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EZ Algo isn't an algo script It's your entire trading stack ✅️ Signals ✅️ Scanners ✅️ Price Action Layouts ✅️ AI ✅️ Automation ✅️ Risk tools ✅️ 1 Click bulk Alerts 26 professional suites in one subscription 8 years building it Here's what's inside 🧵👇

EZ ALGO ⋀⋁

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32,333 просмотров • 4 месяцев назад •via X (Twitter)

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George Matlou vs. Ts Galaxy🔝 He was very instrumental in how we moved the ball. Always looking for that forward pass. One thing I like about his game is how he constantly breaks lines with purpose and urgency. George Matlou: ♤ 54 touches✅️ ♤ 32/35 [91%] pass accuracy✅️ ♤ 25/27 [93%] passes in opposition half✅️ ♤ 7/8 [88%] passes in own half✅️ ♤ 1/3 [33] long balls ♤ 0/2 shots on target ♤ 6 recoveries✅️ ♤ 1 clearance✅️ ♤ 1 Interception✅️ ♤ 1 tackle✅️ Matlou’s influence was undeniable. ♤ He was the rhythm of that midfield. Every phase of play had his fingerprints on it. He never hid, always making himself available to receive, recycle, advance the ball, or make timely recoveries. ♤ He made it easy for his teammates to play through him. Whether we were building from deep, connecting in the middle, or trying to break lines higher up, he was there to support the play and keep the team’s tempo alive. His presence gave the midfield balance, purpose, and continuity throughout the minutes he played. Lehlohonolo George Matlou🔝❤️✌🏾

George Matlou vs. Ts Galaxy🔝 He was very instrumental in how we moved the ball. Always looking for that forward pass. One thing I like about his game is how he constantly breaks lines with purpose and urgency. George Matlou: ♤ 54 touches✅️ ♤ 32/35 [91%] pass accuracy✅️ ♤ 25/27 [93%] passes in opposition half✅️ ♤ 7/8 [88%] passes in own half✅️ ♤ 1/3 [33] long balls ♤ 0/2 shots on target ♤ 6 recoveries✅️ ♤ 1 clearance✅️ ♤ 1 Interception✅️ ♤ 1 tackle✅️ Matlou’s influence was undeniable. ♤ He was the rhythm of that midfield. Every phase of play had his fingerprints on it. He never hid, always making himself available to receive, recycle, advance the ball, or make timely recoveries. ♤ He made it easy for his teammates to play through him. Whether we were building from deep, connecting in the middle, or trying to break lines higher up, he was there to support the play and keep the team’s tempo alive. His presence gave the midfield balance, purpose, and continuity throughout the minutes he played. Lehlohonolo George Matlou🔝❤️✌🏾

El Capitano⚪

22,327 просмотров • 8 месяцев назад

I just tested Motion Sync on Yapper and I like the cinematic Video made in minutes. Let me show you how you can do it 👇 Step 1: Open Step 2: Click on video and select Motion Sync. Then, ✅️Select base video Pick a video with the movement you want — this will drive the animation. ✅️Add reference image Upload your character or image you want to bring to life. ✅️Generate video Hit generate and watch your idea turn into a smooth motion sync clip. Simple, fast, and actually fun to experiment with.

I just tested Motion Sync on Yapper and I like the cinematic Video made in minutes. Let me show you how you can do it 👇 Step 1: Open Step 2: Click on video and select Motion Sync. Then, ✅️Select base video Pick a video with the movement you want — this will drive the animation. ✅️Add reference image Upload your character or image you want to bring to life. ✅️Generate video Hit generate and watch your idea turn into a smooth motion sync clip. Simple, fast, and actually fun to experiment with.

Leonardo

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I’d say look in to Ivermectin and Febenzadole too. ALKALINIZE YOUR BODY Kill Cancer easily. One of the simplest & most powerful things you can do is to drink lemon & sodium bicarbonate in 1 litre of water daily It's a highly alkaline drink that not only kills cancer by eliminating acids from your body, it also supports detoxification, digestion, speeds up metabolism & is super hydrating Drink it daily, use it as a gym recovery drink & enjoy it's taste! Subscribe: Cancer Cure ✅️

I’d say look in to Ivermectin and Febenzadole too. ALKALINIZE YOUR BODY Kill Cancer easily. One of the simplest & most powerful things you can do is to drink lemon & sodium bicarbonate in 1 litre of water daily It's a highly alkaline drink that not only kills cancer by eliminating acids from your body, it also supports detoxification, digestion, speeds up metabolism & is super hydrating Drink it daily, use it as a gym recovery drink & enjoy it's taste! Subscribe: Cancer Cure ✅️

Diana ❤️🇺🇸🙏🐸

2,810,831 просмотров • 1 год назад

South Africa Is Playing With Fire Boardrooms are now battlegrounds. Activists are marching into corporate offices across South Africa demanding that "foreigners" pack up and leave. President Cyril Ramaphosa Cyril Ramaphosa 🇿🇦 this cannot stand and here's why it's not just wrong, it's dangerously short-sighted. Please ask yourself does South Africa know how deeply it is embedded in the rest of this continent? ✅️1. MTN carries hundreds of millions of subscribers across Nigeria, Ghana, Uganda, Rwanda, Côte d'Ivoire, and beyond a South African company thriving on African soil that isn't its own. MTN South Africa ✅️2. Absa now earns nearly a third of its group profit from outside South Africa — Botswana, Ghana, Kenya, Mauritius, Mozambique, Uganda, Zambia, Tanzania and is still acquiring more African banks as we speak. Absa Group ✅️3.Standard Bank remains the continent's largest bank by assets, moving trillions of rand through African economies every single year. Standard Bank ZA ✅️4. MultiChoice/DStv has for decades been the dominant gatekeeper of entertainment and information across dozens of African households. ✅️5. Sanlam and Old Mutual manage insurance and pension savings for millions of East and West Africans. ✅️6. Nandos, Steers, and Debonairs feed households across Kenya, Nigeria, Zambia, and Uganda. ✅️7. Bidvest and Imperial Logistics run supply chains that keep goods moving through multiple African economies. ✅8. ️Then there's tourism an industry South Africa depends on heavily, and where the hypocrisy cuts sharpest. Hospitality groups like Tsogo Sun, City Lodge, and Sun International have expanded into Zambia, Nigeria, and beyond. South African Airways, Airlink, and a web of SA-based travel companies have spent decades marketing Cape Town, Kruger, and the Garden Route to the rest of the continent and Africans have answered in huge numbers. Kenyans, Nigerians, Ghanaians, and others fill Sandton hotels, Table Mountain cable cars, and Kruger safari lodges every year, injecting real foreign currency into the South African economy. So which is it? Do you want African wallets in your tills and African bodies in your hotel beds, but not African faces in your boardrooms? Now imagine just for a moment Nigeria, Kenya, Zambia, or Ghana adopting the same logic. Boardrooms in Lagos or Nairobi staging walk-ins demanding MTN executives leave, Absa hand back its banking licenses, or Kenyan immigration quietly discouraging South African tourists from checking into Mombasa or Diani hotels. The fallout for South Africa would be instant and brutal and tourism, banking, and telecoms would be the first casualties, not the last. This is the essence of glass houses and stones. You cannot build a continental empire in banking, telecoms, insurance, and tourism, and then slam the door on that same continent's citizens the moment it's convenient at home. It's worth remembering South Africa's most celebrated exports thrive precisely because other nations didn't gatekeep opportunity by passport. Trevor Noah built a global career from a studio in New York. Elon Musk, born in Pretoria, built his fortune in Silicon Valley and Texas. Both benefited from economies that judged them on merit, not nationality. Trevor Noah Elon Musk If Africa responds to South Africa's boardroom nativism with mirrored nativism, no one wins least of all South Africans working, investing, and building across this continent. Mr. President, this is a fire worth putting out before it spreads. As always, I choose to remain an optimist. Mohammed Hersi A true African patriot at heart

