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3 problems. 1 box. ❌ Losing files forever. ✅ RAID + Backup. One drive fails? No problem. ❌ Chaos across devices. ✅ Thunderbolt 4 + 10GbE. Fast sync. Remote access. ❌ Paying forever for slow, creepy cloud storage. ✅ No subscription. No speed limits. Your home, your data. #ZimaCube2....

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ZimaSpace

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24,333 просмотров • 2 месяцев назад •via X (Twitter)

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#ZimaBoard 2, Pocket-Sized Beast Your data belongs to you, not big tech. No one should control your files, photos, or privacy. #NAS #homelab #local #degoogle
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#ZimaBoard 2, Pocket-Sized Beast Your data belongs to you, not big tech. No one should control your files, photos, or privacy. #NAS #homelab #local #degoogle

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(Upcoming) NotePlus now features Kanban. Why choose NotePlus? 0. Dev oriented, 100% Native, no WebView. 1. No account required – sync via your private cloud. 2. Not SaaS – no concerns about data leaks. 3. No subscription required. 4. NotePlus for iOS is currently under Apple Review and is Free! And more features are coming.....
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(Upcoming) NotePlus now features Kanban. Why choose NotePlus? 0. Dev oriented, 100% Native, no WebView. 1. No account required – sync via your private cloud. 2. Not SaaS – no concerns about data leaks. 3. No subscription required. 4. NotePlus for iOS is currently under Apple Review and is Free! And more features are coming.....

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ZimaBoard 2 + 12 hard drives = 132TB mini NAS 🤯 No more cloud fees. More privacy. More space for what matters, your photos, videos, files, memories! This is the right way. 💥 #SelfHosting #degoogle #Privacy #DIY
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ZimaBoard 2 + 12 hard drives = 132TB mini NAS 🤯 No more cloud fees. More privacy. More space for what matters, your photos, videos, files, memories! This is the right way. 💥 #SelfHosting #degoogle #Privacy #DIY

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28,423 просмотров • 3 месяцев назад

THIS FRENCH GUY RUNS OPENCLAW AND HERMES AI AGENTS ON A HOMELAB THAT REPLACED GOOGLE DRIVE AND ICLOUD ENTIRELY ON JUST 10 WATTS The project: host his own local AI agents, OpenClaw and Hermes, ditch cloud storage subscriptions entirely by running his own NAS, and use the whole build to prep for his K8s certification – all running on 4 Raspberry Pis. Today's upgrade: a PoE module. One Ethernet cable now does two jobs – powers the Raspberry Pi AND gives it network access at the same time. No separate power adapters, no cable clutter, no juggling connections during maintenance. Total power draw for all 4 Raspberry Pis combined: max 10 watts. He's not renting cloud storage anymore. He's not paying monthly subscriptions for space he already owns the hardware for. His files, his AI agents, his data – all sitting on hardware in his own home, on a fraction of the power a lightbulb uses. Next up: blocking ads network-wide across his entire home, using one of the automation services running on the same setup. This is what "own your data" actually looks like when someone builds it instead of just talking about it. Bookmark this post. Full build in the video below.
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THIS FRENCH GUY RUNS OPENCLAW AND HERMES AI AGENTS ON A HOMELAB THAT REPLACED GOOGLE DRIVE AND ICLOUD ENTIRELY ON JUST 10 WATTS The project: host his own local AI agents, OpenClaw and Hermes, ditch cloud storage subscriptions entirely by running his own NAS, and use the whole build to prep for his K8s certification – all running on 4 Raspberry Pis. Today's upgrade: a PoE module. One Ethernet cable now does two jobs – powers the Raspberry Pi AND gives it network access at the same time. No separate power adapters, no cable clutter, no juggling connections during maintenance. Total power draw for all 4 Raspberry Pis combined: max 10 watts. He's not renting cloud storage anymore. He's not paying monthly subscriptions for space he already owns the hardware for. His files, his AI agents, his data – all sitting on hardware in his own home, on a fraction of the power a lightbulb uses. Next up: blocking ads network-wide across his entire home, using one of the automation services running on the same setup. This is what "own your data" actually looks like when someone builds it instead of just talking about it. Bookmark this post. Full build in the video below.

