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Static defense

Vitaly

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21,724 次观看 • 10 个月前 •via X (Twitter)

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New BitMind Paper is Out: Our latest paper explores how a decentralized, competitive AI framework can outperform static models in detecting deepfakes. 90%+ accuracy in real-world scenarios Adaptive AI that evolves with new threats Bridging research & real-world application Deepfakes are evolving—our defense is too. Read the full paper:

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❗️❗️❗️🇺🇦🇷🇺An analysis by German newspaper Die Zeit reveals that the massive proliferation of drones on the Ukrainian frontlines has expanded the contested "kill zone" to a depth of up to 40 kilometers, fundamentally paralyzing traditional ground maneuvers. According to observers, this highly contested grey zone continues to grow each month, creating an environment where any visible movement is instantly targeted. The scale of this technological shift is defined by several critical factors: Unprecedented Drone Density: There are now at least ten active reconnaissance and strike drones operating over every single square kilometer of the front line, making it virtually impossible for either side to concentrate armor or infantry for a large-scale offensive. Extreme Cost Asymmetry: The financial disproportion of modern battlefield destruction has reached staggering levels. A mass-produced, Ukrainian or Russian FPV kamikaze drone costing as little as €500 is now routinely capable of turning a modern €20-million main battle tank or air defense system into scrap metal. Autonomous Target Interception: Ground control is increasingly aided by artificial intelligence. Once a drone operator identifies and locks onto a target, machine-vision auto-tracking algorithms take over. The drone autonomously navigates the final, most hazardous phase of its flight to hit the target, even if electronic warfare jamming completely severs the pilot's manual connection. Under these conditions of total battlefield transparency, tanks and armored personnel carriers have nearly disappeared from the immediate zero-line, forcing both militaries to adapt to a highly dispersed, static war of attrition.

NSTRIKE

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Christian Parker is lowkey giving away exactly what he’s looking for, especially on the back end. This is the modern shift in the league. We’re moving away from chalkboard coverages and static defensive calls. It’s not “line up in Cover 3 and live with it.” It’s about matching the offense. Adapting to the offense. Morphing based on what you’re shown. That’s why he keeps emphasizing instincts and processing as a premium. Processing isn’t just “IQ.” It’s not just being smart. It’s taking the scheme you installed during the week, pairing it with your keys and giveaways, recognizing a block set or route combination post-snap, and instantly knowing: “Based on this picture, my responsibility just changed.” And doing that fast enough that you don’t cause a busted coverage. That’s what he’s asking for. He wants all 11 in sync. If the offense presents a certain formation or pattern and we’re in a specific coverage shell, the defense has to immediately morph. That requires communication, awareness, and trust. That’s why Will McClay mentioned the “brain works” aspect. They’re looking for players who naturally react the way the coordinator would react if he were on the field himself. And based on what Parker is saying, get ready for advanced match coverages. Palms. Quarters. Match principles. These aren’t spot-drop zones. These are pattern-match systems where your assignment changes based on receiver release, splits, route stems, and formation. We’re reading and reacting in real time. So yes — he still values physical traits. But he wants physical traits layered on top of elite processing. That’s the key. Thank to 105.3 The FAN

Christian Parker is lowkey giving away exactly what he’s looking for, especially on the back end. This is the modern shift in the league. We’re moving away from chalkboard coverages and static defensive calls. It’s not “line up in Cover 3 and live with it.” It’s about matching the offense. Adapting to the offense. Morphing based on what you’re shown. That’s why he keeps emphasizing instincts and processing as a premium. Processing isn’t just “IQ.” It’s not just being smart. It’s taking the scheme you installed during the week, pairing it with your keys and giveaways, recognizing a block set or route combination post-snap, and instantly knowing: “Based on this picture, my responsibility just changed.” And doing that fast enough that you don’t cause a busted coverage. That’s what he’s asking for. He wants all 11 in sync. If the offense presents a certain formation or pattern and we’re in a specific coverage shell, the defense has to immediately morph. That requires communication, awareness, and trust. That’s why Will McClay mentioned the “brain works” aspect. They’re looking for players who naturally react the way the coordinator would react if he were on the field himself. And based on what Parker is saying, get ready for advanced match coverages. Palms. Quarters. Match principles. These aren’t spot-drop zones. These are pattern-match systems where your assignment changes based on receiver release, splits, route stems, and formation. We’re reading and reacting in real time. So yes — he still values physical traits. But he wants physical traits layered on top of elite processing. That’s the key. Thank to 105.3 The FAN