South Africa Is Playing With Fire Boardrooms are now battlegrounds. Activists are marching into corporate offices across South Africa demanding that "foreigners" pack up and leave. President Cyril Ramaphosa Cyril Ramaphosa 🇿🇦 this cannot stand and here's why it's not just wrong, it's dangerously short-sighted. Please ask yourself does South Africa know how deeply it is embedded in the rest of this continent? ✅️1. MTN carries hundreds of millions of subscribers across Nigeria, Ghana, Uganda, Rwanda, Côte d'Ivoire, and beyond a South African company thriving on African soil that isn't its own. MTN South Africa ✅️2. Absa now earns nearly a third of its group profit from outside South Africa — Botswana, Ghana, Kenya, Mauritius, Mozambique, Uganda, Zambia, Tanzania and is still acquiring more African banks as we speak. Absa Group ✅️3.Standard Bank remains the continent's largest bank by assets, moving trillions of rand through African economies every single year. Standard Bank ZA ✅️4. MultiChoice/DStv has for decades been the dominant gatekeeper of entertainment and information across dozens of African households. ✅️5. Sanlam and Old Mutual manage insurance and pension savings for millions of East and West Africans. ✅️6. Nandos, Steers, and Debonairs feed households across Kenya, Nigeria, Zambia, and Uganda. ✅️7. Bidvest and Imperial Logistics run supply chains that keep goods moving through multiple African economies. ✅8. ️Then there's tourism an industry South Africa depends on heavily, and where the hypocrisy cuts sharpest. Hospitality groups like Tsogo Sun, City Lodge, and Sun International have expanded into Zambia, Nigeria, and beyond. South African Airways, Airlink, and a web of SA-based travel companies have spent decades marketing Cape Town, Kruger, and the Garden Route to the rest of the continent and Africans have answered in huge numbers. Kenyans, Nigerians, Ghanaians, and others fill Sandton hotels, Table Mountain cable cars, and Kruger safari lodges every year, injecting real foreign currency into the South African economy. So which is it? Do you want African wallets in your tills and African bodies in your hotel beds, but not African faces in your boardrooms? Now imagine just for a moment Nigeria, Kenya, Zambia, or Ghana adopting the same logic. Boardrooms in Lagos or Nairobi staging walk-ins demanding MTN executives leave, Absa hand back its banking licenses, or Kenyan immigration quietly discouraging South African tourists from checking into Mombasa or Diani hotels. The fallout for South Africa would be instant and brutal and tourism, banking, and telecoms would be the first casualties, not the last. This is the essence of glass houses and stones. You cannot build a continental empire in banking, telecoms, insurance, and tourism, and then slam the door on that same continent's citizens the moment it's convenient at home. It's worth remembering South Africa's most celebrated exports thrive precisely because other nations didn't gatekeep opportunity by passport. Trevor Noah built a global career from a studio in New York. Elon Musk, born in Pretoria, built his fortune in Silicon Valley and Texas. Both benefited from economies that judged them on merit, not nationality. Trevor Noah Elon Musk If Africa responds to South Africa's boardroom nativism with mirrored nativism, no one wins least of all South Africans working, investing, and building across this continent. Mr. President, this is a fire worth putting out before it spreads. As always, I choose to remain an optimist. Mohammed Hersi A true African patriot at heart

Mohammed Hersi : Mr Optimist

333,682 просмотров • 1 месяц назад

😎 BTS Mystery Box Giveaway! 1 𝗹𝘂𝗰𝗸𝘆 𝘄𝗶𝗻𝗻𝗲𝗿 ! 💜 How to Enter? 1. Create an account on DKshop ( 2. Like + RT this post 3. Comment your favorite BTS song 💜 ✅️Winners will be announced on Nov. 1st. 𝘉𝙮 𝙅𝙤𝙞𝙣𝙞𝙣𝙜 𝙄 𝙖𝙜𝙧𝙚𝙚 𝙩𝙝𝙖𝙩: 1. Winners will be chosen randomly. 2. Winners must claim their prize within one week. 3. Winners are responsible for customs fees or taxes in their country. J-Hope is back, and we'd like to celebrate it with you guys! Share this post with your friends so they can join, too! 🥰 Good luck to all! 🥰 #giveaway #bts #DKshop

😎 BTS Mystery Box Giveaway! 1 𝗹𝘂𝗰𝗸𝘆 𝘄𝗶𝗻𝗻𝗲𝗿 ! 💜 How to Enter? 1. Create an account on DKshop ( 2. Like + RT this post 3. Comment your favorite BTS song 💜 ✅️Winners will be announced on Nov. 1st. 𝘉𝙮 𝙅𝙤𝙞𝙣𝙞𝙣𝙜 𝙄 𝙖𝙜𝙧𝙚𝙚 𝙩𝙝𝙖𝙩: 1. Winners will be chosen randomly. 2. Winners must claim their prize within one week. 3. Winners are responsible for customs fees or taxes in their country. J-Hope is back, and we'd like to celebrate it with you guys! Share this post with your friends so they can join, too! 🥰 Good luck to all! 🥰 #giveaway #bts #DKshop

Delivered Korea

19,212 просмотров • 1 год назад

🚨Uphold REPLACING YOUR BANK?!.. 🤯 Uphold just leveled up the game: ✅ XRP Credit Card ✅ Earn yield on your assets ✅ XRP-backed loans 🎯This isn’t crypto anymore… this is a FULL financial stack 🤯 Now here’s what people are missing 👇 ✅️ Zebec Network is ALREADY plugged into this future. 💳 Zebec Card = real-time spending 💸 Payroll streaming (not bi-weekly… INSTANT) 🌍 Multi-stablecoin + XRP rails potential 🤝 Positioned alongside both Ripple + Uphold ecosystems 👉 Translation: *As XRP rails expand through Uphold… *Zebec becomes the REAL WORLD USAGE LAYER 🙄While others are building speculation… 🛠Zebec is building PAYROLL, CARDS, and DAILY FINANCE ⚠️ This is how adoption actually happens: Infrastructure (Ripple) → Access (Uphold) → Usage (Zebec) Most people still don’t see it… $XRP 🤝 $ZBCN = money movement + money usage 🚀 The stack is forming right in front of you #XRP #Zebec #ZBCN #Crypto #Fintech #Ripple #Uphold #Web3 #Payments #Stablecoins ADP

🚨Uphold REPLACING YOUR BANK?!.. 🤯 Uphold just leveled up the game: ✅ XRP Credit Card ✅ Earn yield on your assets ✅ XRP-backed loans 🎯This isn’t crypto anymore… this is a FULL financial stack 🤯 Now here’s what people are missing 👇 ✅️ Zebec Network is ALREADY plugged into this future. 💳 Zebec Card = real-time spending 💸 Payroll streaming (not bi-weekly… INSTANT) 🌍 Multi-stablecoin + XRP rails potential 🤝 Positioned alongside both Ripple + Uphold ecosystems 👉 Translation: As XRP rails expand through Uphold… Zebec becomes the REAL WORLD USAGE LAYER 🙄While others are building speculation… 🛠Zebec is building PAYROLL, CARDS, and DAILY FINANCE ⚠️ This is how adoption actually happens: Infrastructure (Ripple) → Access (Uphold) → Usage (Zebec) Most people still don’t see it… $XRP 🤝 $ZBCN = money movement + money usage 🚀 The stack is forming right in front of you #XRP #Zebec #ZBCN #Crypto #Fintech #Ripple #Uphold #Web3 #Payments #Stablecoins ADP