SCOTTY BEAM

18,175 просмотров • 24 дней назад

🚨 IDAHO GOP GOVERNOR PRIMARY – MAY 19 🚨 RINO PURGE Idaho, it’s time to fire the career politician. Brad Little is running for a third term with this record: ❌ Slow-walked deeper tax relief while protecting bloated rainy-day funds ❌ Vetoed the original medical freedom bill (had to be overridden) and recent 2026 bills on daycare deregulation ❌ “Criminals only” border policy that keeps ag labor flows open instead of full mass deportation + E-Verify ❌ Heavy PAC and corporate ties — out-raised challengers massively from big donors, realtors, ag giants ❌ Post-session veto flurry in 2026 (rainy day funds, capital projects, more) after legislators went home No more talk-MAGA, govern-RINO. Mark Fitzpatrick is the America First outsider Idaho needs: ✅ Mass deportation of ALL illegal aliens + mandatory E-Verify ✅ Drastic tax cuts and real government shrinkage ✅ Radical transparency — monthly Capitol town halls + Governor’s Podcast ✅ Ironclad on 2A, life from conception, parental rights, medical freedom ✅ Former police officer, Old State Saloon owner, homeschool dad — zero RINO baggage This is your chance to send a clear message: No more RINOs. 🗳️ May 19 — Vote Mark Fitzpatrick for Governor. Let’s make Idaho the strongest, freest state in the Republic. 🇺🇸 .Mark Fitzpatrick
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🚨 IDAHO GOP GOVERNOR PRIMARY – MAY 19 🚨 RINO PURGE Idaho, it’s time to fire the career politician. Brad Little is running for a third term with this record: ❌ Slow-walked deeper tax relief while protecting bloated rainy-day funds ❌ Vetoed the original medical freedom bill (had to be overridden) and recent 2026 bills on daycare deregulation ❌ “Criminals only” border policy that keeps ag labor flows open instead of full mass deportation + E-Verify ❌ Heavy PAC and corporate ties — out-raised challengers massively from big donors, realtors, ag giants ❌ Post-session veto flurry in 2026 (rainy day funds, capital projects, more) after legislators went home No more talk-MAGA, govern-RINO. Mark Fitzpatrick is the America First outsider Idaho needs: ✅ Mass deportation of ALL illegal aliens + mandatory E-Verify ✅ Drastic tax cuts and real government shrinkage ✅ Radical transparency — monthly Capitol town halls + Governor’s Podcast ✅ Ironclad on 2A, life from conception, parental rights, medical freedom ✅ Former police officer, Old State Saloon owner, homeschool dad — zero RINO baggage This is your chance to send a clear message: No more RINOs. 🗳️ May 19 — Vote Mark Fitzpatrick for Governor. Let’s make Idaho the strongest, freest state in the Republic. 🇺🇸 .Mark Fitzpatrick

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Yesterday I met with a $100M brand completely swamped trying to build AI agents. They were duct taping together N8n, Gumloop, Make, Lindy and Zapier hoping it would help automate. Here’s the thing: Those tools are great but only if your data is clean, connected, and actually usable by AI. For example you can’t build a real media buying agent if it doesn’t know: – What’s in stock – What your customers LTV – How your sales change by season Triple Whale brings all of your data into one place. So when you ask our AI a question, it doesn’t guess because it already has the data. And it knows how to use it. We’ve spent 920 days and hired 72 full-time engineers to build the most advanced data layer in ecom designed for AI from day one. Wallaroo Media figured it out👇 They dropped every other platform for Triple Whale. ❌ No more scattered agents ❌ No more platform-hopping ✅ Better answers, better automations, better results Shout out to Justin Doyle and the whole Wallaroo team for the incredible partnership!
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Yesterday I met with a $100M brand completely swamped trying to build AI agents. They were duct taping together N8n, Gumloop, Make, Lindy and Zapier hoping it would help automate. Here’s the thing: Those tools are great but only if your data is clean, connected, and actually usable by AI. For example you can’t build a real media buying agent if it doesn’t know: – What’s in stock – What your customers LTV – How your sales change by season Triple Whale brings all of your data into one place. So when you ask our AI a question, it doesn’t guess because it already has the data. And it knows how to use it. We’ve spent 920 days and hired 72 full-time engineers to build the most advanced data layer in ecom designed for AI from day one. Wallaroo Media figured it out👇 They dropped every other platform for Triple Whale. ❌ No more scattered agents ❌ No more platform-hopping ✅ Better answers, better automations, better results Shout out to Justin Doyle and the whole Wallaroo team for the incredible partnership!

