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NQ Example Bearish Breaker after Turtle Soup Into Buyside, Draw On Sellside Liquidity, STDDIV -2 Intraday Swing Model No DOM Required.

The Inner Circle Trader

813,583 subscribers

186,094 次观看 • 3 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

11 条评论

Sir Pickle 的头像

Sir Pickle3 年前

i knew you'd dose us before 1:30pm ;)

Quant Data 的头像

Quant Data2 年前

🚨 $NVDA is down over 3% again 🚨 Yesterday morning, we tweeted about the $15.2M+ in bearish golden sweeps on $NVDA for 6/21 (today) and 7/19 expiries. $NVDA fell $10+ yesterday and $4+ today. Want to see trades like this? Try our 7 day free trial at:

Chappy 的头像

Chappy3 年前

Right there with you 🤝

Wolf 的头像

Wolf3 年前

I'm in the long. It's a ping pong.

Master Fren ❤️💛💚💙 的头像

Master Fren ❤️💛💚💙3 年前

Thank you. Love these. So you turtle souped the opening bell? Bravo.

Pepethuglife 的头像

Pepethuglife3 年前

I see the DOM

Trader Pip 的头像

Trader Pip3 年前

just riding the FVG higher and TRAIL MY STOP

Trader Pip 的头像

Trader Pip3 年前

look at that RUN, can someone HIT that FOLLOW BUTTON TO show some LOVE

Vila 的头像

Vila3 年前

The speed

SmartMoneyIdeology 的头像

SmartMoneyIdeology3 年前

It looks like we exchanged liquidity 😂

Ody 的头像

Ody3 年前

Bro tf is DOM 😂 Saw that on TradingView today was like: “damn these people really be missing all of it..”

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NQ Live Execution: Bearish Market Bias NQ retraced higher into a Bearish Breaker with obvious 20556.50 Sellside Liquidity for the taking. Music Credit: The Teskey Brothers - Rain

NQ Live Execution: Bearish Market Bias NQ retraced higher into a Bearish Breaker with obvious 20556.50 Sellside Liquidity for the taking. Music Credit: The Teskey Brothers - Rain

The Inner Circle Trader

134,345 次观看 • 1 年前

Most traders know CRT but very few know where to enter, that’s the difference between analysis and precision. In this 12-min lecture, there is an in-depth break down of the Top Entry Models that make CRT deadly accurate: • The TURTLE SOUP MODEL • The MODEL #1 (OB/CSD) • The INVERSION ENTRY (advanced MSS) • The BREAKER ENTRY CRT is not just about spotting the range, it’s about knowing the exact point price is ready to move. This is where most traders get it wrong, they see the setup but enter at the wrong time, after this video you’ll know exactly what to wait for. No more early entries. No more late entries. Just precision. 🎥 12 minutes that can change how you execute forever.

Most traders know CRT but very few know where to enter, that’s the difference between analysis and precision. In this 12-min lecture, there is an in-depth break down of the Top Entry Models that make CRT deadly accurate: • The TURTLE SOUP MODEL • The MODEL #1 (OB/CSD) • The INVERSION ENTRY (advanced MSS) • The BREAKER ENTRY CRT is not just about spotting the range, it’s about knowing the exact point price is ready to move. This is where most traders get it wrong, they see the setup but enter at the wrong time, after this video you’ll know exactly what to wait for. No more early entries. No more late entries. Just precision. 🎥 12 minutes that can change how you execute forever.

R1X TRADES (GER40)

28,674 次观看 • 3 个月前

🚨 SPACEX IPO WILL DRAIN THE WHOLE MARKET LIQUIDITY!! $1,750,000,000,000 valuation on day one. a $30 TRILLION projected AI ceiling. 95% of shares sitting in insider hands. That's not an IPO. That's the largest scheduled liquidity extraction in history. Every dollar that flows into SpaceX comes from somewhere: - Stocks - Crypto - High beta tech - Everything retail is holding right now I'm watching for a 30-50% drawdown on SPX after the launch. Super bearish on shorter timeframes right now. The IPO is June 12. Insiders have been waiting years for this door to open. By November, 93% of their shares are free to sell. Draw your own conclusions. This sounds SCARY, but I'll be tracking every move around this listing. I will post my moves here so my FOLLOWERS can SAVE their money. Follow me and turn NOTIFICATIONS ON, as I will share my strategy soon. Many will regret not following me earlier...