Jetpack J0

69,486 просмотров • 3 месяцев назад

Today, we're making Error Tracking by Better Stack generally available. Sentry-compatible. AI-native. At 1/6th the price. Here's why we built it, and how to get the most out of it. What's wrong with error tracking today? Most teams use Sentry. It's solid! But at scale, the bills get brutal. Just 100M exceptions with 90 day lookback? ~$30,000 on Sentry. We charge ~$5,000 for the exact same thing. The math isn't subtle. And so most teams still end up sampling. Which means missing the exact exception that caused the outage. The bigger problem: errors are orphaned data. Your exception lands in Sentry. Your logs are in Datadog. Your traces are somewhere else. Root cause analysis becomes a multi-tab archaeology project at 3 am. We built error tracking natively inside Better Stack: the same platform where your logs, traces, metrics, uptime checks, and on-call schedules already live. Errors are just another signal. They belong together. The part that changes how your team works: Our AI SRE doesn't just surface errors. It fixes them. See a new exception? One click. The AI SRE analyzes the full context, from stack traces, environment variables, browser sessions, related logs and recent deploys, and opens a pull request. Not a ticket. Not a summary. A pull request with the fix. This is what happens when error tracking is fully integrated with the rest of your observability stack instead of bolted on separately. The AI has everything it needs to actually act. The migration is trivial: 1. Keep your existing Sentry SDK. Don't touch a single line of instrumentation code. 2. Point the DSN at Better Stack. 3. Done. Errors flow in. Your dashboards work. Your alerts work. 4. New exception appears. Click "Fix with AI SRE." Pull request lands in your repo. 5. Review, merge, close. That's the whole workflow. The AI angle is real, not a marketing badge. LLMs are genuinely good at fixing bugs if they have full context. The reason AI coding assistants sometimes frustrate engineers is incomplete information, not the model. We solve that by giving the AI SRE your entire telemetry stack as context. Stack traces, logs, traces, service maps, previous incidents and much more. All of it, in one place, at the moment it matters. Observability tools are only useful if you actually ingest all your data. At current prices of other tools, most teams can't afford to. Now you can, and your AI SRE can actually do something about it.

Today, we're making Error Tracking by Better Stack generally available. Sentry-compatible. AI-native. At 1/6th the price. Here's why we built it, and how to get the most out of it. What's wrong with error tracking today? Most teams use Sentry. It's solid! But at scale, the bills get brutal. Just 100M exceptions with 90 day lookback? ~$30,000 on Sentry. We charge ~$5,000 for the exact same thing. The math isn't subtle. And so most teams still end up sampling. Which means missing the exact exception that caused the outage. The bigger problem: errors are orphaned data. Your exception lands in Sentry. Your logs are in Datadog. Your traces are somewhere else. Root cause analysis becomes a multi-tab archaeology project at 3 am. We built error tracking natively inside Better Stack: the same platform where your logs, traces, metrics, uptime checks, and on-call schedules already live. Errors are just another signal. They belong together. The part that changes how your team works: Our AI SRE doesn't just surface errors. It fixes them. See a new exception? One click. The AI SRE analyzes the full context, from stack traces, environment variables, browser sessions, related logs and recent deploys, and opens a pull request. Not a ticket. Not a summary. A pull request with the fix. This is what happens when error tracking is fully integrated with the rest of your observability stack instead of bolted on separately. The AI has everything it needs to actually act. The migration is trivial: 1. Keep your existing Sentry SDK. Don't touch a single line of instrumentation code. 2. Point the DSN at Better Stack. 3. Done. Errors flow in. Your dashboards work. Your alerts work. 4. New exception appears. Click "Fix with AI SRE." Pull request lands in your repo. 5. Review, merge, close. That's the whole workflow. The AI angle is real, not a marketing badge. LLMs are genuinely good at fixing bugs if they have full context. The reason AI coding assistants sometimes frustrate engineers is incomplete information, not the model. We solve that by giving the AI SRE your entire telemetry stack as context. Stack traces, logs, traces, service maps, previous incidents and much more. All of it, in one place, at the moment it matters. Observability tools are only useful if you actually ingest all your data. At current prices of other tools, most teams can't afford to. Now you can, and your AI SRE can actually do something about it.

Juraj Masar

15,063 просмотров • 4 месяцев назад

Stephen Boyd, Stanford professor: "Citadel will pay a 22 year old $400K to run this optimizer. The textbook that teaches it is free, and so was my course." this free stanford lecture holds the entire "optimal portfolio, perfect allocation" secret the trading gurus sell you. and the man teaching it didn't lock it inside a fund, he gave away the whole course and a textbook that costs $0. at the board it's simple: once you can write down your expected return, your risk, and your limits, convex optimization finds the single best portfolio, provably, not by trial and error. that's how a fund turns a messy pile of signals into one clean allocation. no guru required. it's not new either. boyd and vandenberghe's textbook has been free since 2004. same stack i mapped in the article above, old and free and sitting on a website the whole time. here's the honest part the course skips: the optimizer only allocates the edge you feed it. garbage in, garbage out. it can't invent an advantage that isn't already in your signals, it just sizes the real one without blowing you up. the solver is free. having an actual edge to optimize is the part nobody can sell you.

Stephen Boyd, Stanford professor: "Citadel will pay a 22 year old $400K to run this optimizer. The textbook that teaches it is free, and so was my course." this free stanford lecture holds the entire "optimal portfolio, perfect allocation" secret the trading gurus sell you. and the man teaching it didn't lock it inside a fund, he gave away the whole course and a textbook that costs $0. at the board it's simple: once you can write down your expected return, your risk, and your limits, convex optimization finds the single best portfolio, provably, not by trial and error. that's how a fund turns a messy pile of signals into one clean allocation. no guru required. it's not new either. boyd and vandenberghe's textbook has been free since 2004. same stack i mapped in the article above, old and free and sitting on a website the whole time. here's the honest part the course skips: the optimizer only allocates the edge you feed it. garbage in, garbage out. it can't invent an advantage that isn't already in your signals, it just sizes the real one without blowing you up. the solver is free. having an actual edge to optimize is the part nobody can sell you.