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I was paying $500/month for 9 apps that made me feel productive without actually being productive! Notion. Roam. Evernote. Todoist. Bear. Readwise. Cron. Craft. Superhuman. Every one of them solved a real problem. Every one of them created a new one. Information scattered across nine places. Twenty minutes a day minimum switching between them. $500 leaving the account every month. Then everything moved into Obsidian. One app. Local files. No subscription. No sync issues. No data locked in a proprietary format. No "your account has been downgraded" email from an app that got acquired. That was 18 months ago. This is the exact vault setup. The folder structure. The templates. The plugins. The workflows. All of it. Free to take and adapt. $500 a month back in your pocket and everything in one place. Bookmark this gem so you do not lose it! Follow NeilXbt for more Obsidian and productivity builds..
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I was paying $500/month for 9 apps that made me feel productive without actually being productive! Notion. Roam. Evernote. Todoist. Bear. Readwise. Cron. Craft. Superhuman. Every one of them solved a real problem. Every one of them created a new one. Information scattered across nine places. Twenty minutes a day minimum switching between them. $500 leaving the account every month. Then everything moved into Obsidian. One app. Local files. No subscription. No sync issues. No data locked in a proprietary format. No "your account has been downgraded" email from an app that got acquired. That was 18 months ago. This is the exact vault setup. The folder structure. The templates. The plugins. The workflows. All of it. Free to take and adapt. $500 a month back in your pocket and everything in one place. Bookmark this gem so you do not lose it! Follow NeilXbt for more Obsidian and productivity builds..

NeilXbt

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𝗥𝘂𝗻 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗚𝗲𝗺𝗺𝗮 𝟰 + 𝗢𝗽𝗲𝗻𝗖𝗹𝗮𝘄 𝗮𝘀 𝗮 𝗳𝗿𝗲𝗲 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗶𝗻 𝟯 𝘀𝘁𝗲𝗽𝘀. No API bills. No usage limits. No subscription. Nothing leaves your computer. Here's the full setup: → Step 1: Go to Download and install. Update to version 0.2.2.0 or higher. → Step 2: Open terminal. Type: ollama pull gemma4. Downloads the model. Done. → Step 3: Install OpenClaw. Select Ollama as your provider. Point it at port 11434. Pick Gemma 4. That's it. Your AI agent is now running locally. Message it through Telegram, Slack, Discord, or WhatsApp like a coworker. Read files. Write code. Remember context across every conversation. All on your own hardware. Gemma 4 ranked number 3 on the global open model leaderboard on launch day. It beat models with 20 times more parameters. The 26B version activates only 4B parameters at a time so you get near large-model quality at small-model speed. Every AI subscription you're paying for right now could be replaced with this.
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𝗥𝘂𝗻 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗚𝗲𝗺𝗺𝗮 𝟰 + 𝗢𝗽𝗲𝗻𝗖𝗹𝗮𝘄 𝗮𝘀 𝗮 𝗳𝗿𝗲𝗲 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗶𝗻 𝟯 𝘀𝘁𝗲𝗽𝘀. No API bills. No usage limits. No subscription. Nothing leaves your computer. Here's the full setup: → Step 1: Go to Download and install. Update to version 0.2.2.0 or higher. → Step 2: Open terminal. Type: ollama pull gemma4. Downloads the model. Done. → Step 3: Install OpenClaw. Select Ollama as your provider. Point it at port 11434. Pick Gemma 4. That's it. Your AI agent is now running locally. Message it through Telegram, Slack, Discord, or WhatsApp like a coworker. Read files. Write code. Remember context across every conversation. All on your own hardware. Gemma 4 ranked number 3 on the global open model leaderboard on launch day. It beat models with 20 times more parameters. The 26B version activates only 4B parameters at a time so you get near large-model quality at small-model speed. Every AI subscription you're paying for right now could be replaced with this.

