正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

If this contract had gone live, attackers would have drained every single ETH within hours. A crowdfunding platform. Clean-looking Solidity. Ready for mainnet. The dev team was confident. Then we ran it through V2's AI Smart Contract Auditor. 9 vulnerabilities detected. 1 critical. 2 high. Under 20 minutes. The... critical finding? A textbook reentrancy bug in claimRefund(). The contract sent ETH before zeroing the user's balance. An attacker could re-enter the function during the transfer and drain the same refund repeatedly until the entire contract was empty. Every backer's funds across every campaign, gone in one transaction. It also caught an emergencyWithdraw() function that let the owner pull the entire contract balance with zero restrictions. Active campaigns, pending refunds, everything. One compromised key and it's a complete rug. Plus an uncapped platform fee that could be set to 100% (creators get nothing) or above 100% (permanent DoS on all fund claims). The AI generated a 24-page report with severity classifications, exact code locations, detailed attack scenarios, and specific fixes for each issue. Final score: 89/100. Not because it was "pretty good," but because the architecture was sound and the issues were fixable. Without catching them though? Catastrophic. A traditional audit runs $5K to $50K and takes weeks. This took under 20 minutes. That's the power of V2 ⚡️show more

ChainGPT

796,492 subscribers

26,504 次观看 • 6 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

We were tracking 50+ campaigns manually. Viktor turned it into one report in minutes. We skipped a project coordinator hire. We're a 12-person team managing 140+ AI and tech creators. For months, campaign tracking meant someone on the team manually checking spreadsheets, chasing creators over DM, and pulling together a status update before every client call. It worked, but it was the kind of work that quietly ate hours nobody had to spare. We tried solving it by splitting the work across the team. That just meant more people spending time on the same repetitive problem instead of one. Then we gave Viktor one job: own the campaign tracker. Pull status across all active campaigns, flag what's overdue, what's pending approval, what needs a payment follow-up, and what's coming up next. Post it directly into our Slack workspace before the week starts. Inside Slack, Viktor flagged overdue tasks, pending approvals, payment follow-ups, and upcoming deadlines. All from one report, posted directly into our workspace. Everything stayed read-only. Nothing was edited or sent without approval. The team stopped spending the first hour of every Monday piecing together what was happening across 50+ campaigns. That time went back to the work that actually moves clients forward. The teams still tracking this in spreadsheets are one missed deadline away from a client conversation they don't want to have. A copilot helps you work. An AI employee works when you don't. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #CreatorMarketing #CampaignOps Paid Partnership

We were tracking 50+ campaigns manually. Viktor turned it into one report in minutes. We skipped a project coordinator hire. We're a 12-person team managing 140+ AI and tech creators. For months, campaign tracking meant someone on the team manually checking spreadsheets, chasing creators over DM, and pulling together a status update before every client call. It worked, but it was the kind of work that quietly ate hours nobody had to spare. We tried solving it by splitting the work across the team. That just meant more people spending time on the same repetitive problem instead of one. Then we gave Viktor one job: own the campaign tracker. Pull status across all active campaigns, flag what's overdue, what's pending approval, what needs a payment follow-up, and what's coming up next. Post it directly into our Slack workspace before the week starts. Inside Slack, Viktor flagged overdue tasks, pending approvals, payment follow-ups, and upcoming deadlines. All from one report, posted directly into our workspace. Everything stayed read-only. Nothing was edited or sent without approval. The team stopped spending the first hour of every Monday piecing together what was happening across 50+ campaigns. That time went back to the work that actually moves clients forward. The teams still tracking this in spreadsheets are one missed deadline away from a client conversation they don't want to have. A copilot helps you work. An AI employee works when you don't. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #CreatorMarketing #CampaignOps Paid Partnership

Nova Reid

103,724 次观看 • 2 个月前

I showed this video to a few friends before telling them it was AI. Every single one believed it was a real creator. They thought someone had picked up a camera, filmed the shots, recorded the voiceover, and edited everything together. None of that happened. The person isn't real. The voice isn't real. The video wasn't filmed. The entire thing was created using Arcads AI. What's fascinating isn't just how realistic the face looks—it's the tiny details. The natural expressions, the eye contact, the pacing, the body language, and the way everything comes together to feel like authentic UGC. We're moving into a world where creating high-quality content no longer requires a camera, actors, or a production team. If you have an idea, AI can turn it into a video in minutes. This technology is improving incredibly fast, and the gap between AI-generated content and traditional creator content is getting smaller with every update. If you're a founder, marketer, or creator, it's worth paying attention. Comment "Send" and I'll share the exact workflow I used to create this video.

I showed this video to a few friends before telling them it was AI. Every single one believed it was a real creator. They thought someone had picked up a camera, filmed the shots, recorded the voiceover, and edited everything together. None of that happened. The person isn't real. The voice isn't real. The video wasn't filmed. The entire thing was created using Arcads AI. What's fascinating isn't just how realistic the face looks—it's the tiny details. The natural expressions, the eye contact, the pacing, the body language, and the way everything comes together to feel like authentic UGC. We're moving into a world where creating high-quality content no longer requires a camera, actors, or a production team. If you have an idea, AI can turn it into a video in minutes. This technology is improving incredibly fast, and the gap between AI-generated content and traditional creator content is getting smaller with every update. If you're a founder, marketer, or creator, it's worth paying attention. Comment "Send" and I'll share the exact workflow I used to create this video.

