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Retro gaming shouldn’t be complicated. ROCKNIX fixes that. • install once → everything works • supports dozens of consoles out of the box • no manual emulator setup Built for gamers: • fast, stable Linux system • optimized for handheld devices • smooth performance across systems Features: • RetroAchievements...

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Techjunkie Aman

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24,491 次观看 • 3 个月前 •via X (Twitter)

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This is what Windows should’ve been. Zorin OS is a Linux distro made for normal users. No learning curve. Feels familiar from day one. You get: • Windows/macOS-style layouts • Smooth, fast performance • No ads, no bloat, no tracking • Built-in apps (ready out of the box) More: • Runs on PCs up to 10–15 years old • Lightweight but powerful • Secure + virus-resistant • Software store with thousands of apps • Can run many Windows apps (.exe support) • Easy dual-boot with Windows Plus: • Gaming support (Steam, Proton, Lutris) • GPU drivers pre-installed + optimized • Works great on laptops + desktops • Try it directly from USB (no install needed) • Long-term updates (years of support) Extra: • LibreOffice included (MS Office alternative) • Multiple desktop layouts (switch anytime) • Zorin Connect (phone ↔ PC integration) • Works offline, no account required No tweaking. No terminal needed. Just install → use.
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This is what Windows should’ve been. Zorin OS is a Linux distro made for normal users. No learning curve. Feels familiar from day one. You get: • Windows/macOS-style layouts • Smooth, fast performance • No ads, no bloat, no tracking • Built-in apps (ready out of the box) More: • Runs on PCs up to 10–15 years old • Lightweight but powerful • Secure + virus-resistant • Software store with thousands of apps • Can run many Windows apps (.exe support) • Easy dual-boot with Windows Plus: • Gaming support (Steam, Proton, Lutris) • GPU drivers pre-installed + optimized • Works great on laptops + desktops • Try it directly from USB (no install needed) • Long-term updates (years of support) Extra: • LibreOffice included (MS Office alternative) • Multiple desktop layouts (switch anytime) • Zorin Connect (phone ↔ PC integration) • Works offline, no account required No tweaking. No terminal needed. Just install → use.

Techjunkie Aman

23,901 次观看 • 3 个月前

Welcome to CyberNetwork, where the future of gaming meets the power of blockchain technology. CyberNetwork is revolutionising the gaming industry with a decentralised platform that offers unparalleled opportunities for gamers, developers, and investors. By leveraging blockchain technology, we ensure transparency, security, and fairness, transforming traditional gaming models into more immersive and rewarding experiences. CyberNetwork is built on SovereignChain technology from Multiversᕽ, featuring: ⚡️ 100K Transactions Per Second (TPS): Ensuring fast and seamless interactions. ⚡️ 1-Second Block Timing: Providing near-instantaneous transaction confirmations. ⚡️ 2-Second Finality: Guaranteeing rapid settlement and reduced waiting times. This robust infrastructure supports our mission to create a more equitable, transparent, and rewarding gaming ecosystem. Leveraging #MultiversX's Interoperability layer, CyberNetwork is able to seamlessly interact with major blockchains like #Bitcoin, #Ethereum, and #Solana. CyberNetwork Features: 1. Gaming Launchpad 🚀: Empowering game developers to launch and scale their projects. 2. Game Store 🏪: A comprehensive game distribution platform for discovering and purchasing games. 3. Digital Asset Marketplace 🎨: Facilitating the creation, buying, and selling of in-game assets. 4. DEX 💱: Enabling decentralized exchanges of gaming tokens and assets. 5. Gamer's Passport 🎮: A unified identity and achievement system across games. 6. Metaverse Services 🌌: A service for game developers to create high-density virtual worlds. CyberNetwork is set to transform how we experience and engage with games. In the upcoming days, we’ll be unveiling each of these exciting features one by one. Stay tuned for a journey of discovery! For more info: Telegram Discord #Web3Gaming #BlockchainGaming
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Welcome to CyberNetwork, where the future of gaming meets the power of blockchain technology. CyberNetwork is revolutionising the gaming industry with a decentralised platform that offers unparalleled opportunities for gamers, developers, and investors. By leveraging blockchain technology, we ensure transparency, security, and fairness, transforming traditional gaming models into more immersive and rewarding experiences. CyberNetwork is built on SovereignChain technology from Multiversᕽ, featuring: ⚡️ 100K Transactions Per Second (TPS): Ensuring fast and seamless interactions. ⚡️ 1-Second Block Timing: Providing near-instantaneous transaction confirmations. ⚡️ 2-Second Finality: Guaranteeing rapid settlement and reduced waiting times. This robust infrastructure supports our mission to create a more equitable, transparent, and rewarding gaming ecosystem. Leveraging #MultiversX's Interoperability layer, CyberNetwork is able to seamlessly interact with major blockchains like #Bitcoin, #Ethereum, and #Solana. CyberNetwork Features: 1. Gaming Launchpad 🚀: Empowering game developers to launch and scale their projects. 2. Game Store 🏪: A comprehensive game distribution platform for discovering and purchasing games. 3. Digital Asset Marketplace 🎨: Facilitating the creation, buying, and selling of in-game assets. 4. DEX 💱: Enabling decentralized exchanges of gaming tokens and assets. 5. Gamer's Passport 🎮: A unified identity and achievement system across games. 6. Metaverse Services 🌌: A service for game developers to create high-density virtual worlds. CyberNetwork is set to transform how we experience and engage with games. In the upcoming days, we’ll be unveiling each of these exciting features one by one. Stay tuned for a journey of discovery! For more info: Telegram Discord #Web3Gaming #BlockchainGaming

CyberNetwork

74,805 次观看 • 2 年前

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.
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Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Guri Singh