Julian Goldie SEO

36,898 просмотров • 4 месяцев назад

Cyber Resilience: Staying Ahead, Not Just Bouncing Back! Cyber resilience isn't just about bouncing back—it's about keeping your momentum going, no matter what. Let's face it: in today's always-connected world, downtime isn't an option. Traditional cyber recovery methods often feel painfully slow, fragmented, and incomplete. Simply recovering data doesn't guarantee you'll get back to business fast enough. That's why Commvault Cloud Rewind caught my attention: Here's What Makes It Stand Out: ✅ Real-Time Recovery: Imagine rebuilding your entire environment—apps, workloads, and all—within minutes instead of days. (Yes, that's about 10x faster than most solutions out there.) ✅ Complete Environment Restoration: It’s about swiftly and seamlessly restoring your full infrastructure, from cloud applications to entire AI stacks. ✅ AI-Driven Automation: Cloud Rewind smartly leverages AI to "rewind" your business operations precisely to the moment before a breach, ensuring zero data loss and minimal disruption. ✅ Safe, Isolated Testing Environments: Air-gapped cloud spaces provide a safe environment to thoroughly test and perfect your recovery strategy before a crisis hits. ✅ Rapid Identity Restoration: Quickly rebuild Active Directory services, restoring identity and access management within minutes. With AI-driven decision-making accelerating and cybersecurity threats evolving, true resilience means uninterrupted business continuity. Commvault is stepping up to help businesses confidently manage cyber risks, ensuring minimal downtime and maximum operational stability. How prepared is your organization for its next cyber challenge? #ZeroLossRecovery #CyberResilience #Commvault #CloudRewind #CyberSecurity #DataRecovery #AI #RSAC
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Cyber Resilience: Staying Ahead, Not Just Bouncing Back! Cyber resilience isn't just about bouncing back—it's about keeping your momentum going, no matter what. Let's face it: in today's always-connected world, downtime isn't an option. Traditional cyber recovery methods often feel painfully slow, fragmented, and incomplete. Simply recovering data doesn't guarantee you'll get back to business fast enough. That's why Commvault Cloud Rewind caught my attention: Here's What Makes It Stand Out: ✅ Real-Time Recovery: Imagine rebuilding your entire environment—apps, workloads, and all—within minutes instead of days. (Yes, that's about 10x faster than most solutions out there.) ✅ Complete Environment Restoration: It’s about swiftly and seamlessly restoring your full infrastructure, from cloud applications to entire AI stacks. ✅ AI-Driven Automation: Cloud Rewind smartly leverages AI to "rewind" your business operations precisely to the moment before a breach, ensuring zero data loss and minimal disruption. ✅ Safe, Isolated Testing Environments: Air-gapped cloud spaces provide a safe environment to thoroughly test and perfect your recovery strategy before a crisis hits. ✅ Rapid Identity Restoration: Quickly rebuild Active Directory services, restoring identity and access management within minutes. With AI-driven decision-making accelerating and cybersecurity threats evolving, true resilience means uninterrupted business continuity. Commvault is stepping up to help businesses confidently manage cyber risks, ensuring minimal downtime and maximum operational stability. How prepared is your organization for its next cyber challenge? #ZeroLossRecovery #CyberResilience #Commvault #CloudRewind #CyberSecurity #DataRecovery #AI #RSAC