Rossst.03

72,607 просмотров • 1 месяц назад

most traders pay $3,000+/month for tools GitHub replaced for free. 9 repos. zero subscriptions. 1. OpenBB → replaces Bloomberg Terminal ($2,000/mo) financial data platform built for AI agents and quants connects natively to claude via MCP. most people don't know this. 2. freqtrade → replaces paid crypto bot services ($100/mo) ML strategy optimization. runs on binance, bybit, hyperliquid and 10+ others 34,000 stars. free and always will be. 3. hummingbot → replaces HFT bot platforms ($200/mo) $34B+ in user-generated trading volume has a native claude MCP integration. connect your AI directly to 140+ exchanges. 4. FinGPT → replaces financial AI subscriptions ($150/mo) open-source LLMs that outperform GPT-4 on market sentiment bloomberg spent $3M training theirs. this costs $17 to fine-tune. 5. NautilusTrader → replaces institutional trading platforms ($500/mo) production-grade. rust-native. fast enough to train RL trading agents same codebase for backtesting and live. zero rewrite needed. 6. QuantConnect Lean → replaces paid quant research platforms ($100/mo) professional algo trading engine. python + C# from backtest to live in one click. used by 200K+ quants worldwide. 7. jesse → replaces TradingView algo subscriptions ($25/mo) advanced crypto trading framework for serious strategy builders clean. powerful. no bloat. 8. vectorbt → replaces paid backtesting tools ($80/mo) fastest backtesting library in existence tests thousands of strategies in seconds. pandas-based. 9. FinRL → replaces custom AI trading infrastructure ($300/mo) financial reinforcement learning. train your own trading AI. from the same team behind FinGPT. 10. AlphaCartel Setup → replaces hedge fund signal services ($300/mo) this is where all 9 repos above connect into one working system claude-powered bots. live signals. no-code setup. community of traders already printing. total before: ~$3,455/month total now: $0 + alphacartel like + bookmark. you'll need this.

most traders pay $3,000+/month for tools GitHub replaced for free. 9 repos. zero subscriptions. 1. OpenBB → replaces Bloomberg Terminal ($2,000/mo) financial data platform built for AI agents and quants connects natively to claude via MCP. most people don't know this. 2. freqtrade → replaces paid crypto bot services ($100/mo) ML strategy optimization. runs on binance, bybit, hyperliquid and 10+ others 34,000 stars. free and always will be. 3. hummingbot → replaces HFT bot platforms ($200/mo) $34B+ in user-generated trading volume has a native claude MCP integration. connect your AI directly to 140+ exchanges. 4. FinGPT → replaces financial AI subscriptions ($150/mo) open-source LLMs that outperform GPT-4 on market sentiment bloomberg spent $3M training theirs. this costs $17 to fine-tune. 5. NautilusTrader → replaces institutional trading platforms ($500/mo) production-grade. rust-native. fast enough to train RL trading agents same codebase for backtesting and live. zero rewrite needed. 6. QuantConnect Lean → replaces paid quant research platforms ($100/mo) professional algo trading engine. python + C# from backtest to live in one click. used by 200K+ quants worldwide. 7. jesse → replaces TradingView algo subscriptions ($25/mo) advanced crypto trading framework for serious strategy builders clean. powerful. no bloat. 8. vectorbt → replaces paid backtesting tools ($80/mo) fastest backtesting library in existence tests thousands of strategies in seconds. pandas-based. 9. FinRL → replaces custom AI trading infrastructure ($300/mo) financial reinforcement learning. train your own trading AI. from the same team behind FinGPT. 10. AlphaCartel Setup → replaces hedge fund signal services ($300/mo) this is where all 9 repos above connect into one working system claude-powered bots. live signals. no-code setup. community of traders already printing. total before: ~$3,455/month total now: $0 + alphacartel like + bookmark. you'll need this.

AI Bulls

107,531 просмотров • 4 месяцев назад

Automation consultants just got OBLITERATED. I built a complete AI-powered viral content bot in TWO SENTENCES. Agencies charge $8K and quote 3 weeks for this exact system. Here's what I actually said: Sentence 1: "Create an X posting bot that searches viral content and makes me famous" → Result: Complete Reddit scraper + AI content synthesizer Sentence 2: "Now make it predominantly AI agent focused" → Result: Full autonomous AI agent with research tools Time: 11 minutes total Code written: 0 lines Cost: $20/month n8n Consultant price: $8,000 What it actually does: - Scrapes viral posts from r/artificial, r/MachineLearning, r/technology, r/OpenAI, r/singularity - AI analyzes what makes content viral - Synthesizes original tweets optimized for engagement - Posts automatically to X - Logs everything to Google Sheets Built entirely in plain English with AI agents. While your consultant is scheduling "discovery calls" and creating project timelines... I already shipped, tested, and deployed. The automation industry is about to get absolutely destroyed by people who learned to speak to AI instead of learning to code. Your $8K consultant? They're learning the same way. They just won't admit it. Full build video below 👇 This is the future: AI agents building AI agents. And it costs $20/month, not $8K. What's your consultant charging you for something this simple?

Automation consultants just got OBLITERATED. I built a complete AI-powered viral content bot in TWO SENTENCES. Agencies charge $8K and quote 3 weeks for this exact system. Here's what I actually said: Sentence 1: "Create an X posting bot that searches viral content and makes me famous" → Result: Complete Reddit scraper + AI content synthesizer Sentence 2: "Now make it predominantly AI agent focused" → Result: Full autonomous AI agent with research tools Time: 11 minutes total Code written: 0 lines Cost: $20/month n8n Consultant price: $8,000 What it actually does: - Scrapes viral posts from r/artificial, r/MachineLearning, r/technology, r/OpenAI, r/singularity - AI analyzes what makes content viral - Synthesizes original tweets optimized for engagement - Posts automatically to X - Logs everything to Google Sheets Built entirely in plain English with AI agents. While your consultant is scheduling "discovery calls" and creating project timelines... I already shipped, tested, and deployed. The automation industry is about to get absolutely destroyed by people who learned to speak to AI instead of learning to code. Your $8K consultant? They're learning the same way. They just won't admit it. Full build video below 👇 This is the future: AI agents building AI agents. And it costs $20/month, not $8K. What's your consultant charging you for something this simple?

Alton Syn

32,377 просмотров • 9 месяцев назад

I found a site that tracks $1.3M+ in free credits for startups. And it's completely FREE to access ! Here's what makes it insane: 1/ Cloud Infrastructure Gold > Google Cloud: $350K in credits > AWS Activate: $100K covered > Microsoft Azure: $150K + OpenAI credits > Cloudflare: $250K for 3 domains That's $850K saved before writing a single line of code. 2/ AI API Access > Anthropic Claude: $25K in credits > ElevenLabs: 33M characters (680+ hours of voice) > Priority rate limits included > Access to technical resources Build AI products without burning your runway on API costs. 3/ Developer Tools Stack > GitHub Enterprise: 20 seats free > Retool: $60K worth (1 year free) > Datadog: Full monitoring suite > PostHog: 30M events/month covered Your entire dev infrastructure for $0. 4/ Analytics Without the Bill > Mixpanel: $50K (Growth plan) > PostHog: All-in-one platform > Session replay included > Unlimited seats Track every user action without the $5K/month analytics bill. 5/ Constantly Updated > 41 total perks across 8 categories > Direct application links > Eligibility requirements listed > Featured programs highlighted Bookmark it. Apply to everything. Save your runway.

I found a site that tracks $1.3M+ in free credits for startups. And it's completely FREE to access ! Here's what makes it insane: 1/ Cloud Infrastructure Gold > Google Cloud: $350K in credits > AWS Activate: $100K covered > Microsoft Azure: $150K + OpenAI credits > Cloudflare: $250K for 3 domains That's $850K saved before writing a single line of code. 2/ AI API Access > Anthropic Claude: $25K in credits > ElevenLabs: 33M characters (680+ hours of voice) > Priority rate limits included > Access to technical resources Build AI products without burning your runway on API costs. 3/ Developer Tools Stack > GitHub Enterprise: 20 seats free > Retool: $60K worth (1 year free) > Datadog: Full monitoring suite > PostHog: 30M events/month covered Your entire dev infrastructure for $0. 4/ Analytics Without the Bill > Mixpanel: $50K (Growth plan) > PostHog: All-in-one platform > Session replay included > Unlimited seats Track every user action without the $5K/month analytics bill. 5/ Constantly Updated > 41 total perks across 8 categories > Direct application links > Eligibility requirements listed > Featured programs highlighted Bookmark it. Apply to everything. Save your runway.

