, irked Trump and many Senate Republicans. The bill was advanced out of committee by an 8-7 vote, with Hawley joining Democrats to approve the bill. Shortly after the vote, Trump drilled into Hawley in a Truth Social post in which he called him a "pawn" and "second-tier" senator. "The Democrats, because of our tremendous ACHIEVEMENTS and SUCCESS, have been trying to ‘Target’ me for a long period of time, and they’re using Josh Hawley, who I got elected TWICE, as a pawn to help them," he wrote. "I wonder why Hawley would pass a Bill that Nancy Pelosi is in absolute love with — He is playing right into the dirty hands of the Democrats. It’s a great Bill for her, and her ‘husband,’ but so bad for our Country! I don’t think real Republicans want to see their President, who has had unprecedented success, TARGETED, because of the ‘whims’ of a second-tier Senator named Josh Hawley!" Despite the pushback, Hawley told Fox News Digital, "I want to get this done." "I want to get this banned. So, I will work with anybody, I said to my colleagues, like if you've got good faith changes you want to make, you think will make this stronger, I'm all for it. I will do it," he went on. "What I will not do, though, is consensus stuff that's going to kill the bill." He blasted an amendment proposed by Sen. Rick Scott, R-Fla., and promoted by Trump, which he said, "would have gutted the bill." "Scott made really clear he's opposed to this bill, and he was attempting to kill the bill," said Hawley. "I’ve seen this for six years now. Members campaign on banning stocks, and then they get here, and they're like, well, this isn't the right time, or maybe let's do it later, or let's never do it, or you heard today, let's have another hearing. We've had hearings for years," For his part, Scott told Fox News Digital that "Trump's on the right side." "This was a bill that Senator Hawley teamed up with Democrats to attack Trump," he said. "Here's a guy that went through Russiagate, went through an indictment, went through conviction, went through all this stuff, and then this is just a new bill to go target the president." Sen. Ron Johnson, R-Wisc., meanwhile, told Fox News Digital that he thinks it is "hypocritical" of Pelosi to be supporting the bill after years of her husband engaging in lucrative trading. "I thought it was interesting that all the Democrats, together with Senator Hawley, voted against Rick Scott's excellent amendment, which would have asked GAO to do an investigation of how did she get so wealthy," he said. "If they really wanted to get to the bottom of that, they would have supported that amendment. The fact that they voted against it speaks volumes." "The idea is good, the execution is not as good," said Sen. James Lankford, R-Okla. "I wanted to be able to see a bill that's actually fixed and that actually works. This bill bans things like cryptocurrency, digital currency, it says you can't use stablecoin," he explained. "It's a straightforward idea. But we've got to be able to clean up the language of this particular version of it to make sure it's right." Sen. Rand Paul, R-Ky., also took issue with the bill specifically for exempting Trump. He told Fox News Digital, "If it were a good bill, they would apply it to Donald Trump. The fact that they're excluding Donald Trump, and he's going to be exempt from it, probably means it's not a very good bill." He said that if passed, the bill would "deter" leaders with strong business acumen. "I think you want to bring people like that. You're going to deter a lot of people like from coming, not just banning them from owning stock, but saying they have to sell all of their businesses. I think it's over the top and not well thought out," said Paul. On the other side of the aisle, Sen. John Fetterman, D-Pa., voiced support for the bill, saying, "treat everyone equally, and don't trade stock if you're a member here." Read more:
6:26

WATCH: Trump ‘wants to get' stock-trading ban done, Hawley insists after president's brutal attack on bill | Peter Pinedo, Fox News President Donald Trump drilled into Sen Josh Hawley in over the bill, calling him a 'pawn' and 'second-tier' senator Despite President Donald Trump's harsh criticism over his bill to ban stock trading among top government officials, Sen. Josh Hawley, R-Mo., insists he has a good rapport with the president, who shares his goal of enacting such a ban. Speaking with Fox News Digital on Thursday, Hawley said that following the criticism, "the president and I had a really good conversation" and "he wants to get it done." Hawley also addressed the controversy on the Jesse Watters Show the night before, when he notes that he and the president are in agreement that stock trading should be banned for members of Congress, saying, "The point of this bill is to ban members of Congress from trading on the information that only they have. I’ve supported this bill for years, Jesse. I think we called it the Pelosi Act. It is named for her because she is the poster child of this kind of behavior. It ought to be illegal and it ought to be prosecutable." Addressing his phone call with Trump, Hawley told Watters, "I think that the president, a number of people who are opposed to banning stock trading, had said to the president that he would be covered by the bill. He’d have to sell Mar-a-Lago and sell assets. Not the case at all. The president and the vice president, all of their assets are totally exempted." Regarding a proposal to investigate Rep. Nancy Pelosi, D-Calif., for allegedly engaging in insider stock trading, Hawley said, "Pelosi shouldn't just be investigated; she should be prosecuted. And we need to make what she is doing, and other members of Congress are doing illegal. I mean, you shouldn’t be able to go up to Congress and get rich by trading on information that only you have and not members of the public. Right now, lots of members of Congress are getting by with it. We need to make the whole thing illegal." Speaking with Fox News Digital on Thursday, Hawley reiterated, "What the White House wanted was that the president, the vice president not be covered. They're not, the offices are, but it'll be the next office holders." The Missouri Republican added that "in fairness, we did the same thing for Joe Biden. We passed this last year, and we set the date out so that it would be the next president who had to comply." "So, Trump and Vance are not covered, but all the members of Congress are. And it's not a perfect bill, but it's pretty tough," said Hawley. Hawley’s measure, originally named the PELOSI Act but switched to the HONEST Act after Senate Democrats agreed to support it, irked Trump and many Senate Republicans. The bill was advanced out of committee by an 8-7 vote, with Hawley joining Democrats to approve the bill. Shortly after the vote, Trump drilled into Hawley in a Truth Social post in which he called him a "pawn" and "second-tier" senator. "The Democrats, because of our tremendous ACHIEVEMENTS and SUCCESS, have been trying to ‘Target’ me for a long period of time, and they’re using Josh Hawley, who I got elected TWICE, as a pawn to help them," he wrote. "I wonder why Hawley would pass a Bill that Nancy Pelosi is in absolute love with — He is playing right into the dirty hands of the Democrats. It’s a great Bill for her, and her ‘husband,’ but so bad for our Country! I don’t think real Republicans want to see their President, who has had unprecedented success, TARGETED, because of the ‘whims’ of a second-tier Senator named Josh Hawley!" Despite the pushback, Hawley told Fox News Digital, "I want to get this done." "I want to get this banned. So, I will work with anybody, I said to my colleagues, like if you've got good faith changes you want to make, you think will make this stronger, I'm all for it. I will do it," he went on. "What I will not do, though, is consensus stuff that's going to kill the bill." He blasted an amendment proposed by Sen. Rick Scott, R-Fla., and promoted by Trump, which he said, "would have gutted the bill." "Scott made really clear he's opposed to this bill, and he was attempting to kill the bill," said Hawley. "I’ve seen this for six years now. Members campaign on banning stocks, and then they get here, and they're like, well, this isn't the right time, or maybe let's do it later, or let's never do it, or you heard today, let's have another hearing. We've had hearings for years," For his part, Scott told Fox News Digital that "Trump's on the right side." "This was a bill that Senator Hawley teamed up with Democrats to attack Trump," he said. "Here's a guy that went through Russiagate, went through an indictment, went through conviction, went through all this stuff, and then this is just a new bill to go target the president." Sen. Ron Johnson, R-Wisc., meanwhile, told Fox News Digital that he thinks it is "hypocritical" of Pelosi to be supporting the bill after years of her husband engaging in lucrative trading. "I thought it was interesting that all the Democrats, together with Senator Hawley, voted against Rick Scott's excellent amendment, which would have asked GAO to do an investigation of how did she get so wealthy," he said. "If they really wanted to get to the bottom of that, they would have supported that amendment. The fact that they voted against it speaks volumes." "The idea is good, the execution is not as good," said Sen. James Lankford, R-Okla. "I wanted to be able to see a bill that's actually fixed and that actually works. This bill bans things like cryptocurrency, digital currency, it says you can't use stablecoin," he explained. "It's a straightforward idea. But we've got to be able to clean up the language of this particular version of it to make sure it's right." Sen. Rand Paul, R-Ky., also took issue with the bill specifically for exempting Trump. He told Fox News Digital, "If it were a good bill, they would apply it to Donald Trump. The fact that they're excluding Donald Trump, and he's going to be exempt from it, probably means it's not a very good bill." He said that if passed, the bill would "deter" leaders with strong business acumen. "I think you want to bring people like that. You're going to deter a lot of people like from coming, not just banning them from owning stock, but saying they have to sell all of their businesses. I think it's over the top and not well thought out," said Paul. On the other side of the aisle, Sen. John Fetterman, D-Pa., voiced support for the bill, saying, "treat everyone equally, and don't trade stock if you're a member here." Read more:

Owen Gregorian

83,975 görüntüleme • 11 ay önce

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.
0:28

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,735 görüntüleme • 1 yıl önce

A study proved that $40 million was extracted from Polymarket in one year using a single mathematical formula I found a wallet that is using it right now on Iran war markets and made $1.4M in one week. Most people on Polymarket try to predict the future. Will there be a war. Who will win the election. What will happen next. I spent months doing the same thing. Reading news. Watching debates. Building my little models of what I thought should happen. And losing money. Not because I was wrong about events. Because I was wrong about the game itself. The game is not about predictions. And the wallet I'm about to show you is living proof. Three weeks ago I pulled the full trade history of this wallet: What I saw at first didn't make sense. He was opening the same market more than 30 times. US strikes Iran by January 11. US strikes Iran by January 12. January 13. January 14. January 15. January 16. January 17. The same event. Different dates. Over and over. First thought: this person is obsessed with Iran. Second thought: this person doesn't care about Iran at all. Here's what he's actually doing. Polymarket creates separate markets for the same event with different deadlines. Will the US strike Iran by March. By April. By June. These are not independent questions. If the strike happens in March then April and June automatically resolve to YES as well. But Polymarket prices each market separately. And the crowd prices them emotionally. Fear spikes on Tuesday night because someone tweeted something. One market jumps. The others lag behind. For a few minutes and sometimes hours prices on related markets stop converging. When you buy NO across multiple dates and the total cost is 94 cents and the guaranteed payout is $1 regardless of what happens you're not betting. You're collecting a 6% return on mathematical inevitability. That's the entire strategy. He buys dollars for 94 cents. I checked his numbers. On the Iran series alone he pulled $247,000 in realized profit across seven markets with different dates. Average purchase price of NO positions from 72 to 95 cents. Each one resolved at $1. The biggest hit was the government shutdown market. $88,000 in profit. Same logic. Buy both sides when the total cost is less than a dollar. One side pays. Math does the rest. 85% of his capital is in political markets. Wars. Elections. Geopolitics. Not because he has strong geopolitical convictions. Because political markets on Polymarket are where the math breaks most often. Why political markets specifically? Because they generate the most emotion. When CNN runs breaking news about Iran at 11 PM thousands of people rush to buy YES on the nearest date. They overbid the price. They panic. They push one market out of line with the rest. That panic is his paycheck. And now the part that actually matters. I dug deeper into how this type of arbitrage works at scale and found a study that made everything click. A team analyzed every trade on Polymarket over 12 months. They found 17,218 market conditions. 41% of them had an exploitable pricing error. And the total profit extracted by arbitrageurs was $40 million. The top single wallet made $2 million using one algorithm. The Frank-Wolfe method. I'll explain without math because the concept is simple even if the calculations aren't. Imagine you walk into a store that sells lottery tickets for 7 different drawings. Each ticket is priced separately. The store doesn't coordinate prices between drawings. You notice that if you buy a certain combination of tickets across all 7 drawings the total cost is $94 but you're guaranteed to win exactly $100 no matter which drawing hits. You don't need to predict which drawing will win. You just need to notice that the store mispriced the tickets. Here's Frank-Wolfe in one sentence. It scans thousands of related markets simultaneously and finds combinations where the total price is less than the guaranteed payout. Then it calculates the exact amounts to buy on each side to maximize the spread. The reason a human can't do this manually is scale. There are hundreds of active markets on Polymarket. Many are connected by logic. If event A happens then event B must also happen. If candidate X wins state Y then the national result shifts. The number of possible combinations grows exponentially. While you're checking 10 markets by hand the algorithm has scanned 17,000. What anoin123 does is a manual version of this. He picks one cluster of related markets like the Iran date series and runs the logic in his head. Buy NO across seven dates. Total cost less than a dollar. Wait. Collect. The automated version does the same thing but across all markets on the platform simultaneously. My personal takeaway after three weeks of studying this. I spent months trying to be smarter than the crowd. Reading polls. Watching news. Forming opinions. And the whole time there was a category of traders who had zero opinions about anything. They just waited for the crowd to misprice related markets and collected the difference. The uncomfortable realization is that prediction markets are not actually about predictions for those who make the most money. They're about math. And the math breaks every day because people trade on emotions and the platform prices markets independently of each other. I don't have the infrastructure to run Frank-Wolfe at scale. But I don't need to. Wallets like anoin123 do this in plain sight. Every trade on the blockchain. Every entry price. Every exit. Every timestamp. I stopped trying to predict events. I started watching wallets that make money regardless of what happens. The difference in my results is so stark it's uncomfortable to think about. If you want to understand the full math behind this the study is publicly available. Search for Arbitrage in Prediction Markets on arXiv. But the short version is this. Every time the crowd panics about a war or an election and pushes one market out of line with its related markets someone on the other side quietly buys dollars for 94 cents. The question is not whether they'll strike Iran. The question is whether you noticed that seven markets about the same event are priced as if they have nothing to do with each other. That gap is where the money lives.
0:55