2,180,357 次观看 • 4 个月前

A 17 year old high schooler told his mom he needed a Steam Deck for school. She said no, it's a gaming console. He said it runs Linux. She didn't know what that means. Bought it for his birthday. $280. He never installed a single game on it. Opened the terminal, installed Claude Code and typed his first command while holding the device like a PlayStation controller. Thumbsticks on both sides. Code editor in the middle. The most ridiculous dev setup anyone has ever seen. At second 0:09 you can read what he typed into the terminal: claude your code looks like absolute shit Claude didn't argue. Just started rewriting the shader, adding bloom effects, fixing chromatic aberration and improving the particle system. On a gaming console held in two hands on a couch. His friends play Fortnite on their Steam Decks. He builds software on his while lying in bed. He set up Claude Code with custom skills, hooks that auto run tests every time a file is saved and memory that remembers every project across sessions. The stuff most developers pay $200 a month for and use at maybe 20% capacity. He runs it on a $280 handheld and squeezes out every feature. Within three weeks he had built and sold four small apps to local businesses. A booking page for a barber shop, an inventory tracker for a vape store, a menu site for a taco truck and a scheduling tool for a dog groomer. All built on a Steam Deck in his bedroom. All coded by Claude while he gave instructions with his thumbs. Made over $13,000 in his first month. His mom still thinks he plays games on it. His teacher caught him using it during study hall. Looked at the screen expecting a game. Saw green code scrolling and Claude asking: Do you want to make this edit to main.js ? Teacher had no idea what she was looking at. Told him to put it away. He closed the lid. Claude kept running inside. A $280 gaming console that his mom bought thinking it was a toy is now a development workstation that earns more per month than her car payment. Setup time: 20 minutes once. Time he saves every day: 3 to 5 hours. Money made in month one: $13,000. Games installed: zero. His grandpa asked him to install FIFA last weekend. He said the console is busy. Grandpa asked doing what. He said working. Grandpa didn't ask again.
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A 17 year old high schooler told his mom he needed a Steam Deck for school. She said no, it's a gaming console. He said it runs Linux. She didn't know what that means. Bought it for his birthday. $280. He never installed a single game on it. Opened the terminal, installed Claude Code and typed his first command while holding the device like a PlayStation controller. Thumbsticks on both sides. Code editor in the middle. The most ridiculous dev setup anyone has ever seen. At second 0:09 you can read what he typed into the terminal: claude your code looks like absolute shit Claude didn't argue. Just started rewriting the shader, adding bloom effects, fixing chromatic aberration and improving the particle system. On a gaming console held in two hands on a couch. His friends play Fortnite on their Steam Decks. He builds software on his while lying in bed. He set up Claude Code with custom skills, hooks that auto run tests every time a file is saved and memory that remembers every project across sessions. The stuff most developers pay $200 a month for and use at maybe 20% capacity. He runs it on a $280 handheld and squeezes out every feature. Within three weeks he had built and sold four small apps to local businesses. A booking page for a barber shop, an inventory tracker for a vape store, a menu site for a taco truck and a scheduling tool for a dog groomer. All built on a Steam Deck in his bedroom. All coded by Claude while he gave instructions with his thumbs. Made over $13,000 in his first month. His mom still thinks he plays games on it. His teacher caught him using it during study hall. Looked at the screen expecting a game. Saw green code scrolling and Claude asking: Do you want to make this edit to main.js ? Teacher had no idea what she was looking at. Told him to put it away. He closed the lid. Claude kept running inside. A $280 gaming console that his mom bought thinking it was a toy is now a development workstation that earns more per month than her car payment. Setup time: 20 minutes once. Time he saves every day: 3 to 5 hours. Money made in month one: $13,000. Games installed: zero. His grandpa asked him to install FIFA last weekend. He said the console is busy. Grandpa asked doing what. He said working. Grandpa didn't ask again.

Marlow

3,236,654 次观看 • 2 个月前

68 college students played video games an hour a day for 30 weeks. They got measurably smarter. EEG brain scans confirmed it. The setup was simple. Half the group played League of Legends, an action game. The other half played Legends of the Three Kingdoms, a strategy card game. Same hours, same schedule, no gaming experience for anyone going in. Both groups improved on attention, working memory, and executive function. The League group's gains were significantly larger in spatial attention and spatial working memory. The benefits were still measurable 10 weeks after the gaming stopped. None of this is new. Daphne Bavelier's lab at the University of Geneva has been replicating this finding since the early 2000s. Her 2018 meta-analysis in Psychological Bulletin pulled data from 8,970 participants across 15 years and found the same thing. Action games train attentional control, a brain skill that transfers to other tasks. Strategy games train deliberation, which mostly stays inside the strategy game. The mechanism is the counterintuitive part. Action games train your brain by giving you no time to think. The brain can't deliberate. League of Legends throws 9 champions, hundreds of minions, dozens of abilities, mana, cooldowns, and map state at you, all updating in milliseconds. The brain learns to perceive faster instead. That perceptual speed transfers to anything else that demands the same skill. Including surgery. The 2007 Rosser study in Archives of Surgery found that laparoscopic surgeons who played video games more than 3 hours a week made 37% fewer errors, completed procedures 27% faster, and scored 42% higher on overall performance. The top third of gamers made 47% fewer errors. Laparoscopic surgery is a 2D screen with distorted depth perception, remote-controlled instruments, and multiple data streams updating in real time. The cognitive profile is almost identical to an action video game. The 10-week persistence is the part that should change how this gets discussed. If the gains were just from practicing the game, they would have disappeared the moment the students stopped playing. They didn't. The 30 weeks rewired the perceptual system, and the rewiring stayed.
0:19

68 college students played video games an hour a day for 30 weeks. They got measurably smarter. EEG brain scans confirmed it. The setup was simple. Half the group played League of Legends, an action game. The other half played Legends of the Three Kingdoms, a strategy card game. Same hours, same schedule, no gaming experience for anyone going in. Both groups improved on attention, working memory, and executive function. The League group's gains were significantly larger in spatial attention and spatial working memory. The benefits were still measurable 10 weeks after the gaming stopped. None of this is new. Daphne Bavelier's lab at the University of Geneva has been replicating this finding since the early 2000s. Her 2018 meta-analysis in Psychological Bulletin pulled data from 8,970 participants across 15 years and found the same thing. Action games train attentional control, a brain skill that transfers to other tasks. Strategy games train deliberation, which mostly stays inside the strategy game. The mechanism is the counterintuitive part. Action games train your brain by giving you no time to think. The brain can't deliberate. League of Legends throws 9 champions, hundreds of minions, dozens of abilities, mana, cooldowns, and map state at you, all updating in milliseconds. The brain learns to perceive faster instead. That perceptual speed transfers to anything else that demands the same skill. Including surgery. The 2007 Rosser study in Archives of Surgery found that laparoscopic surgeons who played video games more than 3 hours a week made 37% fewer errors, completed procedures 27% faster, and scored 42% higher on overall performance. The top third of gamers made 47% fewer errors. Laparoscopic surgery is a 2D screen with distorted depth perception, remote-controlled instruments, and multiple data streams updating in real time. The cognitive profile is almost identical to an action video game. The 10-week persistence is the part that should change how this gets discussed. If the gains were just from practicing the game, they would have disappeared the moment the students stopped playing. They didn't. The 30 weeks rewired the perceptual system, and the rewiring stayed.