Lou’s Takes ✭

37,326 次观看 • 7 个月前

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🚨🇮🇷🇺🇸 THE STEALTH MYTH JUST TOOK A HIT, AND THAT SHOULD WORRY EVERYONE A U.S. F-35, the crown jewel of modern airpower, just limped home after taking fire over Iran. Not destroyed. Not confirmed shot down. But hit. And that alone changes the conversation. For years, the F-35 has been sold as invisible, untouchable. A jet designed to slip through enemy airspace like a ghost. So what happened? First, let’s kill the myth: stealth does not mean invisible. Every aircraft reflects radar. The trick is reducing that signal, bending it, scattering it, shrinking it into something harder to detect. Harder. Not impossible. And that distinction may be the whole story. Because the skies over Iran right now aren’t a one-off mission. They’re crowded, repetitive, predictable. F-35s have been flying there a lot. Same routes. Same altitudes. Same mission profiles. That matters. Air defense isn’t static. It learns. Modern systems don’t just “see” aircraft; they collect data over time. Patterns. Angles. Frequencies. Tiny radar returns that look like noise until they don’t. Do that enough times, and the noise starts to look like a signature. Then there’s the second possibility: this wasn’t just Iranian ingenuity. Iran doesn’t build its air defense ecosystem in isolation. It buys. It reverse-engineers. It integrates. Russia has spent years refining systems specifically designed to counter Western stealth. China has invested heavily in multi-band radar, systems that trade precision for detection, spotting stealth aircraft at longer ranges even if they can’t track them perfectly. Individually, these systems have limits. Together? They create something closer to a net. The third explanation is the simplest and the least comfortable: War is messy. Even the most advanced aircraft in history can be hit under the right conditions. A lucky shot. A brief exposure. A pilot forced into a less-than-ideal flight path. The same conflict has already seen drones shot down, friendly fire incidents, and dense, overlapping air defenses lighting up the sky. In that environment, “stealth” becomes less of a shield and more of an advantage, one that can be eroded. If Iran, with a patchwork of imported systems and domestic improvisation, can even touch an F-35, then the future of air warfare looks very different than advertised.

Mario Nawfal

701,946 次观看 • 6 个月前

🇷🇺🇺🇦⚔️ The center of gravity of the ground war has increasingly shifted toward the Sloviansk-Kramatorsk axis. Since roughly October 2023, Russian forces have held the broader battlefield initiative in Donbas, advancing slowly but persistently. Today, the heaviest pressure is around Kostiantynivka and its surrounding fortified areas, which form one of Ukraine’s last major defensive belts from the south and southeast before the Sloviansk-Kramatorsk agglomeration, effectively the final major Ukrainian stronghold in Donetsk Oblast. Moscow has concentrated substantial assault and maneuver formations on this axis in an effort to break through Ukrainian defenses and open the road toward Kramatorsk. The offensive is costing Russia heavily, particularly because Ukrainian positions are saturated with FPV drones, but the political and military value of Sloviansk and Kramatorsk is high enough for Moscow to tolerate extraordinary losses. Russia is simultaneously attempting to approach the agglomeration from the north through the Lyman axis, where Ukraine’s 3rd Army Corps under Andriy Biletsky has been containing Russian pressure. Elsewhere, the frontline remains comparatively static: Ukraine has recovered some contested “gray zones” where Russian troop density is lower, while both sides continue limited infiltrations and localized actions without producing major operational changes. This makes the battle for Sloviansk-Kramatorsk much larger than a fight over another piece of territory. Capturing the remaining Ukrainian-held core of Donetsk Oblast would give Putin something he could present domestically as fulfillment of a central objective of his so-called “special military operation,” potentially strengthening Moscow’s position before negotiations. Ukraine therefore has every incentive not only to hold the agglomeration, but to make Russia pay the maximum possible price for attempting to take it. If Russian assault formations are severely depleted here without achieving an operational breakthrough, Moscow could eventually lose the offensive initiative it has held for years. At the same time, there are currently neither sufficient Ukrainian manpower and military-technical resources nor visible battlefield preparations for a major operational-level Ukrainian counteroffensive, making attrition and defense the more realistic immediate strategy.

