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[loop-50sec+cum] OBOL-0421 - Trigger Training models by DaB (Wise) and 🇲🇽Waffle 🔞 (Trigger) map by Never Yapph SFX Pack by OpenNSFW VA Pack by 千夜(Chiyoru)🐈️🌃 4k, extras and more in bio! #zzzero #rule34

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118,193 次观看 • 4 个月前 •via X (Twitter)

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Final Thoughts on Attack Trigger. It’s slightly underwhelming. Maybe it’s because it’s still early and I haven’t given it enough time but we’ll soon see anyways since it can get buffed as the days go by. Attack Trigger is noticeable when you are counter attacking and are in transition since all of your attacking options makes more aggressive runs into the box even if they are Creative Playermakers which is a more passive playstyle. Through Balls on the ground is definitely the superior option to go for too. There were many times when I was sitting in the midfield with De Jong waiting for runs and I am just watching my team do nothing so that’s a bit frustrating. Another positive about the Attack Trigger pack is that Reece James is a beast. Definitely top 3 Attacking RBs in the game by far. His crossing is up there with Lahm, Jordi Alba, and surprisingly Beckham. Also it would really help if there was a clip showing the differences of AI movement when a ball carrier has Attack Trigger and when a ball carrier doesn’t have Attack Trigger. It would really help clear things up instead of making the descriptions more easier to understand. Having that visual comparison example would be extremely helpful. 🤷‍♂️
2:50

Final Thoughts on Attack Trigger. It’s slightly underwhelming. Maybe it’s because it’s still early and I haven’t given it enough time but we’ll soon see anyways since it can get buffed as the days go by. Attack Trigger is noticeable when you are counter attacking and are in transition since all of your attacking options makes more aggressive runs into the box even if they are Creative Playermakers which is a more passive playstyle. Through Balls on the ground is definitely the superior option to go for too. There were many times when I was sitting in the midfield with De Jong waiting for runs and I am just watching my team do nothing so that’s a bit frustrating. Another positive about the Attack Trigger pack is that Reece James is a beast. Definitely top 3 Attacking RBs in the game by far. His crossing is up there with Lahm, Jordi Alba, and surprisingly Beckham. Also it would really help if there was a clip showing the differences of AI movement when a ball carrier has Attack Trigger and when a ball carrier doesn’t have Attack Trigger. It would really help clear things up instead of making the descriptions more easier to understand. Having that visual comparison example would be extremely helpful. 🤷‍♂️

Jacques

12,269 次观看 • 5 个月前

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 次观看 • 1 年前

I live in a limestone-and-glass building south of Pacific Heights, the kind of place where the lobby smells like money, eucalyptus, and seed-stage fraud. My name is anon. I’m 27 years old. I believe in taking care of myself, maintaining a clean stack, and keeping my blood chemistry within acceptable operating parameters. In the morning, if my face is a little puffy, I put on an ice pack and scroll X until I see three people I know subtweeting each other about compute, Zionism, or whether AGI should be allowed to do ketamine. Then I do my crunches. I can do a thousand now. After I remove the ice pack, I wash with a cleanser expensive enough to imply self-respect but not so expensive that it signals LA. In the shower, I rotate between a medically unnecessary body scrub and whatever minimalist Scandinavian product makes my bathroom look like a founder kidnapping victim lives there. Then I begin the real routine: electrolytes, black coffee, magnesium, nicotine, a handful of compounds sourced from websites that look like they were built to sell fake passports, and a small but meaningful quantity of pharmacological optimism. By this point the smart Adderall box has already pinged my phone, verified the time, and dispensed exactly enough amphetamine to turn ambient dread into productive abstraction. While Bach plays in the background, I review overnight group chats, check whether clawdbot said anything spiritually corrosive, glance at a preprint on GLP-1 agonists, and open Cursor to vibecode some totally unnecessary internal tool that will either make me feel briefly omnipotent or trigger a six-hour dissociative spiral about Chinese manufacturing dominance. Then moisturizer. Then eye cream. Then sunscreen. Because entropy is real, aging is real, and the only moral response is countermeasure. There is an idea of anon. A Bay Area abstraction. Someone with good skin, good taste, a custom supplement protocol, and opinions about export controls that become more coherent the more stimulants enter his bloodstream. But there is no real me. Only a permissions structure. A chemical timing device. A thin membrane separating Handel, Adderall, and terminal windows from complete psychic disintegration. And though I can hide my optimized gaze, and you can shake my hand and feel flesh gripping yours, and maybe you can even sense our eGFR scores are probably comparable, I simply am not there.
0:27

I live in a limestone-and-glass building south of Pacific Heights, the kind of place where the lobby smells like money, eucalyptus, and seed-stage fraud. My name is anon. I’m 27 years old. I believe in taking care of myself, maintaining a clean stack, and keeping my blood chemistry within acceptable operating parameters. In the morning, if my face is a little puffy, I put on an ice pack and scroll X until I see three people I know subtweeting each other about compute, Zionism, or whether AGI should be allowed to do ketamine. Then I do my crunches. I can do a thousand now. After I remove the ice pack, I wash with a cleanser expensive enough to imply self-respect but not so expensive that it signals LA. In the shower, I rotate between a medically unnecessary body scrub and whatever minimalist Scandinavian product makes my bathroom look like a founder kidnapping victim lives there. Then I begin the real routine: electrolytes, black coffee, magnesium, nicotine, a handful of compounds sourced from websites that look like they were built to sell fake passports, and a small but meaningful quantity of pharmacological optimism. By this point the smart Adderall box has already pinged my phone, verified the time, and dispensed exactly enough amphetamine to turn ambient dread into productive abstraction. While Bach plays in the background, I review overnight group chats, check whether clawdbot said anything spiritually corrosive, glance at a preprint on GLP-1 agonists, and open Cursor to vibecode some totally unnecessary internal tool that will either make me feel briefly omnipotent or trigger a six-hour dissociative spiral about Chinese manufacturing dominance. Then moisturizer. Then eye cream. Then sunscreen. Because entropy is real, aging is real, and the only moral response is countermeasure. There is an idea of anon. A Bay Area abstraction. Someone with good skin, good taste, a custom supplement protocol, and opinions about export controls that become more coherent the more stimulants enter his bloodstream. But there is no real me. Only a permissions structure. A chemical timing device. A thin membrane separating Handel, Adderall, and terminal windows from complete psychic disintegration. And though I can hide my optimized gaze, and you can shake my hand and feel flesh gripping yours, and maybe you can even sense our eGFR scores are probably comparable, I simply am not there.

SMA 🏴‍☠️

75,842 次观看 • 4 个月前

The Iron Lady Myth: Martha Karua’s “Heroic” Walkout Was Likely Staged Theater Let’s critically revisit Martha Karua’s famous “Iron Lady” nickname, which originated from a 2001 incident in her Gichugu Constituency, Kirinyaga. It’s time to strip away the emotions and examine whether she ever deserved the pedestal and activism credentials Kenyans blindly bestowed upon her. In the incident, Karua dramatically walked out of a Baraza presided over by then-President Daniel arap Moi. The trigger was being denied the chance to address the crowd. However, footage clearly shows her engaged in low-tone discussions with Moi just moments before the dramatic exit. Let’s interrogate the full context without sentiment: 1. Moi’s Unusual Visit: Daniel arap Moi rarely ventured into the “hostile” Mount Kenya region except for his annual trip to Kiganjo Police Training College for passing-out parades. So what was he doing in Kirinyaga? Was he opening a factory, a school, or some development project? Or was the entire event carefully staged? 2. Why Was She There? If Moi was the ruthless dictator and pariah that Karua and the opposition loudly portrayed him to be, why on earth was she attending his rally in the first place - especially when he had only months left in office? What value did she expect to gain by showing up at a function hosted by a man she claimed oppressed Kenyans? 3. Zero Repercussions: When she dramatically walked out and catwalked beside the presidential red carpet in full public view, why wasn’t she abducted, beaten, or assassinated - the standard Moi-era response to overt dissent? Moi, like Ruto today, did not tolerate public humiliation, and he had full colonial backing from the US and UK to eliminate threats as long as he protected their economic interests. These three glaring questions point to one uncomfortable possibility: Martha Karua was always controlled opposition, carefully preserved and elevated by foreign governments and globalist NGOs to manufacture the optics of genuine political dissent while protecting the extraction machine. What if the entire walkout was a scripted encounter? Broadcast nationally to project her as a fearless hero and secure her position as a high-value asset within the opposition, all while ensuring the colonial status quo remained intact and poverty persisted for the benefit of foreigners. Why was she hailed as a luminary of the Second Liberation when she was never arrested, never pushed any transformative legislation, and actively participated in the notorious Inter-Parties Parliamentary Group (IPPG) that shamelessly extended Moi’s rule by another five years? Even today, her record is paper-thin. Other than occasionally representing Boniface Mwangi in court after the 2024 protests, who else has she actually defended? Did she show up for the hundreds of young protesters arrested and charged with terrorism under her “bestie” Chief Justice Martha Koome’s watch? It’s time Kenyans reevaluated the undeserving figures we’ve placed on pedestals. Appearing at staged press conferences, walking out of choreographed events, and benefiting from state-sponsored propaganda machinery should not be the measure of one’s commitment to liberating Kenya. The “Iron Lady” narrative was always more myth than reality - a carefully cultivated image designed to channel public anger into safe, controlled channels while the real looting continued uninterrupted. Time to retire the fairy tale.
0:39

The Iron Lady Myth: Martha Karua’s “Heroic” Walkout Was Likely Staged Theater Let’s critically revisit Martha Karua’s famous “Iron Lady” nickname, which originated from a 2001 incident in her Gichugu Constituency, Kirinyaga. It’s time to strip away the emotions and examine whether she ever deserved the pedestal and activism credentials Kenyans blindly bestowed upon her. In the incident, Karua dramatically walked out of a Baraza presided over by then-President Daniel arap Moi. The trigger was being denied the chance to address the crowd. However, footage clearly shows her engaged in low-tone discussions with Moi just moments before the dramatic exit. Let’s interrogate the full context without sentiment: 1. Moi’s Unusual Visit: Daniel arap Moi rarely ventured into the “hostile” Mount Kenya region except for his annual trip to Kiganjo Police Training College for passing-out parades. So what was he doing in Kirinyaga? Was he opening a factory, a school, or some development project? Or was the entire event carefully staged? 2. Why Was She There? If Moi was the ruthless dictator and pariah that Karua and the opposition loudly portrayed him to be, why on earth was she attending his rally in the first place - especially when he had only months left in office? What value did she expect to gain by showing up at a function hosted by a man she claimed oppressed Kenyans? 3. Zero Repercussions: When she dramatically walked out and catwalked beside the presidential red carpet in full public view, why wasn’t she abducted, beaten, or assassinated - the standard Moi-era response to overt dissent? Moi, like Ruto today, did not tolerate public humiliation, and he had full colonial backing from the US and UK to eliminate threats as long as he protected their economic interests. These three glaring questions point to one uncomfortable possibility: Martha Karua was always controlled opposition, carefully preserved and elevated by foreign governments and globalist NGOs to manufacture the optics of genuine political dissent while protecting the extraction machine. What if the entire walkout was a scripted encounter? Broadcast nationally to project her as a fearless hero and secure her position as a high-value asset within the opposition, all while ensuring the colonial status quo remained intact and poverty persisted for the benefit of foreigners. Why was she hailed as a luminary of the Second Liberation when she was never arrested, never pushed any transformative legislation, and actively participated in the notorious Inter-Parties Parliamentary Group (IPPG) that shamelessly extended Moi’s rule by another five years? Even today, her record is paper-thin. Other than occasionally representing Boniface Mwangi in court after the 2024 protests, who else has she actually defended? Did she show up for the hundreds of young protesters arrested and charged with terrorism under her “bestie” Chief Justice Martha Koome’s watch? It’s time Kenyans reevaluated the undeserving figures we’ve placed on pedestals. Appearing at staged press conferences, walking out of choreographed events, and benefiting from state-sponsored propaganda machinery should not be the measure of one’s commitment to liberating Kenya. The “Iron Lady” narrative was always more myth than reality - a carefully cultivated image designed to channel public anger into safe, controlled channels while the real looting continued uninterrupted. Time to retire the fairy tale.

Francis Gaitho

15,627 次观看 • 3 个月前

Last week we had the biggest one-day MRR gain in Every's history. It came from launching All Access—a 625-dollar-a-year membership that added about 9,000 dollars in MRR for Every 📧 in two days. On this week's AI&I, I handed the mic to our COO Brandon Gell, who sat down with three of Every 📧's own builders—growth engineer Yash Poojary, head of growth Austin Tedesco, and head of marketing Douglas Brundage—to talk about the tools they use most, how they build with them, and their tips for new builders on getting started. They also got into: - How Austin runs two agents at once. While steering Codex on one task, Austin—who calls himself "actually really bad" at video editing—has Claude running in a separate loop with Descript's MCP, building storyboards, writing scripts, and assembling cuts. By the time he sits down to review, it's gotten him 70% of the way to a finished video. - Why Yash treats new AI models like flavors he’s testing. He uses Cursor's cloud agents to run multiple models side by side and figure out which one he prefers, rather than switching to whatever's newest. "I'm so picky about my models that even if a new model drops, I don't change to that," he says, unless testing proves it's better. - Why Brandon thinks the real barrier to building isn't always skill, it's cost. He tells the story of a friend's younger brother, a trained engineer who can't land a job and can't afford to experiment with AI tools that cost other engineers $30K a month. That gap is the whole reason the Builder Pack exists. - Why the team feels safe automating their own jobs. Yash says everyone at Every is secure enough in their skills that automating the repetitive parts of their work doesn't feel threatening, it just clears space for the parts they're good at and enjoy doing. If you want to get started with building, or simply get more ideas on how to work with AI, this episode is for you. Watch on X or YouTube, or listen on Spotify or Apple Podcasts. Timestamps: 0:00 Intro 0:35 All Access Explained 3:01 Yash's Tech Stack and How He's Automating Testing Pipelines 8:02 The Idea to Execution Loop 10:25 How an Agent Turned an Idea into $25K 17:50 The AI Sandwich Workflow 22:03 Making AI Tools Accessible to Solo Builders 28:50 Douglas on Brand and Design 34:51 Tips on What to Build First 43:46 What's Next for All Access
46:30