A study proved that $40 million was extracted from Polymarket in one year using a single mathematical formula I found a wallet that is using it right now on Iran war markets and made $1.4M in one week. Most people on Polymarket try to predict the future. Will there be a war. Who will win the election. What will happen next. I spent months doing the same thing. Reading news. Watching debates. Building my little models of what I thought should happen. And losing money. Not because I was wrong about events. Because I was wrong about the game itself. The game is not about predictions. And the wallet I'm about to show you is living proof. Three weeks ago I pulled the full trade history of this wallet: What I saw at first didn't make sense. He was opening the same market more than 30 times. US strikes Iran by January 11. US strikes Iran by January 12. January 13. January 14. January 15. January 16. January 17. The same event. Different dates. Over and over. First thought: this person is obsessed with Iran. Second thought: this person doesn't care about Iran at all. Here's what he's actually doing. Polymarket creates separate markets for the same event with different deadlines. Will the US strike Iran by March. By April. By June. These are not independent questions. If the strike happens in March then April and June automatically resolve to YES as well. But Polymarket prices each market separately. And the crowd prices them emotionally. Fear spikes on Tuesday night because someone tweeted something. One market jumps. The others lag behind. For a few minutes and sometimes hours prices on related markets stop converging. When you buy NO across multiple dates and the total cost is 94 cents and the guaranteed payout is $1 regardless of what happens you're not betting. You're collecting a 6% return on mathematical inevitability. That's the entire strategy. He buys dollars for 94 cents. I checked his numbers. On the Iran series alone he pulled $247,000 in realized profit across seven markets with different dates. Average purchase price of NO positions from 72 to 95 cents. Each one resolved at $1. The biggest hit was the government shutdown market. $88,000 in profit. Same logic. Buy both sides when the total cost is less than a dollar. One side pays. Math does the rest. 85% of his capital is in political markets. Wars. Elections. Geopolitics. Not because he has strong geopolitical convictions. Because political markets on Polymarket are where the math breaks most often. Why political markets specifically? Because they generate the most emotion. When CNN runs breaking news about Iran at 11 PM thousands of people rush to buy YES on the nearest date. They overbid the price. They panic. They push one market out of line with the rest. That panic is his paycheck. And now the part that actually matters. I dug deeper into how this type of arbitrage works at scale and found a study that made everything click. A team analyzed every trade on Polymarket over 12 months. They found 17,218 market conditions. 41% of them had an exploitable pricing error. And the total profit extracted by arbitrageurs was $40 million. The top single wallet made $2 million using one algorithm. The Frank-Wolfe method. I'll explain without math because the concept is simple even if the calculations aren't. Imagine you walk into a store that sells lottery tickets for 7 different drawings. Each ticket is priced separately. The store doesn't coordinate prices between drawings. You notice that if you buy a certain combination of tickets across all 7 drawings the total cost is $94 but you're guaranteed to win exactly $100 no matter which drawing hits. You don't need to predict which drawing will win. You just need to notice that the store mispriced the tickets. Here's Frank-Wolfe in one sentence. It scans thousands of related markets simultaneously and finds combinations where the total price is less than the guaranteed payout. Then it calculates the exact amounts to buy on each side to maximize the spread. The reason a human can't do this manually is scale. There are hundreds of active markets on Polymarket. Many are connected by logic. If event A happens then event B must also happen. If candidate X wins state Y then the national result shifts. The number of possible combinations grows exponentially. While you're checking 10 markets by hand the algorithm has scanned 17,000. What anoin123 does is a manual version of this. He picks one cluster of related markets like the Iran date series and runs the logic in his head. Buy NO across seven dates. Total cost less than a dollar. Wait. Collect. The automated version does the same thing but across all markets on the platform simultaneously. My personal takeaway after three weeks of studying this. I spent months trying to be smarter than the crowd. Reading polls. Watching news. Forming opinions. And the whole time there was a category of traders who had zero opinions about anything. They just waited for the crowd to misprice related markets and collected the difference. The uncomfortable realization is that prediction markets are not actually about predictions for those who make the most money. They're about math. And the math breaks every day because people trade on emotions and the platform prices markets independently of each other. I don't have the infrastructure to run Frank-Wolfe at scale. But I don't need to. Wallets like anoin123 do this in plain sight. Every trade on the blockchain. Every entry price. Every exit. Every timestamp. I stopped trying to predict events. I started watching wallets that make money regardless of what happens. The difference in my results is so stark it's uncomfortable to think about. If you want to understand the full math behind this the study is publicly available. Search for Arbitrage in Prediction Markets on arXiv. But the short version is this. Every time the crowd panics about a war or an election and pushes one market out of line with its related markets someone on the other side quietly buys dollars for 94 cents. The question is not whether they'll strike Iran. The question is whether you noticed that seven markets about the same event are priced as if they have nothing to do with each other. That gap is where the money lives.