Harshil Tomar

29,003 просмотров • 6 месяцев назад

🚨 WARNING: THE WORST DAY OF 2026 IS TOMORROW. JPMorgan is preparing to dump $165,000,000,000 into the market right at open. Thinking this won’t move the market? You’re in for the rudest awakening of your life. Every time JP Morgan sells stocks, the S&P 500 drops 10–20%. And this isn't just about the stock market. It's about liquidity. It's about investor sentiment. And it's about a market that isn't prepared for what's coming. Let me explain: JPMorgan isn't some retail trader taking profits. It's one of the largest and most influential financial institutions on the planet. When they move capital at scale, markets pay attention. And history shows that large institutional selling rarely happens in a vacuum. It usually signals something bigger. A shift in risk appetite. A change in liquidity conditions. Or growing concerns beneath the surface that most investors haven't recognized yet. Now here's the part almost nobody talks about. The direct impact isn't limited to the stocks being sold. Because when a major institution dumps billions of dollars worth of equities, it affects sentiment across the entire market. Selling creates more selling. Liquidity gets thinner. Volatility increases. And risk assets everywhere start to feel the pressure. That's why this isn't just an S&P 500 story. The S&P 500 is the first domino. But the effects will spread into AI stocks. International equities. Commodities. Credit markets. And even digital assets. Today, people are positioned for stability. They're positioned for higher prices. They're positioned for the rally to continue. Which means they're vulnerable if liquidity suddenly moves in the opposite direction. THIS IS THE WARNING. Not because one institution is selling. But because markets often underestimate what large-scale institutional selling can trigger. The risk isn't the transaction itself. The risk is how everyone else reacts to it. Markets aren't pricing that possibility today. But eventually, they will. I've spent more than a decade studying macro and market cycles. I've called some of the biggest market tops and bottoms of the past 10+ years. And I'll call the next market crash in 2026 before the crowd sees it coming. Follow and turn notifications on. I'll post my next market call here first.

🚨 WARNING: THE WORST DAY OF 2026 IS TOMORROW. JPMorgan is preparing to dump $165,000,000,000 into the market right at open. Thinking this won’t move the market? You’re in for the rudest awakening of your life. Every time JP Morgan sells stocks, the S&P 500 drops 10–20%. And this isn't just about the stock market. It's about liquidity. It's about investor sentiment. And it's about a market that isn't prepared for what's coming. Let me explain: JPMorgan isn't some retail trader taking profits. It's one of the largest and most influential financial institutions on the planet. When they move capital at scale, markets pay attention. And history shows that large institutional selling rarely happens in a vacuum. It usually signals something bigger. A shift in risk appetite. A change in liquidity conditions. Or growing concerns beneath the surface that most investors haven't recognized yet. Now here's the part almost nobody talks about. The direct impact isn't limited to the stocks being sold. Because when a major institution dumps billions of dollars worth of equities, it affects sentiment across the entire market. Selling creates more selling. Liquidity gets thinner. Volatility increases. And risk assets everywhere start to feel the pressure. That's why this isn't just an S&P 500 story. The S&P 500 is the first domino. But the effects will spread into AI stocks. International equities. Commodities. Credit markets. And even digital assets. Today, people are positioned for stability. They're positioned for higher prices. They're positioned for the rally to continue. Which means they're vulnerable if liquidity suddenly moves in the opposite direction. THIS IS THE WARNING. Not because one institution is selling. But because markets often underestimate what large-scale institutional selling can trigger. The risk isn't the transaction itself. The risk is how everyone else reacts to it. Markets aren't pricing that possibility today. But eventually, they will. I've spent more than a decade studying macro and market cycles. I've called some of the biggest market tops and bottoms of the past 10+ years. And I'll call the next market crash in 2026 before the crowd sees it coming. Follow and turn notifications on. I'll post my next market call here first.

WhaleTwits

340,862 просмотров • 2 месяцев назад

🚨 WARNING: TOMORROW WILL BE THE WORST DAY OF 2026!! JPMorgan will dump $165 BILLION in U.S. stocks right after the market opens. If you think this is a "drop in the ocean" and it won’t affect the markets... YOU ARE COMPLETELY WRONG. Every time JP Morgan sells stocks, the S&P 500 drops 10–20%. And this isn't just about the stock market. It's about liquidity. It's about investor sentiment. And it's about a market that isn't prepared for what's coming. Let me explain: JPMorgan isn't some retail trader taking profits. It's one of the largest and most influential financial institutions on the planet. When they move capital at scale, markets pay attention. And history shows that large institutional selling rarely happens in a vacuum. It usually signals something bigger. A shift in risk appetite. A change in liquidity conditions. Or growing concerns beneath the surface that most investors haven't recognized yet. Now here's the part almost nobody talks about. The direct impact isn't limited to the stocks being sold. Because when a major institution dumps billions of dollars worth of equities, it affects sentiment across the entire market. Selling creates more selling. Liquidity gets thinner. Volatility increases. And risk assets everywhere start to feel the pressure. That's why this isn't just an S&P 500 story. The S&P 500 is the first domino. But the effects will spread into AI stocks. International equities. Commodities. Credit markets. And even digital assets. Today, people are positioned for stability. They're positioned for higher prices. They're positioned for the rally to continue. Which means they're vulnerable if liquidity suddenly moves in the opposite direction. THIS IS THE WARNING. Not because one institution is selling. But because markets often underestimate what large-scale institutional selling can trigger. The risk isn't the transaction itself. The risk is how everyone else reacts to it. Markets aren't pricing that possibility today. But eventually, they will. I've spent more than a decade studying macro and market cycles. I've called some of the biggest market tops and bottoms of the past 10+ years. And I'll call the next market crash in 2026 before the crowd sees it coming. Follow and turn notifications on. I'll post my next market call here first.

🚨 WARNING: TOMORROW WILL BE THE WORST DAY OF 2026!! JPMorgan will dump $165 BILLION in U.S. stocks right after the market opens. If you think this is a "drop in the ocean" and it won’t affect the markets... YOU ARE COMPLETELY WRONG. Every time JP Morgan sells stocks, the S&P 500 drops 10–20%. And this isn't just about the stock market. It's about liquidity. It's about investor sentiment. And it's about a market that isn't prepared for what's coming. Let me explain: JPMorgan isn't some retail trader taking profits. It's one of the largest and most influential financial institutions on the planet. When they move capital at scale, markets pay attention. And history shows that large institutional selling rarely happens in a vacuum. It usually signals something bigger. A shift in risk appetite. A change in liquidity conditions. Or growing concerns beneath the surface that most investors haven't recognized yet. Now here's the part almost nobody talks about. The direct impact isn't limited to the stocks being sold. Because when a major institution dumps billions of dollars worth of equities, it affects sentiment across the entire market. Selling creates more selling. Liquidity gets thinner. Volatility increases. And risk assets everywhere start to feel the pressure. That's why this isn't just an S&P 500 story. The S&P 500 is the first domino. But the effects will spread into AI stocks. International equities. Commodities. Credit markets. And even digital assets. Today, people are positioned for stability. They're positioned for higher prices. They're positioned for the rally to continue. Which means they're vulnerable if liquidity suddenly moves in the opposite direction. THIS IS THE WARNING. Not because one institution is selling. But because markets often underestimate what large-scale institutional selling can trigger. The risk isn't the transaction itself. The risk is how everyone else reacts to it. Markets aren't pricing that possibility today. But eventually, they will. I've spent more than a decade studying macro and market cycles. I've called some of the biggest market tops and bottoms of the past 10+ years. And I'll call the next market crash in 2026 before the crowd sees it coming. Follow and turn notifications on. I'll post my next market call here first.