Oliver

12,783 次观看 • 1 个月前

The OriginTrail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using Claude Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in Zcash 🛡️’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As Anthropic framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): Bug bounty program and honeypot details:

The OriginTrail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using Claude Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in Zcash 🛡️’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As Anthropic framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): Bug bounty program and honeypot details:

OriginTrail Developers

466,082 次观看 • 3 个月前

I GAVE GROK BOT $54, CLOSED THE LAPTOP, AND WENT TO SLEEP At 05:30 it handed me an $8,900 balance. Net profit: $8,846. I did not leave one bot staring at a chart. Eight agents worked the same live desk through the night: SEARCH scanned fresh meme signals. RISK checked contracts, mint authority, liquidity, and exit depth. SNIPER waited for a cleared entry window and executed it. WHALE tracked large-wallet movement. RUG watched the developer wallet for a liquidity touch. SHILL compared social acceleration with market activity. EXIT moved stops and took profit through a ladder. LEDGER kept every position, rejection, and handoff in shared memory. Every setup moved from agent to agent. SEARCH could not buy. SNIPER could not approve its own risk. RISK could veto the entire chain. EXIT needed a route out before execution was allowed in. HEAD OF DESK read the combined state and released only the trades that cleared the rules I had approved before going offline. If a setup failed a contract, wallet, liquidity, or attention check, it died before capital touched it. If it cleared, SNIPER entered, EXIT managed the position, and LEDGER carried the result into the next decision. By morning the desk had one brief ready: start: $54 balance: $8,900 net: +$8,846 open risk: reviewed owner decisions waiting: 1 This is the result of one run, not a promise that the next one repeats it. The useful part is that the agents kept researching, checking, executing, and managing risk while the laptop was closed.

I GAVE GROK BOT $54, CLOSED THE LAPTOP, AND WENT TO SLEEP At 05:30 it handed me an $8,900 balance. Net profit: $8,846. I did not leave one bot staring at a chart. Eight agents worked the same live desk through the night: SEARCH scanned fresh meme signals. RISK checked contracts, mint authority, liquidity, and exit depth. SNIPER waited for a cleared entry window and executed it. WHALE tracked large-wallet movement. RUG watched the developer wallet for a liquidity touch. SHILL compared social acceleration with market activity. EXIT moved stops and took profit through a ladder. LEDGER kept every position, rejection, and handoff in shared memory. Every setup moved from agent to agent. SEARCH could not buy. SNIPER could not approve its own risk. RISK could veto the entire chain. EXIT needed a route out before execution was allowed in. HEAD OF DESK read the combined state and released only the trades that cleared the rules I had approved before going offline. If a setup failed a contract, wallet, liquidity, or attention check, it died before capital touched it. If it cleared, SNIPER entered, EXIT managed the position, and LEDGER carried the result into the next decision. By morning the desk had one brief ready: start: $54 balance: $8,900 net: +$8,846 open risk: reviewed owner decisions waiting: 1 This is the result of one run, not a promise that the next one repeats it. The useful part is that the agents kept researching, checking, executing, and managing risk while the laptop was closed.

88n77

22,995 次观看 • 16 天前

🚨 Do you understand what Claude just quietly dropped while everyone was distracted? 1 million tokens. Let me explain what that actually means because the number alone doesn't hit right. > A senior engineer joins a company and spends 3 to 6 months just reading code.. Understanding how things connect. Learning where the bugs hide. Why that one file nobody touches exists. It takes months because a codebase is massive and human memory is small. > Claude just loaded the entire thing in one prompt. 30 seconds. Every file, Every function, Every line. All of it. Sitting in memory like it's been working there for years. And it scored highest among every single frontier model. Not GPT.. Not Gemini, Nobody. > Yesterday Amazon's AI nuked production because it couldn't see the full picture - it made a decision with partial context and deleted everything. Today an AI can hold 1 million tokens of context at once. That's the fix. That's the "before and after" moment for AI coding. > 600 images in one request. Entire PDFs. Full repos. And they dropped it on a Friday on all plans like it was a patch note. The scariest AI updates aren't the ones with press conferences. They're the ones that drop in a tweet at 6pm and change everything by Monday morning.

🚨 Do you understand what Claude just quietly dropped while everyone was distracted? 1 million tokens. Let me explain what that actually means because the number alone doesn't hit right. > A senior engineer joins a company and spends 3 to 6 months just reading code.. Understanding how things connect. Learning where the bugs hide. Why that one file nobody touches exists. It takes months because a codebase is massive and human memory is small. > Claude just loaded the entire thing in one prompt. 30 seconds. Every file, Every function, Every line. All of it. Sitting in memory like it's been working there for years. And it scored highest among every single frontier model. Not GPT.. Not Gemini, Nobody. > Yesterday Amazon's AI nuked production because it couldn't see the full picture - it made a decision with partial context and deleted everything. Today an AI can hold 1 million tokens of context at once. That's the fix. That's the "before and after" moment for AI coding. > 600 images in one request. Entire PDFs. Full repos. And they dropped it on a Friday on all plans like it was a patch note. The scariest AI updates aren't the ones with press conferences. They're the ones that drop in a tweet at 6pm and change everything by Monday morning.