Aakash Gupta

1,415,907 次观看 • 2 个月前

Get ready for an epic 2024: GameSwift Modular Blockchain will propel web3 gaming to the next level. GameSwift AI 🎮 has a modular architecture designed to provide flexibility and scalability, tailor-made for the dynamic world of gaming. Forget monolithic limitations. GameSwift's modular magic allows you to customize and optimize your blockchain experience like never before. Think of it like a Lego set for your dApps, where you slot in the perfect pieces to create the ultimate gaming experience. 🧱 The GameSwift Modular Blockchain has it all: • Adaptability: Need a super fast chain for ultrafast transactions? zkEVM handles the heavy lifting while the modular design keeps everything optimized. • Flexibility: As technology evolves, the GameSwift ecosystem can adapt. Upgrade modules, integrate new features and stay cutting edge. GameSwift's modularity lets you mix and match capabilities to craft the perfect platform for your game. • Scalability: GameSwift's modular design effortlessly adapts to demand, ensuring smooth gameplay for all, even as your game explodes in popularity. CoinGecko includes GameSwift in Modular Blockchain category One important milestone has been crypto data platform CoinGecko recognizing GameSwift by including it in their "Top Modular Blockchain Coins by Market Cap" category. This emphasizes GameSwift's credibility, potential and hard work in the wider crypto marketplace. Notably, of the 3 projects in this category, GameSwift has the lowest market cap - making it a true gem for the next BTC halving and bull run. 🔥 The Success of Modular Blockchains As we all know, Celestia is a modular blockchain that has also seen great achievements, showing the power and potential of modular designs. With GameSwift being another highly innovative modular blockchain and its 2024 roadmap, we can be certain this project will continue to surge. And with its low market cap, GSWIFT token is undoubtedly the gem of modular blockchains in crypto. But wait, there's more! GameSwift's modularity also opens up a world of new possibilities for developers: • Optimize efficiency - focus on creating incredible playability without worrying about exorbitant fees. Speed up contract execution and build games that are fun and financially sustainable. • Focus on what matters - Forget security worries, GameSwift's ZK Shared Security system keeps your game safe while you concentrate on crafting the best possible experience. • Use cross-chains - seamlessly connect with other blockchains, expanding your reach and unlocking new opportunities for your game. And for $GSWIFT holders? Get ready for some sweet sidechain action! Stake your tokens to earn rewards from the games built on GameSwift's modular ecosystem. This is a win-win situation: developers get the tools they need to build amazing games and you get rewarded for being part of the GameSwift ecosystem. In conclusion, the GameSwift modular blockchain is the key to unlocking a world of possibilities, where developers can create without limits and players can experience games like never before. With GameSwift, the future of Web3 gaming is modular, adaptable, and limitless. 🚀
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Get ready for an epic 2024: GameSwift Modular Blockchain will propel web3 gaming to the next level. GameSwift AI 🎮 has a modular architecture designed to provide flexibility and scalability, tailor-made for the dynamic world of gaming. Forget monolithic limitations. GameSwift's modular magic allows you to customize and optimize your blockchain experience like never before. Think of it like a Lego set for your dApps, where you slot in the perfect pieces to create the ultimate gaming experience. 🧱 The GameSwift Modular Blockchain has it all: • Adaptability: Need a super fast chain for ultrafast transactions? zkEVM handles the heavy lifting while the modular design keeps everything optimized. • Flexibility: As technology evolves, the GameSwift ecosystem can adapt. Upgrade modules, integrate new features and stay cutting edge. GameSwift's modularity lets you mix and match capabilities to craft the perfect platform for your game. • Scalability: GameSwift's modular design effortlessly adapts to demand, ensuring smooth gameplay for all, even as your game explodes in popularity. CoinGecko includes GameSwift in Modular Blockchain category One important milestone has been crypto data platform CoinGecko recognizing GameSwift by including it in their "Top Modular Blockchain Coins by Market Cap" category. This emphasizes GameSwift's credibility, potential and hard work in the wider crypto marketplace. Notably, of the 3 projects in this category, GameSwift has the lowest market cap - making it a true gem for the next BTC halving and bull run. 🔥 The Success of Modular Blockchains As we all know, Celestia is a modular blockchain that has also seen great achievements, showing the power and potential of modular designs. With GameSwift being another highly innovative modular blockchain and its 2024 roadmap, we can be certain this project will continue to surge. And with its low market cap, GSWIFT token is undoubtedly the gem of modular blockchains in crypto. But wait, there's more! GameSwift's modularity also opens up a world of new possibilities for developers: • Optimize efficiency - focus on creating incredible playability without worrying about exorbitant fees. Speed up contract execution and build games that are fun and financially sustainable. • Focus on what matters - Forget security worries, GameSwift's ZK Shared Security system keeps your game safe while you concentrate on crafting the best possible experience. • Use cross-chains - seamlessly connect with other blockchains, expanding your reach and unlocking new opportunities for your game. And for $GSWIFT holders? Get ready for some sweet sidechain action! Stake your tokens to earn rewards from the games built on GameSwift's modular ecosystem. This is a win-win situation: developers get the tools they need to build amazing games and you get rewarded for being part of the GameSwift ecosystem. In conclusion, the GameSwift modular blockchain is the key to unlocking a world of possibilities, where developers can create without limits and players can experience games like never before. With GameSwift, the future of Web3 gaming is modular, adaptable, and limitless. 🚀

ETHachi Uchiha | Crypto DEGENius

10,982 次观看 • 2 年前

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.
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Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Varun