Blaze

31,373 görüntüleme • 5 ay önce

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026
2:49

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 görüntüleme • 4 ay önce

When I was 8 years old, growing up in Taipei, I called my aunt in San Francisco and asked: What is the best science and technology school in the world? She said MIT. I went on the internet, found it, and decided that was where I was going. All because of a Steven Spielberg movie about a little robot boy who wanted to find his mom. I grew up as an only child. What stayed with me from that movie was not just the technology. It was the possibility that one day, an artificial companion could understand how I felt. That was the first time I remember being moved by a technology that could change how humans experience reality. Years later, I did get to MIT. I studied AI before it became obvious. I became a machine learning engineer, built my first company, joined a $3.5B VC fund, left to build again, failed, started again, moved to New York alone, and built through one of the hardest crypto markets as a solo founder after the collapse of FTX. I kept going because I have always been drawn to technologies that change how humans understand the world. AI was the first version of that. Crypto and prediction markets are the next. I believe the future I am building toward is inevitable. The only question is whether I get to be one of the people who helps realize it. That future is a world where markets become information-first. The old model of trading was asset-first. It rewarded people with capital, financial education, institutional access, and better tools. But the next generation of markets will be shaped by information flow, narrative, attention, politics, culture, sentiment, and collective belief. Prediction markets make this shift obvious. They are one of the first asset classes where the value is informational, not purely financial in the traditional sense. Your edge does not have to come from technical analysis or a traditional finance background. Your edge can come from knowing something before it becomes consensus. From seeing reality shift before the market prices it in. Someone with firsthand knowledge of an unfolding event can have more alpha than an institution with a much bigger balance sheet. They turn belief into price. But price alone is not enough. Polymarket shows what the market thinks will happen. ARES is built to understand why the market is changing. We are building an information-first trading platform for prediction markets and other narrative-driven assets. One that does not just show traders what is moving, but helps them understand why odds are shifting, why narratives are forming, and why the future is moving in a certain direction. But the bigger vision is not just a better trading terminal. We want to turn every trade into an information object. Every position can become a piece of content. Every market view can become a signal. Every trader can build a reputation around conviction and accuracy. Most feeds rank information by engagement. Who got the most likes. Who already has the biggest audience. Markets allow us to rank information differently. How much are you willing to stake on what you believe? How often have you been right? That creates a fundamentally different kind of media feed. One powered by conviction, track record, and market incentives. One that becomes harder to fake. One that can help people understand not just what the market thinks will happen, but why reality is changing. I also believe prediction markets are one of the few markets where humans can still have a real edge over AI. AI knows what is already on the internet. But humans experience reality before it becomes data. We see things before they become headlines. We hear things before they become reports. We feel shifts before they become consensus. If those signals can be priced, organized, and made legible, then more people can gain access to financial opportunity, information agency, and power. That is what Ares is building toward. I spent years watching founders from the VC side of the table, always thinking: I wish that was me. Now it is. I talked about this journey and the thesis behind Ares in my conversation with Dmitry on Predict Time If you are building, trading, investing, or thinking deeply about prediction markets and information markets, I would love for you to watch it. And if you want to collaborate on what we are building, contribute to the vision, or join the team, we are always open to exceptional people across functions. DMs are open.
1:42:20

When I was 8 years old, growing up in Taipei, I called my aunt in San Francisco and asked: What is the best science and technology school in the world? She said MIT. I went on the internet, found it, and decided that was where I was going. All because of a Steven Spielberg movie about a little robot boy who wanted to find his mom. I grew up as an only child. What stayed with me from that movie was not just the technology. It was the possibility that one day, an artificial companion could understand how I felt. That was the first time I remember being moved by a technology that could change how humans experience reality. Years later, I did get to MIT. I studied AI before it became obvious. I became a machine learning engineer, built my first company, joined a $3.5B VC fund, left to build again, failed, started again, moved to New York alone, and built through one of the hardest crypto markets as a solo founder after the collapse of FTX. I kept going because I have always been drawn to technologies that change how humans understand the world. AI was the first version of that. Crypto and prediction markets are the next. I believe the future I am building toward is inevitable. The only question is whether I get to be one of the people who helps realize it. That future is a world where markets become information-first. The old model of trading was asset-first. It rewarded people with capital, financial education, institutional access, and better tools. But the next generation of markets will be shaped by information flow, narrative, attention, politics, culture, sentiment, and collective belief. Prediction markets make this shift obvious. They are one of the first asset classes where the value is informational, not purely financial in the traditional sense. Your edge does not have to come from technical analysis or a traditional finance background. Your edge can come from knowing something before it becomes consensus. From seeing reality shift before the market prices it in. Someone with firsthand knowledge of an unfolding event can have more alpha than an institution with a much bigger balance sheet. They turn belief into price. But price alone is not enough. Polymarket shows what the market thinks will happen. ARES is built to understand why the market is changing. We are building an information-first trading platform for prediction markets and other narrative-driven assets. One that does not just show traders what is moving, but helps them understand why odds are shifting, why narratives are forming, and why the future is moving in a certain direction. But the bigger vision is not just a better trading terminal. We want to turn every trade into an information object. Every position can become a piece of content. Every market view can become a signal. Every trader can build a reputation around conviction and accuracy. Most feeds rank information by engagement. Who got the most likes. Who already has the biggest audience. Markets allow us to rank information differently. How much are you willing to stake on what you believe? How often have you been right? That creates a fundamentally different kind of media feed. One powered by conviction, track record, and market incentives. One that becomes harder to fake. One that can help people understand not just what the market thinks will happen, but why reality is changing. I also believe prediction markets are one of the few markets where humans can still have a real edge over AI. AI knows what is already on the internet. But humans experience reality before it becomes data. We see things before they become headlines. We hear things before they become reports. We feel shifts before they become consensus. If those signals can be priced, organized, and made legible, then more people can gain access to financial opportunity, information agency, and power. That is what Ares is building toward. I spent years watching founders from the VC side of the table, always thinking: I wish that was me. Now it is. I talked about this journey and the thesis behind Ares in my conversation with Dmitry on Predict Time If you are building, trading, investing, or thinking deeply about prediction markets and information markets, I would love for you to watch it. And if you want to collaborate on what we are building, contribute to the vision, or join the team, we are always open to exceptional people across functions. DMs are open.