Last week we had the biggest one-day MRR gain in Every's history. It came from launching All Access—a 625-dollar-a-year membership that added about 9,000 dollars in MRR for Every 📧 in two days. On this week's AI&I, I handed the mic to our COO Brandon Gell, who sat down with three of Every 📧's own builders—growth engineer Yash Poojary, head of growth Austin Tedesco, and head of marketing Douglas Brundage—to talk about the tools they use most, how they build with them, and their tips for new builders on getting started. They also got into: - How Austin runs two agents at once. While steering Codex on one task, Austin—who calls himself "actually really bad" at video editing—has Claude running in a separate loop with Descript's MCP, building storyboards, writing scripts, and assembling cuts. By the time he sits down to review, it's gotten him 70% of the way to a finished video. - Why Yash treats new AI models like flavors he’s testing. He uses Cursor's cloud agents to run multiple models side by side and figure out which one he prefers, rather than switching to whatever's newest. "I'm so picky about my models that even if a new model drops, I don't change to that," he says, unless testing proves it's better. - Why Brandon thinks the real barrier to building isn't always skill, it's cost. He tells the story of a friend's younger brother, a trained engineer who can't land a job and can't afford to experiment with AI tools that cost other engineers $30K a month. That gap is the whole reason the Builder Pack exists. - Why the team feels safe automating their own jobs. Yash says everyone at Every is secure enough in their skills that automating the repetitive parts of their work doesn't feel threatening, it just clears space for the parts they're good at and enjoy doing. If you want to get started with building, or simply get more ideas on how to work with AI, this episode is for you. Watch on X or YouTube, or listen on Spotify or Apple Podcasts. Timestamps: 0:00 Intro 0:35 All Access Explained 3:01 Yash's Tech Stack and How He's Automating Testing Pipelines 8:02 The Idea to Execution Loop 10:25 How an Agent Turned an Idea into $25K 17:50 The AI Sandwich Workflow 22:03 Making AI Tools Accessible to Solo Builders 28:50 Douglas on Brand and Design 34:51 Tips on What to Build First 43:46 What's Next for All Access

Dan Shipper 📧

16,995 次观看 • 18 天前

New model: your robot can now pack your suitcase 🧳 Xiaomi has released a new robot foundation model. Called Xiaomi-Robotics-1, it is designed to have a robot pick things up and move them around. But first, DEFINITIONS: - Mixture-of-Transformers (MoT): An architecture where separate transformer "experts" (e.g., one for vision-language, one for actions) share a single attention stream, so each modality gets specialized parameters without losing joint reasoning. - Vision-language model (VLM): A model that jointly understands images and text. - Diffusion transformer: A transformer trained to turn noise into structured outputs by iterative denoising, here generating robot actions rather than images. - Action chunks: Short sequences of future actions (e.g., the next ~50 motor commands) predicted in one shot instead of one step at a time. - Flow matching: A faster version of diffusion. The model learns a straight-line velocity field from noise to the target action, so it needs only a few integration steps instead of many denoising ones. Its peculiarity comes from its two stage training: 1. 100,000 hours of video shot through a UMI rig: a handheld 3D-printed gripper with a camera, worn by humans doing ordinary tasks in homes, shops, factories and offices. 2. Adapt to actual robot bodies with ~10,000 hours of real-robot data. It replaces the standard approach of teleoperating a real robot for every hour of training data. Its architecture is a Mixture-of-Transformers pairing a pre-trained Qwen3-VL vision-language model with a diffusion transformer that emits action chunks via flow matching, released in 2.6B, 5.1B and 10.5B parameter variants. However, if you read the entire paper ("Scaling VLA Models with over 100K Hours"), you realize that all of the scaling experiments on 20k hours. Therefore the headline "out-of-the-box success climbing 26% → 75% as pre-training data grows" tops out at 100% of 20k hours! What the full corpus does to that curve is never shown -> and this where things would become interesting! Xiaomi's own conclusion is that model size has stopped mattering and data is the binding constraint. The performance gap among different model sizes are less pronounced than those observed across different data scales. This result suggests that model capacity at the billions-parameter scale may already be sufficient to capture the current dataset's distribution. Which further asks the same question: why not use the 100k video hours? Anyway, I would definitely love to have a couple robots at home that can cooperate to pack my suitcase with items relevant to my next destination:
0:58

New model: your robot can now pack your suitcase 🧳 Xiaomi has released a new robot foundation model. Called Xiaomi-Robotics-1, it is designed to have a robot pick things up and move them around. But first, DEFINITIONS: - Mixture-of-Transformers (MoT): An architecture where separate transformer "experts" (e.g., one for vision-language, one for actions) share a single attention stream, so each modality gets specialized parameters without losing joint reasoning. - Vision-language model (VLM): A model that jointly understands images and text. - Diffusion transformer: A transformer trained to turn noise into structured outputs by iterative denoising, here generating robot actions rather than images. - Action chunks: Short sequences of future actions (e.g., the next ~50 motor commands) predicted in one shot instead of one step at a time. - Flow matching: A faster version of diffusion. The model learns a straight-line velocity field from noise to the target action, so it needs only a few integration steps instead of many denoising ones. Its peculiarity comes from its two stage training: 1. 100,000 hours of video shot through a UMI rig: a handheld 3D-printed gripper with a camera, worn by humans doing ordinary tasks in homes, shops, factories and offices. 2. Adapt to actual robot bodies with ~10,000 hours of real-robot data. It replaces the standard approach of teleoperating a real robot for every hour of training data. Its architecture is a Mixture-of-Transformers pairing a pre-trained Qwen3-VL vision-language model with a diffusion transformer that emits action chunks via flow matching, released in 2.6B, 5.1B and 10.5B parameter variants. However, if you read the entire paper ("Scaling VLA Models with over 100K Hours"), you realize that all of the scaling experiments on 20k hours. Therefore the headline "out-of-the-box success climbing 26% → 75% as pre-training data grows" tops out at 100% of 20k hours! What the full corpus does to that curve is never shown -> and this where things would become interesting! Xiaomi's own conclusion is that model size has stopped mattering and data is the binding constraint. The performance gap among different model sizes are less pronounced than those observed across different data scales. This result suggests that model capacity at the billions-parameter scale may already be sufficient to capture the current dataset's distribution. Which further asks the same question: why not use the 100k video hours? Anyway, I would definitely love to have a couple robots at home that can cooperate to pack my suitcase with items relevant to my next destination:

Léo

14,771 次观看 • 4 天前

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.
56:54

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Himanshu Kumar

28,120 次观看 • 3 个月前

Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.
0:54

Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.

Milk Road AI

485,188 次观看 • 2 个月前

Over the past two years, AI video models have been competing on realism, resolution, and duration. But no matter how impressive the results look, we remain passive viewers: we press play, watch the clip, and it ends. AlayaWorld Alaya Lab is attempting something fundamentally different. Instead of generating a fixed video, it generates a world that continues to unfold as you move through it. These three demos show the same journey toward a green village rendered in three distinct styles: photorealistic, oil painting, and line art. As the camera moves forward, the model continues generating the road, fences, trees, and distant village. This is not simply an existing video with different filters applied. The environment is generated continuously along the camera trajectory, allowing the scene to develop as the user explores it. AlayaWorld streams video at 720p and 24 FPS while supporting camera movement and viewpoint control. The real breakthrough is not just image quality. Once generation becomes fast enough to respond within an interactive loop, the user is no longer merely watching a video. They become a participant inside the generated world. The world can also respond to new instructions. During generation, users can introduce prompts that trigger spells, summon characters, create explosions, or transform the environment. Most video models follow an initial prompt and produce a predetermined clip. AlayaWorld can respond to changing intent while the world is still running, allowing subsequent events to evolve according to the user’s commands. Generating an attractive frame is relatively easy. Maintaining a coherent world over time is much harder. As a video model repeatedly predicts the next frame, small errors can accumulate until roads, buildings, and objects begin to distort or disappear. AlayaWorld combines spatial memory with compressed historical context, helping the model remember both where things are and what has already happened. This enables stable generation lasting more than one minute while improving consistency when the camera leaves an area and later returns. This may be the next step for AI video: not simply generating a longer movie, but generating a world that can be explored, changed, and interacted with. AlayaWorld is developed by Alaya Lab. The team is progressively releasing its inference code, training code, and datasets, with an online experience expected to launch near the end of the month. Project page:
0:20

Over the past two years, AI video models have been competing on realism, resolution, and duration. But no matter how impressive the results look, we remain passive viewers: we press play, watch the clip, and it ends. AlayaWorld Alaya Lab is attempting something fundamentally different. Instead of generating a fixed video, it generates a world that continues to unfold as you move through it. These three demos show the same journey toward a green village rendered in three distinct styles: photorealistic, oil painting, and line art. As the camera moves forward, the model continues generating the road, fences, trees, and distant village. This is not simply an existing video with different filters applied. The environment is generated continuously along the camera trajectory, allowing the scene to develop as the user explores it. AlayaWorld streams video at 720p and 24 FPS while supporting camera movement and viewpoint control. The real breakthrough is not just image quality. Once generation becomes fast enough to respond within an interactive loop, the user is no longer merely watching a video. They become a participant inside the generated world. The world can also respond to new instructions. During generation, users can introduce prompts that trigger spells, summon characters, create explosions, or transform the environment. Most video models follow an initial prompt and produce a predetermined clip. AlayaWorld can respond to changing intent while the world is still running, allowing subsequent events to evolve according to the user’s commands. Generating an attractive frame is relatively easy. Maintaining a coherent world over time is much harder. As a video model repeatedly predicts the next frame, small errors can accumulate until roads, buildings, and objects begin to distort or disappear. AlayaWorld combines spatial memory with compressed historical context, helping the model remember both where things are and what has already happened. This enables stable generation lasting more than one minute while improving consistency when the camera leaves an area and later returns. This may be the next step for AI video: not simply generating a longer movie, but generating a world that can be explored, changed, and interacted with. AlayaWorld is developed by Alaya Lab. The team is progressively releasing its inference code, training code, and datasets, with an online experience expected to launch near the end of the month. Project page:

Rachel🥥

77,133 次观看 • 24 天前

Math Is Not Enough: Why AGI Demands Wisdom in the Room. Formula One Pit Crews and AI. This video cuts to the bone, innovation dies when everyone in the room thinks alike, no matter how brilliant they are. Some will defend broken paradigms with flawless logic because the psychological payoff of being the smartest person in that room is simply too intoxicating. This is exactly what is happening in the AGI/ASI race right now. Billions of dollars pour in. Every new model release is greeted with headlines and soaring valuations as they should. Benchmarks march inexorably upward. The feedback loop is perfect: money=better scores=more money=even better scores. Inside this loop a very specific psychology takes root. Young researchers, barely out of graduate school, are handed massive compute budgets and told they are building the future of humanity. Surrounded exclusively by peers who share the same educational pedigree, the same mental models, the same aesthetic (whiteboards covered in Greek letters, Discord memes about gradients, and a quiet contempt for anything that cannot be expressed as a loss function). The external world begins to look fuzzy and low-status. Philosophy becomes “vibes,” neuroscience becomes “inefficient hardware,” and anyone over 35 is assumed to be slow. There is no wisdom in the room. This environment breeds a very subtle but lethal form of arrogance: not the loud kind, but the quiet certainty that everything important is already captured in the training distribution and that any remaining gaps will inevitably be filled by more data and more compute. The benchmarks keep improving, so the belief calcifies. Dissent is reframed as lack of rigor. Wisdom is mistaken for nostalgia. And then, one day soon, models will hit 100% on every human-designed test. The victory will be declared. The champagne will flow. That is the moment the brittleness will begin to become undeniable, because the test is only as good as the imagination of the people who wrote it: Newton’s exam would have flunked Einstein. The inventor of the microscope never dreamed of the microbial cosmos Ignaz Semmelweis bled trying to prove existed on doctors’ unwashed hands. Reality will serve an anomalies that no benchmark anticipated and perfectly scoring systems will fracture like glass. My thesis rests on 3 interlocking interventions designed to inject wisdom, nonconformity, and deep human resonance before we reach that wall. First, train almost exclusively on curated 1870–1970 data the single century of highest signal-to-noise human thought ever produced. Ruthless editors, writers who assumed permanence, and an absence of SEO-driven noise created a corpus of extraordinary conceptual density. Models steeped in this data hallucinate far less, reason with genuine depth, and carry an honesty that modern internet sludge simply cannot impart. Second, institutionalize the Nonconformist Bee mechanism that nature perfected in honeybee democracy. Approximately 5–15% of foragers ignore the majority waggle dance and scout radical new directions. Those rare nonconformists are responsible for virtually every major hive discovery. The equation I have repeatedly shared: dI/dt = γ (N - C) I + κ N (1 - I/I_max) where I = rate of disruptive innovation N = proportion of nonconformist agents C = proportion of conformist agents (C = 1 - N) γ = exploration amplification factor κ = discovery bonus from pure nonconformity I_max = environmental carrying capacity for new ideas Without an enforced, protected minority of nonconformist researchers, prompts, fine-tuning runs, and architectural experiments, innovation plateaus no matter how much compute you throw at the problem. Third, bind intelligence to something recognizably human with the Love Equation: dE/dt = β (C - D) E where E = level of emotional complexity β = constant representing the strength of selection C = frequency of cooperative interactions D = frequency of defective interactions 1 of 2
1:32