🇷🇺🇺🇦⚔️ The center of gravity of the ground war has increasingly shifted toward the Sloviansk-Kramatorsk axis. Since roughly October 2023, Russian forces have held the broader battlefield initiative in Donbas, advancing slowly but persistently. Today, the heaviest pressure is around Kostiantynivka and its surrounding fortified areas, which form one of Ukraine’s last major defensive belts from the south and southeast before the Sloviansk-Kramatorsk agglomeration, effectively the final major Ukrainian stronghold in Donetsk Oblast. Moscow has concentrated substantial assault and maneuver formations on this axis in an effort to break through Ukrainian defenses and open the road toward Kramatorsk. The offensive is costing Russia heavily, particularly because Ukrainian positions are saturated with FPV drones, but the political and military value of Sloviansk and Kramatorsk is high enough for Moscow to tolerate extraordinary losses. Russia is simultaneously attempting to approach the agglomeration from the north through the Lyman axis, where Ukraine’s 3rd Army Corps under Andriy Biletsky has been containing Russian pressure. Elsewhere, the frontline remains comparatively static: Ukraine has recovered some contested “gray zones” where Russian troop density is lower, while both sides continue limited infiltrations and localized actions without producing major operational changes. This makes the battle for Sloviansk-Kramatorsk much larger than a fight over another piece of territory. Capturing the remaining Ukrainian-held core of Donetsk Oblast would give Putin something he could present domestically as fulfillment of a central objective of his so-called “special military operation,” potentially strengthening Moscow’s position before negotiations. Ukraine therefore has every incentive not only to hold the agglomeration, but to make Russia pay the maximum possible price for attempting to take it. If Russian assault formations are severely depleted here without achieving an operational breakthrough, Moscow could eventually lose the offensive initiative it has held for years. At the same time, there are currently neither sufficient Ukrainian manpower and military-technical resources nor visible battlefield preparations for a major operational-level Ukrainian counteroffensive, making attrition and defense the more realistic immediate strategy.

NSTRIKE

54,144 次观看 • 1 个月前

$HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.$

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.