0xNobler

375,877 просмотров • 2 месяцев назад

I asked Claude Fable 5 (Extra High) to build an arb bot for Polymarket. One rule: trade only when YES + NO in 2 hours it almost doubled it (+$96.31) > in 5 hours it showed +$579 PnL > current balance: +$3,799.73 Cost: 10M tokens. Here's how it works and why: Every BTC Up/Down market on Polymarket has exactly two outcomes. YES and NO. When the market resolves, one pays $1. The other pays $0. That means owning BOTH sides should always cost exactly $1. But markets aren't perfect. Sometimes YES trades at $0.48 while NO trades at $0.49. Together that's only $0.97 so the bot instantly buys both. A position worth $1... for just $0.97. And Claude knows it, this is the simplest arb that exists. You can paste this to it and use this logic in your prompt. This $0.03 difference is locked in regardless of whether Bitcoin pumps, dumps, or goes sideways. No prediction required. And difficult part isn't finding the opportunity, it's execution. These pricing gaps usually disappear in seconds. If one order fills but the other doesn't, the trade can become a loss. So my bot constantly scans every BTC market, checks fees, validates liquidity, places both orders almost simultaneously and skips what isn't worth the risk. It's less like trading and more like catching tiny accounting mistakes before everyone else notices them. Funny enoughh, the hardest part was not writing arb logic. It was making the execution reliable enough that free money actually stayed free. This completely changed how I think about trading. What's the point of it if you can just fill mispriced BTC markets?? Automatically. The biggest edge is getting AI to execute simple ideas faster and more consistently than any human ever could. Shared the exact build in my last article, leaving it below. Good luck!

I asked Claude Fable 5 (Extra High) to build an arb bot for Polymarket. One rule: trade only when YES + NO in 2 hours it almost doubled it (+$96.31) > in 5 hours it showed +$579 PnL > current balance: +$3,799.73 Cost: 10M tokens. Here's how it works and why: Every BTC Up/Down market on Polymarket has exactly two outcomes. YES and NO. When the market resolves, one pays $1. The other pays $0. That means owning BOTH sides should always cost exactly $1. But markets aren't perfect. Sometimes YES trades at $0.48 while NO trades at $0.49. Together that's only $0.97 so the bot instantly buys both. A position worth $1... for just $0.97. And Claude knows it, this is the simplest arb that exists. You can paste this to it and use this logic in your prompt. This $0.03 difference is locked in regardless of whether Bitcoin pumps, dumps, or goes sideways. No prediction required. And difficult part isn't finding the opportunity, it's execution. These pricing gaps usually disappear in seconds. If one order fills but the other doesn't, the trade can become a loss. So my bot constantly scans every BTC market, checks fees, validates liquidity, places both orders almost simultaneously and skips what isn't worth the risk. It's less like trading and more like catching tiny accounting mistakes before everyone else notices them. Funny enoughh, the hardest part was not writing arb logic. It was making the execution reliable enough that free money actually stayed free. This completely changed how I think about trading. What's the point of it if you can just fill mispriced BTC markets?? Automatically. The biggest edge is getting AI to execute simple ideas faster and more consistently than any human ever could. Shared the exact build in my last article, leaving it below. Good luck!

Oracle Boar

155,166 просмотров • 1 месяц назад

I asked Claude Fable 5 (Extra High) to build an arb bot for Polymarket. One rule: trade only when YES + NO in 2 hours it almost doubled it (+$98.40) > in 5 hours it showed +$493 PnL > current balance: +$3,279.73 Cost: 7.5M tokens. Here's how it works and why: Every BTC Up/Down market on Polymarket has exactly two outcomes. YES and NO. When the market resolves, one pays $1. The other pays $0. That means owning BOTH sides should always cost exactly $1. But markets aren't perfect. Sometimes YES trades at $0.48 while NO trades at $0.49. Together that's only $0.97 so the bot instantly buys both. A position worth $1... for just $0.97. And Claude knows it, this is the simplest arb that exists. You can paste this to it and use this logic in your prompt. This $0.03 difference is locked in regardless of whether Bitcoin pumps, dumps, or goes sideways. No prediction required. And difficult part isn't finding the opportunity, it's execution. These pricing gaps usually disappear in seconds. If one order fills but the other doesn't, the trade can become a loss. So my bot constantly scans every BTC market, checks fees, validates liquidity, places both orders almost simultaneously and skips what isn't worth the risk. It's less like trading and more like catching tiny accounting mistakes before everyone else notices them. Funny enoughh, the hardest part was not writing arb logic. It was making the execution reliable enough that free money actually stayed free. This completely changed how I think about trading. What's the point of it if you can just fill mispriced BTC markets?? Automatically. The biggest edge is getting AI to execute simple ideas faster and more consistently than any human ever could. Shared the exact build in my last article, leaving it below.

I asked Claude Fable 5 (Extra High) to build an arb bot for Polymarket. One rule: trade only when YES + NO in 2 hours it almost doubled it (+$98.40) > in 5 hours it showed +$493 PnL > current balance: +$3,279.73 Cost: 7.5M tokens. Here's how it works and why: Every BTC Up/Down market on Polymarket has exactly two outcomes. YES and NO. When the market resolves, one pays $1. The other pays $0. That means owning BOTH sides should always cost exactly $1. But markets aren't perfect. Sometimes YES trades at $0.48 while NO trades at $0.49. Together that's only $0.97 so the bot instantly buys both. A position worth $1... for just $0.97. And Claude knows it, this is the simplest arb that exists. You can paste this to it and use this logic in your prompt. This $0.03 difference is locked in regardless of whether Bitcoin pumps, dumps, or goes sideways. No prediction required. And difficult part isn't finding the opportunity, it's execution. These pricing gaps usually disappear in seconds. If one order fills but the other doesn't, the trade can become a loss. So my bot constantly scans every BTC market, checks fees, validates liquidity, places both orders almost simultaneously and skips what isn't worth the risk. It's less like trading and more like catching tiny accounting mistakes before everyone else notices them. Funny enoughh, the hardest part was not writing arb logic. It was making the execution reliable enough that free money actually stayed free. This completely changed how I think about trading. What's the point of it if you can just fill mispriced BTC markets?? Automatically. The biggest edge is getting AI to execute simple ideas faster and more consistently than any human ever could. Shared the exact build in my last article, leaving it below.

Oracle Boar

78,062 просмотров • 11 дней назад

Facebook ads might be one of the easiest places to run an agentic loop. Here's why it works: running an A/B test on an ad gives you a clear winner. Build it in 5 parts: 1) Raw material. - Create a folder. - Every day, yap at the camera for 30 seconds. - That is your entire job here. 2) Access. - Point it at the ad account, not at your description of the ad account. - It needs the spend and the performance numbers itself. 3) Action. - Let it cut what's in the folder into variants. - Different hooks, different angles, a different line of copy. - On Google search ads it's just copy, which is the easiest thing in the world for an AI to swap. 4) Judgment. - Every two or three days it looks back at what actually ran. - Not hourly. The data isn't there yet. 5) Reversal. - "Cutting the losers, doubling down on the winners." The catch: budget. Each variant needs enough spend to tell you something, or you're just reading noise. The other limit is the footage: Fully synthetic ads still lose to ads with a person in them. Go record the 30 seconds and let the loop test the rest.

