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Helldivers 2 adding specialized SEAF Troopers changes squad dynamics completely. Medics prioritize wounded AI and seek out damaged Helldivers to heal, while Squad Leaders direct frontline infantry. It turns standard spawn waves into a genuinely responsive fireteam.

NPC Universe✨

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49,587 просмотров • 13 дней назад •via X (Twitter)

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The wounded of Gaza gathered again today in a sit-in, as they say they must almost every day, to demand that Gaza's Ministry of Health and the World Health Organization allow them out for treatment they cannot get inside the Strip. Many have carried untreated wounds for years, their injuries worsening as painkillers stop working and infection eats into flesh that no functioning hospital remains to repair, while they watch the crossing open day after day for others but never for them. Their suffering is the direct result of the Israeli occupation's grip on Gaza's borders: after seizing the Rafah crossing in May 2024, it cut medical transfers to a trickle, leaving more than 18,500 patients still waiting to be evacuated, over 4,000 of them children, according to the WHO. Gaza's health ministry says six to ten patients die every day waiting to get out, with well over a thousand already dead on the waiting lists, as 94 percent of the territory's hospitals lie destroyed or damaged and the occupation keeps the sick and wounded from leaving to seek the care that exists just across the border. gassan_.elkhaldi

The wounded of Gaza gathered again today in a sit-in, as they say they must almost every day, to demand that Gaza's Ministry of Health and the World Health Organization allow them out for treatment they cannot get inside the Strip. Many have carried untreated wounds for years, their injuries worsening as painkillers stop working and infection eats into flesh that no functioning hospital remains to repair, while they watch the crossing open day after day for others but never for them. Their suffering is the direct result of the Israeli occupation's grip on Gaza's borders: after seizing the Rafah crossing in May 2024, it cut medical transfers to a trickle, leaving more than 18,500 patients still waiting to be evacuated, over 4,000 of them children, according to the WHO. Gaza's health ministry says six to ten patients die every day waiting to get out, with well over a thousand already dead on the waiting lists, as 94 percent of the territory's hospitals lie destroyed or damaged and the occupation keeps the sick and wounded from leaving to seek the care that exists just across the border. gassan_.elkhaldi

Translating Falasteen (Palestine)

19,887 просмотров • 1 месяц назад

❗️❗️❗️🇺🇦🇷🇺The Ukrainian defense tech cluster Brave 1 has officially unleashed the Firefly guided munition for heavy bomber drones, a massive technological breakthrough that will systematically terrorize Russian occupiers hiding in frontline positions. This lethal innovation is already fully prepared for serial production, signaling an immediate, high-volume influx of precision-guided firepower directly to the front lines. The Firefly can be deployed from a maximum altitude of 800 meters, completely eliminating the dangerous requirement for bomber drones to descend to low altitudes where they are vulnerable to small arms fire. Featuring an advanced automatic guidance system that independently calculates and adjusts the flight trajectory, the Firefly transforms standard drone drops into unstoppable, high-precision strikes. When released from its maximum altitude, the munition generates devastating kinetic energy and explosive force, making it highly effective at obliterating fortified Russian positions, bunker complexes, and wooden field fortifications. This technological leap strikes a crippling blow to the Kremlin's dug-in forces and completely rewrites the rules of trench warfare. While Russian soldiers rely on rudimentary trenches and desperate defenses, Ukraine is deploying mass-produced, AI-assisted precision weaponry that can liquidate enemy positions from the safety of the upper skies. By removing drones from the reach of infantry fire while drastically increasing their destructive accuracy, Brave 1 has delivered a weapon system that will turn every Russian fortification into a inescapable trap, ensuring that the invading forces are systematically hunted and neutralized with absolute impunity.

❗️❗️❗️🇺🇦🇷🇺The Ukrainian defense tech cluster Brave 1 has officially unleashed the Firefly guided munition for heavy bomber drones, a massive technological breakthrough that will systematically terrorize Russian occupiers hiding in frontline positions. This lethal innovation is already fully prepared for serial production, signaling an immediate, high-volume influx of precision-guided firepower directly to the front lines. The Firefly can be deployed from a maximum altitude of 800 meters, completely eliminating the dangerous requirement for bomber drones to descend to low altitudes where they are vulnerable to small arms fire. Featuring an advanced automatic guidance system that independently calculates and adjusts the flight trajectory, the Firefly transforms standard drone drops into unstoppable, high-precision strikes. When released from its maximum altitude, the munition generates devastating kinetic energy and explosive force, making it highly effective at obliterating fortified Russian positions, bunker complexes, and wooden field fortifications. This technological leap strikes a crippling blow to the Kremlin's dug-in forces and completely rewrites the rules of trench warfare. While Russian soldiers rely on rudimentary trenches and desperate defenses, Ukraine is deploying mass-produced, AI-assisted precision weaponry that can liquidate enemy positions from the safety of the upper skies. By removing drones from the reach of infantry fire while drastically increasing their destructive accuracy, Brave 1 has delivered a weapon system that will turn every Russian fortification into a inescapable trap, ensuring that the invading forces are systematically hunted and neutralized with absolute impunity.

NSTRIKE

44,285 просмотров • 1 месяц назад

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

87,330 просмотров • 1 месяц назад

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category

D4rsh🦅

13,266 просмотров • 1 год назад

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.

Big Brain AI

366,516 просмотров • 5 месяцев назад

Step 1: Get clocked doing 97 mph. Step 2: Admit your insurance check bounced. Step 3: Threaten the cop while playing the "veteran card." This might be the gold standard for how not to handle a traffic stop. The chaotic breakdown: A Georgia State Patrol trooper pulls over Mrs. McNair for driving 97 mph in a 70 mph zone. When asked for paperwork, she admits she does not have valid auto insurance, explaining that her insurance company supposedly returned her check. The trooper explains that he cannot legally just let her drive away without insurance in case something happens. Trying to avoid towing her vehicle, he offers her a choice: either get insured right then and there over the phone/online, or the car will have to be towed. Instead of taking the opportunity to resolve the insurance issue, Mrs. McNair gives the trooper an attitude. The trooper walks back to his squad car to consult with another officer. He reports that the interaction is going downhill, noting that he spotted a half-filled, missing bottle of tequila in her car and smelled what he suspected to be an illegal substance. When the trooper returns to the vehicle to remove her from the car—now dealing with a suspected DUI/contraband situation alongside the lack of insurance—the situation completely self-destructs. Mrs. McNair panics and refuses to exit. The trooper opens her door and attempts to pull her out. She clings desperately to the steering wheel, screaming for help. Backup arrives, and multiple troopers forcefully pull her from the vehicle, wrestling her onto the highway shoulder as she flails and tucks her arms to avoid being handcuffed. Once the officers finally overpower her and place her in handcuffs, her defiance continues. As she is being marched over and put into the back of the squad car, she repeatedly threatens the trooper and loudly claims she is a military veteran, explicitly stating she will use her veteran status in her favor to get out of the situation. Ultimately, she is booked on charges of speeding, driving without insurance, three counts of resisting arrest, and obstruction.