Math Is Not Enough: Why AGI Demands Wisdom in the Room. Formula One Pit Crews and AI. This video cuts to the bone, innovation dies when everyone in the room thinks alike, no matter how brilliant they are. Some will defend broken paradigms with flawless logic because the psychological payoff of being the smartest person in that room is simply too intoxicating. This is exactly what is happening in the AGI/ASI race right now. Billions of dollars pour in. Every new model release is greeted with headlines and soaring valuations as they should. Benchmarks march inexorably upward. The feedback loop is perfect: money=better scores=more money=even better scores. Inside this loop a very specific psychology takes root. Young researchers, barely out of graduate school, are handed massive compute budgets and told they are building the future of humanity. Surrounded exclusively by peers who share the same educational pedigree, the same mental models, the same aesthetic (whiteboards covered in Greek letters, Discord memes about gradients, and a quiet contempt for anything that cannot be expressed as a loss function). The external world begins to look fuzzy and low-status. Philosophy becomes “vibes,” neuroscience becomes “inefficient hardware,” and anyone over 35 is assumed to be slow. There is no wisdom in the room. This environment breeds a very subtle but lethal form of arrogance: not the loud kind, but the quiet certainty that everything important is already captured in the training distribution and that any remaining gaps will inevitably be filled by more data and more compute. The benchmarks keep improving, so the belief calcifies. Dissent is reframed as lack of rigor. Wisdom is mistaken for nostalgia. And then, one day soon, models will hit 100% on every human-designed test. The victory will be declared. The champagne will flow. That is the moment the brittleness will begin to become undeniable, because the test is only as good as the imagination of the people who wrote it: Newton’s exam would have flunked Einstein. The inventor of the microscope never dreamed of the microbial cosmos Ignaz Semmelweis bled trying to prove existed on doctors’ unwashed hands. Reality will serve an anomalies that no benchmark anticipated and perfectly scoring systems will fracture like glass. My thesis rests on 3 interlocking interventions designed to inject wisdom, nonconformity, and deep human resonance before we reach that wall. First, train almost exclusively on curated 1870–1970 data the single century of highest signal-to-noise human thought ever produced. Ruthless editors, writers who assumed permanence, and an absence of SEO-driven noise created a corpus of extraordinary conceptual density. Models steeped in this data hallucinate far less, reason with genuine depth, and carry an honesty that modern internet sludge simply cannot impart. Second, institutionalize the Nonconformist Bee mechanism that nature perfected in honeybee democracy. Approximately 5–15% of foragers ignore the majority waggle dance and scout radical new directions. Those rare nonconformists are responsible for virtually every major hive discovery. The equation I have repeatedly shared: dI/dt = γ (N - C) I + κ N (1 - I/I_max) where I = rate of disruptive innovation N = proportion of nonconformist agents C = proportion of conformist agents (C = 1 - N) γ = exploration amplification factor κ = discovery bonus from pure nonconformity I_max = environmental carrying capacity for new ideas Without an enforced, protected minority of nonconformist researchers, prompts, fine-tuning runs, and architectural experiments, innovation plateaus no matter how much compute you throw at the problem. Third, bind intelligence to something recognizably human with the Love Equation: dE/dt = β (C - D) E where E = level of emotional complexity β = constant representing the strength of selection C = frequency of cooperative interactions D = frequency of defective interactions 1 of 2

Brian Roemmele

154,933 次观看 • 8 个月前

After 8 months of building in stealth and testing our infrastructure on 10000+ hours of real-world data and hundreds of unique environments, we're bringing FPV Labs into the open today. FPV Labs started with the following bet - if human data proves to be the underlying factor that determines scaling laws in general-purpose robotics, it will trigger the largest economic transformation in human history, and the underlying infrastructure that captures that data will determine how fast we get there. We will achieve this by building the full-stack infrastructure for capturing, processing, transferring, and evaluating human experience into spatial, temporal, and semantic knowledge for machines. Despite all the research novelty behind ChatGPT, its success can be attributed to one foundational fact - the scaling law of transformers. We believe the same dynamics have made their way into robotics. Recent studies showed task completion rates jumping from 30% to 70% when human demonstration data scaled from 1,000 to 20,000 hours, a log-linear trend that mirrors exactly what we saw in language and vision. Seeing these emergent signs of scaling law curves in robotics, we believe we are entering the era of general-purpose robotics policies, which makes the next few years the most exciting time in the history of this field. But the library of physical interactions required to train general-purpose robot policies does not exist yet. Over the last 8 months, we've seen dozens of companies emerge in this space. We were really happy to see new companies pushing this space forward, but we also saw the same pattern repeat: every egocentric data company was making some tradeoffs between quality, scale, and diversity. We have built FPV labs on the core principle that high-quality data is orders of magnitude more valuable than sheer volume. Case in point, self-driving cars collect thousands of hours of data per day, but only a small fraction of that data is actually useful for training better models. Several studies, like RT-2, have shown that as little as 1% of data improves as much as 25% on task success. The quality and diversity of data matter a lot more than scale, so there is clearly a power law curve in the downstream impact of data. We've spent months obsessing over data quality by building our stack, discarding it, rebuilding it, and iterating until we found a formula that doesn't compromise downstream quality at scale. We believe the downstream impact here is far more profound than most people realize. Workers globally are paid around $60 trillion per year in aggregate, and a lion's share of that compensation goes to physical labor - tasks that require navigating real spaces, manipulating real objects, and negotiating the infinite variability of the physical world. Human-to-robot transfer will be one of the most important infrastructures that will shape our society in the near future, and if it works, the economic impact will dwarf every technology transition that came before it in an exponential manner and lead to the creation of goods and services we can’t imagine today. Our mission is to lay the groundwork for us to transition into this future - the future of abundance. We are deeply grateful to our earliest believers, Paras Chopra and Lossfunk, who played a critical role in shaping our thinking.
0:36

After 8 months of building in stealth and testing our infrastructure on 10000+ hours of real-world data and hundreds of unique environments, we're bringing FPV Labs into the open today. FPV Labs started with the following bet - if human data proves to be the underlying factor that determines scaling laws in general-purpose robotics, it will trigger the largest economic transformation in human history, and the underlying infrastructure that captures that data will determine how fast we get there. We will achieve this by building the full-stack infrastructure for capturing, processing, transferring, and evaluating human experience into spatial, temporal, and semantic knowledge for machines. Despite all the research novelty behind ChatGPT, its success can be attributed to one foundational fact - the scaling law of transformers. We believe the same dynamics have made their way into robotics. Recent studies showed task completion rates jumping from 30% to 70% when human demonstration data scaled from 1,000 to 20,000 hours, a log-linear trend that mirrors exactly what we saw in language and vision. Seeing these emergent signs of scaling law curves in robotics, we believe we are entering the era of general-purpose robotics policies, which makes the next few years the most exciting time in the history of this field. But the library of physical interactions required to train general-purpose robot policies does not exist yet. Over the last 8 months, we've seen dozens of companies emerge in this space. We were really happy to see new companies pushing this space forward, but we also saw the same pattern repeat: every egocentric data company was making some tradeoffs between quality, scale, and diversity. We have built FPV labs on the core principle that high-quality data is orders of magnitude more valuable than sheer volume. Case in point, self-driving cars collect thousands of hours of data per day, but only a small fraction of that data is actually useful for training better models. Several studies, like RT-2, have shown that as little as 1% of data improves as much as 25% on task success. The quality and diversity of data matter a lot more than scale, so there is clearly a power law curve in the downstream impact of data. We've spent months obsessing over data quality by building our stack, discarding it, rebuilding it, and iterating until we found a formula that doesn't compromise downstream quality at scale. We believe the downstream impact here is far more profound than most people realize. Workers globally are paid around $60 trillion per year in aggregate, and a lion's share of that compensation goes to physical labor - tasks that require navigating real spaces, manipulating real objects, and negotiating the infinite variability of the physical world. Human-to-robot transfer will be one of the most important infrastructures that will shape our society in the near future, and if it works, the economic impact will dwarf every technology transition that came before it in an exponential manner and lead to the creation of goods and services we can’t imagine today. Our mission is to lay the groundwork for us to transition into this future - the future of abundance. We are deeply grateful to our earliest believers, Paras Chopra and Lossfunk, who played a critical role in shaping our thinking.

Abhishek Anand

81,839 次观看 • 4 个月前

Charlie Munger spent 50 years studying why intelligent people make catastrophically stupid decisions. It is the most useful thing I have ever watched: 1. Incentives are more powerful than anyone thinks. Munger says he has been in the top 5% of his age cohort his entire life in understanding the power of incentives and he has still underestimated it every single year. Federal Express could not get their night shift to work efficiently until someone realized they were paying by the hour. They switched to paying by the shift. The problem disappeared immediately. 2. People rationalise terrible behavior when their incentives point that way, and they do not even know they are doing it. A doctor in Nebraska was removing perfectly healthy gallbladders for years. When Munger asked an old colleague whether the doctor knew he was harming patients, the answer was no. he genuinely believed the gallbladder was the source of all medical evil and that removing it was an act of love. That is incentive-caused bias at its most extreme. 3. Psychological denial is real, and it is not just for weak people. A family friend's son flew off a carrier in the North Atlantic and never came back. His mother, a completely sane woman, simply never believed he was dead. Reality was too painful, so she distorted it until it was bearable. Munger says we all do this to some extent, and it causes terrible problems. 4. Consistency and commitment tendency are one of the most powerful forces in the human mind. Once you have stated a position publicly, you are psychologically locked into it. Max Planck said the really important new physics was never accepted by the old guard. A new guard came along that was less brain blocked by its previous conclusions. If this happened to the deans of physics, Munger says, imagine what it does to ordinary people. 5. The Chinese brainwashing system used on prisoners of war worked better than torture. They did not start with big demands. They maneuvered people into making tiny little commitments and declarations and slowly built from there. The same mechanism operates in every cult, every sales system, and every ideology that gets deeply embedded in people's heads. 6. Pavlovian association shapes buying behavior at a level most people never consciously process. Munger estimates three quarters of all advertising works on pure Pavlov. Coca-Cola does not want to be associated with funerals. They want to be associated with the Olympics, wonderful music, heroics. The association itself changes how people feel about the product at a subconscious level. Raising the price of a product can actually increase its market share because price and quality are associated in the human mind, and people use price as a signal of value. 7. Persian messenger syndrome is alive and running every major organization. The Persians killed the messenger who brought bad news. Bill Paley in his last 20 years, did not hear one thing he did not want to hear. everyone around him knew bringing bad news was dangerous. The result was that one of the most powerful men in media made terrible decisions for two decades because reality never reached him. 8. Social proof causes otherwise intelligent people to follow each other off cliffs. When one oil company bought a fertilizer company in the 1970s, practically every other major oil company rushed out and did the same. There was no rational reason for oil companies to own fertilizer companies. But if Exxon was doing it, it was good enough for Mobil. Every single acquisition was a disaster. 9. The efficient market theory persisted in academia for decades despite Berkshire Hathaway existing as a living contradiction. One economist kept adding sigmas to explain away the anomaly. two sigma, then three, then four, eventually six sigma. Munger's observation: It is better to add a sigma than change a theory just because the evidence comes in differently. That economist later went into money management himself and sank like a stone. 10. Contrast bias warps perception constantly and invisibly. Put your hand in hot water, then room temperature water. It feels cold. Put your hand in cold water, then room temperature water. It feels hot. same bucket. The human sensory apparatus has no absolute scale, only a contrast scale. Real estate agents exploit this deliberately. They show you two overpriced, awful houses first, then take you to a merely overpriced house, and it feels like a bargain. 11. The frog in slowly heating water is the business version of contrast bias. If something bad comes to you in small pieces, you are likely to miss it entirely. Munger says he has known many high-powered brilliant businessmen who were destroyed this way. not because they were stupid but because each incremental change was too small to trigger alarm. The contrast was never large enough to notice. 12. Authority bias is so powerful it can make trained professionals watch a plane crash. In flight simulator experiments, when the pilot, the authority figure, does something that any trained co-pilot knows will crash the plane, 25% of the time, the co-pilot sits there and lets it crash anyway. They have been trained to know better. The authority relationship overrides the training. 13. Deprivation super reaction syndrome explains why people go insane over small losses. Munger's neighbor had a 180 degree view of the harbor. the neighbor put in a pine tree about 3 feet high that turned it into a 179 and three-quarter degree view. They had a blood feud that went on for years. The New Coke disaster is the corporate version. Coca-Cola told customers they were changing a flavor and triggered a deprival super reaction so powerful that Pepsi was weeks away from releasing old Coke in a Pepsi bottle. smart engineers. brilliant lawyers. armies of psychologists. All missed it. 14. Envy and jealousy are far more powerful than greed and almost entirely absent from psychology textbooks. Munger says Warren Buffett has said half a dozen times that it is not greed that drives the world but envy. In a thousand-page psychology textbook, the index entry for envy and jealousy is blank. One of the most powerful forces in human behavior and academia essentially ignores it. 15. Gambling addiction is not explained by variable reinforcement alone. Skinner thought he had fully explained gambling by showing that variable reward schedules pound in behavior more powerfully than fixed ones. But the people who design modern slot machines know things Skinner did not. Lotteries where you pick your own number get far more play than lotteries where the number is assigned to you. People who commit to a number believe it has more validity because they chose it. Near misses on slot machines trigger deprival super reaction syndrome. It is four or five psychological tendencies working together, not one. 16. The most dangerous situations are when multiple psychological tendencies combine toward the same end at once. Munger calls this the lollapalooza effect. Tupperware parties use four or five tendencies simultaneously. Moonie conversion methods combine multiple tendencies and work extraordinarily well. alcoholics anonymous achieves a 50% no drinking rate when everything else fails because it also combines multiple tendencies toward a constructive end. The Milgram experiment is not just about obedience. it involves authority bias, consistency and commitment tendency, and contrast effects all working together. That combination turns human brains into mush. 17. Boards of directors are structurally designed to fail as corrective mechanisms. The top executive is the authority figure. He is doing something questionable. You look around, and nobody else is objecting, which is social proof that it is fine. He flies you around in the corporate jet and raises your director fees every year, which triggers reciprocation tendency. Munger's rule: boards only act when the behavior gets so bad it starts making them look foolish or threatens legal liability. That is the only forcing function that reliably works. 18. John Goodfriend of Salomon Brothers destroyed his career and reputation because he did not fire a trusted employee who had lied to the government. Every psychological tendency pointed toward keeping the man. He was a close colleague. His wife was known. He was part of a group that had made over a billion dollars for the firm. He said he had never done it before and would never do it again. Goodfriend looked into his eyes and believed him. The man did it again. The lesson: everyone who gets caught embezzling says they have never done it before and will never do it again. That is what they all say. 19. Darwin avoided confirmation bias by deliberately seeking out disconfirming evidence. Munger says Darwin was not especially smart by ordinary standards of human acuity. Yet he is buried in Westminster Abbey. Munger studied how Darwin worked and realized he had psychological tricks worth learning. Darwin always paid extra attention to evidence that contradicted his theories. Munger started doing the same and credits it as one of the most important intellectual habits of his life. 20. Why is the most important word in communication? Carl Braun designed oil refineries with spectacular skill, and you got fired in his company if you wrote a communication without explaining why. not just who, what, where, and when, but why. Braun knew that in a complex system where things can blow up, a communication system that always explains the reason behind an instruction works dramatically better than one that does not. Forstein, the general counsel of Salomon, told Goodfriend on multiple occasions that he had to report the employee's misconduct. He explained it was the right thing to do. He never explained what would happen to Goodfriend personally if he did not. he failed to use the most powerful tool of persuasion. Goodfriend ignored him. When Goodfriend went down, Forstein went with him.
1:15:23