YanXbt

29,658 次观看 • 1 个月前

2009. Alex Karp goes on Charlie Rose. He almost never does television. He's running a company called Palantir that has zero salespeople, took three years to build before generating a dollar of revenue, and is named after the seeing stones in The Lord of the Rings. His resume makes no sense for a tech CEO. Stanford Law, then a PhD in neoclassical social philosophy at Goethe University in Frankfurt. He spent years studying questions like "what does it mean to know something," and says the work was intelligible to maybe 30 or 40 people on earth. He co-founded Palantir in 2003 with Peter Thiel and a group of former PayPal engineers because he believed Silicon Valley should be involved in the fight against terrorism. That's the actual origin story. A philosopher and a bunch of payment processing guys decided to go after Al-Qaeda. What he says in this interview is wild in hindsight. Everyone in government at the time was doing broad data mining, casting giant algorithmic nets across datasets and hoping something useful came back. Karp called that approach broken. His argument had two parts. One: terrorists are entrepreneurs. They figure out how the last guy got caught and change their patterns overnight. A static algorithm against someone who adapts every week is a losing game. Two: when algorithms nobody understands are scanning everyone, civil liberties disappear because there's no record of what the government actually looked at or why. So Palantir built the opposite. A platform where a human analyst sits in the loop, interacts with data directly, spots patterns in real time, and every single step gets tagged. If the government looked at you, there's a paper trail. Where did the evidence come from? Were they allowed to see it? Did they use data from one case and sneak it into another? Karp called it "predicate-based search." Find the needle. Leave the haystack alone. Then he drops this line about cyber warfare: "What would have taken a large government organization can now be done with two or three teenagers in a coffee shop." In 2009. He described the attribution problem years before most people had heard the word. You get attacked, you don't know if it's a nation-state or a kid in a basement, and you, as the president, have to decide how to respond. He warned that if Americans ever felt like there was a trade-off between security and freedom, "we'll lose." The company took 17 years to go public. Seventeen. They did a direct listing in September 2020 at $9.50 a share, valued at around $16 billion. Wall Street was skeptical. Many people hated Palantir for its government work. The stock trades around $155 today. Market cap is roughly $370 billion. That's the 30th-most-valuable company on Earth, with about 4,400 employees. Revenue has crossed $4 billion a year. The guy studying what it means to know something at a German university in the 1990s is now running one of the most important defense and AI companies alive, and the interview where he laid out exactly why is sitting on YouTube with barely any views.

2009. Alex Karp goes on Charlie Rose. He almost never does television. He's running a company called Palantir that has zero salespeople, took three years to build before generating a dollar of revenue, and is named after the seeing stones in The Lord of the Rings. His resume makes no sense for a tech CEO. Stanford Law, then a PhD in neoclassical social philosophy at Goethe University in Frankfurt. He spent years studying questions like "what does it mean to know something," and says the work was intelligible to maybe 30 or 40 people on earth. He co-founded Palantir in 2003 with Peter Thiel and a group of former PayPal engineers because he believed Silicon Valley should be involved in the fight against terrorism. That's the actual origin story. A philosopher and a bunch of payment processing guys decided to go after Al-Qaeda. What he says in this interview is wild in hindsight. Everyone in government at the time was doing broad data mining, casting giant algorithmic nets across datasets and hoping something useful came back. Karp called that approach broken. His argument had two parts. One: terrorists are entrepreneurs. They figure out how the last guy got caught and change their patterns overnight. A static algorithm against someone who adapts every week is a losing game. Two: when algorithms nobody understands are scanning everyone, civil liberties disappear because there's no record of what the government actually looked at or why. So Palantir built the opposite. A platform where a human analyst sits in the loop, interacts with data directly, spots patterns in real time, and every single step gets tagged. If the government looked at you, there's a paper trail. Where did the evidence come from? Were they allowed to see it? Did they use data from one case and sneak it into another? Karp called it "predicate-based search." Find the needle. Leave the haystack alone. Then he drops this line about cyber warfare: "What would have taken a large government organization can now be done with two or three teenagers in a coffee shop." In 2009. He described the attribution problem years before most people had heard the word. You get attacked, you don't know if it's a nation-state or a kid in a basement, and you, as the president, have to decide how to respond. He warned that if Americans ever felt like there was a trade-off between security and freedom, "we'll lose." The company took 17 years to go public. Seventeen. They did a direct listing in September 2020 at $9.50 a share, valued at around $16 billion. Wall Street was skeptical. Many people hated Palantir for its government work. The stock trades around $155 today. Market cap is roughly $370 billion. That's the 30th-most-valuable company on Earth, with about 4,400 employees. Revenue has crossed $4 billion a year. The guy studying what it means to know something at a German university in the 1990s is now running one of the most important defense and AI companies alive, and the interview where he laid out exactly why is sitting on YouTube with barely any views.

Anish Moonka

112,707 次观看 • 6 个月前