Step 1: Get clocked doing 97 mph. Step 2: Admit your insurance check bounced. Step 3: Threaten the cop while playing the "veteran card." This might be the gold standard for how not to handle a traffic stop. The chaotic breakdown: A Georgia State Patrol trooper pulls over Mrs. McNair for driving 97 mph in a 70 mph zone. When asked for paperwork, she admits she does not have valid auto insurance, explaining that her insurance company supposedly returned her check. The trooper explains that he cannot legally just let her drive away without insurance in case something happens. Trying to avoid towing her vehicle, he offers her a choice: either get insured right then and there over the phone/online, or the car will have to be towed. Instead of taking the opportunity to resolve the insurance issue, Mrs. McNair gives the trooper an attitude. The trooper walks back to his squad car to consult with another officer. He reports that the interaction is going downhill, noting that he spotted a half-filled, missing bottle of tequila in her car and smelled what he suspected to be an illegal substance. When the trooper returns to the vehicle to remove her from the car—now dealing with a suspected DUI/contraband situation alongside the lack of insurance—the situation completely self-destructs. Mrs. McNair panics and refuses to exit. The trooper opens her door and attempts to pull her out. She clings desperately to the steering wheel, screaming for help. Backup arrives, and multiple troopers forcefully pull her from the vehicle, wrestling her onto the highway shoulder as she flails and tucks her arms to avoid being handcuffed. Once the officers finally overpower her and place her in handcuffs, her defiance continues. As she is being marched over and put into the back of the squad car, she repeatedly threatens the trooper and loudly claims she is a military veteran, explicitly stating she will use her veteran status in her favor to get out of the situation. Ultimately, she is booked on charges of speeding, driving without insurance, three counts of resisting arrest, and obstruction.

✨️Serenitee♡Sam✨️

23,460 просмотров • 3 месяцев назад

The absolute "Main Character" delusion of ordering expensive food, refusing to pay because of the price, and then escalating it to a DUI arrest... when you already have 3 prior DUIs. You cannot make this level of entitlement up. A couple’s night out took a sharp turn for the worse after they refused to pay for an expensive round of food and drinks, despite knowing the prices from the menu beforehand. The establishment called the Palm Beach Gardens Police Department to intervene, but what began as a dispute over a hefty dining tab quickly spiraled into a criminal arrest. When officers arrived on the scene, the couple was already separated. While his partner stayed behind at the bar area, attempting to negotiate a lower total due to how expensive the bill was, Ruby Colon made his way out to the parking lot. According to police reports, the two gave completely conflicting accounts of what had transpired during their dinner and why they were refusing to settle up. Instead of presenting a cohesive story, the situation devolved into a chaotic back-and-forth with responding officers. The confrontation took a critical turn outside. Despite the bill dispute remaining completely unresolved, Ruby intended to get into his vehicle and drive away from the property. However, when officers confronted Ruby near the vehicle, they immediately noticed physical indicators of heavy intoxication. The call shifted instantly from a restaurant dispute to a full DUI investigation. Officers conducted Field Sobriety Exercises right there in the parking lot, which Ruby failed to perform satisfactorily. A standard background check revealed a heavily marred driving record. Ruby was operating the vehicle with a suspended and revoked license—a restriction resulting from three previous DUI charges. While Ruby was formally placed in handcuffs, processed for driving under the influence as a repeat offender, and transported to a local holding facility, his partner faced her own logistical hurdle. Claiming she had no remaining funds on her credit cards to order an Uber, an officer ultimately transported her home in the back of a squad car. What began as an attempt to haggle out of a high dinner bill ended with one partner in a jail cell and the other getting a police escort home.

Sensitive content

The absolute "Main Character" delusion of ordering expensive food, refusing to pay because of the price, and then escalating it to a DUI arrest... when you already have 3 prior DUIs. You cannot make this level of entitlement up. A couple’s night out took a sharp turn for the worse after they refused to pay for an expensive round of food and drinks, despite knowing the prices from the menu beforehand. The establishment called the Palm Beach Gardens Police Department to intervene, but what began as a dispute over a hefty dining tab quickly spiraled into a criminal arrest. When officers arrived on the scene, the couple was already separated. While his partner stayed behind at the bar area, attempting to negotiate a lower total due to how expensive the bill was, Ruby Colon made his way out to the parking lot. According to police reports, the two gave completely conflicting accounts of what had transpired during their dinner and why they were refusing to settle up. Instead of presenting a cohesive story, the situation devolved into a chaotic back-and-forth with responding officers. The confrontation took a critical turn outside. Despite the bill dispute remaining completely unresolved, Ruby intended to get into his vehicle and drive away from the property. However, when officers confronted Ruby near the vehicle, they immediately noticed physical indicators of heavy intoxication. The call shifted instantly from a restaurant dispute to a full DUI investigation. Officers conducted Field Sobriety Exercises right there in the parking lot, which Ruby failed to perform satisfactorily. A standard background check revealed a heavily marred driving record. Ruby was operating the vehicle with a suspended and revoked license—a restriction resulting from three previous DUI charges. While Ruby was formally placed in handcuffs, processed for driving under the influence as a repeat offender, and transported to a local holding facility, his partner faced her own logistical hurdle. Claiming she had no remaining funds on her credit cards to order an Uber, an officer ultimately transported her home in the back of a squad car. What began as an attempt to haggle out of a high dinner bill ended with one partner in a jail cell and the other getting a police escort home.