Tuki

206,309 次观看 • 6 个月前

The ChainGPT AI skill for Claude Code is one of the most complete Web3-AI dev environment on the market. Let me prove it. Open Claude Code with the installed skill, and you have direct access to: • Built-in wallets across 33+ chains • DEX trading, perps, and Hyperliquid execution • Smart contract generation and auditing • NFT generation across 22 chains • Real-time crypto news API • Fine-tuned crypto LLM with live on-chain data Every part of the Web3 stack, one prompt away. Here's what that looks like in practice. I built a real-time on-chain whale tracker in a single afternoon. It's called Whale Watch. → Pulls live swap data from Ethereum DEX pools → Filters every trade over $100K → Runs each whale through the ChainGPT LLM for a trader-grade live analysis → Routes the user into 1inch with the token pair pre-loaded if they want to follow the trade Real data. Real AI. Real action layer. The skill wrote the server. It hit the right APIs. It generated the UI. It debugged itself when something broke. The only thing I supplied was the idea and the polish. Open Claude Code. Ship something with ChainGPT AI this weekend! /plugin install ChainGPT-org/chaingpt-claude-skill

The ChainGPT AI skill for Claude Code is one of the most complete Web3-AI dev environment on the market. Let me prove it. Open Claude Code with the installed skill, and you have direct access to: • Built-in wallets across 33+ chains • DEX trading, perps, and Hyperliquid execution • Smart contract generation and auditing • NFT generation across 22 chains • Real-time crypto news API • Fine-tuned crypto LLM with live on-chain data Every part of the Web3 stack, one prompt away. Here's what that looks like in practice. I built a real-time on-chain whale tracker in a single afternoon. It's called Whale Watch. → Pulls live swap data from Ethereum DEX pools → Filters every trade over $100K → Runs each whale through the ChainGPT LLM for a trader-grade live analysis → Routes the user into 1inch with the token pair pre-loaded if they want to follow the trade Real data. Real AI. Real action layer. The skill wrote the server. It hit the right APIs. It generated the UI. It debugged itself when something broke. The only thing I supplied was the idea and the polish. Open Claude Code. Ship something with ChainGPT AI this weekend! /plugin install ChainGPT-org/chaingpt-claude-skill

ChainGPT

25,853 次观看 • 4 个月前

A 16-YEAR-OLD DEVELOPER BUILT AN AI EMPLOYEE THAT WORKS 24 HOURS A DAY FOR LESS THAN $20 He stopped treating AI like a chatbot and turned it into a complete production system powered by Claude, Cursor and automated workflows Every morning the pipeline generated new ideas, wrote scripts, organized research and prepared content before he even opened his laptop What used to take an entire afternoon now takes less than 30 minutes, cutting production costs while multiplying output every single week The biggest lesson wasn't that AI works faster It was that the people building repeatable systems are moving ahead of creators still doing everything manually The biggest advantage in 2026 won't be talent It will be automation Bookmark this

A 16-YEAR-OLD DEVELOPER BUILT AN AI EMPLOYEE THAT WORKS 24 HOURS A DAY FOR LESS THAN $20 He stopped treating AI like a chatbot and turned it into a complete production system powered by Claude, Cursor and automated workflows Every morning the pipeline generated new ideas, wrote scripts, organized research and prepared content before he even opened his laptop What used to take an entire afternoon now takes less than 30 minutes, cutting production costs while multiplying output every single week The biggest lesson wasn't that AI works faster It was that the people building repeatable systems are moving ahead of creators still doing everything manually The biggest advantage in 2026 won't be talent It will be automation Bookmark this

RetroChainer

13,378 次观看 • 2 个月前

As a graphics engine coder I think when you look at a flickering bug like this one in the video below it’s not immediately obvious what is going on. The key here is observation - to study this flickering/bugged render carefully - what do we see? Firstly for me it was very obvious that nearly all of the scene shadows were flashing on and off - but (but!) there was a secondary issue where some buildings and parts of the sky were also flashing purple. Hmmmm. Interesting. I initially thought then this might be two separate bugs - but because the sky purple element could only based on full screen post fx and not 3D rendering I looked at this first with a few GPU captures to step through all our post processing to find the rendering stage which made these pixels turn purple: When I did this I found the colour 3D texture LUT grading that makes our different biomes have unique colour palettes was going very wrong - colours near 0 or 1 were wrapping and making the purple elements that we see in the said sky and base parts. The only way this could happen was if the texture was corrupt (which it was not) or if the 3D texture sampling was wrapping and not clamped as intended. That was the Eureka moment - because if the post fx had the wrong texture sampler then the disappearing shadows which also require an exact texture sampler for comparing depth might be also wrong because of the same kind of texture sampling issue! So with this idea that the engine was using the wrong texture samplers, but only in very high draw call scenes like the big base here I the looked at some engine limits and found the bug very quickly - a circular dx12 descriptor buffer for samplers running out over multiple frames, reusing the wrong data for new scenes inflight. Hence the flickering, as the GPU randomly got wrong samplers for some post textures or shadow depth. Easy to fix with triple limits for future expansion and also adding an assert/debug spam in case this limit is ever reached again - QA testers would see this message and report if they ever saw a flicker with this style of bug. My bug and my bad from 2017 porting NMS to DX12 without foreseeing how massively complex bases and our game would grow.

As a graphics engine coder I think when you look at a flickering bug like this one in the video below it’s not immediately obvious what is going on. The key here is observation - to study this flickering/bugged render carefully - what do we see? Firstly for me it was very obvious that nearly all of the scene shadows were flashing on and off - but (but!) there was a secondary issue where some buildings and parts of the sky were also flashing purple. Hmmmm. Interesting. I initially thought then this might be two separate bugs - but because the sky purple element could only based on full screen post fx and not 3D rendering I looked at this first with a few GPU captures to step through all our post processing to find the rendering stage which made these pixels turn purple: When I did this I found the colour 3D texture LUT grading that makes our different biomes have unique colour palettes was going very wrong - colours near 0 or 1 were wrapping and making the purple elements that we see in the said sky and base parts. The only way this could happen was if the texture was corrupt (which it was not) or if the 3D texture sampling was wrapping and not clamped as intended. That was the Eureka moment - because if the post fx had the wrong texture sampler then the disappearing shadows which also require an exact texture sampler for comparing depth might be also wrong because of the same kind of texture sampling issue! So with this idea that the engine was using the wrong texture samplers, but only in very high draw call scenes like the big base here I the looked at some engine limits and found the bug very quickly - a circular dx12 descriptor buffer for samplers running out over multiple frames, reusing the wrong data for new scenes inflight. Hence the flickering, as the GPU randomly got wrong samplers for some post textures or shadow depth. Easy to fix with triple limits for future expansion and also adding an assert/debug spam in case this limit is ever reached again - QA testers would see this message and report if they ever saw a flicker with this style of bug. My bug and my bad from 2017 porting NMS to DX12 without foreseeing how massively complex bases and our game would grow.