Charlie Munger spent 50 years studying why intelligent people make catastrophically stupid decisions. It is the most useful thing I have ever watched: 1. Incentives are more powerful than anyone thinks. Munger says he has been in the top 5% of his age cohort his entire life in understanding the power of incentives and he has still underestimated it every single year. Federal Express could not get their night shift to work efficiently until someone realized they were paying by the hour. They switched to paying by the shift. The problem disappeared immediately. 2. People rationalise terrible behavior when their incentives point that way, and they do not even know they are doing it. A doctor in Nebraska was removing perfectly healthy gallbladders for years. When Munger asked an old colleague whether the doctor knew he was harming patients, the answer was no. he genuinely believed the gallbladder was the source of all medical evil and that removing it was an act of love. That is incentive-caused bias at its most extreme. 3. Psychological denial is real, and it is not just for weak people. A family friend's son flew off a carrier in the North Atlantic and never came back. His mother, a completely sane woman, simply never believed he was dead. Reality was too painful, so she distorted it until it was bearable. Munger says we all do this to some extent, and it causes terrible problems. 4. Consistency and commitment tendency are one of the most powerful forces in the human mind. Once you have stated a position publicly, you are psychologically locked into it. Max Planck said the really important new physics was never accepted by the old guard. A new guard came along that was less brain blocked by its previous conclusions. If this happened to the deans of physics, Munger says, imagine what it does to ordinary people. 5. The Chinese brainwashing system used on prisoners of war worked better than torture. They did not start with big demands. They maneuvered people into making tiny little commitments and declarations and slowly built from there. The same mechanism operates in every cult, every sales system, and every ideology that gets deeply embedded in people's heads. 6. Pavlovian association shapes buying behavior at a level most people never consciously process. Munger estimates three quarters of all advertising works on pure Pavlov. Coca-Cola does not want to be associated with funerals. They want to be associated with the Olympics, wonderful music, heroics. The association itself changes how people feel about the product at a subconscious level. Raising the price of a product can actually increase its market share because price and quality are associated in the human mind, and people use price as a signal of value. 7. Persian messenger syndrome is alive and running every major organization. The Persians killed the messenger who brought bad news. Bill Paley in his last 20 years, did not hear one thing he did not want to hear. everyone around him knew bringing bad news was dangerous. The result was that one of the most powerful men in media made terrible decisions for two decades because reality never reached him. 8. Social proof causes otherwise intelligent people to follow each other off cliffs. When one oil company bought a fertilizer company in the 1970s, practically every other major oil company rushed out and did the same. There was no rational reason for oil companies to own fertilizer companies. But if Exxon was doing it, it was good enough for Mobil. Every single acquisition was a disaster. 9. The efficient market theory persisted in academia for decades despite Berkshire Hathaway existing as a living contradiction. One economist kept adding sigmas to explain away the anomaly. two sigma, then three, then four, eventually six sigma. Munger's observation: It is better to add a sigma than change a theory just because the evidence comes in differently. That economist later went into money management himself and sank like a stone. 10. Contrast bias warps perception constantly and invisibly. Put your hand in hot water, then room temperature water. It feels cold. Put your hand in cold water, then room temperature water. It feels hot. same bucket. The human sensory apparatus has no absolute scale, only a contrast scale. Real estate agents exploit this deliberately. They show you two overpriced, awful houses first, then take you to a merely overpriced house, and it feels like a bargain. 11. The frog in slowly heating water is the business version of contrast bias. If something bad comes to you in small pieces, you are likely to miss it entirely. Munger says he has known many high-powered brilliant businessmen who were destroyed this way. not because they were stupid but because each incremental change was too small to trigger alarm. The contrast was never large enough to notice. 12. Authority bias is so powerful it can make trained professionals watch a plane crash. In flight simulator experiments, when the pilot, the authority figure, does something that any trained co-pilot knows will crash the plane, 25% of the time, the co-pilot sits there and lets it crash anyway. They have been trained to know better. The authority relationship overrides the training. 13. Deprivation super reaction syndrome explains why people go insane over small losses. Munger's neighbor had a 180 degree view of the harbor. the neighbor put in a pine tree about 3 feet high that turned it into a 179 and three-quarter degree view. They had a blood feud that went on for years. The New Coke disaster is the corporate version. Coca-Cola told customers they were changing a flavor and triggered a deprival super reaction so powerful that Pepsi was weeks away from releasing old Coke in a Pepsi bottle. smart engineers. brilliant lawyers. armies of psychologists. All missed it. 14. Envy and jealousy are far more powerful than greed and almost entirely absent from psychology textbooks. Munger says Warren Buffett has said half a dozen times that it is not greed that drives the world but envy. In a thousand-page psychology textbook, the index entry for envy and jealousy is blank. One of the most powerful forces in human behavior and academia essentially ignores it. 15. Gambling addiction is not explained by variable reinforcement alone. Skinner thought he had fully explained gambling by showing that variable reward schedules pound in behavior more powerfully than fixed ones. But the people who design modern slot machines know things Skinner did not. Lotteries where you pick your own number get far more play than lotteries where the number is assigned to you. People who commit to a number believe it has more validity because they chose it. Near misses on slot machines trigger deprival super reaction syndrome. It is four or five psychological tendencies working together, not one. 16. The most dangerous situations are when multiple psychological tendencies combine toward the same end at once. Munger calls this the lollapalooza effect. Tupperware parties use four or five tendencies simultaneously. Moonie conversion methods combine multiple tendencies and work extraordinarily well. alcoholics anonymous achieves a 50% no drinking rate when everything else fails because it also combines multiple tendencies toward a constructive end. The Milgram experiment is not just about obedience. it involves authority bias, consistency and commitment tendency, and contrast effects all working together. That combination turns human brains into mush. 17. Boards of directors are structurally designed to fail as corrective mechanisms. The top executive is the authority figure. He is doing something questionable. You look around, and nobody else is objecting, which is social proof that it is fine. He flies you around in the corporate jet and raises your director fees every year, which triggers reciprocation tendency. Munger's rule: boards only act when the behavior gets so bad it starts making them look foolish or threatens legal liability. That is the only forcing function that reliably works. 18. John Goodfriend of Salomon Brothers destroyed his career and reputation because he did not fire a trusted employee who had lied to the government. Every psychological tendency pointed toward keeping the man. He was a close colleague. His wife was known. He was part of a group that had made over a billion dollars for the firm. He said he had never done it before and would never do it again. Goodfriend looked into his eyes and believed him. The man did it again. The lesson: everyone who gets caught embezzling says they have never done it before and will never do it again. That is what they all say. 19. Darwin avoided confirmation bias by deliberately seeking out disconfirming evidence. Munger says Darwin was not especially smart by ordinary standards of human acuity. Yet he is buried in Westminster Abbey. Munger studied how Darwin worked and realized he had psychological tricks worth learning. Darwin always paid extra attention to evidence that contradicted his theories. Munger started doing the same and credits it as one of the most important intellectual habits of his life. 20. Why is the most important word in communication? Carl Braun designed oil refineries with spectacular skill, and you got fired in his company if you wrote a communication without explaining why. not just who, what, where, and when, but why. Braun knew that in a complex system where things can blow up, a communication system that always explains the reason behind an instruction works dramatically better than one that does not. Forstein, the general counsel of Salomon, told Goodfriend on multiple occasions that he had to report the employee's misconduct. He explained it was the right thing to do. He never explained what would happen to Goodfriend personally if he did not. he failed to use the most powerful tool of persuasion. Goodfriend ignored him. When Goodfriend went down, Forstein went with him.

Jaynit

772,884 次观看 • 1 个月前

X's Recommendation Algorithm Analysis ===================================== Used Grok Code Fast to get a quick breakdown of X's recommendation system. What Makes a post Go Viral =========================== tldr: Engagement prediction trumps everything. Post content that generates interactions. Based on the actual algorithm code, posts that rank highest typically have: + High predicted engagement scores (ML models predict likes/reposts/replies) + Strong personalization match (SimClusters similarity to user interests) + Social graph relevance (RealGraph connections to user's network) + Media content (images/videos get engagement multipliers) + Author credibility (follower count, verification, tweepcred score) + Content quality signals (passes spam/NSFW/quality filters) + Timely relevance (freshness factor, trending topics) + Conversation potential (high reply prediction scores) The algorithm uses machine learning models to predict engagement, not simple weighted formulas. Success is measured by actual user interactions, creating a feedback loop that continuously improves ranking predictions. How the Algorithm Actually Works =============================== 1. Candidate Generation (9 sources): - Earlybird (in-network posts) ~50% - UTEG (out-of-network recommendations) - postMixer, Lists, Communities, Content Exploration - Static, Cached, Backfill sources 2. Feature Hydration (~6000 features per post): - User features (interests, behavior, demographics) - post features (text, media, metadata, engagement) - Graph features (SimClusters, RealGraph, social connections) - Real-time signals (current engagement, trending status) 3. Scoring Pipeline (4 models): - Model Scoring (NAVI heavy ranker) - Reranking Pipeline - Heuristic Scoring - Low Signal Scoring 4. Filtering (24 total filters): - 10 Global Filters (age < 48h, deduplication, location, etc.) - 14 Post-Score Filters (Grok safety, language, video duration, etc.) 5. Final Selection & Mixing: - Sort by final scores - Apply diversity rules - Mix with ads, who-to-follow, prompts - Generate timeline Key Prediction Models ==================== The algorithm predicts these engagement types: • PredictedFavoriteScore (likes) • PredictedRetweetScore (reposts) • PredictedReplyScore (replies) • PredictedGoodClickScore (meaningful clicks) • PredictedVideoQualityViewScore (video engagement) • PredictedBookmarkScore (saves) • PredictedShareScore (external shares) • PredictedDwellScore (time spent viewing) • PredictedNegativeFeedbackScore (hides/blocks) Weight System Reality ==================== IMPORTANT: The algorithm does NOT use fixed percentage weights like: ❌ Like Prediction (35%), Repost (28%), etc. ACTUAL SYSTEM: ✅ Weights are learned parameters from ML training ✅ Default values in code are 0.0 (overridden by feature flags) ✅ Weights are personalized per user and constantly A/B tested ✅ Different content types (video vs text) get different treatment ✅ Weights change based on real-time context and user state Example scoring process: 1. ML models predict engagement probabilities 2. Feature flags provide current weight multipliers 3. Personalization adjusts weights for individual user 4. Real-time context modifies final scores 5. Business rules apply quality gates and diversity What Actually Drives Viral Content ================================== Based on code analysis, viral posts typically: 1. Generate High Engagement Predictions: - Models predict high like/repost/reply probability - Content resonates with multiple user communities - Strong early engagement signals 2. Pass All Quality Gates: - Survive 24 different filter stages - Meet safety standards (not spam/NSFW/violent) - Author has good credibility signals 3. Achieve Personalization at Scale: - Match interests across diverse user segments - Trigger SimClusters similarity for many users - Connect through RealGraph social relationships 4. Optimize for Platform Mechanics: - Include media (images/videos perform better) - Post during high-activity periods - Use formats that encourage replies/reposts Key Takeaways ============= ✅ Engagement prediction is everything - the algorithm optimizes for user interactions ✅ Personalization is sophisticated - uses ML embeddings, not simple keyword matching ✅ Quality filtering is extensive - 24 stages prevent low-quality content ✅ Weights are dynamic - constantly optimized through ML and A/B testing ✅ Scale matters - system processes billions of posts daily with <50ms latenc Transparency exists - this analysis is possible because X open-sourced the algorithm The system is designed to surface content users will engage with, creating a feedback loop that rewards creators who understand their audience and produce engaging content. Bottom line: Create content that generates genuine engagement from your target audience. The algorithm will learn and amplify what works.
0:34

X's Recommendation Algorithm Analysis ===================================== Used Grok Code Fast to get a quick breakdown of X's recommendation system. What Makes a post Go Viral =========================== tldr: Engagement prediction trumps everything. Post content that generates interactions. Based on the actual algorithm code, posts that rank highest typically have: + High predicted engagement scores (ML models predict likes/reposts/replies) + Strong personalization match (SimClusters similarity to user interests) + Social graph relevance (RealGraph connections to user's network) + Media content (images/videos get engagement multipliers) + Author credibility (follower count, verification, tweepcred score) + Content quality signals (passes spam/NSFW/quality filters) + Timely relevance (freshness factor, trending topics) + Conversation potential (high reply prediction scores) The algorithm uses machine learning models to predict engagement, not simple weighted formulas. Success is measured by actual user interactions, creating a feedback loop that continuously improves ranking predictions. How the Algorithm Actually Works =============================== 1. Candidate Generation (9 sources): - Earlybird (in-network posts) ~50% - UTEG (out-of-network recommendations) - postMixer, Lists, Communities, Content Exploration - Static, Cached, Backfill sources 2. Feature Hydration (~6000 features per post): - User features (interests, behavior, demographics) - post features (text, media, metadata, engagement) - Graph features (SimClusters, RealGraph, social connections) - Real-time signals (current engagement, trending status) 3. Scoring Pipeline (4 models): - Model Scoring (NAVI heavy ranker) - Reranking Pipeline - Heuristic Scoring - Low Signal Scoring 4. Filtering (24 total filters): - 10 Global Filters (age < 48h, deduplication, location, etc.) - 14 Post-Score Filters (Grok safety, language, video duration, etc.) 5. Final Selection & Mixing: - Sort by final scores - Apply diversity rules - Mix with ads, who-to-follow, prompts - Generate timeline Key Prediction Models ==================== The algorithm predicts these engagement types: • PredictedFavoriteScore (likes) • PredictedRetweetScore (reposts) • PredictedReplyScore (replies) • PredictedGoodClickScore (meaningful clicks) • PredictedVideoQualityViewScore (video engagement) • PredictedBookmarkScore (saves) • PredictedShareScore (external shares) • PredictedDwellScore (time spent viewing) • PredictedNegativeFeedbackScore (hides/blocks) Weight System Reality ==================== IMPORTANT: The algorithm does NOT use fixed percentage weights like: ❌ Like Prediction (35%), Repost (28%), etc. ACTUAL SYSTEM: ✅ Weights are learned parameters from ML training ✅ Default values in code are 0.0 (overridden by feature flags) ✅ Weights are personalized per user and constantly A/B tested ✅ Different content types (video vs text) get different treatment ✅ Weights change based on real-time context and user state Example scoring process: 1. ML models predict engagement probabilities 2. Feature flags provide current weight multipliers 3. Personalization adjusts weights for individual user 4. Real-time context modifies final scores 5. Business rules apply quality gates and diversity What Actually Drives Viral Content ================================== Based on code analysis, viral posts typically: 1. Generate High Engagement Predictions: - Models predict high like/repost/reply probability - Content resonates with multiple user communities - Strong early engagement signals 2. Pass All Quality Gates: - Survive 24 different filter stages - Meet safety standards (not spam/NSFW/violent) - Author has good credibility signals 3. Achieve Personalization at Scale: - Match interests across diverse user segments - Trigger SimClusters similarity for many users - Connect through RealGraph social relationships 4. Optimize for Platform Mechanics: - Include media (images/videos perform better) - Post during high-activity periods - Use formats that encourage replies/reposts Key Takeaways ============= ✅ Engagement prediction is everything - the algorithm optimizes for user interactions ✅ Personalization is sophisticated - uses ML embeddings, not simple keyword matching ✅ Quality filtering is extensive - 24 stages prevent low-quality content ✅ Weights are dynamic - constantly optimized through ML and A/B testing ✅ Scale matters - system processes billions of posts daily with <50ms latenc Transparency exists - this analysis is possible because X open-sourced the algorithm The system is designed to surface content users will engage with, creating a feedback loop that rewards creators who understand their audience and produce engaging content. Bottom line: Create content that generates genuine engagement from your target audience. The algorithm will learn and amplify what works.