✨️Serenitee♡Sam✨️

108,863 просмотров • 3 месяцев назад

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Blaze

89,575 просмотров • 2 месяцев назад

A friend of mine works in market research Her job involves calling different brands, asking questions, and collecting insights that later turn into reports. She needs to capture everything accurately while the conversation is still happening Earlier, she used to note everything down manually during the call. It was stressful and she often had to spend extra time later cleaning up messy notes. Now she does something much simpler. She keeps the call on speaker on her laptop, turns on the Typeless mic, and it converts the conversation into clean notes in real time. After the call, she just asks it to turn those notes into a proper summary or email for her team. No scrambling through rough notes. No spending another 20 minutes rewriting everything. She even does the same thing on mobile when she needs to send a quick update after a call. What makes it powerful is how it handles real speech. We don’t talk in perfect sentences. We pause, repeat ourselves, change direction mid-sentence, and add filler words. Typeless understands that. It removes filler words, fixes repetitions, catches mid-sentence changes, and turns messy speech into clean, ready to send text, whether that’s an email, a bullet list, notes, or a message. One feature that really stands out is voice based editing. You can refine sentences, change the tone, or tweak wording just by speaking again. No typing needed. It works anywhere you write WhatsApp, Slack, email, Notes, even ChatGPT and supports 100+ languages with a personal dictionary. Privacy is also a big focus. The platform is HIPAA compliant and GDPR compliant, with zero cloud data retention. Your data is never used to train AI models, and your history stays stored locally on your device. For professionals working with sensitive information, that matters a lot. They’re also working toward SOC 2 Type II and ISO 27001 compliance. Speaking your thoughts and getting polished writing instantly is honestly much faster than typing everything. That’s basically what Typeless does. Try it here: Android: (check the video 👇) Trust center:

A friend of mine works in market research Her job involves calling different brands, asking questions, and collecting insights that later turn into reports. She needs to capture everything accurately while the conversation is still happening Earlier, she used to note everything down manually during the call. It was stressful and she often had to spend extra time later cleaning up messy notes. Now she does something much simpler. She keeps the call on speaker on her laptop, turns on the Typeless mic, and it converts the conversation into clean notes in real time. After the call, she just asks it to turn those notes into a proper summary or email for her team. No scrambling through rough notes. No spending another 20 minutes rewriting everything. She even does the same thing on mobile when she needs to send a quick update after a call. What makes it powerful is how it handles real speech. We don’t talk in perfect sentences. We pause, repeat ourselves, change direction mid-sentence, and add filler words. Typeless understands that. It removes filler words, fixes repetitions, catches mid-sentence changes, and turns messy speech into clean, ready to send text, whether that’s an email, a bullet list, notes, or a message. One feature that really stands out is voice based editing. You can refine sentences, change the tone, or tweak wording just by speaking again. No typing needed. It works anywhere you write WhatsApp, Slack, email, Notes, even ChatGPT and supports 100+ languages with a personal dictionary. Privacy is also a big focus. The platform is HIPAA compliant and GDPR compliant, with zero cloud data retention. Your data is never used to train AI models, and your history stays stored locally on your device. For professionals working with sensitive information, that matters a lot. They’re also working toward SOC 2 Type II and ISO 27001 compliance. Speaking your thoughts and getting polished writing instantly is honestly much faster than typing everything. That’s basically what Typeless does. Try it here: Android: (check the video 👇) Trust center:

aditii

59,829 просмотров • 5 месяцев назад

Subnet 64 Chutes just confirmed in their latest interview with Jesus Martinez that they have the capability to run 1 trillion parameter models. let's put that into perspective for a second: templar was making waves for running 72b models and rightfully so, that was genuinely impressive. but Chamath Palihapitiya, when he brought up templar in a recent interview with Jensen Huang from NVIDIA, thought they were running 4b models. not 72b. 4b. and he was still bullish on it. now imagine when he finds out what chutes confirmed they can do.. 1 trillion parameters. on a decentralised subnet. no AWS. no Google Cloud. no centralised infrastructure pulling the strings. just Bittensor doing what it was always supposed to do - incentivising the best compute in the world to show up and compete. Jensen Huang talks about whoever is pushing the frontier of compute. that's the conversation chutes just walked into. this is the kind of milestone that doesn't stay quiet for long. when people outside of crypto start connecting the dots that a bittensor subnet is running inference at a scale that rivals or exceeds what the top AI labs are deploying, the narrative around $TAO changes completely. Chamath gets it wrong on the model size and is still impressed. Jensen sees who's actually moving compute forward. and chutes just put a number on the table that neither of them can ignore. the noise hasn't started yet. but it will.

Subnet 64 Chutes just confirmed in their latest interview with Jesus Martinez that they have the capability to run 1 trillion parameter models. let's put that into perspective for a second: templar was making waves for running 72b models and rightfully so, that was genuinely impressive. but Chamath Palihapitiya, when he brought up templar in a recent interview with Jensen Huang from NVIDIA, thought they were running 4b models. not 72b. 4b. and he was still bullish on it. now imagine when he finds out what chutes confirmed they can do.. 1 trillion parameters. on a decentralised subnet. no AWS. no Google Cloud. no centralised infrastructure pulling the strings. just Bittensor doing what it was always supposed to do - incentivising the best compute in the world to show up and compete. Jensen Huang talks about whoever is pushing the frontier of compute. that's the conversation chutes just walked into. this is the kind of milestone that doesn't stay quiet for long. when people outside of crypto start connecting the dots that a bittensor subnet is running inference at a scale that rivals or exceeds what the top AI labs are deploying, the narrative around $TAO changes completely. Chamath gets it wrong on the model size and is still impressed. Jensen sees who's actually moving compute forward. and chutes just put a number on the table that neither of them can ignore. the noise hasn't started yet. but it will.

Alchemist - τ

18,417 просмотров • 4 месяцев назад

$Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.$

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.

Milk Road AI

27,908 просмотров • 2 месяцев назад

Officers have to remove 2 from a bar that were trying to start a brawl but after fighting with the officers end up in cuffs. An evening at the Cajun Boil & Bar in Champaign, Illinois, took a chaotic turn when a call to remove unruly customers quickly escalated into an all-out brawl with responding officers. What started as a routine escort out of the building exploded right at the beginning of the interaction, captured clearly on police body camera footage. Champaign Police officers were initially dispatched to the restaurant after staff requested the removal of two women who were reportedly lingering in the restroom and causing a major disturbance. According to employees, the women had been involved in a verbal argument with a male customer inside the restroom prior to police arrival. As officers attempted to guide the group out of the dining area, tension filled the room as the individuals demanded to know why they were being forced to leave. Suddenly, chaos erupted. Things moved out to the sidewalk as the woman in the grey sweatshirt Dajae T. Dawson—turns and throws a direct punch into an officer's chest. The officer immediately moves to tackle and restrain her, explicitly stating on camera, "You punched me." More came out regarding physical altercation inside that involved the second woman, in the orange sweatshirt, and a restaurant employee. Officers did have tu detain her to get thy full story. Even after being placed in handcuffs and escorted outside, the resistance didn't stop. Dawson continued to aggressively argue with officers, refused to cooperate with identification requests, and began violently kicking at the windows of the squad car once placed inside. While a physical fight with law enforcement on camera often points to assault, local prosecutors officially booked Dawson under the state's specific statutes for striking law enforcement. Dawson was officially charged with: Aggravated Battery to a Peace Officer (a felony charge in Illinois for striking an officer) Resisting a Peace Officer Criminal Damage to Property (for the damage caused to the police vehicle) Currently there is no update I can find on court results.