Martin Griffiths

72,855 次观看 • 1 年前

Jeff Bezos just described AI in three words that make most of the economy temporary. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Not a product. Not a platform. Not a feature. A layer. Underneath everything. Everyone is asking which AI company wins. Bezos is telling you that is the wrong question entirely. A horizontal layer does not produce winners. It produces a new floor. Everything standing on the old one either gets rebuilt or gets erased. This has happened exactly twice in modern history. Electricity. The internet. Both times the same pattern. The new layer appeared. The old economy kept running above it. Revenue held. Careers continued. Everything looked normal. Then quietly and permanently the entire structure reorganized around the new substrate. The people who did not move were not outcompeted. They were made structurally irrelevant. Not because they were wrong. Because the ground they stood on stopped being ground. Bezos is telling you it is happening a third time. Not with a product. Not with a platform. With intelligence itself becoming infrastructure. A horizontal layer does not compete with the expert. It makes expertise free. It hands a 22 year old with zero credentials the same cognitive output you spent a decade and a quarter million dollars learning to produce. For $20 a month. That is not disruption. Disruption replaces a product with a better product. This dissolves the scarcity your entire career was priced on. Not because the work disappeared. Because the wall around it did. Every profession that exists because knowledge is hard to acquire. Every company that profits because analysis takes time. Every industry that survives because complexity locks outsiders out. All of it rests on a single assumption. That cognition is scarce. AI does not challenge that assumption. It retires it. The people who understand this are already rebuilding. Quietly. Deliberately. While everyone else argues about whether the thing underneath them is real. Bezos did not give you a prediction. He gave you a position on a map. You are either above the new layer or beneath it.

Jeff Bezos just described AI in three words that make most of the economy temporary. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Not a product. Not a platform. Not a feature. A layer. Underneath everything. Everyone is asking which AI company wins. Bezos is telling you that is the wrong question entirely. A horizontal layer does not produce winners. It produces a new floor. Everything standing on the old one either gets rebuilt or gets erased. This has happened exactly twice in modern history. Electricity. The internet. Both times the same pattern. The new layer appeared. The old economy kept running above it. Revenue held. Careers continued. Everything looked normal. Then quietly and permanently the entire structure reorganized around the new substrate. The people who did not move were not outcompeted. They were made structurally irrelevant. Not because they were wrong. Because the ground they stood on stopped being ground. Bezos is telling you it is happening a third time. Not with a product. Not with a platform. With intelligence itself becoming infrastructure. A horizontal layer does not compete with the expert. It makes expertise free. It hands a 22 year old with zero credentials the same cognitive output you spent a decade and a quarter million dollars learning to produce. For $20 a month. That is not disruption. Disruption replaces a product with a better product. This dissolves the scarcity your entire career was priced on. Not because the work disappeared. Because the wall around it did. Every profession that exists because knowledge is hard to acquire. Every company that profits because analysis takes time. Every industry that survives because complexity locks outsiders out. All of it rests on a single assumption. That cognition is scarce. AI does not challenge that assumption. It retires it. The people who understand this are already rebuilding. Quietly. Deliberately. While everyone else argues about whether the thing underneath them is real. Bezos did not give you a prediction. He gave you a position on a map. You are either above the new layer or beneath it.