Morgan Lai

302,663 görüntüleme • 2 ay önce

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns
1:01

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

133,696 görüntüleme • 14 gün önce

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen
49:49

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

Moon Dev

26,010 görüntüleme • 4 ay önce

🚨BREAKING🚨: UFOs have been monitoring sensitive nuclear and military sites all over the world. “New Jersey Drones” are a complete and total misnomer. These unexplainable “New Jersey Drones” have now been spotted at 19 sensitive military sites GLOBALLY. And those are just the bases publicly reported on by Western media that share information with the United States. They’ve also been spotted at nuclear sites like the PSEG Salem Plant in New Jersey. It’s impossible to make sense of this if you don’t know that 167 WHISTLEBLOWERS—employees at nuclear bases, often with Q clearances—have already come forward bearing witness to UFOs flying in and around American critical infrastructure since the 1940s. This is documented in an incredible book written by Robert Hastings, aptly named UFOs & Nukes. We produced a documentary with Hastings and interviewed two of his best witnesses directly—Bob Jacobs (Vandenberg, 1964) and Mario Woods (Ellsworth Air Force Base, 1977). This conversation will blow your mind but also help you make sense of the incoming president’s statements on UFOs and sightings all around the world today. Link in reply. Key Takeaways: 1. UFOs have been systematically surveilling nuclear sites GLOBALLY (not just in America). Lino Fukushima (Japan)—a town obsessed with UFOs, even housing a dedicated museum—sits near a civilian nuclear grid built in the ’70s. Bariloche (Argentina), also with a nuclear research facility, has had multiple sightings. Chernobyl had multiple eyewitness UFO reports after their spill as documented by Harvard PhD Jensine Andresen. Roswell was home to the 509th Atomic Bomber Squadron (the world’s first). The Ariel School sighting in Zimbabwe involved 60+ children right next to a uranium mine. The nuclear connection is widespread and ubiquitous. 2. Witnesses describe UFOs disabling and tampering with nuclear missiles, including at Malmstrom AFB (Montana) and F.E. Warren AFB (Wyoming). In 1967 at Malmstrom, 10 missiles were rendered INOPERABLE by a blazing red UFO flying overhead, witnessed by multiple ICBM security personnel—often called the ‘Echo Flight’ incident. It reportedly involved at least a dozen on-duty personnel who saw the bright object hovering near the silos. Captain Robert Salas insists it was a genuine unidentified craft—one of the most widely discussed UFO disruptions of a nuclear system on record. In 2010, The Atlantic covered an hour-long power disruption at F.E. Warren AFB (October 23), taking 50 Minuteman III missiles offline. President Obama was briefed. Robert Hastings spoke with missile technicians there who attributed the outage to a cigar-shaped “tic tac” UFO hovering near the base. 3. Whistleblowers and military veterans involved in these UFO-nuclear incidents have faced intimidation, stalled promotions, and in some cases, demotions. Many served under the military’s Personal Reliability Program, rigorously screening their mental and physical health before receiving their jobs. As guardians of America’s most critical defense assets, they work under immense pressure — all of their incentives cut against speaking out highlighting the veracity of their testimonies. 4. Mysterious drones and UFOs continue to appear over sensitive sites like New Jersey’s Salem nuclear plant, Wright-Patterson AFB, and beyond. Multiple security personnel at Salem reported unexplained aerial objects in restricted airspace (2018), prompting heightened alerts. Wright-Patterson—long rumored to store recovered UFO materials since Project Blue Book—has also logged similar reports. These ongoing incidents align with Hastings’ extensive documentation showing UFOs targeting nuclear research and defense sites. 5. During the 1962 “BLUEGILL TRIPLE PRIME” nuclear test—part of Operation Fishbowl at the height of the Cuban Missile Crisis—footage reportedly shows a bright object “tumbling out” of the nuclear fireball near Johnston Atoll. In an interview with Ross Coulthart (Ross Coulthart), former Australian intelligence official Geoff Cruickshank cited U.S. Navy deck logs indicating that the USS Safeguard and USNS Point Barrow recovered unusual, highly radioactive “spherical” debris afterward (the video of the test hides this UFO with a inexplicable white triangle superimposed on it). Harald Malmgrem, advisor to 4 different presidents (and JFK at the time), a man with credentials beyond reproach, discusses this UFO wreckage ending up at Los Alamos where JFK visited in November of 62’! This POSSIBLY explains JFK’s request to see data on UFOs from then CIA director John McCone right before his death. And incoming FBI director Kash Patel’s hints of some weirdness we wouldn’t expect hidden in the JFK file. 6. Top UFO “debunkers” often hail from America’s atomic programs—like Dr. Edward Condon (a Manhattan Project alumnus whose 1969 report shut down Project Blue Book). Condon was an extremely close confidante of Oppenheimer and Vannevar Bush. And now, Dr. Sean Kirkpatrick (AARO Director with past work at Oak Ridge National Laboratory etc.). Could their nuclear program backgrounds point to a coordinated effort to dismiss the “UFOs & Nukes” connection—despite decades of credible reports linking UFOs to critical nuclear sites? We are on the verge of a massive paradigm shift for humanity. You can ignore this data or throw it in the junk pile, but refusing to contend with it won’t change anything. It’s there—and requires serious scrutiny and analysis. This is especially true if we want any real, non-brain-dead understanding of today’s situation. The patriots documented by Robert Hastings need to be heard. Full video👇🏻:
1:45