Officers have to remove 2 from a bar that were trying to start a brawl but after fighting with the officers end up in cuffs. An evening at the Cajun Boil & Bar in Champaign, Illinois, took a chaotic turn when a call to remove unruly customers quickly escalated into an all-out brawl with responding officers. What started as a routine escort out of the building exploded right at the beginning of the interaction, captured clearly on police body camera footage. Champaign Police officers were initially dispatched to the restaurant after staff requested the removal of two women who were reportedly lingering in the restroom and causing a major disturbance. According to employees, the women had been involved in a verbal argument with a male customer inside the restroom prior to police arrival. As officers attempted to guide the group out of the dining area, tension filled the room as the individuals demanded to know why they were being forced to leave. Suddenly, chaos erupted. Things moved out to the sidewalk as the woman in the grey sweatshirt Dajae T. Dawson—turns and throws a direct punch into an officer's chest. The officer immediately moves to tackle and restrain her, explicitly stating on camera, "You punched me." More came out regarding physical altercation inside that involved the second woman, in the orange sweatshirt, and a restaurant employee. Officers did have tu detain her to get thy full story. Even after being placed in handcuffs and escorted outside, the resistance didn't stop. Dawson continued to aggressively argue with officers, refused to cooperate with identification requests, and began violently kicking at the windows of the squad car once placed inside. While a physical fight with law enforcement on camera often points to assault, local prosecutors officially booked Dawson under the state's specific statutes for striking law enforcement. Dawson was officially charged with: Aggravated Battery to a Peace Officer (a felony charge in Illinois for striking an officer) Resisting a Peace Officer Criminal Damage to Property (for the damage caused to the police vehicle) Currently there is no update I can find on court results.

Giggling Ganon

106,906 просмотров • 3 месяцев назад

‼️‼️🇺🇦🇷🇺 In a devastating maritime blitz that marks one of the most extensive asymmetric assaults on naval logistics in modern history, Ukraine’s Unmanned Systems Forces have completely paralyzed Russian commercial and military shipping across the Sea of Azov. The catastrophic operational fallout has forced Russian border authorities to abruptly suspend all vessel movement through the critical Don-Donbas-Azov shipping canal and completely freeze transit applications for the strategic Kerch Strait. According to an official briefing by the Commander of Ukraine's Unmanned Systems Forces, Robert "Madyar" Brovdi, elite strike units operating under the SBS Birds command executed an industrial-scale drone operation overnight. Over the course of a single night, Ukrainian drone swarms successfully hunted and battered 28 vessels belonging to Russia's illicit "shadow fleet" in the Sea of Azov. This single-night victory caps off a relentless 6-day maritime blockade. In total, Ukrainian drone operators have achieved 73 confirmed precision hits across 76 hostile vessels, systematically dismantling the Kremlin's maritime supply chain. The official damage log confirmed by the SBS Birds command includes: 21 shadow-fleet oil tankers aggressively neutralized to choke off fuel supplies. 4 maritime tugboats and 2 dry cargo vessels heavily damaged. 1 specialized engineering vessel (dredger) critical for port infrastructure knocked out. Simultaneously, the large-scale operation projected immense kinetic power deep into the enemy's operational rear. Across occupied Crimea and the southern frontline sectors, Ukrainian forces successfully obliterated 53 legitimate military targets—obliterating crucial air defense coordinates, radar stations, and energy infrastructure. By turning the Sea of Azov into a burning trap, Ukraine has effectively shattered Russia’s southern military logistics while severing the vital economic channels Moscow uses to fund its war machine. See the latest updates with us: Visioner

‼️‼️🇺🇦🇷🇺 In a devastating maritime blitz that marks one of the most extensive asymmetric assaults on naval logistics in modern history, Ukraine’s Unmanned Systems Forces have completely paralyzed Russian commercial and military shipping across the Sea of Azov. The catastrophic operational fallout has forced Russian border authorities to abruptly suspend all vessel movement through the critical Don-Donbas-Azov shipping canal and completely freeze transit applications for the strategic Kerch Strait. According to an official briefing by the Commander of Ukraine's Unmanned Systems Forces, Robert "Madyar" Brovdi, elite strike units operating under the SBS Birds command executed an industrial-scale drone operation overnight. Over the course of a single night, Ukrainian drone swarms successfully hunted and battered 28 vessels belonging to Russia's illicit "shadow fleet" in the Sea of Azov. This single-night victory caps off a relentless 6-day maritime blockade. In total, Ukrainian drone operators have achieved 73 confirmed precision hits across 76 hostile vessels, systematically dismantling the Kremlin's maritime supply chain. The official damage log confirmed by the SBS Birds command includes: 21 shadow-fleet oil tankers aggressively neutralized to choke off fuel supplies. 4 maritime tugboats and 2 dry cargo vessels heavily damaged. 1 specialized engineering vessel (dredger) critical for port infrastructure knocked out. Simultaneously, the large-scale operation projected immense kinetic power deep into the enemy's operational rear. Across occupied Crimea and the southern frontline sectors, Ukrainian forces successfully obliterated 53 legitimate military targets—obliterating crucial air defense coordinates, radar stations, and energy infrastructure. By turning the Sea of Azov into a burning trap, Ukraine has effectively shattered Russia’s southern military logistics while severing the vital economic channels Moscow uses to fund its war machine. See the latest updates with us: Visioner