Facebook ads might be one of the easiest places to run an agentic loop. Here's why it works: running an A/B test on an ad gives you a clear winner. Build it in 5 parts: 1) Raw material. - Create a folder. - Every day, yap at the camera for 30 seconds. - That is your entire job here. 2) Access. - Point it at the ad account, not at your description of the ad account. - It needs the spend and the performance numbers itself. 3) Action. - Let it cut what's in the folder into variants. - Different hooks, different angles, a different line of copy. - On Google search ads it's just copy, which is the easiest thing in the world for an AI to swap. 4) Judgment. - Every two or three days it looks back at what actually ran. - Not hourly. The data isn't there yet. 5) Reversal. - "Cutting the losers, doubling down on the winners." The catch: budget. Each variant needs enough spend to tell you something, or you're just reading noise. The other limit is the footage: Fully synthetic ads still lose to ads with a person in them. Go record the 30 seconds and let the loop test the rest.

The Startup Ideas Podcast (SIP) 🧃

12,504 просмотров • 19 дней назад

I'm making over $10K a month selling AI services. I got there in under a year with a three-stage model: a free assessment, a paid assessment, and an AI Concierge retainer. My lowest paying client is $1,200 a month. My highest is $2,000. Here's the entire model: 1) Stage one: a free 15-minute mini assessment. One pain point, one prescribed fix. Do 1 to 3 max, purely for testimonials. 2) Stage two: the paid assessment. 45 minutes, 3 to 7 opportunities scored on effort vs impact, sold for $1,000 to $2,000. (I open sourced my paid assessment template. It's yours (free) at 3) Do the ROI math in their words. $250 an hour times 5 hours a week in email is $1,250 a week. Cut it to 1 hour and they can't argue. 4) Stage three: AI Concierge. Two 45-minute calls a month at $1,000 to $2,000. That's $750 to $1,250 an hour. 5) Add unlimited Voxer access. Feels like an AI expert in their pocket, so they pay top dollar. They barely use it. 6) Every call runs AOA: Audit, Optimize, Automate. Cut the 14-step process to 11, then turn it into a Claude skill. 7) Standardize on one ecosystem. Everything runs in Claude Cowork building Claude skills. No custom stack per client, no vendor lock-in. 8) Target owner-led service businesses doing $3M to $10M. Repetitive work, visible bottlenecks, real budget. 9) Your first client is in your phone. Text 10 business owners you know. 10 texts equals 5 to 6 free assessments, 2 to 3 paid, 1 concierge client. 10) Raise your price $250 with every yes. My next client won't be under $2,500 a month. Two things that make this work: 1) Boundaries are the offer. Free stops at the roadmap, concierge stops at two calls. Everything else is billed separately. 2) Every stage sells the next one. The free assessment promises seven more fixes. The paid report walkthrough pitches the retainer. Full breakdown below. (also available on the Build With AI podcast wherever you get your pods)

I'm making over $10K a month selling AI services. I got there in under a year with a three-stage model: a free assessment, a paid assessment, and an AI Concierge retainer. My lowest paying client is $1,200 a month. My highest is $2,000. Here's the entire model: 1) Stage one: a free 15-minute mini assessment. One pain point, one prescribed fix. Do 1 to 3 max, purely for testimonials. 2) Stage two: the paid assessment. 45 minutes, 3 to 7 opportunities scored on effort vs impact, sold for $1,000 to $2,000. (I open sourced my paid assessment template. It's yours (free) at 3) Do the ROI math in their words. $250 an hour times 5 hours a week in email is $1,250 a week. Cut it to 1 hour and they can't argue. 4) Stage three: AI Concierge. Two 45-minute calls a month at $1,000 to $2,000. That's $750 to $1,250 an hour. 5) Add unlimited Voxer access. Feels like an AI expert in their pocket, so they pay top dollar. They barely use it. 6) Every call runs AOA: Audit, Optimize, Automate. Cut the 14-step process to 11, then turn it into a Claude skill. 7) Standardize on one ecosystem. Everything runs in Claude Cowork building Claude skills. No custom stack per client, no vendor lock-in. 8) Target owner-led service businesses doing $3M to $10M. Repetitive work, visible bottlenecks, real budget. 9) Your first client is in your phone. Text 10 business owners you know. 10 texts equals 5 to 6 free assessments, 2 to 3 paid, 1 concierge client. 10) Raise your price $250 with every yes. My next client won't be under $2,500 a month. Two things that make this work: 1) Boundaries are the offer. Free stops at the roadmap, concierge stops at two calls. Everything else is billed separately. 2) Every stage sells the next one. The free assessment promises seven more fixes. The paid report walkthrough pitches the retainer. Full breakdown below. (also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

59,298 просмотров • 13 дней назад

A single tweet just vaporized BILLIONS from cybersecurity stocks. CrowdStrike down 8%. Cloudflare down 8.1%. Okta down 9.2%. SailPoint down 9.4%. The Global X Cybersecurity ETF just hit its lowest level since November 2023. What happened? Anthropic dropped Claude Code Security. It scans your entire codebase for vulnerabilities and suggests patches. Sounds boring. Until you read what it actually did: In testing, Claude found over 500 HIGH-SEVERITY BUGS in production open-source codebases. Bugs that had been sitting there for DECADES. Despite years of expert review. Despite fuzzing campaigns. Despite penetration testing. Despite million-dollar security audits. Nobody found them. An AI did. In hours. Here's the terrifying part: Traditional security tools work by pattern matching. They look for known vulnerabilities in a database. Claude doesn't do that. It READS code the way a human security researcher would. Traces data flows. Understands how components interact. Catches logic flaws that rule-based tools can't see. And it just outperformed every cybersecurity tool on the market. Combined. Wall Street figured this out fast. If an AI can find what your $500k/year security team missed... Why do you need the team? If an AI catches bugs that CrowdStrike, Okta, and Cloudflare couldn't... Why are you paying those subscriptions? Barclays came out saying the selloff was "illogical" and Claude "doesn't compete" with these companies. But here's what Barclays missed: It's not about what Claude competes with TODAY. It's about what it replaces TOMORROW. The SaaS apocalypse hit legal software 3 weeks ago. Thomson Reuters dropped 18% in one day. $285 billion wiped from software stocks. Now it's cybersecurity's turn. The pattern is obvious: Every industry that sells "expertise as a service" is about to get repriced. Legal research? Done. Code vulnerability scanning? Done. Compliance checking? Coming soon. Financial analysis? On deck. Companies that spent 20 years building "moats" around specialized knowledge are watching AI swim right over them. CrowdStrike is worth $95 billion. They have 30,000 customers. Claude just found 500 bugs their tools missed. Do the math. The smart money already sees what's happening. The iShares Expanded Tech-Software Sector ETF is down 23% YTD. Heading for its largest quarterly decline since the 2008 financial crisis. Not because software is dying... But because software companies that charge per-seat subscriptions for AI-replicable work are dying. Anthropic just proved that a general-purpose AI can outperform DECADES of specialized cybersecurity infrastructure. In a single product release. While still in "limited research preview." It's not even fully launched yet. What happens when it scales? We're about to find out.