Dustin

104,327 次观看 • 2 个月前

🚨💔Navi w0nderful and his girlfriend qqsis had a terrible situation with their dog: ”It all started when Ihor really wanted a dog. We went looking. We bought a perfect puppy (completely healthy, with veterinary confirmation and KSU documents). We started looking for a transporter. It so happened that the transporter suggested by the breeder had bad reviews, so I went to search on my own. I found a person who supposedly understands dogs (according to him - super responsible and takes full responsibility for the animal). Of all the possible problems, only a 1–2 day delay in delivery was discussed. Spoiler: they promised delivery in 3–4 days, it took 2 weeks… Before the breeder handed over the dog, this person was constantly in touch, communicating, etc. After the dog arrived in Kyiv from Kryvyi Rih, the transporter (whose name is Artem +380973444587 and his wife Alina +380 95 764 98 86 / all communication was through his wife’s number, which is listed on his website) sent us only one video. Then I explained that we had already paid for the dog and it was already ours, but nothing changed after that. The dog was taken to Moldova and they wrote: “there is no veterinarian at the border who issues the EU certificate, so we are waiting for one to appear.” We were outraged but agreed, because the agreement was for LEGAL transportation!!! Remember this moment, because later Artem (the transporter) will explain everything with this EU certificate. As it turned out later, the dog was with his relatives in Moldova, who also deal with transportation (Ruslan +373 695 71 399). The transportation of the dog was ultimately carried out by a passenger transport company (Baltik Tour — +380 63 060 5614). In the second week, we started calling, arguing, and saying that we would pick up the dog from Moldova and transport it ourselves. Eventually, we contacted Ruslan, and he said that in 2–3 days the dog would definitely be sent to us. All this time, we did not know how exactly the dog would be transported - we were told that it would travel ALONE WITH THE DRIVER. In the end, the dog was sent to us. It was supposed to arrive on March 7–8, but on March 6 at 8 a.m., a woman called me and said: “Your dog is dying, we actually thought it was already dead. I wrote you a message on WhatsApp, but we gave it sugar and it seems to have recovered a bit. Go pick it up and take it to a veterinarian if you want it to survive…” Then we went to pick up the dog 100 km from Warsaw. It was barely breathing. They told us it had just been running. We take the dog, go to a taxi, and they tell us: “Where is the money? Pay or we won’t let you take the dog. If it dies - you will deal with the transporter.” Then we went to a clinic and did tests. With 90% probability, it was a stroke, which led to severe neurological issues: the dog could not walk, eat, or drink, and was relieving itself while lying down. After the transport, it lost 30% of its weight, which has now caused gastritis, it also picked up parasites somewhere, and many other problems. Please, be careful and only trust animals to people you know personally. Oh, and one more thing: when I asked what happened to the dog, I was told: “I left it in the carrier for just an hour, and when I came back, it started screaming, squealing, and lost consciousness.” How could anyone think to leave a dog like that… If we had known this could happen - we didn’t even suspect that something like this is possible in the modern world. And what if we didn’t have money for treatment? What if we didn’t make it in time? This is simply horror and hell, I wouldn’t wish this on anyone. Once again, listing everyone involved: Artem - the one who communicated with us and was responsible for the transportation: +380973444587 His wife/manager Alina (or possibly a fake account of Artem): +380 95 764 98 86 His relative or partner Ruslan: +373 695 71 399 The company illegally transporting animals into the EU: +380 63 060 5614...“

🚨💔Navi w0nderful and his girlfriend qqsis had a terrible situation with their dog: ”It all started when Ihor really wanted a dog. We went looking. We bought a perfect puppy (completely healthy, with veterinary confirmation and KSU documents). We started looking for a transporter. It so happened that the transporter suggested by the breeder had bad reviews, so I went to search on my own. I found a person who supposedly understands dogs (according to him - super responsible and takes full responsibility for the animal). Of all the possible problems, only a 1–2 day delay in delivery was discussed. Spoiler: they promised delivery in 3–4 days, it took 2 weeks… Before the breeder handed over the dog, this person was constantly in touch, communicating, etc. After the dog arrived in Kyiv from Kryvyi Rih, the transporter (whose name is Artem +380973444587 and his wife Alina +380 95 764 98 86 / all communication was through his wife’s number, which is listed on his website) sent us only one video. Then I explained that we had already paid for the dog and it was already ours, but nothing changed after that. The dog was taken to Moldova and they wrote: “there is no veterinarian at the border who issues the EU certificate, so we are waiting for one to appear.” We were outraged but agreed, because the agreement was for LEGAL transportation!!! Remember this moment, because later Artem (the transporter) will explain everything with this EU certificate. As it turned out later, the dog was with his relatives in Moldova, who also deal with transportation (Ruslan +373 695 71 399). The transportation of the dog was ultimately carried out by a passenger transport company (Baltik Tour — +380 63 060 5614). In the second week, we started calling, arguing, and saying that we would pick up the dog from Moldova and transport it ourselves. Eventually, we contacted Ruslan, and he said that in 2–3 days the dog would definitely be sent to us. All this time, we did not know how exactly the dog would be transported - we were told that it would travel ALONE WITH THE DRIVER. In the end, the dog was sent to us. It was supposed to arrive on March 7–8, but on March 6 at 8 a.m., a woman called me and said: “Your dog is dying, we actually thought it was already dead. I wrote you a message on WhatsApp, but we gave it sugar and it seems to have recovered a bit. Go pick it up and take it to a veterinarian if you want it to survive…” Then we went to pick up the dog 100 km from Warsaw. It was barely breathing. They told us it had just been running. We take the dog, go to a taxi, and they tell us: “Where is the money? Pay or we won’t let you take the dog. If it dies - you will deal with the transporter.” Then we went to a clinic and did tests. With 90% probability, it was a stroke, which led to severe neurological issues: the dog could not walk, eat, or drink, and was relieving itself while lying down. After the transport, it lost 30% of its weight, which has now caused gastritis, it also picked up parasites somewhere, and many other problems. Please, be careful and only trust animals to people you know personally. Oh, and one more thing: when I asked what happened to the dog, I was told: “I left it in the carrier for just an hour, and when I came back, it started screaming, squealing, and lost consciousness.” How could anyone think to leave a dog like that… If we had known this could happen - we didn’t even suspect that something like this is possible in the modern world. And what if we didn’t have money for treatment? What if we didn’t make it in time? This is simply horror and hell, I wouldn’t wish this on anyone. Once again, listing everyone involved: Artem - the one who communicated with us and was responsible for the transportation: +380973444587 His wife/manager Alina (or possibly a fake account of Artem): +380 95 764 98 86 His relative or partner Ruslan: +373 695 71 399 The company illegally transporting animals into the EU: +380 63 060 5614...“