Visioner

102,348 просмотров • 1 месяц назад

Finally video emerges of an actual Ukrainian attack in Toretsk, two weeks after their supposed ninja counteroffensive kicked off... or is it?⬇️ I'm referring to this video, which was published Wednesday, showing the destruction of two AFU M113s withdrawing troops from a southern suburb of Toretsk (Zabalka) that basically all mappers had placed well inside Russian lines (video 1, see figure 3 for the map). Video emerged later that day of Russian infantry destroying an apparently Ukrainian-held house in the same area with a satchel charge after suppressing the defenders with an RPG and small arms (the first segment of video 2). Interesting, I thought. So I looked at the map again, harder. And then I looked at the geolocated positions of Russian units, which the TG channel Creamy Caprice keeps a map of (figure 3, showing the location of the first video - the red and blue dots mark Russian and Ukrainian positions logged over the entire course of the battle). And that's when it struck me - Russian troops have never been spotted in the northwestern corner of the Zabalka district. They've been seen in the central part and they've been seen around the slag heaps, but not the northwest corner. And there's a relatively short route into it through the Ukrainian-held forest to the northwest, although the last mile is through an open field around the base of (and dominated by) the nearby slag heap. It would be an extremely dangerous journey. Now we come to what the video actually depicts - the withdrawal of troops. The first APC is hit while withdrawing and the escaping dismounts are effectively engaged after going to ground in the forest. The second APC then arrives in Zabalka, loads up, and is heavily hit and knocked out as it withdraws. A couple soldiers are seen heading deeper into the city on foot as it departs, but not a full squad - there may not have been room for them on the transport, or they may have (correctly) thought their chances were better on foot. Then the subsequent video emerged of Russian infantry clearing holdouts in the area, which could have very well been those men. So what happened here? Well, I pointed out earlier that it's standard practice to back-clear an urban area after taking it, to clear bypassed enemy strongholds and booby-traps and render the area safe to support further operations. I suspect there was actually a pocket of bypassed Ukrainian troops holed up in the western Zabalka District, a couple kilometers behind Russian lines, whom the AFU command tried to evacuate via APC earlier this week while they still had the chance to do so. And the Russians may very well have allowed those APCs to pull in and load up so they could - as they did - very coldly kill them while they were packed with infantry to withdraw. Why did the Ukrainian command undertake such a high-risk operation instead of simply writing these men off and telling them that it was every man for himself? Probably because these were Azov fighters and thus entitled to special treatment and consideration - one of their brigades operates in the area. In any event, far from suggesting a Ukrainian counterattack into south Toretsk, this turn of events suggests to me that the Russians are instead mopping up remaining resistance in the city. (As an addendum, the people behind Creamy Caprice think that the third segment of video 2 shows Ukrainian activity somewhat farther into the northwest corner of the Zabalka Dictrict, but that segment also doesn't show live troops - for all we know they were bombing an AFU comms repeater or something on the roof of that building. The second segment of that video is old judging by the snow on the ground.)

Finally video emerges of an actual Ukrainian attack in Toretsk, two weeks after their supposed ninja counteroffensive kicked off... or is it?⬇️ I'm referring to this video, which was published Wednesday, showing the destruction of two AFU M113s withdrawing troops from a southern suburb of Toretsk (Zabalka) that basically all mappers had placed well inside Russian lines (video 1, see figure 3 for the map). Video emerged later that day of Russian infantry destroying an apparently Ukrainian-held house in the same area with a satchel charge after suppressing the defenders with an RPG and small arms (the first segment of video 2). Interesting, I thought. So I looked at the map again, harder. And then I looked at the geolocated positions of Russian units, which the TG channel Creamy Caprice keeps a map of (figure 3, showing the location of the first video - the red and blue dots mark Russian and Ukrainian positions logged over the entire course of the battle). And that's when it struck me - Russian troops have never been spotted in the northwestern corner of the Zabalka district. They've been seen in the central part and they've been seen around the slag heaps, but not the northwest corner. And there's a relatively short route into it through the Ukrainian-held forest to the northwest, although the last mile is through an open field around the base of (and dominated by) the nearby slag heap. It would be an extremely dangerous journey. Now we come to what the video actually depicts - the withdrawal of troops. The first APC is hit while withdrawing and the escaping dismounts are effectively engaged after going to ground in the forest. The second APC then arrives in Zabalka, loads up, and is heavily hit and knocked out as it withdraws. A couple soldiers are seen heading deeper into the city on foot as it departs, but not a full squad - there may not have been room for them on the transport, or they may have (correctly) thought their chances were better on foot. Then the subsequent video emerged of Russian infantry clearing holdouts in the area, which could have very well been those men. So what happened here? Well, I pointed out earlier that it's standard practice to back-clear an urban area after taking it, to clear bypassed enemy strongholds and booby-traps and render the area safe to support further operations. I suspect there was actually a pocket of bypassed Ukrainian troops holed up in the western Zabalka District, a couple kilometers behind Russian lines, whom the AFU command tried to evacuate via APC earlier this week while they still had the chance to do so. And the Russians may very well have allowed those APCs to pull in and load up so they could - as they did - very coldly kill them while they were packed with infantry to withdraw. Why did the Ukrainian command undertake such a high-risk operation instead of simply writing these men off and telling them that it was every man for himself? Probably because these were Azov fighters and thus entitled to special treatment and consideration - one of their brigades operates in the area. In any event, far from suggesting a Ukrainian counterattack into south Toretsk, this turn of events suggests to me that the Russians are instead mopping up remaining resistance in the city. (As an addendum, the people behind Creamy Caprice think that the third segment of video 2 shows Ukrainian activity somewhat farther into the northwest corner of the Zabalka Dictrict, but that segment also doesn't show live troops - for all we know they were bombing an AFU comms repeater or something on the roof of that building. The second segment of that video is old judging by the snow on the ground.)

Armchair Warlord

15,294 просмотров • 1 год назад

#Battlefield6 Season 4 'Top Gun' - Carrier Strike Mode Overview 🚢 "Carrier Strike is a reworked version of classic Battlefield modes Carrier Assault (Battlefield 4) and Titan Mode (Battlefield 2142). Your objective is to take control of Strategic Surface-to-Surface Missile (SSM) batteries to weaken the enemy fleet, then lead the final, decisive assault to bring their carrier down (NATO's USS Grant and Pax Armata's PXS Condor)." The Basics Carrier Strike preserves the fun and spectacle of a large-scale assault on an aircraft carrier, while adding 'new and exciting ways' to get the job done. 🚢 Carrier Strike is playable on the Tsuru Reef's map. 🚢 32v32, single-round mode with a 30-minute timer. 🚢 Your goal is to reduce the enemy carrier's HP to zero or to destroy their M-COMs when they become available to detonate. A game also ends automatically after 30-minutes; the team with the highest carrier health wins and draws, while designed to be rare, can be achieved if both carriers have the exact same amount of health. 🚢 Your carrier serves as your mobile HQ and your primary target. In addition to the flight deck, where you can launch jets off for maximum altitude, the carrier includes an interior Headquarters spawn area and M-COMs. When a carrier takes damage, note the smoke, hull damage, and debris. When it drops to 50% health, it enters a "critical state” where an explosion rocks the deck, and the interior M-COM rooms become accessible from the water. "A carrier can be damaged by multiple means, but is primarily impacted by SSM batteries, five in total scattered around the map like Conquest flags." 🚢 Teams can control the SSM batteries by capturing them (remaining in its control area for a short period of time) and keeping them from enemy control. 🚢 SSM batteries periodically fire missiles at the enemy carrier, dealing consistent damage every 15 seconds. When a battery changes ownership, its firing cooldown resets, so it is possible to stop an upcoming salvo by capturing a battery before it fires off. 🚢 In addition to causing significant carrier damage, controlling the majority of SSM batteries also grants your teams access to automatic AA emplacements. This can disrupt incoming enemy aerial vehicles alongside your squads infantry and vehicular anti-air means. You can also damage the Carrier by direct fire, including explosives and weapons mounted on vehicles. "Direct damage is the other means of destroying the Carrier prior to its critical state. Jet bombs, explosives, tank ammo, and shoulder-fired launchers fired directly onto the enemy carrier’s deck or hull cause damage. However, among all these damage sources, jet bombs deal as much damage as the missiles from SSM Batteries, so consider putting your best air-to-ground specialist on strafing run duty." 🚢 Finally, once the carrier is in a critical state, its M-COM stations can be directly accessed and primed for detonation. At first, one M-COM room opens up to those invading by sea, while the Headquarters spawn allows the defending team to move immediately into the room after respawning. Once that M-COM is destroyed, a set of blast doors open to the other M-COM in the Carrier’s main reactor room.