A single tweet just vaporized BILLIONS from cybersecurity stocks. CrowdStrike down 8%. Cloudflare down 8.1%. Okta down 9.2%. SailPoint down 9.4%. The Global X Cybersecurity ETF just hit its lowest level since November 2023. What happened? Anthropic dropped Claude Code Security. It scans your entire codebase for vulnerabilities and suggests patches. Sounds boring. Until you read what it actually did: In testing, Claude found over 500 HIGH-SEVERITY BUGS in production open-source codebases. Bugs that had been sitting there for DECADES. Despite years of expert review. Despite fuzzing campaigns. Despite penetration testing. Despite million-dollar security audits. Nobody found them. An AI did. In hours. Here's the terrifying part: Traditional security tools work by pattern matching. They look for known vulnerabilities in a database. Claude doesn't do that. It READS code the way a human security researcher would. Traces data flows. Understands how components interact. Catches logic flaws that rule-based tools can't see. And it just outperformed every cybersecurity tool on the market. Combined. Wall Street figured this out fast. If an AI can find what your $500k/year security team missed... Why do you need the team? If an AI catches bugs that CrowdStrike, Okta, and Cloudflare couldn't... Why are you paying those subscriptions? Barclays came out saying the selloff was "illogical" and Claude "doesn't compete" with these companies. But here's what Barclays missed: It's not about what Claude competes with TODAY. It's about what it replaces TOMORROW. The SaaS apocalypse hit legal software 3 weeks ago. Thomson Reuters dropped 18% in one day. $285 billion wiped from software stocks. Now it's cybersecurity's turn. The pattern is obvious: Every industry that sells "expertise as a service" is about to get repriced. Legal research? Done. Code vulnerability scanning? Done. Compliance checking? Coming soon. Financial analysis? On deck. Companies that spent 20 years building "moats" around specialized knowledge are watching AI swim right over them. CrowdStrike is worth $95 billion. They have 30,000 customers. Claude just found 500 bugs their tools missed. Do the math. The smart money already sees what's happening. The iShares Expanded Tech-Software Sector ETF is down 23% YTD. Heading for its largest quarterly decline since the 2008 financial crisis. Not because software is dying... But because software companies that charge per-seat subscriptions for AI-replicable work are dying. Anthropic just proved that a general-purpose AI can outperform DECADES of specialized cybersecurity infrastructure. In a single product release. While still in "limited research preview." It's not even fully launched yet. What happens when it scales? We're about to find out.

Ricardo

110,899 просмотров • 6 месяцев назад

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Ricardo

50,564 просмотров • 3 месяцев назад

after reviewing 97 wave 3 submissions, these are my personal favorite demos. this is not a ranking, just the projects that impressed me the most because of their technical quality, execution, and attention to detail. every team that shipped deserves respect, building is never easy. 1. BasisDesk – A delta neutral basis trading platform that earns funding rates while reducing price risk. - what impressed me: every financial calculation uses fixed point arithmetic instead of floating point, making it one of the most accurate implementations I reviewed. - best for: traders looking for funding-rate opportunities with lower market risk. 2. SoNarr – turns crypto narratives into real onchain trades using SoSoValue data and SoDEX. - what impressed me: It showed three confirmed mainnet orders with careful checks before execution. - best for: analysts and index creators who want to automate narrative-based investing. 3. Helix – A news-to-signal platform that analyzes headlines and generates trading signals. - what impressed me: It includes a correlation heatmap to prove which signals actually worked instead of just making claims. - best for: traders who want transparent and evidence-based signals. 4. Conviction Matrix – scores crypto sectors using ETF flows, VC activity, and treasury data. - what impressed me: uses proper statistical methods like Wilson confidence intervals and verifies predictions over time. - best for: anyone who values measurable and data-driven signals. 5. Sonar – An AI hedge fund assistant that turns ETF flow data into trading ideas. - what impressed me: the builder openly fixed 34 issues found during an audit instead of hiding them. - best for: small funds and solo traders who value transparency. 6. SoSoMind – A complete AI trading terminal with research, execution, and portfolio tools. - what impressed me: It never shows fake data. If information isn't available, it simply says "unavailable." - best for: active onchain traders who want everything in one place. 7. MARA – An ai macro trading agent that records its reasoning onchain. - what impressed me: strong historical validation with a very rigorous performance evaluation. - best for: traders who want transparent and verifiable AI decisions. 8. SoSo Analyst – A protocol that lets analysts build an onchain reputation through verified predictions. - what impressed me: It showed one of the strongest end-to-end execution examples I reviewed. - best for: researchers and users who want to follow analysts with proven records. 9. Mosaic – builds crypto index portfolios from simple natural-language investment ideas. - what impressed me: the team found a major execution bug, fixed it, and added tests to prevent it from happening again. - best for: Investors who want easy thematic investing. 10. Prism – A quantitative index builder using a deterministic five-factor model. -what impressed me: AI is only used to understand the investment idea. Portfolio construction stays fully deterministic and reproducible. - best for: Index creators and quantitative researchers. These were my personal favorites after reviewing 97 submissions on SoSoValue there were many great projects that didn't make this list, and every builder deserves credit for shipping something. I'm excited to see which of these projects continue to grow after the buildathon. which demos impressed you the most? 👇

after reviewing 97 wave 3 submissions, these are my personal favorite demos. this is not a ranking, just the projects that impressed me the most because of their technical quality, execution, and attention to detail. every team that shipped deserves respect, building is never easy. 1. BasisDesk – A delta neutral basis trading platform that earns funding rates while reducing price risk. - what impressed me: every financial calculation uses fixed point arithmetic instead of floating point, making it one of the most accurate implementations I reviewed. - best for: traders looking for funding-rate opportunities with lower market risk. 2. SoNarr – turns crypto narratives into real onchain trades using SoSoValue data and SoDEX. - what impressed me: It showed three confirmed mainnet orders with careful checks before execution. - best for: analysts and index creators who want to automate narrative-based investing. 3. Helix – A news-to-signal platform that analyzes headlines and generates trading signals. - what impressed me: It includes a correlation heatmap to prove which signals actually worked instead of just making claims. - best for: traders who want transparent and evidence-based signals. 4. Conviction Matrix – scores crypto sectors using ETF flows, VC activity, and treasury data. - what impressed me: uses proper statistical methods like Wilson confidence intervals and verifies predictions over time. - best for: anyone who values measurable and data-driven signals. 5. Sonar – An AI hedge fund assistant that turns ETF flow data into trading ideas. - what impressed me: the builder openly fixed 34 issues found during an audit instead of hiding them. - best for: small funds and solo traders who value transparency. 6. SoSoMind – A complete AI trading terminal with research, execution, and portfolio tools. - what impressed me: It never shows fake data. If information isn't available, it simply says "unavailable." - best for: active onchain traders who want everything in one place. 7. MARA – An ai macro trading agent that records its reasoning onchain. - what impressed me: strong historical validation with a very rigorous performance evaluation. - best for: traders who want transparent and verifiable AI decisions. 8. SoSo Analyst – A protocol that lets analysts build an onchain reputation through verified predictions. - what impressed me: It showed one of the strongest end-to-end execution examples I reviewed. - best for: researchers and users who want to follow analysts with proven records. 9. Mosaic – builds crypto index portfolios from simple natural-language investment ideas. - what impressed me: the team found a major execution bug, fixed it, and added tests to prevent it from happening again. - best for: Investors who want easy thematic investing. 10. Prism – A quantitative index builder using a deterministic five-factor model. -what impressed me: AI is only used to understand the investment idea. Portfolio construction stays fully deterministic and reproducible. - best for: Index creators and quantitative researchers. These were my personal favorites after reviewing 97 submissions on SoSoValue there were many great projects that didn't make this list, and every builder deserves credit for shipping something. I'm excited to see which of these projects continue to grow after the buildathon. which demos impressed you the most? 👇

DAVIS (❖,❖)

38,186 просмотров • 29 дней назад