🚨BREAKING🚨: UFOs have been monitoring sensitive nuclear and military sites all over the world. “New Jersey Drones” are a complete and total misnomer. These unexplainable “New Jersey Drones” have now been spotted at 19 sensitive military sites GLOBALLY. And those are just the bases publicly reported on by Western media that share information with the United States. They’ve also been spotted at nuclear sites like the PSEG Salem Plant in New Jersey. It’s impossible to make sense of this if you don’t know that 167 WHISTLEBLOWERS—employees at nuclear bases, often with Q clearances—have already come forward bearing witness to UFOs flying in and around American critical infrastructure since the 1940s. This is documented in an incredible book written by Robert Hastings, aptly named UFOs & Nukes. We produced a documentary with Hastings and interviewed two of his best witnesses directly—Bob Jacobs (Vandenberg, 1964) and Mario Woods (Ellsworth Air Force Base, 1977). This conversation will blow your mind but also help you make sense of the incoming president’s statements on UFOs and sightings all around the world today. Link in reply. Key Takeaways: 1. UFOs have been systematically surveilling nuclear sites GLOBALLY (not just in America). Lino Fukushima (Japan)—a town obsessed with UFOs, even housing a dedicated museum—sits near a civilian nuclear grid built in the ’70s. Bariloche (Argentina), also with a nuclear research facility, has had multiple sightings. Chernobyl had multiple eyewitness UFO reports after their spill as documented by Harvard PhD Jensine Andresen. Roswell was home to the 509th Atomic Bomber Squadron (the world’s first). The Ariel School sighting in Zimbabwe involved 60+ children right next to a uranium mine. The nuclear connection is widespread and ubiquitous. 2. Witnesses describe UFOs disabling and tampering with nuclear missiles, including at Malmstrom AFB (Montana) and F.E. Warren AFB (Wyoming). In 1967 at Malmstrom, 10 missiles were rendered INOPERABLE by a blazing red UFO flying overhead, witnessed by multiple ICBM security personnel—often called the ‘Echo Flight’ incident. It reportedly involved at least a dozen on-duty personnel who saw the bright object hovering near the silos. Captain Robert Salas insists it was a genuine unidentified craft—one of the most widely discussed UFO disruptions of a nuclear system on record. In 2010, The Atlantic covered an hour-long power disruption at F.E. Warren AFB (October 23), taking 50 Minuteman III missiles offline. President Obama was briefed. Robert Hastings spoke with missile technicians there who attributed the outage to a cigar-shaped “tic tac” UFO hovering near the base. 3. Whistleblowers and military veterans involved in these UFO-nuclear incidents have faced intimidation, stalled promotions, and in some cases, demotions. Many served under the military’s Personal Reliability Program, rigorously screening their mental and physical health before receiving their jobs. As guardians of America’s most critical defense assets, they work under immense pressure — all of their incentives cut against speaking out highlighting the veracity of their testimonies. 4. Mysterious drones and UFOs continue to appear over sensitive sites like New Jersey’s Salem nuclear plant, Wright-Patterson AFB, and beyond. Multiple security personnel at Salem reported unexplained aerial objects in restricted airspace (2018), prompting heightened alerts. Wright-Patterson—long rumored to store recovered UFO materials since Project Blue Book—has also logged similar reports. These ongoing incidents align with Hastings’ extensive documentation showing UFOs targeting nuclear research and defense sites. 5. During the 1962 “BLUEGILL TRIPLE PRIME” nuclear test—part of Operation Fishbowl at the height of the Cuban Missile Crisis—footage reportedly shows a bright object “tumbling out” of the nuclear fireball near Johnston Atoll. In an interview with Ross Coulthart (Ross Coulthart), former Australian intelligence official Geoff Cruickshank cited U.S. Navy deck logs indicating that the USS Safeguard and USNS Point Barrow recovered unusual, highly radioactive “spherical” debris afterward (the video of the test hides this UFO with a inexplicable white triangle superimposed on it). Harald Malmgrem, advisor to 4 different presidents (and JFK at the time), a man with credentials beyond reproach, discusses this UFO wreckage ending up at Los Alamos where JFK visited in November of 62’! This POSSIBLY explains JFK’s request to see data on UFOs from then CIA director John McCone right before his death. And incoming FBI director Kash Patel’s hints of some weirdness we wouldn’t expect hidden in the JFK file. 6. Top UFO “debunkers” often hail from America’s atomic programs—like Dr. Edward Condon (a Manhattan Project alumnus whose 1969 report shut down Project Blue Book). Condon was an extremely close confidante of Oppenheimer and Vannevar Bush. And now, Dr. Sean Kirkpatrick (AARO Director with past work at Oak Ridge National Laboratory etc.). Could their nuclear program backgrounds point to a coordinated effort to dismiss the “UFOs & Nukes” connection—despite decades of credible reports linking UFOs to critical nuclear sites? We are on the verge of a massive paradigm shift for humanity. You can ignore this data or throw it in the junk pile, but refusing to contend with it won’t change anything. It’s there—and requires serious scrutiny and analysis. This is especially true if we want any real, non-brain-dead understanding of today’s situation. The patriots documented by Robert Hastings need to be heard. Full video👇🏻:

Jesse Michels

765,087 görüntüleme • 1 yıl önce

Israel Moved Gaza’s Yellow Line And Then Shelled Palestinians For Being On The Wrong Side Drop Site News reports that the IDF quietly moved part of the “yellow line” which divides Gaza 300 meters forward, and then started shelling Palestinians for being on the “wrong side” of the line. They just keep finding new ways to carve off more pieces of Gaza and murder more Palestinians. ❖ Haaretz has a disturbing story out about a 14 year-old Palestinian boy who was waiting for his school bus in the West Bank on a quiet street eating a cookie, when suddenly a bunch of IDF vehicles pulled up and a soldier shot him directly in the face with a teargas canister. Then they sped off. The boy lost his right eye in the attack. Israelis will just casually shoot a Palestinian kid in the fucking face for no fucking reason, and then go online and call you hateful for opposing them. ❖ Leftists are going to hate Israel for leftist reasons and rightists are going to hate Israel for rightist reasons. This is not a difference that needs to be reconciled or a problem that needs to be solved. Some groyper hating the same genocidal apartheid state I hate doesn’t say anything about me or my politics anymore than our having the same opinion on the importance of dental hygiene. It doesn’t make me the same as him. It doesn’t make him my friend. It doesn’t mean I have to be nice to him. It doesn’t mean I have to stop opposing Israel and its atrocities. It doesn’t mean I am obligated to do anything to protect Israel from the western rightists who’ve been turning against it in rapidly increasing numbers. It doesn’t mean anything. It just is what it is. You’ll see Israel supporters point to the rising number of rightists who oppose Israel and trying to marry it to pro-Palestine leftists in some way. I recently saw former Israeli spokesman Eylon Levy claiming that antizionism “builds coalitions on both extremes — and increasingly between them — by mobilizing politics against Jews.” But that’s just some nonsense they’re making up in order to justify their genocidal atrocities. Leftist opposition to Israel is about justice, equality, anticolonialism, antiracism, anti-imperialism, antiwar and anti-apartheid activism, and has nothing whatsoever to do with hating Jews. Rightist opposition to Israel is more about nationalism, anti-interventionism, America First ideology, and yes, in many cases a hatred of Jews. These are two completely different things. You never saw Zionists bitching about the far right until they started pivoting against Israel; until then they were happy to make alliances with them, and still are as long as they remain supportive of Israel. And half the time you see them citing anti-Israel sentiments on the right it’s only to play guilt-by-association by framing them as the same as the pro-Palestine left. It’s just more empty narrative-diddling from Israel apologists. It’s not a real argument, and doesn’t require a counter-argument. It’s just them flailing around trying anything they can to stop the entire western political spectrum from flushing Israel down the toilet. ❖ New York Times war propagandist Bret Stephens has a new article out titled “The Case for Overthrowing Maduro” in which he argues for US regime change interventionism in Venezuela on the basis that “the regime’s close economic and strategic ties to China, Russia and Iran give America’s enemies a significant foothold in the Americas.” This would be the same Bret Stephens who in 2023 wrote an article in the New York Times titled “20 Years On, I Don’t Regret Supporting the Iraq War,” by the way. On a related note, the leftist outlet Current Affairs presently has a special going where if you cancel your New York Times subscription and send proof of cancelation to help@currentaffairs.org right now, you’ll get a free year-long digital subscription to their vastly superior publication. ❖ Right wingers think a mother should be at home raising her children, an arrangement that many mothers would be on board with, but if you say this requires either state support or for employers to be forced to increase pay so that single-income families can exist they say “No that’s socialism!” They want the mothers to stay at home while the fathers work 80-hour work weeks for ten bucks an hour so that billionaires can become trillionaires. ❖ Elon Musk says that in 10 to 20 years work will be optional and money will be meaningless because AI and automation will eliminate the need for labor and make everything wonderful. Something tells me the guy who just took a trillion-dollar pay package from Tesla doesn’t really believe money is going to be meaningless anytime soon. This soon-to-be-trillionaire whose ego is so fragile and infantile that his AI chatbot tells people he’s smarter than da Vinci and more athletic than LeBron James is asking us to believe that revolutionary change isn’t necessary because capitalists like himself are going to fix it so that everyone lives in luxury. Yeah sure, buddy. A likely story. ❖ OpenAI reportedly plans on building 250 gigawatts of capacity by 2033 to use for its energy-consuming servers, about the same amount of electricity that’s used by 1.5 billion people in India. So, no. No to this. Your right to extend your fist ends at my nose. You don’t get to just add this giant burden to the already severely overburdened ecosystem we all depend on for survival in order to expand your chatbot project. The collective is entitled to stop you. By force. Reading by Tim Foley:
6:29

Israel Moved Gaza’s Yellow Line And Then Shelled Palestinians For Being On The Wrong Side Drop Site News reports that the IDF quietly moved part of the “yellow line” which divides Gaza 300 meters forward, and then started shelling Palestinians for being on the “wrong side” of the line. They just keep finding new ways to carve off more pieces of Gaza and murder more Palestinians. ❖ Haaretz has a disturbing story out about a 14 year-old Palestinian boy who was waiting for his school bus in the West Bank on a quiet street eating a cookie, when suddenly a bunch of IDF vehicles pulled up and a soldier shot him directly in the face with a teargas canister. Then they sped off. The boy lost his right eye in the attack. Israelis will just casually shoot a Palestinian kid in the fucking face for no fucking reason, and then go online and call you hateful for opposing them. ❖ Leftists are going to hate Israel for leftist reasons and rightists are going to hate Israel for rightist reasons. This is not a difference that needs to be reconciled or a problem that needs to be solved. Some groyper hating the same genocidal apartheid state I hate doesn’t say anything about me or my politics anymore than our having the same opinion on the importance of dental hygiene. It doesn’t make me the same as him. It doesn’t make him my friend. It doesn’t mean I have to be nice to him. It doesn’t mean I have to stop opposing Israel and its atrocities. It doesn’t mean I am obligated to do anything to protect Israel from the western rightists who’ve been turning against it in rapidly increasing numbers. It doesn’t mean anything. It just is what it is. You’ll see Israel supporters point to the rising number of rightists who oppose Israel and trying to marry it to pro-Palestine leftists in some way. I recently saw former Israeli spokesman Eylon Levy claiming that antizionism “builds coalitions on both extremes — and increasingly between them — by mobilizing politics against Jews.” But that’s just some nonsense they’re making up in order to justify their genocidal atrocities. Leftist opposition to Israel is about justice, equality, anticolonialism, antiracism, anti-imperialism, antiwar and anti-apartheid activism, and has nothing whatsoever to do with hating Jews. Rightist opposition to Israel is more about nationalism, anti-interventionism, America First ideology, and yes, in many cases a hatred of Jews. These are two completely different things. You never saw Zionists bitching about the far right until they started pivoting against Israel; until then they were happy to make alliances with them, and still are as long as they remain supportive of Israel. And half the time you see them citing anti-Israel sentiments on the right it’s only to play guilt-by-association by framing them as the same as the pro-Palestine left. It’s just more empty narrative-diddling from Israel apologists. It’s not a real argument, and doesn’t require a counter-argument. It’s just them flailing around trying anything they can to stop the entire western political spectrum from flushing Israel down the toilet. ❖ New York Times war propagandist Bret Stephens has a new article out titled “The Case for Overthrowing Maduro” in which he argues for US regime change interventionism in Venezuela on the basis that “the regime’s close economic and strategic ties to China, Russia and Iran give America’s enemies a significant foothold in the Americas.” This would be the same Bret Stephens who in 2023 wrote an article in the New York Times titled “20 Years On, I Don’t Regret Supporting the Iraq War,” by the way. On a related note, the leftist outlet Current Affairs presently has a special going where if you cancel your New York Times subscription and send proof of cancelation to [email protected] right now, you’ll get a free year-long digital subscription to their vastly superior publication. ❖ Right wingers think a mother should be at home raising her children, an arrangement that many mothers would be on board with, but if you say this requires either state support or for employers to be forced to increase pay so that single-income families can exist they say “No that’s socialism!” They want the mothers to stay at home while the fathers work 80-hour work weeks for ten bucks an hour so that billionaires can become trillionaires. ❖ Elon Musk says that in 10 to 20 years work will be optional and money will be meaningless because AI and automation will eliminate the need for labor and make everything wonderful. Something tells me the guy who just took a trillion-dollar pay package from Tesla doesn’t really believe money is going to be meaningless anytime soon. This soon-to-be-trillionaire whose ego is so fragile and infantile that his AI chatbot tells people he’s smarter than da Vinci and more athletic than LeBron James is asking us to believe that revolutionary change isn’t necessary because capitalists like himself are going to fix it so that everyone lives in luxury. Yeah sure, buddy. A likely story. ❖ OpenAI reportedly plans on building 250 gigawatts of capacity by 2033 to use for its energy-consuming servers, about the same amount of electricity that’s used by 1.5 billion people in India. So, no. No to this. Your right to extend your fist ends at my nose. You don’t get to just add this giant burden to the already severely overburdened ecosystem we all depend on for survival in order to expand your chatbot project. The collective is entitled to stop you. By force. Reading by Tim Foley:

Caitlin Johnstone

36,316 görüntüleme • 8 ay önce

it's my birthday. sometimes I feel like I'm 10 years behind in life. deep down I know that that this year will be the best and hardest year of my life but I gotta be honest the five folks who care for a minute. spent the past decade depressed, embarrassed that I wasn't more talented or more successful, guilt ridden for not being mature enough to handle life the way I would've liked when I was younger, keeping my head down trying to work on myself and trying to hone my skills to be a better storyteller. just years of telling myself I'm not good enough, telling myself I'm not old enough or lucky enough. telling myself who the fuck cares about what I do or create. like how the fuck can I do what so many others do. fuck off for even thinking you can do it stephen. I'm not a special person, I'm just a dude. some idiot. who has been in the film industry since I was a kid. an industry I left for a while because I needed to disconnect, I needed time to figure out my life after working since the second grade. needed to find my love and passion. and I did. which is making things for people to enjoy. but it hasn't been that simple. that pivot was like a hard reset. suddenly everything I'd ever achieved meant nothing. it's been a constant grind every day while trying to keep a roof over my head taking on retail jobs, service jobs, handyman gigs after leading shows and movies. and that's okay. like I've gotten clowned on it but you gotta make it work. in between all of that I've been lucky to work with huge brands, do stellar uncredited work on amazing flicks and slowly chip away on my own goals. for the past decade I haven't been able to sleep easily. can't turn off my brain. thinking about how I'm never doing enough for hours just in bed. telling myself maybe it'll be different tomorrow while hiding from the world making unhealthy decisions, not taking care of myself. a lot of times I do feel like I've missed out on my life, especially the past ten years. I've just been working. when I'm not working I'm working on the side with nothing to show for it. just endless chasing rent while being delusional about creating a better life. if you're not careful this kinda dream can suck the life out of you. you lose your passion for it. but I haven't. so much of me has just been waiting in the background of my own life, thinking there would be some moment of realization when I've worked on myself enough and I suddenly I feel like "oh I've got this." waited for that moment but it never came. don't think it ever will. I'm tired of waiting, I'm tired of thinking I'm not ready, I'm tired of telling myself I'm not good enough. it's not true. I won't give up. I won't give up on trying to entertain people. I won't give up on my dream of helping my friends fulfill their own. I never will. love and appreciate all of you for sticking with me and watching what I do and being here for me. 90% of my body is made of movies, games and soundtracks; so because I'm a cringe dork, I have meme'd for years that I feel like reclusive bruce wayne in the dark knight rises (but broke and less handsome) afflicted by failures unable to accept that my life can go on. but that's bullshit. maybe I needed an era to change and hurt and struggle and learn and become who I wanted to be. life comes and goes in eras. and I'm in my begins era now baby.
0:37

it's my birthday. sometimes I feel like I'm 10 years behind in life. deep down I know that that this year will be the best and hardest year of my life but I gotta be honest the five folks who care for a minute. spent the past decade depressed, embarrassed that I wasn't more talented or more successful, guilt ridden for not being mature enough to handle life the way I would've liked when I was younger, keeping my head down trying to work on myself and trying to hone my skills to be a better storyteller. just years of telling myself I'm not good enough, telling myself I'm not old enough or lucky enough. telling myself who the fuck cares about what I do or create. like how the fuck can I do what so many others do. fuck off for even thinking you can do it stephen. I'm not a special person, I'm just a dude. some idiot. who has been in the film industry since I was a kid. an industry I left for a while because I needed to disconnect, I needed time to figure out my life after working since the second grade. needed to find my love and passion. and I did. which is making things for people to enjoy. but it hasn't been that simple. that pivot was like a hard reset. suddenly everything I'd ever achieved meant nothing. it's been a constant grind every day while trying to keep a roof over my head taking on retail jobs, service jobs, handyman gigs after leading shows and movies. and that's okay. like I've gotten clowned on it but you gotta make it work. in between all of that I've been lucky to work with huge brands, do stellar uncredited work on amazing flicks and slowly chip away on my own goals. for the past decade I haven't been able to sleep easily. can't turn off my brain. thinking about how I'm never doing enough for hours just in bed. telling myself maybe it'll be different tomorrow while hiding from the world making unhealthy decisions, not taking care of myself. a lot of times I do feel like I've missed out on my life, especially the past ten years. I've just been working. when I'm not working I'm working on the side with nothing to show for it. just endless chasing rent while being delusional about creating a better life. if you're not careful this kinda dream can suck the life out of you. you lose your passion for it. but I haven't. so much of me has just been waiting in the background of my own life, thinking there would be some moment of realization when I've worked on myself enough and I suddenly I feel like "oh I've got this." waited for that moment but it never came. don't think it ever will. I'm tired of waiting, I'm tired of thinking I'm not ready, I'm tired of telling myself I'm not good enough. it's not true. I won't give up. I won't give up on trying to entertain people. I won't give up on my dream of helping my friends fulfill their own. I never will. love and appreciate all of you for sticking with me and watching what I do and being here for me. 90% of my body is made of movies, games and soundtracks; so because I'm a cringe dork, I have meme'd for years that I feel like reclusive bruce wayne in the dark knight rises (but broke and less handsome) afflicted by failures unable to accept that my life can go on. but that's bullshit. maybe I needed an era to change and hurt and struggle and learn and become who I wanted to be. life comes and goes in eras. and I'm in my begins era now baby.

Stephen Ford

30,135 görüntüleme • 1 yıl önce