#Battlefield6 Season 4 'Top Gun' - Carrier Strike Mode Overview 🚢 "Carrier Strike is a reworked version of classic Battlefield modes Carrier Assault (Battlefield 4) and Titan Mode (Battlefield 2142). Your objective is to take control of Strategic Surface-to-Surface Missile (SSM) batteries to weaken the enemy fleet, then lead the final, decisive assault to bring their carrier down (NATO's USS Grant and Pax Armata's PXS Condor)." The Basics Carrier Strike preserves the fun and spectacle of a large-scale assault on an aircraft carrier, while adding 'new and exciting ways' to get the job done. 🚢 Carrier Strike is playable on the Tsuru Reef's map. 🚢 32v32, single-round mode with a 30-minute timer. 🚢 Your goal is to reduce the enemy carrier's HP to zero or to destroy their M-COMs when they become available to detonate. A game also ends automatically after 30-minutes; the team with the highest carrier health wins and draws, while designed to be rare, can be achieved if both carriers have the exact same amount of health. 🚢 Your carrier serves as your mobile HQ and your primary target. In addition to the flight deck, where you can launch jets off for maximum altitude, the carrier includes an interior Headquarters spawn area and M-COMs. When a carrier takes damage, note the smoke, hull damage, and debris. When it drops to 50% health, it enters a "critical state” where an explosion rocks the deck, and the interior M-COM rooms become accessible from the water. "A carrier can be damaged by multiple means, but is primarily impacted by SSM batteries, five in total scattered around the map like Conquest flags." 🚢 Teams can control the SSM batteries by capturing them (remaining in its control area for a short period of time) and keeping them from enemy control. 🚢 SSM batteries periodically fire missiles at the enemy carrier, dealing consistent damage every 15 seconds. When a battery changes ownership, its firing cooldown resets, so it is possible to stop an upcoming salvo by capturing a battery before it fires off. 🚢 In addition to causing significant carrier damage, controlling the majority of SSM batteries also grants your teams access to automatic AA emplacements. This can disrupt incoming enemy aerial vehicles alongside your squads infantry and vehicular anti-air means. You can also damage the Carrier by direct fire, including explosives and weapons mounted on vehicles. "Direct damage is the other means of destroying the Carrier prior to its critical state. Jet bombs, explosives, tank ammo, and shoulder-fired launchers fired directly onto the enemy carrier’s deck or hull cause damage. However, among all these damage sources, jet bombs deal as much damage as the missiles from SSM Batteries, so consider putting your best air-to-ground specialist on strafing run duty." 🚢 Finally, once the carrier is in a critical state, its M-COM stations can be directly accessed and primed for detonation. At first, one M-COM room opens up to those invading by sea, while the Headquarters spawn allows the defending team to move immediately into the room after respawning. Once that M-COM is destroyed, a set of blast doors open to the other M-COM in the Carrier’s main reactor room.

Battlefield Bulletin

80,619 просмотров • 20 дней назад

i built an AI web development agent that creates full websites from voice commands. (Save 15+ hours per project, eliminate designer bottlenecks, deliver websites while you sleep) Here's how the automation works: → AI voice agent takes natural language requests through 11Labs → Automatically scrapes existing websites for context and content → Generates comprehensive product requirement documents → Controls browser automation to build sites in Lovable → Handles client feedback and edits without human intervention → Delivers production-ready websites in hours, not weeks This system turns last-minute panic into competitive advantage. No more "can you have this done by tomorrow" stress. The secret sauce? Agent orchestration. Instead of one mega-prompt trying to do everything, I built specialized sub-agents: ✅ Website Planner Agent (scraping & requirements) ✅ Browser Control Agent (Lovable automation) ✅ Parent Orchestrator (intelligent routing) Result: What used to take a web dev team 2-3 weeks now happens in 3-4 hours, completely automated. Want the full blueprint? Here are the steps: 1. Like & RT this post 2. Follow me so I can DM you 3. Comment 'BLUEPRINT' I'll send you an entire step-by-step video walkthrough & the full n8n workflow system for free (including all of the prompts). Let's get it.

i built an AI web development agent that creates full websites from voice commands. (Save 15+ hours per project, eliminate designer bottlenecks, deliver websites while you sleep) Here's how the automation works: → AI voice agent takes natural language requests through 11Labs → Automatically scrapes existing websites for context and content → Generates comprehensive product requirement documents → Controls browser automation to build sites in Lovable → Handles client feedback and edits without human intervention → Delivers production-ready websites in hours, not weeks This system turns last-minute panic into competitive advantage. No more "can you have this done by tomorrow" stress. The secret sauce? Agent orchestration. Instead of one mega-prompt trying to do everything, I built specialized sub-agents: ✅ Website Planner Agent (scraping & requirements) ✅ Browser Control Agent (Lovable automation) ✅ Parent Orchestrator (intelligent routing) Result: What used to take a web dev team 2-3 weeks now happens in 3-4 hours, completely automated. Want the full blueprint? Here are the steps: 1. Like & RT this post 2. Follow me so I can DM you 3. Comment 'BLUEPRINT' I'll send you an entire step-by-step video walkthrough & the full n8n workflow system for free (including all of the prompts). Let's get it.