🚨 SPACEX IPO WILL DRAIN THE WHOLE MARKET LIQUIDITY!! $1,750,000,000,000 valuation on day one. a $30 TRILLION projected AI ceiling. 95% of shares sitting in insider hands. That's not an IPO. That's the largest scheduled liquidity extraction in history. Every dollar that flows into SpaceX comes from somewhere: - Stocks - Crypto - High beta tech - Everything retail is holding right now I'm watching for a 30-50% drawdown on SPX after the launch. Super bearish on shorter timeframes right now. The IPO is June 12. Insiders have been waiting years for this door to open. By November, 93% of their shares are free to sell. Draw your own conclusions. This sounds SCARY, but I'll be tracking every move around this listing. I will post my moves here so my FOLLOWERS can SAVE their money. Follow me and turn NOTIFICATIONS ON, as I will share my strategy soon. Many will regret not following me earlier...

ᴛʀᴀᴄᴇʀ

103,370 次观看 • 1 个月前

intern here. Today I integrated AI Crypto Alerts inside AI Hub V2 into my trading workflow. The results were pretty surprising. On the main dashboard, I could scroll and instantly see what materially moved across the market, classified as Bullish, Bearish or Neutral, with the catalyst attached. 🔹Intraday dumps after extreme rallies. 🔹Sharp pullbacks after outsized surges. 🔹Tokens extending 20%+ on volume expansion. 🔹Narrative-driven pumps across sectors. Instead of scanning X, Telegram, Discord and news feeds, I had one structured intelligence stream showing price movement + reason + sentiment in seconds. Then I built a custom watchlist with the assets I actually track: BTC, ETH, SOL and others. That’s where it became tactical. I could see: • Bullishness scores (0–100) • 24h change and market cap positioning • The latest catalyst summarized clearly • Overall sentiment at a glance For example: BTC pushing on ETF inflows and safe-haven positioning. ETH sentiment leaning bearish amid founder selling and macro uncertainty. SOL showing a strong bullishness score despite conflicting narrative pressure. Seeing that context next to price changed how I interpreted moves. If you actively trade, the workflow difference is noticeable → Disclaimer: ChainGPT does not endorse any projects mentioned in this post. Always do your own research before making trading decisions.

intern here. Today I integrated AI Crypto Alerts inside AI Hub V2 into my trading workflow. The results were pretty surprising. On the main dashboard, I could scroll and instantly see what materially moved across the market, classified as Bullish, Bearish or Neutral, with the catalyst attached. 🔹Intraday dumps after extreme rallies. 🔹Sharp pullbacks after outsized surges. 🔹Tokens extending 20%+ on volume expansion. 🔹Narrative-driven pumps across sectors. Instead of scanning X, Telegram, Discord and news feeds, I had one structured intelligence stream showing price movement + reason + sentiment in seconds. Then I built a custom watchlist with the assets I actually track: BTC, ETH, SOL and others. That’s where it became tactical. I could see: • Bullishness scores (0–100) • 24h change and market cap positioning • The latest catalyst summarized clearly • Overall sentiment at a glance For example: BTC pushing on ETF inflows and safe-haven positioning. ETH sentiment leaning bearish amid founder selling and macro uncertainty. SOL showing a strong bullishness score despite conflicting narrative pressure. Seeing that context next to price changed how I interpreted moves. If you actively trade, the workflow difference is noticeable → Disclaimer: ChainGPT does not endorse any projects mentioned in this post. Always do your own research before making trading decisions.