NaviFanpage 🇺🇦

162,465 次观看 • 5 个月前

Today I had my first demo drive in a Tesla. It was also my first time ever sitting in one. This was the first car I’ve ever sat in the driver’s seat of where I didn’t touch the steering wheel for over 20 miles. Before I even got to the car, the people who had demoed it before me were an older married couple who were absolutely euphoric. They thought it was so cool that the car could drive itself. The Tesla employee told me this happens all the time. People come back from demo drives and tell the next test driver that they’re about to have an amazing experience. Little did I know, I’d end up carrying on the torch to the next couple demoing it after me. There was a ton of construction where I demoed the car, and FSD handled the entire drive extremely well. And yes, it can go through a drive-thru and stop at each window. The only thing I had to do was tap the pedal because it wouldn’t leave on its own, but it was still wild seeing the AI stop perfectly at the second window and wait. There are a million things I could write about why a Tesla feels like a better car and how much more it offers compared to a regular car. But for now, I’ll stick to FSD. There were only two moments that made me a little uneasy. The first was pretty minor. The car slightly hesitated going up a driveway, but quickly made up its mind. The second was more noticeable. I didn’t realize the car was nagging me. Once I touched the steering wheel, nothing happened, so I pulled it right a little harder, then let go. After that, the car turned left and crossed a double yellow on a backroad. (and yes I know you can sue the volume knob) I’m not totally sure if it was trying to pull over or what it was doing. I wanted to see how it would handle the situation, but there were cars coming, so I took over and corrected it. One of the coolest moments was when I thought FSD was glitching because it came to a complete stop in the middle of a busy road. Then I looked around and realized why. On the right side, there was a bicyclist waiting at a yellow crosswalk. The cars behind me didn’t honk, and the Tesla stopping actually incentivized another car in the right lane to stop and let him pass. The car is almost too nice to pedestrians, because 99.999% of humans would’ve blown through that, especially with no flashing light. For 99.9% of the drive, the car navigated confidently and smoothly. It was a real “feel the AGI” moment. Please do not let the media, the general public, or anyone else convince you that this technology is just some kind of auto assist or glorified cruise control. This is undoubtedly getting extremely close to feeling superhuman. You still have to pay attention to the road, but after experiencing it myself, I’d be shocked if HW4 Teslas aren’t unsupervised within the next couple years. The car was extremely smooth. There was no harsh braking, and it even avoided something in the road that I didn’t see. Driving with FSD made me realize I probably wasn’t driving as well as I could be. Hopefully, eventually, everyone’s car can be as mindful as a Tesla. I’ve never seen a brand so far removed from the public’s sentiment. I’m so happy I ordered one.

Chris

18,859 次观看 • 2 个月前

According to the Book of Ezekiel, he was standing by the river Chebar when a storm cloud approached, filled with fire, brightness, and something metallic looking. Inside it were four living beings, each with four faces and four wings, moving in perfect coordination. Beside them were wheels within wheels that sparkled like beryl and could move in any direction without turning. The entire structure moved with intelligence and purpose. Ezekiel was very clear that this was not symbolic to him. It was something he saw. Check out this line in the text. “As I looked, I saw a windstorm coming out of the north, an immense cloud with flashing lightning and surrounded by brilliant light. The center of the fire looked like glowing metal.” He also says the wheels were “full of eyes all around,” which many experts try to say is a metaphor, but the language reads like observation. The beings moved, stopped, rose, and descended together, as if part of a single system. Above them was a platform that looked like crystal, and above that a throne like sapphire, with a figure that appeared human but radiant and overpowering. Ezekiel collapses afterward, clearly shaken. So what was this. The traditional explanation is that Ezekiel saw God and angelic beings. I'm not so sure. This could describe advanced beings using technology that an ancient person had no language for. It could be interdimensional entities appearing through a controlled interface. It could be a non human intelligence operating some kind of vehicle or system. Some people even say a breakaway civilization that existed alongside early humanity and later vanished. Whatever it was, to me Ezekiel described machinery, movement, structure, something solid and real. What was it?

According to the Book of Ezekiel, he was standing by the river Chebar when a storm cloud approached, filled with fire, brightness, and something metallic looking. Inside it were four living beings, each with four faces and four wings, moving in perfect coordination. Beside them were wheels within wheels that sparkled like beryl and could move in any direction without turning. The entire structure moved with intelligence and purpose. Ezekiel was very clear that this was not symbolic to him. It was something he saw. Check out this line in the text. “As I looked, I saw a windstorm coming out of the north, an immense cloud with flashing lightning and surrounded by brilliant light. The center of the fire looked like glowing metal.” He also says the wheels were “full of eyes all around,” which many experts try to say is a metaphor, but the language reads like observation. The beings moved, stopped, rose, and descended together, as if part of a single system. Above them was a platform that looked like crystal, and above that a throne like sapphire, with a figure that appeared human but radiant and overpowering. Ezekiel collapses afterward, clearly shaken. So what was this. The traditional explanation is that Ezekiel saw God and angelic beings. I'm not so sure. This could describe advanced beings using technology that an ancient person had no language for. It could be interdimensional entities appearing through a controlled interface. It could be a non human intelligence operating some kind of vehicle or system. Some people even say a breakaway civilization that existed alongside early humanity and later vanished. Whatever it was, to me Ezekiel described machinery, movement, structure, something solid and real. What was it?

Jason Wilde

21,394 次观看 • 7 个月前

$I built a content engine that runs on telegram. Two commands... /discover: sends out to 9 sources across HackerNews, Reddit communities covering AI automation, prompt engineering, vibe coding, and specialist newsletters. Pulls everything published in the last 24 hours, runs each item through an AI extraction layer that scores it against 100+ niche keywords, deduplicates, and drops the relevant ideas into a Notion database. Takes about 90 seconds. Costs fractions of a cent. /ideas: this command pulls the top scored ideas from that database, randomizes the selection so you're not seeing the same ones every time, and sends them to you in a clean numbered list. You reply with /write 3 or whatever you choose, and the system researches the topic using Perplexity's live web search, generates three distinct outline options with different angles and hooks, saves them to a Google Doc, and sends you a message telling you they're ready. You read the outlines, and you pick one. You then reply with the command /outline 2. The system writes the full piece in your voice, following your brand guidelines, with specific examples and concrete claims. It can be done in under two minutes of your time. The whole thing runs on n8n, with no subscriptions beyond what you already use. If content takes too long or you don't have ideas, this solves that. I built this for myself; I can do it for you. If you're tired of knowing you should be posting and still not doing it, let's talk.$