David Roberts

54,964 просмотров • 11 месяцев назад

Anthropic shouldn't have made this free a company doing $47,000,000,000 a year wrote down exactly how they run their AI agents, published the numbers, and charged nobody it's called Graph Engineering: one lead Claude plans a job and hires a swarm of smaller ones, each working its own slice at the same time turns out how many it hires decides everything: → a simple lookup: 1 agent, 3 to 10 tool calls, no swarm → a straight comparison: 2 to 4 workers, 10 to 15 calls each → open-ended research: 10+ workers, one slice of the question each → the lead fires 3 to 5 at once, each running 3+ tools in parallel: up to 90% faster the briefs are where it dies. they told a lead agent to "research the semiconductor shortage" and one worker went off into the 2021 car chip crisis while two others wrote the same 2025 report twice so a worker now gets four things: an objective, an output format, which tools to touch, where its job ends they also pointed one small agent at their own badly written tool descriptions and let it rewrite them every agent that used the new ones finished 40% faster then one grader Claude scores every run 0.0 to 1.0 on five things: factual accuracy, citation accuracy, completeness, source quality, tool efficiency twenty test questions took one of their agents from 30% success to 80% one limit nobody quotes: most coding work has fewer genuinely parallel pieces than research, so a swarm on one repo mostly buys you coordination overhead it pays on wide search and on jobs bigger than one context window free, out of a $965,000,000,000 lab, and almost nobody has copied it yet bookmark this and copy the counts ↓

Anthropic shouldn't have made this free a company doing $47,000,000,000 a year wrote down exactly how they run their AI agents, published the numbers, and charged nobody it's called Graph Engineering: one lead Claude plans a job and hires a swarm of smaller ones, each working its own slice at the same time turns out how many it hires decides everything: → a simple lookup: 1 agent, 3 to 10 tool calls, no swarm → a straight comparison: 2 to 4 workers, 10 to 15 calls each → open-ended research: 10+ workers, one slice of the question each → the lead fires 3 to 5 at once, each running 3+ tools in parallel: up to 90% faster the briefs are where it dies. they told a lead agent to "research the semiconductor shortage" and one worker went off into the 2021 car chip crisis while two others wrote the same 2025 report twice so a worker now gets four things: an objective, an output format, which tools to touch, where its job ends they also pointed one small agent at their own badly written tool descriptions and let it rewrite them every agent that used the new ones finished 40% faster then one grader Claude scores every run 0.0 to 1.0 on five things: factual accuracy, citation accuracy, completeness, source quality, tool efficiency twenty test questions took one of their agents from 30% success to 80% one limit nobody quotes: most coding work has fewer genuinely parallel pieces than research, so a swarm on one repo mostly buys you coordination overhead it pays on wide search and on jobs bigger than one context window free, out of a $965,000,000,000 lab, and almost nobody has copied it yet bookmark this and copy the counts ↓

Argona

117,177 просмотров • 1 месяц назад

🚨🇨🇳 PENTAGON IN PANIC: CHINA'S AI TURNS DAYS OF AIR-WAR PLANNING INTO MINUTES China has revealed details of an intelligent planning system already used by the PLA Air Force to organize large-scale air operations. It can turn hundreds of targets and dozens of formations into a coordinated attack plan within minutes — work that previously took military staffs several days. 🔸 Developed by a team led by senior engineer Deng Jianping, the system uses intelligent algorithms to screen targets, allocate firepower, connect attack chains and calculate the timing of successive strike waves. 🔸 During full-process testing, it completed the planning work three times faster than required while exceeding the coordination-accuracy target by 20%. 🔸 The decisive field test came in 2022 at a high-altitude training area. More than 100 tactical units executed a coordinated strike with timing accurate to a single second. 🔸 Synchronization allows different weapons to reach radars, airfields, missile batteries and command posts almost simultaneously, denying the defender time to warn other units, relocate assets or reorganize the surviving network. 🔸 The system also compresses the gap between intelligence and attack. New targets can be added, weapons reassigned and strike sequences rebuilt within minutes instead of forcing planners to repeat a days-long process. 🔸 In a conflict around Taiwan, China could use the software to coordinate aircraft, drones, missiles and electronic-warfare assets against airfields, radar stations, air defenses, communications nodes and headquarters in one opening assault. More than 100 dispersed tactical units can now be organized to hit different targets at the same second. Taiwan’s defenses would have to detect, identify and engage the entire attack before the first damaged radar or command centre drops out of the network. Which targets would China hit first: airfields, radars, missile batteries or command centres?

🚨🇨🇳 PENTAGON IN PANIC: CHINA'S AI TURNS DAYS OF AIR-WAR PLANNING INTO MINUTES China has revealed details of an intelligent planning system already used by the PLA Air Force to organize large-scale air operations. It can turn hundreds of targets and dozens of formations into a coordinated attack plan within minutes — work that previously took military staffs several days. 🔸 Developed by a team led by senior engineer Deng Jianping, the system uses intelligent algorithms to screen targets, allocate firepower, connect attack chains and calculate the timing of successive strike waves. 🔸 During full-process testing, it completed the planning work three times faster than required while exceeding the coordination-accuracy target by 20%. 🔸 The decisive field test came in 2022 at a high-altitude training area. More than 100 tactical units executed a coordinated strike with timing accurate to a single second. 🔸 Synchronization allows different weapons to reach radars, airfields, missile batteries and command posts almost simultaneously, denying the defender time to warn other units, relocate assets or reorganize the surviving network. 🔸 The system also compresses the gap between intelligence and attack. New targets can be added, weapons reassigned and strike sequences rebuilt within minutes instead of forcing planners to repeat a days-long process. 🔸 In a conflict around Taiwan, China could use the software to coordinate aircraft, drones, missiles and electronic-warfare assets against airfields, radar stations, air defenses, communications nodes and headquarters in one opening assault. More than 100 dispersed tactical units can now be organized to hit different targets at the same second. Taiwan’s defenses would have to detect, identify and engage the entire attack before the first damaged radar or command centre drops out of the network. Which targets would China hit first: airfields, radars, missile batteries or command centres?

NewRulesGeopolitics

20,124 просмотров • 1 месяц назад

In 2026, Venture Capital will eat Private Equity It used to be that venture capital and private equity lived on two separate planets: VC = San Francisco PE = New York They targeted completely different universes of companies: --> PE - people heavy biz services, stable/low growth, predictable cashflows --> VC - tech-forward, high growth, high risk, massive TAM What was the playbook for B2B VC backed startups? --> Grow to unicorn scale by selling to other early adopter tech companies, then Fortune 500s XX> SMB and mid-market services - think field services, IT staffing, accounting, construction, recruiting - were always tough to sell into for startups Why? -->Thin margins, high labor costs, and small IT budgets >> But as AI eats labor, these businesses are in play << There are 3 ways where VC and PE are colliding: 1/ Private Equity funds will become channel partners for startups. PE funds are focused on financial engineering and cost optimization. Startups building AI products and services can sell across their portfolio to automate the backoffice and uplevel sales and marketing. PE funds have made AI their #1 strategic priority and have hired central leaders to oversee their portfolio adoption efforts 2/ PE portfolio pages are a startup idea menu Private equity will often buyout vertical software companies whose TAM didn’t allow venture scaled returns. As software evolves from data storage and collaboration to agents taking action and completing work, AI should massively expand the TAM for these categories. Founders will set their sights on unseating these legacy incumbents backed by private equity. All they have to do is look at their portfolio pages for category ideas 3/ AI Rollups This is one of the most direct ways that VC is eating PE VC backed AI platform businesses are not just selling software but acquiring legacy business services companies to own the value chain end to end. As an example, our speedrun company AgentAstra is acquiring freight forwarding services businesses with mostly debt and integrating AI deeply into their operations These companies aim to increase margins by at least 2x and make them “AI native” tl;dr - While the west coast, Patagonia-wearing VCs and the east coast, PE suits used to live in different universes, in 2026 with AI, I believe, those worlds converge