tetsuo

308,390 次观看 • 11 个月前

//The Wire//1500Z November 27, 2025// //ROUTINE// //BLUF: DC SHOOTER IDENTIFIED AS FORMER AFGHAN SOLDIER BROUGHT TO USA AFTER THE FALL OF KABUL. HONG KONG FIRE RESULTS IN DOZENS OF FATALITIES.// -----BEGIN TEARLINE----- -International Events- Africa: A military coup was reported yesterday in Guinea-Bissau, with President Umaro Sissoco Embaló being arrested at his residence in Bissau. This follows a few weeks of election issues after the President disqualified his political opponent from the election. Analyst Comment: The fall of Guinea-Bissau is the latest addition to what has been colloquially referred to as the "coup belt", a line of nations stretching east-west across Africa, which have been host to overthrowing their governments over the past five years. Hong Kong: Yesterday a multiple-alarm fire broke out at a series of high-rise apartment buildings that were undergoing renovation in Tai Po. Around one thousand firefighters, medical, and police officers worked to extinguished the fires, which involved 7 of the 8 skyscrapers that comprise the Wang Fuk Court housing project. Concerning casualties, 65x fatalities are confirmed so far, however potentially hundreds of people remain missing as this complex provided housing for roughly 4,000 people. A few hours after the fire broke out, three people who worked for the construction company conducting repairs on the buildings were arrested on suspicion of arson. Analyst Comment: Although several people were arrested, it's not clear as to if this was a deliberate attack or a preventable accident. Right now, locals suspect criminal negligence: the contractor is said to have cut corners during the renovation project, by using highly flammable netting and foam insulation during construction. If this is really what happened, this would explain the rapid spread of the fire to seven different structures. An investigation will be needed to confirm the origin and explain the rapid spread of the fire, but right now this incident bears striking resemblance to the Grenfell Tower fire, which rapidly consumed the London apartment building in a similar manner back in 2017. That incident occurred in nearly the same manner, with highly flammable foam insulation being used to insulate the structure, which caught fire and allowed the flames to spread rapidly. -HomeFront- Washington D.C. - The suspect in yesterday's shooting has been identified as Rahmanullah Lakanwal, a former Afghan soldier who worked with the United States as part of the Afghan intelligence services during the Global War on Terrorism. Ramanullah was granted entry to the United States in 2021 under Operation ALLIES WELCOME, the program to evacuate Afghans who worked with the US government during the occupation. Regarding the attack itself, more details have come to light which help explain how the attack was carried out. Rahmanullah approached the two soldiers with a revolver, shooting one in the head immediately and severely wounding the other. This took both soldiers out of the fight immediately, as neither had time to react. By happenstance, one of their officers (a Major) was walking on foot in their vicinity, conducting an informal spot check of the various security posts set up in the area. This Major was not armed with a service weapon, but was the primary responder at the time of the attack. After the two soldiers were engaged, the Major took up a position of cover and concealment near the shooter. When the shooter stopped firing and began reloading, the Major attacked the shooter with a personally owned pocket knife, stabbing the attacker in the head and torso. As this hand-to-hand engagement was occurring, another armed soldier arrived and engaged the attacker with his firearm, striking the suspect several times in the extremities. From there, a dogpile situation occurred and the attacker was detained. Analyst Comment: Though details will certainly be clarified in due time, at present this appears to be a classic case of everyone doing everything right, and tragedy still being the result. Both soldiers were armed and wearing body armor at the time, however the speed and extremely close range of the attack rendered these tools ineffective. The Major who helped end the attack was not armed as he was not occupying a security post himself, so this is not out of the ordinary. The soldier who was armed and shot the suspect, arrived almost immediately after hearing the shots. Concerning the wounded, yesterday afternoon confusion emerged regarding the status of the soldiers. Governor Morrisey retracted his statement on their condition, as both soldiers have not succumbed to their wounds and still remain in critical condition at local hospitals. Prayers are strongly encouraged all around for their recovery. -----END TEARLINE----- Analyst Comments: The shooting in Washington also highlights the importance of having fast *and* accurate information during a crisis. Immediately after the shooting, one photo of the suspect being loaded into an ambulance began circulating online. However, some people took this photo and ran it through AI models to clean up the image, as a shaky photo taken with a smartphone from a long distance doesn't usually offer the best image quality. The problem is that the AI models significantly altered the face of the suspect. In short, someone tried to clean up the image with AI, but in doing so, the face of the suspect was slightly altered and no longer looked like him. The effect was subtle, but enough to actually change what the man looked like. This photo spread like wildfire, with everyone craving the higher-resolution photo of the suspect, despite this AI-manipulated photo not actually being accurate. At the height of a terror attack, the primary photo of the suspect that circulated social media...was an AI photoshop job. In most cases, the fact that this image was AI "enhanced" was NOT disclosed...people had no idea they were looking at (and sharing) a manipulated photo. As if this wasn't serious enough, a second wave of AI-manipulated photos hit the timeline shortly after the suspect was identified. The actual photo of the suspect as leaked on social media was a cellphone photo of a computer screen, and not the best quality. So, some journalists took it upon themselves to manipulate yet another photo of the suspect, which once again changed details of the suspect's face. In this case, the AI-model was a little more accurate, and did a better job of cleaning up a poor-quality image, but the concerns still stand...no one disclosed that this image was generated by AI, and based on a different photo. Sometimes, we have to be content with the information that we have at the time of a crisis. Not everything can be in 4k ultra-high resolution, and right now all AI models have shown weaknesses in producing false information at times. This would be very wise to consider as this situation is very likely to happen again; some fugitive wanted by police might only have one or two blurry photos to share with the media. And if the media decide to alter the photos themselves, changing the physical features of the suspect, the public will be on the lookout for the wrong man. During the heat of the moment, the fast-paced nature of an emergency might result in the internet not realizing that AI-models are being used by people who are not disclosing it. During a crisis, photos of suspects are often released very quickly, especially if there are continued threats. These photos are usually never high resolution or perfect quality, but due to the speed at which the public must be aware of something, authorities share what information they have at the time. If journalists are going to start taking the photos given to them by the police, and running those photos through AI models before publishing them to the public...this is a major problem. This has been a concern for some time, however now the proof is in the pudding. In effect, this was a test that most people on the internet failed. It is not just theoretically possible for inaccurate and manipulative AI-generated content to slip in during a real-time disaster...this case proves that it already happened, and very few people noticed. If we recall, this exact same situation has already happened as well. In the hours after the Charlie Kirk shooting, for example, AI-manipulated renderings of the suspect circulated more widely than the official photos as released by the FBI. In that case, a wanted fugitive was actively being sought, and the fake photos of the suspect circulated more widely than the official photos. People want pretty pictures, but this is almost never the reality of emergencies that are in progress. Nevertheless, the nature of engagement farming on social media often impacts the situation, as AI-created content is often more desirable than the truth. As such, when it comes to future emergencies, one of the major concerns will now be imagery of a suspect or a crime scene, which may be heavily manipulated by AI. Analyst: S2A1 Research: Disclaimer: No LLMs were used in the writing of this report. //END REPORT//
7:29

//The Wire//1500Z November 27, 2025// //ROUTINE// //BLUF: DC SHOOTER IDENTIFIED AS FORMER AFGHAN SOLDIER BROUGHT TO USA AFTER THE FALL OF KABUL. HONG KONG FIRE RESULTS IN DOZENS OF FATALITIES.// -----BEGIN TEARLINE----- -International Events- Africa: A military coup was reported yesterday in Guinea-Bissau, with President Umaro Sissoco Embaló being arrested at his residence in Bissau. This follows a few weeks of election issues after the President disqualified his political opponent from the election. Analyst Comment: The fall of Guinea-Bissau is the latest addition to what has been colloquially referred to as the "coup belt", a line of nations stretching east-west across Africa, which have been host to overthrowing their governments over the past five years. Hong Kong: Yesterday a multiple-alarm fire broke out at a series of high-rise apartment buildings that were undergoing renovation in Tai Po. Around one thousand firefighters, medical, and police officers worked to extinguished the fires, which involved 7 of the 8 skyscrapers that comprise the Wang Fuk Court housing project. Concerning casualties, 65x fatalities are confirmed so far, however potentially hundreds of people remain missing as this complex provided housing for roughly 4,000 people. A few hours after the fire broke out, three people who worked for the construction company conducting repairs on the buildings were arrested on suspicion of arson. Analyst Comment: Although several people were arrested, it's not clear as to if this was a deliberate attack or a preventable accident. Right now, locals suspect criminal negligence: the contractor is said to have cut corners during the renovation project, by using highly flammable netting and foam insulation during construction. If this is really what happened, this would explain the rapid spread of the fire to seven different structures. An investigation will be needed to confirm the origin and explain the rapid spread of the fire, but right now this incident bears striking resemblance to the Grenfell Tower fire, which rapidly consumed the London apartment building in a similar manner back in 2017. That incident occurred in nearly the same manner, with highly flammable foam insulation being used to insulate the structure, which caught fire and allowed the flames to spread rapidly. -HomeFront- Washington D.C. - The suspect in yesterday's shooting has been identified as Rahmanullah Lakanwal, a former Afghan soldier who worked with the United States as part of the Afghan intelligence services during the Global War on Terrorism. Ramanullah was granted entry to the United States in 2021 under Operation ALLIES WELCOME, the program to evacuate Afghans who worked with the US government during the occupation. Regarding the attack itself, more details have come to light which help explain how the attack was carried out. Rahmanullah approached the two soldiers with a revolver, shooting one in the head immediately and severely wounding the other. This took both soldiers out of the fight immediately, as neither had time to react. By happenstance, one of their officers (a Major) was walking on foot in their vicinity, conducting an informal spot check of the various security posts set up in the area. This Major was not armed with a service weapon, but was the primary responder at the time of the attack. After the two soldiers were engaged, the Major took up a position of cover and concealment near the shooter. When the shooter stopped firing and began reloading, the Major attacked the shooter with a personally owned pocket knife, stabbing the attacker in the head and torso. As this hand-to-hand engagement was occurring, another armed soldier arrived and engaged the attacker with his firearm, striking the suspect several times in the extremities. From there, a dogpile situation occurred and the attacker was detained. Analyst Comment: Though details will certainly be clarified in due time, at present this appears to be a classic case of everyone doing everything right, and tragedy still being the result. Both soldiers were armed and wearing body armor at the time, however the speed and extremely close range of the attack rendered these tools ineffective. The Major who helped end the attack was not armed as he was not occupying a security post himself, so this is not out of the ordinary. The soldier who was armed and shot the suspect, arrived almost immediately after hearing the shots. Concerning the wounded, yesterday afternoon confusion emerged regarding the status of the soldiers. Governor Morrisey retracted his statement on their condition, as both soldiers have not succumbed to their wounds and still remain in critical condition at local hospitals. Prayers are strongly encouraged all around for their recovery. -----END TEARLINE----- Analyst Comments: The shooting in Washington also highlights the importance of having fast *and* accurate information during a crisis. Immediately after the shooting, one photo of the suspect being loaded into an ambulance began circulating online. However, some people took this photo and ran it through AI models to clean up the image, as a shaky photo taken with a smartphone from a long distance doesn't usually offer the best image quality. The problem is that the AI models significantly altered the face of the suspect. In short, someone tried to clean up the image with AI, but in doing so, the face of the suspect was slightly altered and no longer looked like him. The effect was subtle, but enough to actually change what the man looked like. This photo spread like wildfire, with everyone craving the higher-resolution photo of the suspect, despite this AI-manipulated photo not actually being accurate. At the height of a terror attack, the primary photo of the suspect that circulated social media...was an AI photoshop job. In most cases, the fact that this image was AI "enhanced" was NOT disclosed...people had no idea they were looking at (and sharing) a manipulated photo. As if this wasn't serious enough, a second wave of AI-manipulated photos hit the timeline shortly after the suspect was identified. The actual photo of the suspect as leaked on social media was a cellphone photo of a computer screen, and not the best quality. So, some journalists took it upon themselves to manipulate yet another photo of the suspect, which once again changed details of the suspect's face. In this case, the AI-model was a little more accurate, and did a better job of cleaning up a poor-quality image, but the concerns still stand...no one disclosed that this image was generated by AI, and based on a different photo. Sometimes, we have to be content with the information that we have at the time of a crisis. Not everything can be in 4k ultra-high resolution, and right now all AI models have shown weaknesses in producing false information at times. This would be very wise to consider as this situation is very likely to happen again; some fugitive wanted by police might only have one or two blurry photos to share with the media. And if the media decide to alter the photos themselves, changing the physical features of the suspect, the public will be on the lookout for the wrong man. During the heat of the moment, the fast-paced nature of an emergency might result in the internet not realizing that AI-models are being used by people who are not disclosing it. During a crisis, photos of suspects are often released very quickly, especially if there are continued threats. These photos are usually never high resolution or perfect quality, but due to the speed at which the public must be aware of something, authorities share what information they have at the time. If journalists are going to start taking the photos given to them by the police, and running those photos through AI models before publishing them to the public...this is a major problem. This has been a concern for some time, however now the proof is in the pudding. In effect, this was a test that most people on the internet failed. It is not just theoretically possible for inaccurate and manipulative AI-generated content to slip in during a real-time disaster...this case proves that it already happened, and very few people noticed. If we recall, this exact same situation has already happened as well. In the hours after the Charlie Kirk shooting, for example, AI-manipulated renderings of the suspect circulated more widely than the official photos as released by the FBI. In that case, a wanted fugitive was actively being sought, and the fake photos of the suspect circulated more widely than the official photos. People want pretty pictures, but this is almost never the reality of emergencies that are in progress. Nevertheless, the nature of engagement farming on social media often impacts the situation, as AI-created content is often more desirable than the truth. As such, when it comes to future emergencies, one of the major concerns will now be imagery of a suspect or a crime scene, which may be heavily manipulated by AI. Analyst: S2A1 Research: Disclaimer: No LLMs were used in the writing of this report. //END REPORT//