308,337 次观看 • 3 个月前

We all remember. We all remember when blockchain was pitched as the next big thing. And today, we feel like we’ve been waiting and waiting. Until recently, Blockchain was too expensive, slow under load, and hard to integrate for most businesses. So enterprises ignored it. It didn’t solve their business problems. That’s changed. Why blockchain, why now? Businesses don’t care about the tech, they care about cost and performance. They’d ask a simple question “Does it save or make me more money?” For a long time, blockchain didn’t clearly do this. That’s no longer true. Blockchain is proving real business cases, especially on Avalanche. On Avalanche, transactions cost fractions of a cent. settle in about a second. And instead of forcing everything onto one shared chain, businesses can launch their own Avalanche L1s with their own rules. To understand this let’s identify the problem and then provide the solution in a way that's easy to understand. Where Businesses Lose Money Most large industries lose money due to operational inefficiencies. Data lives in different systems. Teams spend hours reconciling records that should already match. Intermediaries sit in the middle, taking fees to coordinate all of it. Individually, each step looks small. Together, they create real cost: > Labor spent on manual processes > Capital locked up during settlement delays > Fees paid to intermediaries > Risk introduced by time gaps and mismatched data This is where businesses actually lose money. Not in big, obvious ways. In constant, compounding friction. Take Private Credit, for Example Private credit is loans held outside of traditional banks. It’s a multi-trillion dollar market, and much of it still runs on spreadsheets and weekly reconciliation processes. Loan data is tracked across systems. Teams manually process requests. Funds move on traditional rails, often on delayed cycles. It doesn’t have to be this way Entire teams exist just to keep systems in sync. Now move that system onto Avalanche. Loan data updates in real time. Transactions settle in about a second. Every participant sees the same state instantly. Reconciliation isn’t a separate step because the system itself is the source of truth. The impact is straightforward. > Reduced manual work > Shortened settlement cycles > Fewer layers of coordination between parties Avalanche is Infrastructure for Real Businesses Avalanche is designed to match how businesses actually operate. Instead of sharing a single chain, they can launch their own Avalanche L1s with custom rules, built-in compliance, and predictable performance. They control the system. Avalanche’s Moment For the longest time, blockchain naysayers said this could all be done better with spreadsheets or existing systems. They were right. That’s what the technology allowed. Now it’s changed. Avalanche can replace many of those systems with real-time settlement, shared data, and automated execution. For the first time, the economics work. Built for business. 🔺
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We all remember. We all remember when blockchain was pitched as the next big thing. And today, we feel like we’ve been waiting and waiting. Until recently, Blockchain was too expensive, slow under load, and hard to integrate for most businesses. So enterprises ignored it. It didn’t solve their business problems. That’s changed. Why blockchain, why now? Businesses don’t care about the tech, they care about cost and performance. They’d ask a simple question “Does it save or make me more money?” For a long time, blockchain didn’t clearly do this. That’s no longer true. Blockchain is proving real business cases, especially on Avalanche. On Avalanche, transactions cost fractions of a cent. settle in about a second. And instead of forcing everything onto one shared chain, businesses can launch their own Avalanche L1s with their own rules. To understand this let’s identify the problem and then provide the solution in a way that's easy to understand. Where Businesses Lose Money Most large industries lose money due to operational inefficiencies. Data lives in different systems. Teams spend hours reconciling records that should already match. Intermediaries sit in the middle, taking fees to coordinate all of it. Individually, each step looks small. Together, they create real cost: > Labor spent on manual processes > Capital locked up during settlement delays > Fees paid to intermediaries > Risk introduced by time gaps and mismatched data This is where businesses actually lose money. Not in big, obvious ways. In constant, compounding friction. Take Private Credit, for Example Private credit is loans held outside of traditional banks. It’s a multi-trillion dollar market, and much of it still runs on spreadsheets and weekly reconciliation processes. Loan data is tracked across systems. Teams manually process requests. Funds move on traditional rails, often on delayed cycles. It doesn’t have to be this way Entire teams exist just to keep systems in sync. Now move that system onto Avalanche. Loan data updates in real time. Transactions settle in about a second. Every participant sees the same state instantly. Reconciliation isn’t a separate step because the system itself is the source of truth. The impact is straightforward. > Reduced manual work > Shortened settlement cycles > Fewer layers of coordination between parties Avalanche is Infrastructure for Real Businesses Avalanche is designed to match how businesses actually operate. Instead of sharing a single chain, they can launch their own Avalanche L1s with custom rules, built-in compliance, and predictable performance. They control the system. Avalanche’s Moment For the longest time, blockchain naysayers said this could all be done better with spreadsheets or existing systems. They were right. That’s what the technology allowed. Now it’s changed. Avalanche can replace many of those systems with real-time settlement, shared data, and automated execution. For the first time, the economics work. Built for business. 🔺

Avalanche🔺

13,068 次观看 • 3 个月前

Elon Musk just said something that disqualifies most of Silicon Valley. Musk: “If you’re gonna create a company, the first thing you should try to do is create a working prototype.” Not a pitch deck. Not a business model. Not a funding narrative wrapped in 40 slides designed to make something that doesn’t exist sound inevitable. A working prototype. That alone rules out most of the people who call themselves founders. Musk: “Everything looks great on PowerPoint. You can make anything work on PowerPoint.” That’s not a punchline. It’s an indictment. PowerPoint has no physics. No engineering constraints. No supply chain. No thermal limits. No gravity. Nothing that forces an idea to prove it deserves to exist in the physical world. It is the most effective tool ever built for making the impossible look funded. Spend long enough in that world, and the distance between something that works and something that looks like it works stops feeling important. That is the real damage. Musk: “If you have an actual demonstration article, even if it’s in primitive form, that’s much, much more effective for convincing people.” Even if it barely functions. Even if half of it is held together with tape. Even if it looks like it has no business working yet. Because a crude prototype carries something a polished deck never will. Proof. Not projected proof. Not modeled proof. Not “if we hit these assumptions” proof. The kind that sits on a table and either works or doesn’t. No narrative saves it. No charisma rescues it. No market-sizing slide covers for it. It just has to be real. That is the gap between Musk and the rest of the industry. Not intelligence. Not capital. Not ambition. He builds first. Then talks. Everyone else talks first. Then fundraises to find out if the thing could even be built. The distance between those two sequences is the entire history of why most things never get made. The professional world is engineered to produce one type of person. Someone who can describe, in extraordinary detail, what they would build if given the resources. Not someone who builds. MBA programs train you to present. Accelerators train you to present. Pitch competitions are literally scored on who presents best. The system does not reward builders. It rewards the performance of building. And the people deepest inside that system are the last ones who will ever notice. Musk landed a rocket on a drone ship in the ocean. Then caught a 23-story booster out of the sky with mechanical arms on the launch tower. He didn’t pitch that. He did it. Then the world watched and tried to process what it just saw. That is what separates someone who builds from someone who performs. PowerPoint is where ideas go to be believed. The prototype is where they go to be proven. Most careers begin and end in the first. Not because people can’t build. Because the performance felt so much like progress that nobody ever stopped to check.
0:18

Elon Musk just said something that disqualifies most of Silicon Valley. Musk: “If you’re gonna create a company, the first thing you should try to do is create a working prototype.” Not a pitch deck. Not a business model. Not a funding narrative wrapped in 40 slides designed to make something that doesn’t exist sound inevitable. A working prototype. That alone rules out most of the people who call themselves founders. Musk: “Everything looks great on PowerPoint. You can make anything work on PowerPoint.” That’s not a punchline. It’s an indictment. PowerPoint has no physics. No engineering constraints. No supply chain. No thermal limits. No gravity. Nothing that forces an idea to prove it deserves to exist in the physical world. It is the most effective tool ever built for making the impossible look funded. Spend long enough in that world, and the distance between something that works and something that looks like it works stops feeling important. That is the real damage. Musk: “If you have an actual demonstration article, even if it’s in primitive form, that’s much, much more effective for convincing people.” Even if it barely functions. Even if half of it is held together with tape. Even if it looks like it has no business working yet. Because a crude prototype carries something a polished deck never will. Proof. Not projected proof. Not modeled proof. Not “if we hit these assumptions” proof. The kind that sits on a table and either works or doesn’t. No narrative saves it. No charisma rescues it. No market-sizing slide covers for it. It just has to be real. That is the gap between Musk and the rest of the industry. Not intelligence. Not capital. Not ambition. He builds first. Then talks. Everyone else talks first. Then fundraises to find out if the thing could even be built. The distance between those two sequences is the entire history of why most things never get made. The professional world is engineered to produce one type of person. Someone who can describe, in extraordinary detail, what they would build if given the resources. Not someone who builds. MBA programs train you to present. Accelerators train you to present. Pitch competitions are literally scored on who presents best. The system does not reward builders. It rewards the performance of building. And the people deepest inside that system are the last ones who will ever notice. Musk landed a rocket on a drone ship in the ocean. Then caught a 23-story booster out of the sky with mechanical arms on the launch tower. He didn’t pitch that. He did it. Then the world watched and tried to process what it just saw. That is what separates someone who builds from someone who performs. PowerPoint is where ideas go to be believed. The prototype is where they go to be proven. Most careers begin and end in the first. Not because people can’t build. Because the performance felt so much like progress that nobody ever stopped to check.