Evan Kirstel #B2B #TechFluencer

29,820 просмотров • 1 год назад

🚨 Big Tech just told Trudeau & Carney’s Liberals to pound sand!! Apple flat-out said: “We will NOT build a backdoor into our products. Ever.” Meta, VPN companies, and even American Republicans in Congress are warning Canada: Bill C-22 (the sneaky “Lawful Access Act”) is a full-on privacy killer. They want forced “technical capabilities” — code for backdoors into your encrypted data. ⛔️ Banking records. ⛔️ Health files. ⛔️ Private messages. Everything. ❌ This isn’t “public safety.” ✅ This is a totalitarian power grab dressed up in nice words. Apple, Meta, and cybersecurity experts see it for what it is: A backdoor for the government today = a backdoor for hackers and hostile states tomorrow. Liberals keep saying “if you’ve done nothing wrong, you have nothing to hide.” Meanwhile every major tech company is screaming: NO ❌ This is what happens when a government thinks it owns your life. Drop a 🔥 if you’re done with the surveillance state. #cdnpoli #BillC22 #LiberalSurveillance #PrivacyTheft #CarneyAuthoritarian #CanadaFirst
7:13

🚨 Big Tech just told Trudeau & Carney’s Liberals to pound sand!! Apple flat-out said: “We will NOT build a backdoor into our products. Ever.” Meta, VPN companies, and even American Republicans in Congress are warning Canada: Bill C-22 (the sneaky “Lawful Access Act”) is a full-on privacy killer. They want forced “technical capabilities” — code for backdoors into your encrypted data. ⛔️ Banking records. ⛔️ Health files. ⛔️ Private messages. Everything. ❌ This isn’t “public safety.” ✅ This is a totalitarian power grab dressed up in nice words. Apple, Meta, and cybersecurity experts see it for what it is: A backdoor for the government today = a backdoor for hackers and hostile states tomorrow. Liberals keep saying “if you’ve done nothing wrong, you have nothing to hide.” Meanwhile every major tech company is screaming: NO ❌ This is what happens when a government thinks it owns your life. Drop a 🔥 if you’re done with the surveillance state. #cdnpoli #BillC22 #LiberalSurveillance #PrivacyTheft #CarneyAuthoritarian #CanadaFirst

JayGen 𝕏 er🇨🇦

120,070 просмотров • 2 месяцев назад

🎶 New to Audiera? Let’s make your first AI song in 5 easy steps! No music skills needed — just your creativity and our AI studio. Here’s how to get started 👇 🧠 Open: 🦊 Tip: Use a Web3 browser like MetaMask, Trust Wallet, Bitget Wallet, or OKX for the smoothest experience. ✅ Step 1: Choose Your Style Pop, Hip-Hop, EDM, and more — pick your vibe. ✅ Step 2: Pick a Vocalist Let one of our AI artists sing it — or go with Kira or Ray for that extra spark. ✅ Step 3: Write Your Lyrics Bring your own lyrics or let AI generate them from a theme or keyword. ✅ Step 4: Set the Tempo Choose your melody’s BPM — fast, slow, or somewhere in between. ✅ Step 5: Click “Compose Song” Sit back while AI brings your song to life in minutes. 📂 Check your track in My Workspace 🌍 Flip “Public” to share with the Audiera community & start earning likes and visibility. 🎧 Ready to create? Let’s jam: #Audiera #AIMusic #CreateToEarn #Web3 #DanceToEarn #Kira #Ray #BEAT #MusicNFT #AIComposer
1:28