In 2026, Venture Capital will eat Private Equity It used to be that venture capital and private equity lived on two separate planets: VC = San Francisco PE = New York They targeted completely different universes of companies: --> PE - people heavy biz services, stable/low growth, predictable cashflows --> VC - tech-forward, high growth, high risk, massive TAM What was the playbook for B2B VC backed startups? --> Grow to unicorn scale by selling to other early adopter tech companies, then Fortune 500s XX> SMB and mid-market services - think field services, IT staffing, accounting, construction, recruiting - were always tough to sell into for startups Why? -->Thin margins, high labor costs, and small IT budgets >> But as AI eats labor, these businesses are in play << There are 3 ways where VC and PE are colliding: 1/ Private Equity funds will become channel partners for startups. PE funds are focused on financial engineering and cost optimization. Startups building AI products and services can sell across their portfolio to automate the backoffice and uplevel sales and marketing. PE funds have made AI their #1 strategic priority and have hired central leaders to oversee their portfolio adoption efforts 2/ PE portfolio pages are a startup idea menu Private equity will often buyout vertical software companies whose TAM didn’t allow venture scaled returns. As software evolves from data storage and collaboration to agents taking action and completing work, AI should massively expand the TAM for these categories. Founders will set their sights on unseating these legacy incumbents backed by private equity. All they have to do is look at their portfolio pages for category ideas 3/ AI Rollups This is one of the most direct ways that VC is eating PE VC backed AI platform businesses are not just selling software but acquiring legacy business services companies to own the value chain end to end. As an example, our speedrun company AgentAstra is acquiring freight forwarding services businesses with mostly debt and integrating AI deeply into their operations These companies aim to increase margins by at least 2x and make them “AI native” tl;dr - While the west coast, Patagonia-wearing VCs and the east coast, PE suits used to live in different universes, in 2026 with AI, I believe, those worlds converge

Troy Kirwin

187,377 просмотров • 8 месяцев назад

aster-2:native market analysis. For months, aster-2:native lived between the $0.64-$0.66 range. Every breakout attempt was sold, every move capped. That type of consolidation is not weakness, it’s absorption. Now we finally broke through $0.71, a level that had previously rejected price multiple times. That changes the structure completely. The market has now established a new higher floor. Here’s how I’m looking at it: • $0.71 was the key breakout level • Hold and momentum targets $0.81 next • Break $0.81 and we enter price discovery And once price discovery starts, there are no historical resistance levels overhead. That’s where things get violent. What’s fascinating is that 90% of Aster’s existence has been during terrible market conditions. Most projects spent this period bleeding out, losing users, or disappearing completely. Not aster-2:native . The team kept building through the bear while almost every other project struggled to survive. And every single time the market turns healthy, aster-2:native absolutely rips. People forget this project launched into a real bull environment and ran to $2.40 within weeks. That matters. Now combine that with where the project sits fundamentally: • Over 12% of supply staked • Daily buybacks continuing • Supply constantly tightening • RWA fee upgrades rolling out • Binance ecosystem integration expanding • Perp DEX market structure strengthening Looking at the perp DEX space objectively, Aster 🥷 now looks comfortably positioned at #2 with no real pressure from below. The only direction left to fight for is up. The RWA fee reduction is also being massively underestimated. Slashing taker fees to 0.9 bp while maintaining 0 bp maker fees is not a short-term gimmick. It’s infrastructure positioning for future TradFi flow. Gold, equities, commodities, AI-related exposure, prediction markets. This is clearly becoming much bigger than just another crypto perp exchange. Leonard 💛 Aster 🥷 and CZ 🔶 BNB know exactly what they’re building here. The game plan is obvious: Consolidate supply. Increase staking participation. Expand volume. Grow buybacks. Scale RWAs. Pull Binance order flow deeper into the ecosystem. This feels like the early stages of something much larger.

aster-2:native market analysis. For months, aster-2:native lived between the $0.64-$0.66 range. Every breakout attempt was sold, every move capped. That type of consolidation is not weakness, it’s absorption. Now we finally broke through $0.71, a level that had previously rejected price multiple times. That changes the structure completely. The market has now established a new higher floor. Here’s how I’m looking at it: • $0.71 was the key breakout level • Hold and momentum targets $0.81 next • Break $0.81 and we enter price discovery And once price discovery starts, there are no historical resistance levels overhead. That’s where things get violent. What’s fascinating is that 90% of Aster’s existence has been during terrible market conditions. Most projects spent this period bleeding out, losing users, or disappearing completely. Not aster-2:native . The team kept building through the bear while almost every other project struggled to survive. And every single time the market turns healthy, aster-2:native absolutely rips. People forget this project launched into a real bull environment and ran to $2.40 within weeks. That matters. Now combine that with where the project sits fundamentally: • Over 12% of supply staked • Daily buybacks continuing • Supply constantly tightening • RWA fee upgrades rolling out • Binance ecosystem integration expanding • Perp DEX market structure strengthening Looking at the perp DEX space objectively, Aster 🥷 now looks comfortably positioned at #2 with no real pressure from below. The only direction left to fight for is up. The RWA fee reduction is also being massively underestimated. Slashing taker fees to 0.9 bp while maintaining 0 bp maker fees is not a short-term gimmick. It’s infrastructure positioning for future TradFi flow. Gold, equities, commodities, AI-related exposure, prediction markets. This is clearly becoming much bigger than just another crypto perp exchange. Leonard 💛 Aster 🥷 and CZ 🔶 BNB know exactly what they’re building here. The game plan is obvious: Consolidate supply. Increase staking participation. Expand volume. Grow buybacks. Scale RWAs. Pull Binance order flow deeper into the ecosystem. This feels like the early stages of something much larger.

sdm

13,148 просмотров • 3 месяцев назад