Dustin

16,878 次观看 • 2 天前

Kling 3.0 is out but Sora 2 is still the GOAT when it comes to AI UGC 🤯 And this custom GPT turns your sh*tty Sora 2 prompts into scroll-stopping UGC 🤯 Tell it your product --> get a timeline-based prompt with shot composition, camera angles, lighting, and timing breakdowns. Copy, paste, generate. Perfect for DTC brands and agencies who are tired of AI video output that looks like garbage. Here's the problem: Most people prompt Sora 2 like "make a UGC video of someone using my skincare product" and wonder why the output is unusable. Sora 2 needs hyper-specific instructions—shot type, lighting, scene details, timing cues. Without that, you get slop. This GPT fixes it: → Input your product (supplement, skincare, SaaS, whatever) → It generates a detailed Sora 2 prompt with full scene breakdown → Includes shot composition, camera movement, and timing → Optimized for 9:16 TikTok/Reels format → Copy directly into Sora 2 and generate No 80,000 word "prompting frameworks", just results. What you get: > Professional UGC prompts in 10 seconds > Consistent output quality every time > Prompts built for vertical video formats > Works for any product type Want free access to the Sora 2 Prompt Generator GPT? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)
0:15

Kling 3.0 is out but Sora 2 is still the GOAT when it comes to AI UGC 🤯 And this custom GPT turns your sh*tty Sora 2 prompts into scroll-stopping UGC 🤯 Tell it your product --> get a timeline-based prompt with shot composition, camera angles, lighting, and timing breakdowns. Copy, paste, generate. Perfect for DTC brands and agencies who are tired of AI video output that looks like garbage. Here's the problem: Most people prompt Sora 2 like "make a UGC video of someone using my skincare product" and wonder why the output is unusable. Sora 2 needs hyper-specific instructions—shot type, lighting, scene details, timing cues. Without that, you get slop. This GPT fixes it: → Input your product (supplement, skincare, SaaS, whatever) → It generates a detailed Sora 2 prompt with full scene breakdown → Includes shot composition, camera movement, and timing → Optimized for 9:16 TikTok/Reels format → Copy directly into Sora 2 and generate No 80,000 word "prompting frameworks", just results. What you get: > Professional UGC prompts in 10 seconds > Consistent output quality every time > Prompts built for vertical video formats > Works for any product type Want free access to the Sora 2 Prompt Generator GPT? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)

Mike Futia

22,333 次观看 • 5 个月前

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.
0:24

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 次观看 • 6 个月前

🚀 We Promised to Go Big in August! We’re Going Beyond! This August, we’re bringing Trick or Seek to life, an entirely in-house created comic-style masterpiece built from scratch by the $NAKA team for the #NAKAFAM. Over the past weeks, our entire studio has been working relentlessly, and the journey from concept to launch is no small feat. 💪 Here’s the level of work it takes to create just one top-tier $NAKA game: 1️⃣ Concept & Planning – Crafting the vision, gameplay mechanics, and player journey. 2️⃣ Sketching & Storyboarding – Visualizing every scene, character, and narrative flow. 3️⃣ 3D Modeling & Design – Bringing characters, items, and environments to life with stunning detail. 4️⃣ UI/UX Design – Creating an intuitive, immersive player experience. 5️⃣ Environment Design & Lighting – Setting the mood, atmosphere, and world immersion. 6️⃣ Development & Programming – Building the core gameplay, features, and systems. 7️⃣ Sound Design & Music – Composing original tracks and sound effects for maximum impact. 8️⃣ Testing & Optimization – Fine-tuning performance and eliminating bugs across all devices. 9️⃣ Polish & Publishing – Adding the final touches to deliver nothing short of excellence. Every step is executed at the highest possible quality, in record time, and 100% in-house – because our standard is nothing less than world-class. 🔥 August is the $NAKA month, and this is just one of the many things we have lined up. For the #NAKAFAM, we’re not just building games… we’re building the future of GameFi.
0:19

🚀 We Promised to Go Big in August! We’re Going Beyond! This August, we’re bringing Trick or Seek to life, an entirely in-house created comic-style masterpiece built from scratch by the $NAKA team for the #NAKAFAM. Over the past weeks, our entire studio has been working relentlessly, and the journey from concept to launch is no small feat. 💪 Here’s the level of work it takes to create just one top-tier $NAKA game: 1️⃣ Concept & Planning – Crafting the vision, gameplay mechanics, and player journey. 2️⃣ Sketching & Storyboarding – Visualizing every scene, character, and narrative flow. 3️⃣ 3D Modeling & Design – Bringing characters, items, and environments to life with stunning detail. 4️⃣ UI/UX Design – Creating an intuitive, immersive player experience. 5️⃣ Environment Design & Lighting – Setting the mood, atmosphere, and world immersion. 6️⃣ Development & Programming – Building the core gameplay, features, and systems. 7️⃣ Sound Design & Music – Composing original tracks and sound effects for maximum impact. 8️⃣ Testing & Optimization – Fine-tuning performance and eliminating bugs across all devices. 9️⃣ Polish & Publishing – Adding the final touches to deliver nothing short of excellence. Every step is executed at the highest possible quality, in record time, and 100% in-house – because our standard is nothing less than world-class. 🔥 August is the $NAKA month, and this is just one of the many things we have lined up. For the #NAKAFAM, we’re not just building games… we’re building the future of GameFi.

Nakamoto.Games

26,107 次观看 • 11 个月前

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.
0:10

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.