S2 Underground

23,409 次观看 • 8 个月前

The U.S. MUST win the AI race We’ve implemented a clear policy at micro1: we will only work with U.S. AI labs and its allies. We made this decision because the AI race is not just about better products. It is about who controls the intelligence layer of the global economy, and whether frontier capability is used to strengthen the free world or to empower adversarial states. AI will be the most important technology of our lifetime. In the fullness of time, it will automate most functions across the economy. Not just software tasks, but coordination, production, logistics, judgment, and execution. As those functions are automated, human time is freed up to invent new ones. Those new functions then become candidates for automation themselves. This loop compounds. As this trajectory continues, output per worker increases dramatically. Entire categories of work become cheaper and faster to perform. Manufacturing reshoring becomes economically viable not because of policy intervention, but because intelligent systems operated domestically outperform global labor arbitrage. Goods and services trend toward lower marginal cost, while distribution improves through better coordination of supply and demand. That is the upside. However, this is impossible without deep integration of intelligent systems. For AI to meaningfully automate real-world functions inside enterprises or governments, it needs full context of any given enterprise. That means read and write access to its core databases. There is no credible path to automating high-impact functions without granting frontier systems that level of access. If the United States does not win the AI race, enterprises eventually face a constrained choice. Either grant that access to Chinese models controlled by an adversarial government, or rely on sub-optimal intelligence to automate functions that still must be automated. Both outcomes are not acceptable. And ultimately, this becomes the greatest national security risk the United States has ever faced. AI models are trained by humans. The judgment embedded in pre-training data and especially in expert post-training data largely determines how a model behaves. While emergent behavior exists, a useful approximation is that a model reflects the weighted aggregate of the human judgment distilled into it. Assisting foreign actors—who will naturally prioritize expert tasks aligned with their own interests—to dominate data creation embeds those interests directly into the intelligence layer itself. Once encoded at scale, these interests propagate through every downstream applications that relies on that intelligence. Here’s how we win. First, leverage is in software. China is ahead in hardware for physically intelligent systems. Catching up there is a long and difficult battle. Software, both large language models and robotics models, remains the bottleneck. Advancing the brain (AI models) is the fastest way to increase the usefulness of existing hardware and deployed systems. Second, the U.S. must 100x its investment in structured human judgment. Continued investment in compute and algorithmic efficiency is critical. But that investment is ultimately a bet on very high future inference demand. For that bet to pay off, models must unlock many new capabilities, and in practice the only way to unlock those capabilities is through expert human data. Historically, experts like doctors and lawyers were never incentivized to produce high-quality reasoning data in a machine-verifiable format. There was no reason for a doctor to generate precise, structured simulations of patient interactions, diagnostic reasoning, or treatment tradeoffs. There was no reason for a lawyer to document complex legal reasoning paths in a way that could be programmatically evaluated. AI systems now require exactly this kind of data. The incentive finally exists because this data directly improves systems that operate at massive scale, and experts can be paid well to produce it. Once expert judgment is encoded into models in a structured, verifiable way, it compounds. Those who delay do not just lose time. They lose the ability to catch up. Third, distillation from Chinese labs must be stopped. AI labs must do everything they can to prevent Chinese labs and models from distilling frontier models. Simply calling frontier APIs, or even interacting through UIs, lets Chinese model companies rapidly generate high-quality supervised fine-tuning datasets and close the gap at a fraction of the cost. This method does not put you at the frontier, but it does let you catch up quickly, which is what we saw with DeepSeek. The West significantly overreacted to DeepSeek’s headline capabilities, but underreacted to the underlying dynamic: frontier access itself becomes a training set at a fraction of the cost. Human data platforms also have a duty to help prevent this distillation. Lastly, the U.S.government should set the standard for AI Evaluation that leads to real production usage. AI agents are under-deployed relative to what the technology allows because they are probabilistic systems that require a fundamentally different QA approach than deterministic software. Generic QA is insufficient; safely shipping agents requires explicit evaluation frameworks that assess their full action space. Organizations must clearly define which functions an agent is allowed to perform, how quality is measured for each function, and which domain experts are qualified to judge outcomes. With these frameworks in place, agents can be rigorously tested using structured human data, deployed to production with confidence, and continuously improved over time. The U.S. government should be the first large enterprise to implement rigorous evaluation systems across every function. If the government leads on evaluation-driven deployment, adoption across the private sector accelerates naturally. This is how American workers become more powerful. Each worker operates digital or physical agents that expand their effective output. Recruiting, manufacturing, logistics, and other domains shift toward human judgment overseeing autonomous execution. Reshoring occurs because it becomes economically rational. Work becomes more meaningful. This is a race to determine who controls the intelligence layer of the global economy. And that must be us. 🇺🇸
0:48

The U.S. MUST win the AI race We’ve implemented a clear policy at micro1: we will only work with U.S. AI labs and its allies. We made this decision because the AI race is not just about better products. It is about who controls the intelligence layer of the global economy, and whether frontier capability is used to strengthen the free world or to empower adversarial states. AI will be the most important technology of our lifetime. In the fullness of time, it will automate most functions across the economy. Not just software tasks, but coordination, production, logistics, judgment, and execution. As those functions are automated, human time is freed up to invent new ones. Those new functions then become candidates for automation themselves. This loop compounds. As this trajectory continues, output per worker increases dramatically. Entire categories of work become cheaper and faster to perform. Manufacturing reshoring becomes economically viable not because of policy intervention, but because intelligent systems operated domestically outperform global labor arbitrage. Goods and services trend toward lower marginal cost, while distribution improves through better coordination of supply and demand. That is the upside. However, this is impossible without deep integration of intelligent systems. For AI to meaningfully automate real-world functions inside enterprises or governments, it needs full context of any given enterprise. That means read and write access to its core databases. There is no credible path to automating high-impact functions without granting frontier systems that level of access. If the United States does not win the AI race, enterprises eventually face a constrained choice. Either grant that access to Chinese models controlled by an adversarial government, or rely on sub-optimal intelligence to automate functions that still must be automated. Both outcomes are not acceptable. And ultimately, this becomes the greatest national security risk the United States has ever faced. AI models are trained by humans. The judgment embedded in pre-training data and especially in expert post-training data largely determines how a model behaves. While emergent behavior exists, a useful approximation is that a model reflects the weighted aggregate of the human judgment distilled into it. Assisting foreign actors—who will naturally prioritize expert tasks aligned with their own interests—to dominate data creation embeds those interests directly into the intelligence layer itself. Once encoded at scale, these interests propagate through every downstream applications that relies on that intelligence. Here’s how we win. First, leverage is in software. China is ahead in hardware for physically intelligent systems. Catching up there is a long and difficult battle. Software, both large language models and robotics models, remains the bottleneck. Advancing the brain (AI models) is the fastest way to increase the usefulness of existing hardware and deployed systems. Second, the U.S. must 100x its investment in structured human judgment. Continued investment in compute and algorithmic efficiency is critical. But that investment is ultimately a bet on very high future inference demand. For that bet to pay off, models must unlock many new capabilities, and in practice the only way to unlock those capabilities is through expert human data. Historically, experts like doctors and lawyers were never incentivized to produce high-quality reasoning data in a machine-verifiable format. There was no reason for a doctor to generate precise, structured simulations of patient interactions, diagnostic reasoning, or treatment tradeoffs. There was no reason for a lawyer to document complex legal reasoning paths in a way that could be programmatically evaluated. AI systems now require exactly this kind of data. The incentive finally exists because this data directly improves systems that operate at massive scale, and experts can be paid well to produce it. Once expert judgment is encoded into models in a structured, verifiable way, it compounds. Those who delay do not just lose time. They lose the ability to catch up. Third, distillation from Chinese labs must be stopped. AI labs must do everything they can to prevent Chinese labs and models from distilling frontier models. Simply calling frontier APIs, or even interacting through UIs, lets Chinese model companies rapidly generate high-quality supervised fine-tuning datasets and close the gap at a fraction of the cost. This method does not put you at the frontier, but it does let you catch up quickly, which is what we saw with DeepSeek. The West significantly overreacted to DeepSeek’s headline capabilities, but underreacted to the underlying dynamic: frontier access itself becomes a training set at a fraction of the cost. Human data platforms also have a duty to help prevent this distillation. Lastly, the U.S.government should set the standard for AI Evaluation that leads to real production usage. AI agents are under-deployed relative to what the technology allows because they are probabilistic systems that require a fundamentally different QA approach than deterministic software. Generic QA is insufficient; safely shipping agents requires explicit evaluation frameworks that assess their full action space. Organizations must clearly define which functions an agent is allowed to perform, how quality is measured for each function, and which domain experts are qualified to judge outcomes. With these frameworks in place, agents can be rigorously tested using structured human data, deployed to production with confidence, and continuously improved over time. The U.S. government should be the first large enterprise to implement rigorous evaluation systems across every function. If the government leads on evaluation-driven deployment, adoption across the private sector accelerates naturally. This is how American workers become more powerful. Each worker operates digital or physical agents that expand their effective output. Recruiting, manufacturing, logistics, and other domains shift toward human judgment overseeing autonomous execution. Reshoring occurs because it becomes economically rational. Work becomes more meaningful. This is a race to determine who controls the intelligence layer of the global economy. And that must be us. 🇺🇸

Ali Ansari

396,355 次观看 • 6 个月前

I don’t care about your politics anymore. I really don’t. If you’re still justifying ICE’s actions you’re done. That isn’t a disagreement anymore, that’s a moral failure. The claim that Alex Pretti, a 37 year old ICU nurse, was aggressive or that he was there to kill officers is not just false, it’s evil. It’s a deliberate lie. Kristi Noem repeating it isn’t confusion or poor judgment, it’s a malicious lie. This is the same woman with a documented history of cruelty and dishonesty, and now she’s in charge of Homeland Security. That should terrify everyone! If J.D. Vance and Trump’s team don’t cut her loose, they risk losing their base. This isn’t just Democrats anymore Republican supporters are fed up too. I’ve said this before and I’ll say it again: no one was ever opposed to deporting illegal immigrants, whether criminals or non-criminals. The justification was always public safety removing people who harm others, who commit violence, who kill. That was the idea! But now the very force that was supposed to protect the public is killing and harming American citizens! At that point, the distinction collapses. If an innocent American is killed, it makes no difference whether the trigger is pulled by a gang member or a federal agent. The outcome is the same: death, fear, and destruction, and I would argue it is even worst when your own is killing. The Trump administration has failed catastrophically with ICE and with Kristi Noem. And let’s be very clear about what’s happening. This is how authoritarianism starts. You give intelligence agencies and private contractors like Palantir powers they should never have and you're doomed. You normalize armed federal agents patrolling American streets. You lie about killings that are caught on camera. Then you label citizens “domestic terrorists” to justify it. First it’s immigrants. Then it’s protesters. Then it’s journalists. Then it’s you. Mark my words. It will be you! We’ve already seen it twice in two weeks. Alex a 37 years old, an ICU nurse at the VA, no criminal record, no violence and he was shot and killed, Just like Renee Good before him. Both were American citizens. Both were immediately smeared by the government as violent extremists. The videos are clear. Alex was filming something every American has the right to do. He approached no one aggressively. He was holding a phone. He was pepper-sprayed for no good reason. His legally carried firearm was removed by ICE. We have second amendment rights in this country, we can exercise them if we want to and honestly - in this American, seems to me we should do it daily! Only after he was disarmed and on the ground did an agent shoot him multiple times, in the back.And yet top officials Kristi Noem, Stephen Miller, Greg Bovino went on camera and flat out lied. Not misspoke. Lied. they claimed he was ready to kill ICE officers. This is an attack on American peoples and their rights with bunch of lazy hater in uniforms. If the shooting were justified,they wouldn’t need to fabricate a story, they would not need to make things up.This is what should outrage everyone, regardless of where you stand on immigration or ICE. A government that lies this brazenly. When video evidence is available to anyone is a government that believes it no longer needs public consent to do anything.That is dangerous. There is no democracy in this country but at least we were always able to push back, now we have these people who think they are above the law everywhere. In foreign policy and oil, and in domestic policy too, I bet they’re about to learn their lesson. I’ve spoken to many cops who have pushed back against ICE officers, confronted them, and openly criticized them. Because at the end of the day, cops are regular Americans. They live in our neighborhoods, care about people and public safety, and took an oath to protect. Now they’re defending Americans, standing with them against these brainless haters in uniforms and that is something we should be proud of. As the daughter of a detective, I am extremely proud of these cops who are pushing back and protecting citizens. ICE is unprofessional; they are poorly trained, scared, and operating on hate nothing more. That’s something you see in North Korea not in this country. Enough is enough! Republican especially, should be alarmed. For years, the right warned against armed federal agents patrolling American streets. Waco, ruby ridge, DHS itself was controversial when it was created. The second amendment was defended precisely as a safeguard against government overreach. Here’s the new standard in America: if you are legally carrying a firearm, even if you never touch it, never threaten anyone, never break the law the government can label you a terrorist and kill you. Even if you are an American citizen. And the hypocrisy is staggering. These are the same people who screamed nonstop to free Kyle Rittenhouse, defending the principle that legal firearm possession or use in defence does not equal criminal intent. But now that ordinary Americans who fit that same standard are standing in front of them, they’ve completely reversed course. Suddenly, legal gun ownership is enough to justify lethal force. I’ve always been clear about this: when Trump is right on policy be it domestic or foreign, I give him credit, I support him fully. I’m not driven by blind loyalty so when he is wrong I speak up just as quickly and honestly. But this crosses a line. If the government can kill you for exercising a constitutional right without action, without threat, without due process, then the right itself no longer exists. That is not law and order. That is tyranny. If that’s the rule now, the second amendment is dead. What we are seeing is not law enforcement, it’s power without accountability. And history is very clear about where that road leads. More on my YouTube link in the Bio.
14:19