I built a content engine that runs on telegram. Two commands... /discover: sends out to 9 sources across HackerNews, Reddit communities covering AI automation, prompt engineering, vibe coding, and specialist newsletters. Pulls everything published in the last 24 hours, runs each item through an AI extraction layer that scores it against 100+ niche keywords, deduplicates, and drops the relevant ideas into a Notion database. Takes about 90 seconds. Costs fractions of a cent. /ideas: this command pulls the top scored ideas from that database, randomizes the selection so you're not seeing the same ones every time, and sends them to you in a clean numbered list. You reply with /write 3 or whatever you choose, and the system researches the topic using Perplexity's live web search, generates three distinct outline options with different angles and hooks, saves them to a Google Doc, and sends you a message telling you they're ready. You read the outlines, and you pick one. You then reply with the command /outline 2. The system writes the full piece in your voice, following your brand guidelines, with specific examples and concrete claims. It can be done in under two minutes of your time. The whole thing runs on n8n, with no subscriptions beyond what you already use. If content takes too long or you don't have ideas, this solves that. I built this for myself; I can do it for you. If you're tired of knowing you should be posting and still not doing it, let's talk.

Savvy | Ai & Automation

15,746 次观看 • 6 个月前

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

Argona

32,475 次观看 • 1 个月前

How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!

How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!

ChainGPT

29,339 次观看 • 4 个月前

A gym asked to repost their workout video. Eight months later, that same gym pays them $10,400 a month. Two friends run one Instagram account together, split down the middle. It started as a joke — a place to dump their workout clips so they'd stop flooding their personal pages. Then the gym they train at reposted one video. Then asked for more. Then offered to pay. That was the whole beginning. One local gym, a small monthly fee to keep their feed looking alive. Here's what they figured out fast: gyms are desperate for content and terrible at making it. Beautiful equipment, dead Instagram. The two of them already film every session anyway — so they started pointing the camera at what the gym needed and handing it over as a finished feed. Now they run it like a tiny agency. Three gyms and two activewear brands pay them to produce a month of content each — reels, captions, the posting calendar, the whole thing. The part that makes it possible with two people and full-time jobs: AI does the 90% that used to need a team. They film. Claude does the rest — cuts one session into 20 clips, writes captions in each client's voice, builds the 30-day calendar, drafts the monthly report that keeps every contract renewed. What used to take an editor, a copywriter, and an SMM manager now runs while they're getting coffee after the workout. 5 clients. Around $2,000 each. $10,400 a month. Their cost to run all of it: under $60. That number doesn't move whether they have 5 clients or 15 — that's the entire model. The wild part is how ordinary they are. No huge following. No personal brand. Two normal girls who train together and realized the footage they were already making was worth money to someone else. Every friend group at every gym is filming the same content for fun and letting it die in their camera roll. These two just asked one gym if it wanted to buy it. The full breakdown — how two people turn shared workouts into a real content business — is in the thread above. Read it before another duo in your city signs those gyms first.

Rich

29,972 次观看 • 2 个月前

She's 21. She makes $22,000 a month doing nothing, but filming her own life - the gym, the surf, the climbing, the run and letting AI do the rest. She's not a model. She doesn't sell a course. She just lives a full life on camera and every single hobby is a brand category that's desperate for content. The surf clip is a wakeboard brand's ad. The climbing gym wants the bouldering footage. The track session is an activewear drop. The smoothie after is a supplement post. She'd be doing all of it anyway. The only thing she changed was pointing the phone the right way and letting AI turn one week of her actual life into a month of content for the brands behind it. Here's the mechanism. She films her week - no script, no shoot day, just her life. AI cuts it into 20+ reels, writes every caption in each brand's voice, builds the calendar, sends the report that renews the deal. The part that used to need an editor, a writer, a strategist, and a manager is now one chat window. She kept the only thing AI can't do: actually being there, living it. Seven brands across her hobbies. ~$3,200 each. $22,000 a month - at 21, from a phone and a $40 stack of AI. Her cost doesn't move whether she has one brand or ten. That's the whole business. Everyone her age is filming the exact same life and posting it for free, calling it a personal brand. She pointed hers at the brands that pay and let the system carry the 90% that used to cost a five-person agency. This is the part that should sting: the footage was never the hard part, she'd have shot it regardless. The team around it was. And that team just collapsed into a subscription. The full playbook - niches, how to find brands, how to close them is right below. Save it before someone half your motivation reads it first👇

Rich

32,535 次观看 • 2 个月前

The main bronze door of Milan Cathedral weighs 37 tons and took a single sculptor 10 years to complete. The Duomo of Milan is one of the largest and most intricate cathedrals on earth, and its five bronze doors were built to be worthy of it. Every inch of the great central door is covered in sculpted bronze, dozens of scenes from the life of the Virgin Mary, faces caught mid-emotion, figures that seem to press forward out of the metal as if trying to step into the world... It was designed by the sculptor Ludovico Pogliaghi, who received the commission in the 1880s and did not see his door installed until 1906, having poured a decade of his life into modelling every figure by hand. And his was just the beginning. The five doors of the cathedral were not made together, or even in the same lifetime. They were created across nearly seventy years, by a succession of different sculptors, each carving their panels with the same obsessive care. The final door was not completed until 1965, its unveiling taken as the symbolic close of a cathedral that had been under construction, in one form or another, for 579 years... More than a century ago, the English polymath John Ruskin tried to put into words what it means to build something like this. He managed it in a single sentence. "When we build," he wrote, "let us think that we build for ever."