Kaidu

534,198 次观看 • 1 个月前

React Native now has its own shadcn/ui equivalent — introducing 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜. If you love the flexibility of copying customisable components directly into your project (avoiding heavy, dependency-laden packages), NativeUI is designed for you. 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜 offers beautifully crafted, accessible components tailored for React Native, following the same copy-paste philosophy as shadcn/ui. Built with 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 for fast, declarative, and flexible styling optimised for React Native. ➡️ 𝗖𝗼𝗽𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗰𝗼𝗱𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — no black-box dependencies required. ➡️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁, supporting screen readers and keyboard navigation, and designed to align with native iOS and Android UX patterns. ➡️ 𝗙𝘂𝗹𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗨𝗜 without rebuilding common elements like buttons, inputs, or sliders from scratch. ➡️ 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗼 𝗮𝗻𝗱 𝘃𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗲𝗮𝗰𝘁 𝗡𝗮𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, but not yet integrated with Tamagui’s styling system (future support may be planned). ➡️ 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝘁𝗵𝗲𝗺𝗶𝗻𝗴 𝘃𝗶𝗮 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 — though you’ll need to wire it up manually using Tailwind variables, context providers, and config files. Note: The term “install” in the documentation refers to using the shadcn CLI (e.g., npx shadcn@latest add component) to fetch and copy component code into your project, not adding a package to your dependencies. NativeUI isn’t a plug-and-play library; it’s a lightweight toolbox that empowers you to shape your UI with precision and control. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: npm install a pre-built UI kit for speed, or copy/paste NativeUI components for ultimate customisation? #ReactNative #KeyboardUX #MobileDev #OpenSource #JSDev #Performance #iOSDev #KeyboardExtensions #ReactNativeKeyboard #UIUX #shadcn #nativeui
0:10

React Native now has its own shadcn/ui equivalent — introducing 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜. If you love the flexibility of copying customisable components directly into your project (avoiding heavy, dependency-laden packages), NativeUI is designed for you. 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜 offers beautifully crafted, accessible components tailored for React Native, following the same copy-paste philosophy as shadcn/ui. Built with 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 for fast, declarative, and flexible styling optimised for React Native. ➡️ 𝗖𝗼𝗽𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗰𝗼𝗱𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — no black-box dependencies required. ➡️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁, supporting screen readers and keyboard navigation, and designed to align with native iOS and Android UX patterns. ➡️ 𝗙𝘂𝗹𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗨𝗜 without rebuilding common elements like buttons, inputs, or sliders from scratch. ➡️ 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗼 𝗮𝗻𝗱 𝘃𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗲𝗮𝗰𝘁 𝗡𝗮𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, but not yet integrated with Tamagui’s styling system (future support may be planned). ➡️ 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝘁𝗵𝗲𝗺𝗶𝗻𝗴 𝘃𝗶𝗮 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 — though you’ll need to wire it up manually using Tailwind variables, context providers, and config files. Note: The term “install” in the documentation refers to using the shadcn CLI (e.g., npx shadcn@latest add component) to fetch and copy component code into your project, not adding a package to your dependencies. NativeUI isn’t a plug-and-play library; it’s a lightweight toolbox that empowers you to shape your UI with precision and control. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: npm install a pre-built UI kit for speed, or copy/paste NativeUI components for ultimate customisation? #ReactNative #KeyboardUX #MobileDev #OpenSource #JSDev #Performance #iOSDev #KeyboardExtensions #ReactNativeKeyboard #UIUX #shadcn #nativeui

The React Native Rewind

118,414 次观看 • 11 个月前

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
0:10

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 次观看 • 9 个月前

🇯🇵 A brainless blob reproduced the Tokyo rail network in 26 hours. It was not trying to solve a transport problem. It was trying to eat oat flakes. Physarum polycephalum is, to be generous, a blob. Pale, damp, the size of a thumbnail, it has no brain, no nervous system, and no cells that could reasonably be accused of thinking. Scientists had studied it for years without feeling particularly threatened by it. Then someone put it in a maze. Within hours, Physarum had found the shortest route between entrance and exit. Not by wandering randomly. Not by luck. By something that had no name, because everyone had assumed it required a brain. This was interesting enough. What happened next was embarrassing. In 2010, a researcher named Toshiyuki Nakagaki and his team placed a piece of slime mold at the centre of a damp map of greater Tokyo. Around it, at the locations of 36 surrounding cities, they put small piles of oat flakes. Then they left the room. The organism did what it always does. It explored. Thin tendrils pushed outward in every direction, feeling for food. When a tendril found an oat flake, that connection strengthened. When a path led nowhere useful, it was quietly dismantled. The slime mold was not planning. It was simply following local chemistry, the same way it had been doing for 500 million years. After 26 hours, the exploration was over. What remained was a sparse, elegant network of tubes connecting all 36 cities to each other. Not a tangle. Not a web covering everything. A clean, efficient system with strong main corridors between the busiest points and lighter connections branching where they were needed. The team held it up next to the actual Tokyo rail map. The corridors matched. The branch lines matched. Even the redundant connections, the backup routes engineers had added so the system could survive a single failure, appeared in nearly the same places. The slime mold had not just found the cities. It had independently arrived at the same logic that Japanese railway engineers had spent decades refining. By some measures, its network was more robust than the one humans had built. There is no headquarters inside Physarum, no moment where anyone decides anything. The intelligence, if that is even the right word, lives entirely in one simple rule repeated across millions of connections: strengthen what works, abandon what doesn’t. That rule, applied blindly and without awareness, produces something that looks unnervingly like wisdom. The slime mold was not trying to redesign the Tokyo rail network. It was trying to eat breakfast. It just turns out that the most efficient way to eat breakfast, when your breakfast is scattered across a map of greater Tokyo, looks a great deal like good urban planning 😅 Gandalv / Gandalv
0:10

🇯🇵 A brainless blob reproduced the Tokyo rail network in 26 hours. It was not trying to solve a transport problem. It was trying to eat oat flakes. Physarum polycephalum is, to be generous, a blob. Pale, damp, the size of a thumbnail, it has no brain, no nervous system, and no cells that could reasonably be accused of thinking. Scientists had studied it for years without feeling particularly threatened by it. Then someone put it in a maze. Within hours, Physarum had found the shortest route between entrance and exit. Not by wandering randomly. Not by luck. By something that had no name, because everyone had assumed it required a brain. This was interesting enough. What happened next was embarrassing. In 2010, a researcher named Toshiyuki Nakagaki and his team placed a piece of slime mold at the centre of a damp map of greater Tokyo. Around it, at the locations of 36 surrounding cities, they put small piles of oat flakes. Then they left the room. The organism did what it always does. It explored. Thin tendrils pushed outward in every direction, feeling for food. When a tendril found an oat flake, that connection strengthened. When a path led nowhere useful, it was quietly dismantled. The slime mold was not planning. It was simply following local chemistry, the same way it had been doing for 500 million years. After 26 hours, the exploration was over. What remained was a sparse, elegant network of tubes connecting all 36 cities to each other. Not a tangle. Not a web covering everything. A clean, efficient system with strong main corridors between the busiest points and lighter connections branching where they were needed. The team held it up next to the actual Tokyo rail map. The corridors matched. The branch lines matched. Even the redundant connections, the backup routes engineers had added so the system could survive a single failure, appeared in nearly the same places. The slime mold had not just found the cities. It had independently arrived at the same logic that Japanese railway engineers had spent decades refining. By some measures, its network was more robust than the one humans had built. There is no headquarters inside Physarum, no moment where anyone decides anything. The intelligence, if that is even the right word, lives entirely in one simple rule repeated across millions of connections: strengthen what works, abandon what doesn’t. That rule, applied blindly and without awareness, produces something that looks unnervingly like wisdom. The slime mold was not trying to redesign the Tokyo rail network. It was trying to eat breakfast. It just turns out that the most efficient way to eat breakfast, when your breakfast is scattered across a map of greater Tokyo, looks a great deal like good urban planning 😅 Gandalv / Gandalv

Gandalv

205,841 次观看 • 4 个月前

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.
0:10

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.