I don’t care about your politics anymore. I really don’t. If you’re still justifying ICE’s actions you’re done. That isn’t a disagreement anymore, that’s a moral failure. The claim that Alex Pretti, a 37 year old ICU nurse, was aggressive or that he was there to kill officers is not just false, it’s evil. It’s a deliberate lie. Kristi Noem repeating it isn’t confusion or poor judgment, it’s a malicious lie. This is the same woman with a documented history of cruelty and dishonesty, and now she’s in charge of Homeland Security. That should terrify everyone! If J.D. Vance and Trump’s team don’t cut her loose, they risk losing their base. This isn’t just Democrats anymore Republican supporters are fed up too. I’ve said this before and I’ll say it again: no one was ever opposed to deporting illegal immigrants, whether criminals or non-criminals. The justification was always public safety removing people who harm others, who commit violence, who kill. That was the idea! But now the very force that was supposed to protect the public is killing and harming American citizens! At that point, the distinction collapses. If an innocent American is killed, it makes no difference whether the trigger is pulled by a gang member or a federal agent. The outcome is the same: death, fear, and destruction, and I would argue it is even worst when your own is killing. The Trump administration has failed catastrophically with ICE and with Kristi Noem. And let’s be very clear about what’s happening. This is how authoritarianism starts. You give intelligence agencies and private contractors like Palantir powers they should never have and you're doomed. You normalize armed federal agents patrolling American streets. You lie about killings that are caught on camera. Then you label citizens “domestic terrorists” to justify it. First it’s immigrants. Then it’s protesters. Then it’s journalists. Then it’s you. Mark my words. It will be you! We’ve already seen it twice in two weeks. Alex a 37 years old, an ICU nurse at the VA, no criminal record, no violence and he was shot and killed, Just like Renee Good before him. Both were American citizens. Both were immediately smeared by the government as violent extremists. The videos are clear. Alex was filming something every American has the right to do. He approached no one aggressively. He was holding a phone. He was pepper-sprayed for no good reason. His legally carried firearm was removed by ICE. We have second amendment rights in this country, we can exercise them if we want to and honestly - in this American, seems to me we should do it daily! Only after he was disarmed and on the ground did an agent shoot him multiple times, in the back.And yet top officials Kristi Noem, Stephen Miller, Greg Bovino went on camera and flat out lied. Not misspoke. Lied. they claimed he was ready to kill ICE officers. This is an attack on American peoples and their rights with bunch of lazy hater in uniforms. If the shooting were justified,they wouldn’t need to fabricate a story, they would not need to make things up.This is what should outrage everyone, regardless of where you stand on immigration or ICE. A government that lies this brazenly. When video evidence is available to anyone is a government that believes it no longer needs public consent to do anything.That is dangerous. There is no democracy in this country but at least we were always able to push back, now we have these people who think they are above the law everywhere. In foreign policy and oil, and in domestic policy too, I bet they’re about to learn their lesson. I’ve spoken to many cops who have pushed back against ICE officers, confronted them, and openly criticized them. Because at the end of the day, cops are regular Americans. They live in our neighborhoods, care about people and public safety, and took an oath to protect. Now they’re defending Americans, standing with them against these brainless haters in uniforms and that is something we should be proud of. As the daughter of a detective, I am extremely proud of these cops who are pushing back and protecting citizens. ICE is unprofessional; they are poorly trained, scared, and operating on hate nothing more. That’s something you see in North Korea not in this country. Enough is enough! Republican especially, should be alarmed. For years, the right warned against armed federal agents patrolling American streets. Waco, ruby ridge, DHS itself was controversial when it was created. The second amendment was defended precisely as a safeguard against government overreach. Here’s the new standard in America: if you are legally carrying a firearm, even if you never touch it, never threaten anyone, never break the law the government can label you a terrorist and kill you. Even if you are an American citizen. And the hypocrisy is staggering. These are the same people who screamed nonstop to free Kyle Rittenhouse, defending the principle that legal firearm possession or use in defence does not equal criminal intent. But now that ordinary Americans who fit that same standard are standing in front of them, they’ve completely reversed course. Suddenly, legal gun ownership is enough to justify lethal force. I’ve always been clear about this: when Trump is right on policy be it domestic or foreign, I give him credit, I support him fully. I’m not driven by blind loyalty so when he is wrong I speak up just as quickly and honestly. But this crosses a line. If the government can kill you for exercising a constitutional right without action, without threat, without due process, then the right itself no longer exists. That is not law and order. That is tyranny. If that’s the rule now, the second amendment is dead. What we are seeing is not law enforcement, it’s power without accountability. And history is very clear about where that road leads. More on my YouTube link in the Bio.

ELIZABETH LANE

50,363 次观看 • 6 个月前

I know your timeline is flooded now with word salads of "insane, HER, 10 features you missed, we're so back". Sit down. Chill. Take a deep breath like Mark does in the demo . Let's think step by step: - Technique-wise, OpenAI has figured out a way to map audio to audio directly as first-class modality, and stream videos to a transformer in real-time. These require some new research on tokenization and architecture, but overall it's a data and system optimization problem (as most things are). High-quality data can come from at least 2 sources: 1) Naturally occurring dialogues on YouTube, podcasts, TV series, movies, etc. Whisper can be trained to identify speaker turns in a dialogue or separate overlapping speeches for automated annotation. 2) Synthetic data. Run the slow 3-stage pipeline using the most powerful models: speech1->text1 (ASR), text1->text2 (LLM), text2->speech2 (TTS). The middle LLM can decide when to stop and also simulate how to resume from interruption. It could output additional "thought traces" that are not verbalized to help generate better reply. Then GPT-4o distills directly from speech1->speech2, with optional auxiliary loss functions based on the 3-stage data. After distillation, these behaviors are now baked into the model without emitting intermediate texts. On the system side: the latency would not meet real-time threshold if every video frame is decompressed into an RGB image. OpenAI has likely developed their own neural-first, streaming video codec to transmit the motion deltas as tokens. The communication protocol and NN inference must be co-optimized. For example, there could be a small and energy-efficient NN running on the edge device that decides to transmit more tokens if the video is interesting, and fewer otherwise. - I didn't expect GPT-4o to be closer to GPT-5, the rumored "Arrakis" model that takes multimodal in and out. In fact, it's likely an early checkpoint of GPT-5 that hasn't finished training yet. The branding betrays a certain insecurity. Ahead of Google I/O, OpenAI would rather beat our mental projection of GPT-4.5 than disappoint by missing the sky-high expectation for GPT-5. A smart move to buy more time. - Notably, the assistant is much more lively and even a bit flirty. GPT-4o is trying (perhaps a bit too hard) to sound like HER. OpenAI is eating Character AI's lunch, with almost 100% overlap in form factor and huge distribution channels. It's a pivot towards more emotional AI with strong personality, which OpenAI seemed to actively suppress in the past. - Whoever wins Apple first wins big time. I see 3 levels of integration with iOS: 1) Ditch Siri. OpenAI distills a smaller-tier, purely on-device GPT-4o for iOS, with optional paid upgrade to use the cloud. 2) Native features to stream the camera or screen into the model. Chip-level support for neural audio/video codec. 3) Integrate with iOS system-level action API and smart home APIs. No one uses Siri Shortcuts, but it's time to resurrect. This could become the AI agent product with a billion users from the get-go. The FSD for smartphones with a Tesla-scale data flywheel.
14:22

I know your timeline is flooded now with word salads of "insane, HER, 10 features you missed, we're so back". Sit down. Chill. Take a deep breath like Mark does in the demo . Let's think step by step: - Technique-wise, OpenAI has figured out a way to map audio to audio directly as first-class modality, and stream videos to a transformer in real-time. These require some new research on tokenization and architecture, but overall it's a data and system optimization problem (as most things are). High-quality data can come from at least 2 sources: 1) Naturally occurring dialogues on YouTube, podcasts, TV series, movies, etc. Whisper can be trained to identify speaker turns in a dialogue or separate overlapping speeches for automated annotation. 2) Synthetic data. Run the slow 3-stage pipeline using the most powerful models: speech1->text1 (ASR), text1->text2 (LLM), text2->speech2 (TTS). The middle LLM can decide when to stop and also simulate how to resume from interruption. It could output additional "thought traces" that are not verbalized to help generate better reply. Then GPT-4o distills directly from speech1->speech2, with optional auxiliary loss functions based on the 3-stage data. After distillation, these behaviors are now baked into the model without emitting intermediate texts. On the system side: the latency would not meet real-time threshold if every video frame is decompressed into an RGB image. OpenAI has likely developed their own neural-first, streaming video codec to transmit the motion deltas as tokens. The communication protocol and NN inference must be co-optimized. For example, there could be a small and energy-efficient NN running on the edge device that decides to transmit more tokens if the video is interesting, and fewer otherwise. - I didn't expect GPT-4o to be closer to GPT-5, the rumored "Arrakis" model that takes multimodal in and out. In fact, it's likely an early checkpoint of GPT-5 that hasn't finished training yet. The branding betrays a certain insecurity. Ahead of Google I/O, OpenAI would rather beat our mental projection of GPT-4.5 than disappoint by missing the sky-high expectation for GPT-5. A smart move to buy more time. - Notably, the assistant is much more lively and even a bit flirty. GPT-4o is trying (perhaps a bit too hard) to sound like HER. OpenAI is eating Character AI's lunch, with almost 100% overlap in form factor and huge distribution channels. It's a pivot towards more emotional AI with strong personality, which OpenAI seemed to actively suppress in the past. - Whoever wins Apple first wins big time. I see 3 levels of integration with iOS: 1) Ditch Siri. OpenAI distills a smaller-tier, purely on-device GPT-4o for iOS, with optional paid upgrade to use the cloud. 2) Native features to stream the camera or screen into the model. Chip-level support for neural audio/video codec. 3) Integrate with iOS system-level action API and smart home APIs. No one uses Siri Shortcuts, but it's time to resurrect. This could become the AI agent product with a billion users from the get-go. The FSD for smartphones with a Tesla-scale data flywheel.

Jim Fan

991,793 次观看 • 2 年前

Scientists Admit Long COVID Sufferers Are Actually Suffering From V-AIDS...Vaccine Induced AIDS. The World Renowned Yale University Has Confirmed That COVID mRNA Vaccines Cause Vaccine Acquired Immune Deficiency Syndrome. UnVaxxed Long COVID Sufferers Acquire It From Shedding. The Yale University School of Medicine found that mRNA injections alter human biology to create long term S2 spike protein production that increases over time. The scientists warned that the COVID mRNA vaccines alter T-cell immunophenotypes which triggers VAIDS. The study was led by Bornali Bhattacharjee. People not only collectively in essence felt forced to take an experimental vaccine or risk losing their jobs...we were lied to about it. It never stopped transmission, it never stopped contracting it & it was never safe. The mRNA vaccines cause heart issues, blood clots, autoimmune disorders & so many more grave illnesses & death. This is the most egregious scandal in worldwide history. COVID-19 vaccines cause adverse events such as myocarditis & pericarditis, thrombosis & thrombocytopenia, Guillain–Barre syndrome, transverse myelitis & Bell’s Palsy. In addition, individuals have reported post-vaccination symptoms resembling long COVID beginning shortly after vaccination. This condition, sometimes referred to as post-vaccination syndrome (PVS) or post-acute COVID-19 vaccination syndrome (PACVS), is characterized by symptoms such as exercise intolerance, excessive fatigue, numbness, brain fog, neuropathy, insomnia, palpitations, myalgia, tinnitus, headache, burning sensations & dizziness. Finally, we have scientific confirmation that vaccination against COVID-19 causes a marked decrease in immunity to heterologous pathogens such as viruses, bacteria & fungi.’ A condition known as ‘VAIDS’ vaccine-induced AIDS.” COVID mRNA vaccines cause long-term symptoms by multiple mechanisms: 1⃣ Vaccine 80 components, the mRNA lipid nanoparticles & adenoviral vectors, trigger activation of pattern recognition receptors. Thus, unregulated stimulation of innate immunity leads to chronic inflammation. 2⃣ The S2 spike protein expressed following BNT162b2 or mRNA-1273 vaccination circulates in the plasma as early as one day after vaccination. Interaction with full-length S2 spike, its subunits (S1, S2), and/or peptide fragments with host molecules result in prolonged symptoms. Recently, a subset of non-classical monocytes has been shown to harbor S2 spike protein in patients with PVS18. Further, biodistribution studies on mRNA–LNP platforms in animal models indicate its ability to cross the blood-brain barrier, and the local S2 expression results in neurocognitive symptoms. 3⃣ Vaccine-induced VAIDS immune responses are triggering the stimulation of autoreactive lymphocytes. If you or someone you love is suffering from Long COVID, there are 2 paths to detoxification & healing...& both protocols can also be taken together if desired. 1⃣ Dr Peter McCullough's Detox Protocol involving 3 supplements that detox the spike protein & Inflammation (Link #3 below) 2⃣ Pure Body Zeolite Detoxification Drops that target toxins, chemicals & chelation of heavy metals (Link #4 below) 👇Yale Study: COVID mRNA Cause V-AIDS👇 👇COVID mRNA Vaccines Suppress Immunity👇 👇COVID mRNA Vaccine Detoxification Protoc👇 👇Pure Body Heavy Metal Detox Zeolite Drops👇 Speaker: @officiallizwheeler
1:29