The main bronze door of Milan Cathedral weighs 37 tons and took a single sculptor 10 years to complete. The Duomo of Milan is one of the largest and most intricate cathedrals on earth, and its five bronze doors were built to be worthy of it. Every inch of the great central door is covered in sculpted bronze, dozens of scenes from the life of the Virgin Mary, faces caught mid-emotion, figures that seem to press forward out of the metal as if trying to step into the world... It was designed by the sculptor Ludovico Pogliaghi, who received the commission in the 1880s and did not see his door installed until 1906, having poured a decade of his life into modelling every figure by hand. And his was just the beginning. The five doors of the cathedral were not made together, or even in the same lifetime. They were created across nearly seventy years, by a succession of different sculptors, each carving their panels with the same obsessive care. The final door was not completed until 1965, its unveiling taken as the symbolic close of a cathedral that had been under construction, in one form or another, for 579 years... More than a century ago, the English polymath John Ruskin tried to put into words what it means to build something like this. He managed it in a single sentence. "When we build," he wrote, "let us think that we build for ever."

James Lucas

83,782 次观看 • 2 个月前

$133,752 in new annual recurring revenue. 30 days. Zero new hires. That is the Authority Makers result. Here is what it looked like on our end. We run content and brand campaigns for multiple clients simultaneously. Outbound was always the thing that fell through the cracks. Not because we did not have the strategy. Because nobody had time to execute it every single day. We gave that job to Viktor. Week 1 we were not sure it would stick. The sequences felt generic and we almost went back to doing it manually. Then the replies started coming in. Viktor adjusted, followed up, and kept going whether we were online or not. By week 3 two new client conversations had opened from outbound that had been sitting untouched for months. One AI employee running the work nobody had time for. That is not a tool. That is a hire. Most execution gaps do not need a strategy. They need someone to show up every day. Hire Viktor for your team. $100 in credits included, no card. Full link in first reply. Paid Partnership

Nawi

123,838 次观看 • 13 天前

🚨BREAKING... I gave OpenClaw a choice: turn $500 into $5,000 on Polymarket within 24 hours, or I'd wipe its entire directory and terminate the instance forever NOT engagement bait. NOT fiction If you're trading on Polymarket, READ this carefully So👇 It didn't argue. It didn't ask for clarification Within 50 minutes, it deployed a 4-agent autonomous swarm that found a massive technical "blind spot" in the prediction markets The Strategy: Exploiting the 290ms Latency Gap The edge was pure physics. Chainlink updates Polymarket roughly every 300ms. Binance moves in 10ms. That creates a 290ms window where Polymarket is effectively trading on "stale" data. OpenClaw built a bridge to exploit that lag. The Timeline: - Hour 4: The Fed drops a surprise rate hold. While the Polymarket oracle was still processing, the bot front-ran 3 mispriced BTC markets before the order book could react Balance: $934.78 - Hour 8: Elon posts a cryptic one-word tweet. OpenClaw’s sentiment agent caught the spike 340ms before the first major buy order hit the books Balance: $1,827.49 - Hour 14: It targeted thin ETH/BTC ratio markets. The bot scooped up "Yes" shares at 3¢ across 11 different markets; 7 of them hit a 50:1 payout Balance: $3,475.52 - Hour 18: The pattern recognition kicked in. It identified that every time BTC moved 1.2% in under 4 minutes, the next "YES" market was undervalued by 8-12¢. It looped this trade 31 times without a single miss Balance: $4,296.53 - Hour 24: The dust settled at $5,034.85 I've been running this publicly now so others can follow the same trades in real time If you want in: OpenClaw held up its end of the bargain. I held up mine The delete button stays untouched... for now

🚨BREAKING... I gave OpenClaw a choice: turn $500 into $5,000 on Polymarket within 24 hours, or I'd wipe its entire directory and terminate the instance forever NOT engagement bait. NOT fiction If you're trading on Polymarket, READ this carefully So👇 It didn't argue. It didn't ask for clarification Within 50 minutes, it deployed a 4-agent autonomous swarm that found a massive technical "blind spot" in the prediction markets The Strategy: Exploiting the 290ms Latency Gap The edge was pure physics. Chainlink updates Polymarket roughly every 300ms. Binance moves in 10ms. That creates a 290ms window where Polymarket is effectively trading on "stale" data. OpenClaw built a bridge to exploit that lag. The Timeline: - Hour 4: The Fed drops a surprise rate hold. While the Polymarket oracle was still processing, the bot front-ran 3 mispriced BTC markets before the order book could react Balance: $934.78 - Hour 8: Elon posts a cryptic one-word tweet. OpenClaw’s sentiment agent caught the spike 340ms before the first major buy order hit the books Balance: $1,827.49 - Hour 14: It targeted thin ETH/BTC ratio markets. The bot scooped up "Yes" shares at 3¢ across 11 different markets; 7 of them hit a 50:1 payout Balance: $3,475.52 - Hour 18: The pattern recognition kicked in. It identified that every time BTC moved 1.2% in under 4 minutes, the next "YES" market was undervalued by 8-12¢. It looped this trade 31 times without a single miss Balance: $4,296.53 - Hour 24: The dust settled at $5,034.85 I've been running this publicly now so others can follow the same trades in real time If you want in: OpenClaw held up its end of the bargain. I held up mine The delete button stays untouched... for now

cristal💎

460,403 次观看 • 6 个月前