Blaze

38,242 次观看 • 2 个月前

you're paying $20/mo for something your $500 GPU can already do. Gemma 4 26B A4B QAT MoE + Hermes Agent running on a single RTX 4060 (8GB VRAM). Built a vision capable, 100% free, 100% local, private AI assistant that lives in my Chrome browser. No API keys. No cloud. No subscriptions. 100% vibe coded. 0% handholding. It has full context of whatever's on my screen can answer questions, summarize pages, extract data, and see images. Same local model handles everything, no external calls, ever. keep reading for the model and hermes agent tips i learnt while building this locally. Here's the exact setup for anyone running local LLMs on 6-8 GB VRAM: llama.cpp server flags (on my NVIDIA RTX 4060 8gb VRAM): -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --cache-type-k q8_0 --cache-type-v q8_0 -c 150000 --port 8080 Throughput with quantization: Prefill: 200-250 tokens/sec Decode: 20-25 tokens/sec reduce context if oom on 6 gb vram card. Key learnings: - Quantize KV cache to q8 for faster prefill/decode. Prefill goes from 100-150 (unquantized) to 200-250 tok/s (q8). - But watch out, once actual context grows past ~50k tokens on high entropy workloads, q8 KV quantization can cause hallucinations. Low entropy workloads are mostly unaffected. If you see it happening, drop the quantization. This is common across all local models. - In Hermes Agent settings -> Memory & Context, bump compression threshold from default 0.5 to 0.7. Default triggers way too frequent context compression and eats time. Up next: add persistent memory, web search, tool calling, streaming output and whatever you suggest. Running a 26B MoE with vision + 150k context window on 8GB VRAM would've sounded impossible 6 months ago. Works the same on the NVIDIA RTX 3060 Ti, 3070, 4060 Ti, 5060, 2080, or any 8GB card. VRAM is the only requirement. Local AI agents are closer than people think. You just need to know where the knobs are. Model's Unsloth quant hugging face link in the comments. Have you tried Hermes agent by Nous Research yet? What are you building with local LLMs? Drop it below, let's see what this community is shipping.
0:19

you're paying $20/mo for something your $500 GPU can already do. Gemma 4 26B A4B QAT MoE + Hermes Agent running on a single RTX 4060 (8GB VRAM). Built a vision capable, 100% free, 100% local, private AI assistant that lives in my Chrome browser. No API keys. No cloud. No subscriptions. 100% vibe coded. 0% handholding. It has full context of whatever's on my screen can answer questions, summarize pages, extract data, and see images. Same local model handles everything, no external calls, ever. keep reading for the model and hermes agent tips i learnt while building this locally. Here's the exact setup for anyone running local LLMs on 6-8 GB VRAM: llama.cpp server flags (on my NVIDIA RTX 4060 8gb VRAM): -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --cache-type-k q8_0 --cache-type-v q8_0 -c 150000 --port 8080 Throughput with quantization: Prefill: 200-250 tokens/sec Decode: 20-25 tokens/sec reduce context if oom on 6 gb vram card. Key learnings: - Quantize KV cache to q8 for faster prefill/decode. Prefill goes from 100-150 (unquantized) to 200-250 tok/s (q8). - But watch out, once actual context grows past ~50k tokens on high entropy workloads, q8 KV quantization can cause hallucinations. Low entropy workloads are mostly unaffected. If you see it happening, drop the quantization. This is common across all local models. - In Hermes Agent settings -> Memory & Context, bump compression threshold from default 0.5 to 0.7. Default triggers way too frequent context compression and eats time. Up next: add persistent memory, web search, tool calling, streaming output and whatever you suggest. Running a 26B MoE with vision + 150k context window on 8GB VRAM would've sounded impossible 6 months ago. Works the same on the NVIDIA RTX 3060 Ti, 3070, 4060 Ti, 5060, 2080, or any 8GB card. VRAM is the only requirement. Local AI agents are closer than people think. You just need to know where the knobs are. Model's Unsloth quant hugging face link in the comments. Have you tried Hermes agent by Nous Research yet? What are you building with local LLMs? Drop it below, let's see what this community is shipping.

Alok

36,031 次观看 • 15 天前

🚨SCIENCE🚨: Time just got a remix — and exotic quantum matter started showing up uninvited 🧨 Scientists at California Polytechnic State University just dropped a bombshell on May 4, 2026: by periodically driving magnetic fields in graphene over time, they created entirely new quantum states of matter that flat-out do not exist under any static conditions. These driven phases are dramatically more stable and error-resistant — exactly the kind of breakthrough quantum computing has been starving for. Standard models are left asking why time itself seems to be the missing ingredient. Uniphics sees this as inevitable once you accept the three pillars. Time flow (t_flow) is not a universal constant — it is strictly t_flow = k / E_d, where k = 4.64159 × 10^18 J/m³ is the fixed reference density set by the electron Gyrotron volume. When researchers vary the magnetic field periodically, they are rhythmically modulating local energy density (E_d) in time. That creates transient windows where t_flow itself shifts, opening entirely new minima in the ξM-field potential that negentropy (the drive toward lowest energy, J_neg ≈ −5.66 × 10^{-21} J/K) can lock into stable spin configurations. The Gyrotrons — each a 3D gyroscope of three orthogonal spin quanta (xy, xz, yz planes), every quantum a tempest of whirling energy spinning CW or CCW — access driven phases that static E_d simply cannot sustain. The result: exotic states with no static counterpart, far more resistant to decoherence because they are continuously refreshed by the same negentropy that condensed the first bound matter at the Amorphics-to-Physics transition. No new particles, no extra dimensions, no patches — just the pillars doing what they do best: turning dynamic E_d into order. This is why the new states are so robust. The time-dependent drive keeps the system dancing exactly where unbound energy repels unbound energy just enough to hold the new lock without collapse. How might deliberately engineering time flow gradients in real materials accelerate fault-tolerant quantum computers — or even let us replay the driven phases that built the early universe? A Theory of Everything should be able to answer everything. #Uniphics #QuantumStates #TimeFlow #EnergyDensity #SpinQuanta Grok xAI Uniphics Explained Simply PDF: Chapters 1–10 free: Grokipedia
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🚨SCIENCE🚨: Time just got a remix — and exotic quantum matter started showing up uninvited 🧨 Scientists at California Polytechnic State University just dropped a bombshell on May 4, 2026: by periodically driving magnetic fields in graphene over time, they created entirely new quantum states of matter that flat-out do not exist under any static conditions. These driven phases are dramatically more stable and error-resistant — exactly the kind of breakthrough quantum computing has been starving for. Standard models are left asking why time itself seems to be the missing ingredient. Uniphics sees this as inevitable once you accept the three pillars. Time flow (t_flow) is not a universal constant — it is strictly t_flow = k / E_d, where k = 4.64159 × 10^18 J/m³ is the fixed reference density set by the electron Gyrotron volume. When researchers vary the magnetic field periodically, they are rhythmically modulating local energy density (E_d) in time. That creates transient windows where t_flow itself shifts, opening entirely new minima in the ξM-field potential that negentropy (the drive toward lowest energy, J_neg ≈ −5.66 × 10^{-21} J/K) can lock into stable spin configurations. The Gyrotrons — each a 3D gyroscope of three orthogonal spin quanta (xy, xz, yz planes), every quantum a tempest of whirling energy spinning CW or CCW — access driven phases that static E_d simply cannot sustain. The result: exotic states with no static counterpart, far more resistant to decoherence because they are continuously refreshed by the same negentropy that condensed the first bound matter at the Amorphics-to-Physics transition. No new particles, no extra dimensions, no patches — just the pillars doing what they do best: turning dynamic E_d into order. This is why the new states are so robust. The time-dependent drive keeps the system dancing exactly where unbound energy repels unbound energy just enough to hold the new lock without collapse. How might deliberately engineering time flow gradients in real materials accelerate fault-tolerant quantum computers — or even let us replay the driven phases that built the early universe? A Theory of Everything should be able to answer everything. #Uniphics #QuantumStates #TimeFlow #EnergyDensity #SpinQuanta Grok xAI Uniphics Explained Simply PDF: Chapters 1–10 free: Grokipedia