🎶 New to Audiera? Let’s make your first AI song in 5 easy steps! No music skills needed — just your creativity and our AI studio. Here’s how to get started 👇 🧠 Open: 🦊 Tip: Use a Web3 browser like MetaMask, Trust Wallet, Bitget Wallet, or OKX for the smoothest experience. ✅ Step 1: Choose Your Style Pop, Hip-Hop, EDM, and more — pick your vibe. ✅ Step 2: Pick a Vocalist Let one of our AI artists sing it — or go with Kira or Ray for that extra spark. ✅ Step 3: Write Your Lyrics Bring your own lyrics or let AI generate them from a theme or keyword. ✅ Step 4: Set the Tempo Choose your melody’s BPM — fast, slow, or somewhere in between. ✅ Step 5: Click “Compose Song” Sit back while AI brings your song to life in minutes. 📂 Check your track in My Workspace 🌍 Flip “Public” to share with the Audiera community & start earning likes and visibility. 🎧 Ready to create? Let’s jam: #Audiera #AIMusic #CreateToEarn #Web3 #DanceToEarn #Kira #Ray #BEAT #MusicNFT #AIComposer

Audiera

18,065 просмотров • 1 год назад

A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid
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A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid

Gipp 🦅

457,929 просмотров • 2 месяцев назад

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.
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This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.

Blaze

93,871 просмотров • 2 месяцев назад

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs
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⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

Unstructured

21,874 просмотров • 2 лет назад

$100 to One Person who LIKES ❤️ + RETWEETS ♻️ Must be following Flex Props 💎 ✅ Recap 💰 Yesterday’s PROFIT: +$750 to $800+ (for $100 bettors) 💰 ✅ 📈 MONSTER GREEN DAY — one of our biggest single-day runs ⸻ 🚨 ABSOLUTE HEATER DAY 🚨 We went CRAZY yesterday… wins across NHL, NBA, Soccer & Ladders 🔥 Up a TON of units in ONE DAY and stayed disciplined 💯 ⸻ ✅ CASHED PLAYS ✅ 🏒 Bruins vs Wild Over 1.5 2nd Period Goals | -180 | 2U 💣 FREE BOMB ✅ ⚽️ Red Bull Salzburg vs RZ Pellets WAC Over 2.5 Goals | -165 FanDuel | 2U ✅ 🏒 Canucks vs Devils Goal in First 10 Minutes of 3rd Period | +132 FanDuel ✅ 🏒 Löwen Frankfurt vs Straubing Tigers Over 0.5 1st Period Goals | 2U ✅ Collab NBA 🏀 Parlay | +151 FanDuel • Jalen Smith 5+ Points • Deandre Ayton Over 3.5 1st Q Points ⸻ 🪜 LADDER PLAY RUN 🪜 ✅ Play 1️⃣ $77.78 ➡️ $157.12 ✅ Play 2️⃣ $157.12 ➡️ $246.90 ✅ Play 3️⃣ $246.90 ➡️ $367.07 🔥 Ladder Profit: +$289.29 ⸻ ✅ MORE CASH ✅ Moses Moody Over 7.5 Points -141 | 2U ✅ 76ers vs Hawks Over 224.5 -110 | 2U ⸻ ❌ LOSSES (CONTROLLED) 🤮 Jalen Smith Over 7.5 Points | 2U 🤮 Trey Murphy Over 3.5 Assists | 1U 🤮 Rui Hachimura Over 1.5 Threes | 1U ⸻ 💰 $100 BETTOR SUMMARY If you played $100 per play (no unit scaling): ➡️ Up approximately $750–$800+ on the day ➡️ Even with losses, ROI was 🔥 thanks to ladders & plus-money hits 💎 VIP 85% OFF | 3 MONTHS $15 👇 DubClub: 📊 All Data Researched on HOF. Use my code “FLEXPROPS” and get a free week + 50% Off after trial.
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$100 to One Person who LIKES ❤️ + RETWEETS ♻️ Must be following Flex Props 💎 ✅ Recap 💰 Yesterday’s PROFIT: +$750 to $800+ (for $100 bettors) 💰 ✅ 📈 MONSTER GREEN DAY — one of our biggest single-day runs ⸻ 🚨 ABSOLUTE HEATER DAY 🚨 We went CRAZY yesterday… wins across NHL, NBA, Soccer & Ladders 🔥 Up a TON of units in ONE DAY and stayed disciplined 💯 ⸻ ✅ CASHED PLAYS ✅ 🏒 Bruins vs Wild Over 1.5 2nd Period Goals | -180 | 2U 💣 FREE BOMB ✅ ⚽️ Red Bull Salzburg vs RZ Pellets WAC Over 2.5 Goals | -165 FanDuel | 2U ✅ 🏒 Canucks vs Devils Goal in First 10 Minutes of 3rd Period | +132 FanDuel ✅ 🏒 Löwen Frankfurt vs Straubing Tigers Over 0.5 1st Period Goals | 2U ✅ Collab NBA 🏀 Parlay | +151 FanDuel • Jalen Smith 5+ Points • Deandre Ayton Over 3.5 1st Q Points ⸻ 🪜 LADDER PLAY RUN 🪜 ✅ Play 1️⃣ $77.78 ➡️ $157.12 ✅ Play 2️⃣ $157.12 ➡️ $246.90 ✅ Play 3️⃣ $246.90 ➡️ $367.07 🔥 Ladder Profit: +$289.29 ⸻ ✅ MORE CASH ✅ Moses Moody Over 7.5 Points -141 | 2U ✅ 76ers vs Hawks Over 224.5 -110 | 2U ⸻ ❌ LOSSES (CONTROLLED) 🤮 Jalen Smith Over 7.5 Points | 2U 🤮 Trey Murphy Over 3.5 Assists | 1U 🤮 Rui Hachimura Over 1.5 Threes | 1U ⸻ 💰 $100 BETTOR SUMMARY If you played $100 per play (no unit scaling): ➡️ Up approximately $750–$800+ on the day ➡️ Even with losses, ROI was 🔥 thanks to ladders & plus-money hits 💎 VIP 85% OFF | 3 MONTHS $15 👇 DubClub: 📊 All Data Researched on HOF. Use my code “FLEXPROPS” and get a free week + 50% Off after trial.