Scientists Admit Long COVID Sufferers Are Actually Suffering From V-AIDS...Vaccine Induced AIDS. The World Renowned Yale University Has Confirmed That COVID mRNA Vaccines Cause Vaccine Acquired Immune Deficiency Syndrome. UnVaxxed Long COVID Sufferers Acquire It From Shedding. The Yale University School of Medicine found that mRNA injections alter human biology to create long term S2 spike protein production that increases over time. The scientists warned that the COVID mRNA vaccines alter T-cell immunophenotypes which triggers VAIDS. The study was led by Bornali Bhattacharjee. People not only collectively in essence felt forced to take an experimental vaccine or risk losing their jobs...we were lied to about it. It never stopped transmission, it never stopped contracting it & it was never safe. The mRNA vaccines cause heart issues, blood clots, autoimmune disorders & so many more grave illnesses & death. This is the most egregious scandal in worldwide history. COVID-19 vaccines cause adverse events such as myocarditis & pericarditis, thrombosis & thrombocytopenia, Guillain–Barre syndrome, transverse myelitis & Bell’s Palsy. In addition, individuals have reported post-vaccination symptoms resembling long COVID beginning shortly after vaccination. This condition, sometimes referred to as post-vaccination syndrome (PVS) or post-acute COVID-19 vaccination syndrome (PACVS), is characterized by symptoms such as exercise intolerance, excessive fatigue, numbness, brain fog, neuropathy, insomnia, palpitations, myalgia, tinnitus, headache, burning sensations & dizziness. Finally, we have scientific confirmation that vaccination against COVID-19 causes a marked decrease in immunity to heterologous pathogens such as viruses, bacteria & fungi.’ A condition known as ‘VAIDS’ vaccine-induced AIDS.” COVID mRNA vaccines cause long-term symptoms by multiple mechanisms: 1⃣ Vaccine 80 components, the mRNA lipid nanoparticles & adenoviral vectors, trigger activation of pattern recognition receptors. Thus, unregulated stimulation of innate immunity leads to chronic inflammation. 2⃣ The S2 spike protein expressed following BNT162b2 or mRNA-1273 vaccination circulates in the plasma as early as one day after vaccination. Interaction with full-length S2 spike, its subunits (S1, S2), and/or peptide fragments with host molecules result in prolonged symptoms. Recently, a subset of non-classical monocytes has been shown to harbor S2 spike protein in patients with PVS18. Further, biodistribution studies on mRNA–LNP platforms in animal models indicate its ability to cross the blood-brain barrier, and the local S2 expression results in neurocognitive symptoms. 3⃣ Vaccine-induced VAIDS immune responses are triggering the stimulation of autoreactive lymphocytes. If you or someone you love is suffering from Long COVID, there are 2 paths to detoxification & healing...& both protocols can also be taken together if desired. 1⃣ Dr Peter McCullough's Detox Protocol involving 3 supplements that detox the spike protein & Inflammation (Link #3 below) 2⃣ Pure Body Zeolite Detoxification Drops that target toxins, chemicals & chelation of heavy metals (Link #4 below) 👇Yale Study: COVID mRNA Cause V-AIDS👇 👇COVID mRNA Vaccines Suppress Immunity👇 👇COVID mRNA Vaccine Detoxification Protoc👇 👇Pure Body Heavy Metal Detox Zeolite Drops👇 Speaker: @officiallizwheeler

Valerie Anne Smith

342,842 次观看 • 1 年前

I was asked to write a bio of myself on SportsRecruits. I wanted share what I wrote. I love this one and those in it. • • • OLYMPIC DREAMS. I was 12 years old when I lost my starting position at shortstop, and lost my way into the batting lineup as well. I know it sounds silly being at such a young age, but to me; at the time, it was devastating. If not for my faith in God and my family… I’m not sure if I would be here writing this bio of mine. Hello! My name is Grace Varua. I am 16 years old and currently a sophomore at Pope John High School in Sparta, NJ. I started playing softball when I was 7 years old (video) When I am not playing High School softball Pope John Softball I play for Unity Torres 2028 primarily as a catcher and 3B as my secondary. MY hopes; after you have read, is to feel who I am through this quick bio of myself. Hence, the reason why I started this bio the way I did. A quick backdrop to my story. When this all happened at 12 years old, I wasn’t quite sure how to handle it. I was broken. My parents made it super simple for me. My choices were to quit or get back to work and prove to myself and to “the world” that my story was not finished. My parents have also taught me, that having goals or being goal oriented is super important. Goals give us a sense of purpose or meaning while we sweat and work hard. Goals gives myself an opportunity to plan and stay organized. Most importantly, goals gives myself perspective in paying respect to the time and effort given to me by my coaches, players of my team and parents. Goals keep me disciplined to fight through when I’m tired and all I want to do is rest and pack it in. So I picked a goal that I felt would represent the pinnacle of my sport. That being, to be a member of USA’s Women’s Olympic National Team. USA Softball Women's National Team 🇺🇸 Some may say a far-fetched goal, but I think otherwise. I was taught the philosophy of the 100 hour rule. Roughly 18 minutes a day training for 1 year. Gives myself the opportunity to outperform 95% of the population in my craft. Whether that stands true or not. This philosophy is more about staying disciplined. I can humbly and honestly say, I exceed that 18 minutes and then-some for the reason of hoping to represent my country one day. I’ve been able to catch some of the best youth pitchers in the game of club softball right now. Like Sydney Gonglik (LSU committ) Madelyn Vogan (Penn State committ) Mackenzie Herzog 2028 (Alabama Thunderbolts Premier ‘27) Future big time pitchers Lucy Reis 2029 and Lila Manfredonia 2028 (Ohio Outlaws Wolff/Acord ‘28) Anna Jane (AJ) Jarzab (Beverly Bandits ‘09 Lewis/ Moran) Liv Cilli (Providence Committ), Reese Tobey (UMass committ) and all my current pitchers on UNITY Torres ‘28 Joselyn Bermudez 2028, Adrianna Bryce ,Maddie Falvey 2028 , Gabby Gomez Gabby Perez and OliviaMVidela2028 It has been a blessing to work side by side with all of them. I am the catcher today because of them, my coaches and trainers. I am so thankful to each and every one of them. I have achieved and experienced some awesome things at my age. I made my High School Varsity team as a freshman, was a freshman 1st team All-Conference player, I hit back to back home-runs to help Pope John get to, and win our NJ County Championship in 2025. I am a 4x USA HPP (High Performance Program) invite, USA Softball’s 15U Top Performer and 15U National Team Member. I traveled, played and trained overseas in Paris and the Netherlands. I have a 3.9 G.P.A, I have an amazing older sister and younger brother. I have the best parents in the world and I have GOD with me each and every step of the way through this journey! I am blessed and so grateful! This is who I am. ꜱᴄᴀɴʟᴀɴ ꜱᴘᴏʀᴛꜱ™️ Line Drive Media EXTRA INNING SOFTBALL Sidelines - College Softball 🥎 Jorge Solodkin @FGS_softball 5 Star Elite SB Recruits Thee Softball Report NCAA Down South Softball Carlos Arias Fastpitch Athlete Recruits Cory BIG EAST Conference ACC Softball Southeastern Conference RECRUITING REALITIES Under The Radar 𝕏 EliteSoftballReport Ashley VanBoxmeer | Softball Coach Josh Johnson
0:40

I was asked to write a bio of myself on SportsRecruits. I wanted share what I wrote. I love this one and those in it. • • • OLYMPIC DREAMS. I was 12 years old when I lost my starting position at shortstop, and lost my way into the batting lineup as well. I know it sounds silly being at such a young age, but to me; at the time, it was devastating. If not for my faith in God and my family… I’m not sure if I would be here writing this bio of mine. Hello! My name is Grace Varua. I am 16 years old and currently a sophomore at Pope John High School in Sparta, NJ. I started playing softball when I was 7 years old (video) When I am not playing High School softball Pope John Softball I play for Unity Torres 2028 primarily as a catcher and 3B as my secondary. MY hopes; after you have read, is to feel who I am through this quick bio of myself. Hence, the reason why I started this bio the way I did. A quick backdrop to my story. When this all happened at 12 years old, I wasn’t quite sure how to handle it. I was broken. My parents made it super simple for me. My choices were to quit or get back to work and prove to myself and to “the world” that my story was not finished. My parents have also taught me, that having goals or being goal oriented is super important. Goals give us a sense of purpose or meaning while we sweat and work hard. Goals gives myself an opportunity to plan and stay organized. Most importantly, goals gives myself perspective in paying respect to the time and effort given to me by my coaches, players of my team and parents. Goals keep me disciplined to fight through when I’m tired and all I want to do is rest and pack it in. So I picked a goal that I felt would represent the pinnacle of my sport. That being, to be a member of USA’s Women’s Olympic National Team. USA Softball Women's National Team 🇺🇸 Some may say a far-fetched goal, but I think otherwise. I was taught the philosophy of the 100 hour rule. Roughly 18 minutes a day training for 1 year. Gives myself the opportunity to outperform 95% of the population in my craft. Whether that stands true or not. This philosophy is more about staying disciplined. I can humbly and honestly say, I exceed that 18 minutes and then-some for the reason of hoping to represent my country one day. I’ve been able to catch some of the best youth pitchers in the game of club softball right now. Like Sydney Gonglik (LSU committ) Madelyn Vogan (Penn State committ) Mackenzie Herzog 2028 (Alabama Thunderbolts Premier ‘27) Future big time pitchers Lucy Reis 2029 and Lila Manfredonia 2028 (Ohio Outlaws Wolff/Acord ‘28) Anna Jane (AJ) Jarzab (Beverly Bandits ‘09 Lewis/ Moran) Liv Cilli (Providence Committ), Reese Tobey (UMass committ) and all my current pitchers on UNITY Torres ‘28 Joselyn Bermudez 2028, Adrianna Bryce ,Maddie Falvey 2028 , Gabby Gomez Gabby Perez and OliviaMVidela2028 It has been a blessing to work side by side with all of them. I am the catcher today because of them, my coaches and trainers. I am so thankful to each and every one of them. I have achieved and experienced some awesome things at my age. I made my High School Varsity team as a freshman, was a freshman 1st team All-Conference player, I hit back to back home-runs to help Pope John get to, and win our NJ County Championship in 2025. I am a 4x USA HPP (High Performance Program) invite, USA Softball’s 15U Top Performer and 15U National Team Member. I traveled, played and trained overseas in Paris and the Netherlands. I have a 3.9 G.P.A, I have an amazing older sister and younger brother. I have the best parents in the world and I have GOD with me each and every step of the way through this journey! I am blessed and so grateful! This is who I am. ꜱᴄᴀɴʟᴀɴ ꜱᴘᴏʀᴛꜱ™️ Line Drive Media EXTRA INNING SOFTBALL Sidelines - College Softball 🥎 Jorge Solodkin @FGS_softball 5 Star Elite SB Recruits Thee Softball Report NCAA Down South Softball Carlos Arias Fastpitch Athlete Recruits Cory BIG EAST Conference ACC Softball Southeastern Conference RECRUITING REALITIES Under The Radar 𝕏 EliteSoftballReport Ashley VanBoxmeer | Softball Coach Josh Johnson

GRACE VARUA | NJ UNITY TORRES ‘28 GRAD

142,523 次观看 • 3 个月前

It was two years ago today that two armed federal agents showed up to my home. We knew they chose that day, June 23, 2023, for a specific reason. It was one of the most important days of my life - my graduation from surgical training. Because of all the sacrifice that comes from surgical training, these are one of those moments you never forget. A few hours before the ceremony (so a few hours before this video was filmed) armed agents were at my door. My wife, Andrea, and I knew they chose that day because of what it represented. They were using the value of my accomplishments as the means to extort my compliance with their corrupt investigation. They were holding my entire future hostage and hoping I would be overwhelmed by fear. But that's not what happened. After the agents left, for those first few moments we were terrified but then something changed. We were angry, righteously indignant, hopelessly defiant. The political insanity over the preceding years had taken away so much from us - our country as we knew it, our hopes for the future, the friends we cared about, and so much else. They came to my home that day to take more. But that was the day we decided we weren't going to let them take away anything else. The line in the sand had been drawn. Instead of hiding in fear, Andrea and I opened a few bottles of champaign, sat on our patio, played Vietnam war music, and chain smoked a whole pack of cigarettes (both of us are nonsmokers but it seemed like the right thing to do in the moment). We went to my graduation that night and celebrated everything we accomplished. Today was the first time I saw my graduation video and the speech one of my attendings gave about me. It's overwhelming to think back to that night and to listen to those words. I remember standing on that stage that night and thinking to myself that all of the reasons he gave for why I was a good surgeon were the same reasons federal agents were at my door a few hours before. I was being targeted not because I had done something wrong but because I upheld the principles of my profession. They thought they would destroy me, have me renounce what I knew to be good and true, and turn me to dust. But as we all know now, a totally different story played out. And for the people who were responsible for this treacherous corruption, the story is only just beginning.
2:00

It was two years ago today that two armed federal agents showed up to my home. We knew they chose that day, June 23, 2023, for a specific reason. It was one of the most important days of my life - my graduation from surgical training. Because of all the sacrifice that comes from surgical training, these are one of those moments you never forget. A few hours before the ceremony (so a few hours before this video was filmed) armed agents were at my door. My wife, Andrea, and I knew they chose that day because of what it represented. They were using the value of my accomplishments as the means to extort my compliance with their corrupt investigation. They were holding my entire future hostage and hoping I would be overwhelmed by fear. But that's not what happened. After the agents left, for those first few moments we were terrified but then something changed. We were angry, righteously indignant, hopelessly defiant. The political insanity over the preceding years had taken away so much from us - our country as we knew it, our hopes for the future, the friends we cared about, and so much else. They came to my home that day to take more. But that was the day we decided we weren't going to let them take away anything else. The line in the sand had been drawn. Instead of hiding in fear, Andrea and I opened a few bottles of champaign, sat on our patio, played Vietnam war music, and chain smoked a whole pack of cigarettes (both of us are nonsmokers but it seemed like the right thing to do in the moment). We went to my graduation that night and celebrated everything we accomplished. Today was the first time I saw my graduation video and the speech one of my attendings gave about me. It's overwhelming to think back to that night and to listen to those words. I remember standing on that stage that night and thinking to myself that all of the reasons he gave for why I was a good surgeon were the same reasons federal agents were at my door a few hours before. I was being targeted not because I had done something wrong but because I upheld the principles of my profession. They thought they would destroy me, have me renounce what I knew to be good and true, and turn me to dust. But as we all know now, a totally different story played out. And for the people who were responsible for this treacherous corruption, the story is only just beginning.

Eithan Haim MD

48,101 次观看 • 1 年前