Paul Maley

11,804 次观看 • 2 个月前

Exciting News Alert: Catalyst Joins opBNB Network! 🚀 Hold onto your hats, because Catalyst is back in action, stirring up the world of cross-chain liquidity! We're thrilled to unveil our latest expansion: Catalyst now supports the opBNB network and BNB Chain. This means smoother and more efficient transactions across all chains are now within reach through Catalyst. Let's dive in! 🌊 Catalyst Meets opBNB Our mission to drive interoperability takes another big step as Catalyst teams up with Binance to launch on its domains. The opBNB Testnet deployment will initially support cross-chain swaps with BNB Chain. But wait, there's more! In the coming weeks, more chain connections will be added. Connecting the BNB Chain ecosystem to Catalyst's network is where the magic begins. 🪄 Get ready for a blockchain match made in heaven! Catalyst's prowess in cross-chain liquidity meets opBNB's optimization of the BNB Chain ecosystem. Picture this: high-throughput applications on BNB Chain, supercharged by Catalyst's seamless cross-chain capabilities. It's not just a partnership; it's a gateway to unlocking BNB Chain's full potential! 🚀 opBNB is a Layer-2 superstar built to amplify BNB Chain's might. Powered by the OP Stack, opBNB boasts a block size of 100M, ensuring stable and pocket-friendly gas fees. Whether you're diving into decentralized exchanges, gaming, or everyday transactions, opBNB's got your back with exceptional performance. 🌟 One of opBNB's standout features is its seamless compatibility with the EVM. For developers already well-versed in the EVM world, this means a smooth transition to building and deploying applications on the opBNB network. It's all about making life easier for the crypto community! 🛠️ Catalyst's cross-chain AMM expertise perfectly aligns with opBNB's quest for optimized Layer-2 solutions. The result? Effortless, cost-effective, and high-throughput transactions that benefit users, developers, and the entire BNB Chain ecosystem. Talk about a win-win! 💡 opBNB-Specific Mission Awaits Ready to embark on a thrilling journey within the opBNB Testnet? Whether it's swapping, yield generation, or portfolio management that tickles your fancy, Catalyst on opBNB has got you covered. Get set to explore a world filled with endless possibilities! 🌐 We've crafted an opBNB-specific mission on Catalyst Missions. Completing this mission isn't just rewarding – it also opens doors to the Catalyst community, granting you points and on-chain credentials for a deeper connection. Time to dive in and level up! 🚀 🔗 The integration of Catalyst: Lynx Testnet with the opBNB network is a game-changer. It's a leap toward redefining cross-chain liquidity solutions. Ready to explore endless cross-chain potential? Undertake the opBNB-specific mission and be part of crafting the future of decentralized finance with Catalyst. 🌌
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Exciting News Alert: Catalyst Joins opBNB Network! 🚀 Hold onto your hats, because Catalyst is back in action, stirring up the world of cross-chain liquidity! We're thrilled to unveil our latest expansion: Catalyst now supports the opBNB network and BNB Chain. This means smoother and more efficient transactions across all chains are now within reach through Catalyst. Let's dive in! 🌊 Catalyst Meets opBNB Our mission to drive interoperability takes another big step as Catalyst teams up with Binance to launch on its domains. The opBNB Testnet deployment will initially support cross-chain swaps with BNB Chain. But wait, there's more! In the coming weeks, more chain connections will be added. Connecting the BNB Chain ecosystem to Catalyst's network is where the magic begins. 🪄 Get ready for a blockchain match made in heaven! Catalyst's prowess in cross-chain liquidity meets opBNB's optimization of the BNB Chain ecosystem. Picture this: high-throughput applications on BNB Chain, supercharged by Catalyst's seamless cross-chain capabilities. It's not just a partnership; it's a gateway to unlocking BNB Chain's full potential! 🚀 opBNB is a Layer-2 superstar built to amplify BNB Chain's might. Powered by the OP Stack, opBNB boasts a block size of 100M, ensuring stable and pocket-friendly gas fees. Whether you're diving into decentralized exchanges, gaming, or everyday transactions, opBNB's got your back with exceptional performance. 🌟 One of opBNB's standout features is its seamless compatibility with the EVM. For developers already well-versed in the EVM world, this means a smooth transition to building and deploying applications on the opBNB network. It's all about making life easier for the crypto community! 🛠️ Catalyst's cross-chain AMM expertise perfectly aligns with opBNB's quest for optimized Layer-2 solutions. The result? Effortless, cost-effective, and high-throughput transactions that benefit users, developers, and the entire BNB Chain ecosystem. Talk about a win-win! 💡 opBNB-Specific Mission Awaits Ready to embark on a thrilling journey within the opBNB Testnet? Whether it's swapping, yield generation, or portfolio management that tickles your fancy, Catalyst on opBNB has got you covered. Get set to explore a world filled with endless possibilities! 🌐 We've crafted an opBNB-specific mission on Catalyst Missions. Completing this mission isn't just rewarding – it also opens doors to the Catalyst community, granting you points and on-chain credentials for a deeper connection. Time to dive in and level up! 🚀 🔗 The integration of Catalyst: Lynx Testnet with the opBNB network is a game-changer. It's a leap toward redefining cross-chain liquidity solutions. Ready to explore endless cross-chain potential? Undertake the opBNB-specific mission and be part of crafting the future of decentralized finance with Catalyst. 🌌

Catalyst

72,222 次观看 • 2 年前