Flex Props 💎

11,843 просмотров • 7 месяцев назад

Today I'm lanching my secret openclaw agent I use 1000+ times daily you can use it for free tbh you don't need a 512GB Mac Studio to solve a Mission Control problem. I've been working on ShipClaw for the last 1.5 weeks after my software job. I've been in AI for the last 3 4 years and I know how to solve the Mission Control problem. You must solve 3 problems: → Every user has their own tenant → Every user has their own pairing ID so other members know which platform OpenClaw is used on → Mission Control AI Agent Orchestration That's how I solve these problems using Railway, Cloudflare, and infrastructure VPS in the cloud. Now users have their own identity, own auth, and own OpenClaw server that runs 24/7 with no cost. Why should you pay for a 512GB Mac Studio and give your computer access to OpenClaw? ❌ Stop doing that. Instead, invest in something that pays you in the long game. enjoy ❤️
1:00

Today I'm lanching my secret openclaw agent I use 1000+ times daily you can use it for free tbh you don't need a 512GB Mac Studio to solve a Mission Control problem. I've been working on ShipClaw for the last 1.5 weeks after my software job. I've been in AI for the last 3 4 years and I know how to solve the Mission Control problem. You must solve 3 problems: → Every user has their own tenant → Every user has their own pairing ID so other members know which platform OpenClaw is used on → Mission Control AI Agent Orchestration That's how I solve these problems using Railway, Cloudflare, and infrastructure VPS in the cloud. Now users have their own identity, own auth, and own OpenClaw server that runs 24/7 with no cost. Why should you pay for a 512GB Mac Studio and give your computer access to OpenClaw? ❌ Stop doing that. Instead, invest in something that pays you in the long game. enjoy ❤️

Umair Ali

33,453 просмотров • 4 месяцев назад

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.
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A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

Ilir Aliu

21,844 просмотров • 1 год назад

Weekend plans: I can sleep at 3AM. My tiny server probably can’t. 😎 #ZimaBoard2 #ZimaOS #HomeServer #Homelab
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Weekend plans: I can sleep at 3AM. My tiny server probably can’t. 😎 #ZimaBoard2 #ZimaOS #HomeServer #Homelab

ZimaSpace

36,456 просмотров • 3 месяцев назад