ChainGPT

27,328 次观看 • 4 个月前

Let me walk you through an actual futures trade using our 3-step trading system. This system is what I use for EVERY futures trade—the 3 steps are: Step 1: Break of Structure Step 2: Mark the Zone Step 3: Wait for Entry Let’s dive in… Step 1: Break of Structure On the 5-minute chart, price takes out the previous swing high. Trend = bullish. We're only looking for longs. Step 2: Mark the Zone I draw a demand zone at the lowest consolidation that LED to that break of structure. Now I validate it: • Broke structure ✓ • Created imbalance (gap on 1-minute chart) ✓ • Swept liquidity (double bottom trap for retail) ✓ Step 3: Wait for Entry Price drops back into my zone. Here's the critical part: Retail traders enter immediately. They get excited. They place stops below the low. I do nothing. I wait for price to sweep those stops—the liquidity grab. THEN I enter on the second move up. Here’s my results from a live trade: • Entry: After liquidity sweep • Stop loss: Below sweep candle (10 points) • Take profit: 1:2 ratio (20 points) • Position size: 2 contracts (1% risk on $10K account) Price hesitates. Drops slightly. Then rips to TP. — This is just scratching the surface. In the full 2-hour futures trading masterclass, I break down: • How to calculate exact position sizes • The 3 beginner futures mistakes that cost traders thousands • Live chart examples walking through actual entries and exits step-by-step Just comment "FUTURES" and I'll send you the complete masterclass in the next few minutes.

Let me walk you through an actual futures trade using our 3-step trading system. This system is what I use for EVERY futures trade—the 3 steps are: Step 1: Break of Structure Step 2: Mark the Zone Step 3: Wait for Entry Let’s dive in… Step 1: Break of Structure On the 5-minute chart, price takes out the previous swing high. Trend = bullish. We're only looking for longs. Step 2: Mark the Zone I draw a demand zone at the lowest consolidation that LED to that break of structure. Now I validate it: • Broke structure ✓ • Created imbalance (gap on 1-minute chart) ✓ • Swept liquidity (double bottom trap for retail) ✓ Step 3: Wait for Entry Price drops back into my zone. Here's the critical part: Retail traders enter immediately. They get excited. They place stops below the low. I do nothing. I wait for price to sweep those stops—the liquidity grab. THEN I enter on the second move up. Here’s my results from a live trade: • Entry: After liquidity sweep • Stop loss: Below sweep candle (10 points) • Take profit: 1:2 ratio (20 points) • Position size: 2 contracts (1% risk on $10K account) Price hesitates. Drops slightly. Then rips to TP. — This is just scratching the surface. In the full 2-hour futures trading masterclass, I break down: • How to calculate exact position sizes • The 3 beginner futures mistakes that cost traders thousands • Live chart examples walking through actual entries and exits step-by-step Just comment "FUTURES" and I'll send you the complete masterclass in the next few minutes.

The Trading Geek (Brad Goh)

29,870 次观看 • 6 个月前

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NEXA AI

26,091 次观看 • 8 个月前

🚀 AMM Swaps are LIVE on Google Play & Apple App Store! 🚀 To celebrate, we're launching a massive Airdrop Campaign with our platform token $DXCC ! 🎁 📅 Campaign Period: 48 HOURS ONLY (from the time of this tweet) 🎯 How to Participate & Earn $DXCC: ✅ Task 1: Make a Swap Perform any swap in our AMM to receive 77 $DXCC. ✅ Task 2: Provide Liquidity Add any amount of liquidity to a pool to receive 333 $DXCC. Bonus: Get an additional 1 $DXCC for every 1 xUSD TVL you provide. ✅ Mandatory Social Tasks: - Follow DEX.cc 𐤊 DEX.cc Commons 𐤊 - Like & Retweet this announcement - Tag 2 friends - Reply to this tweet confirming which task you completed (1, 2, or both) and paste your wallet address. Note: Tasks 1 and 2 can be combined for maximum rewards! 📌 Important Highlights about $DXCC: 1⃣DXCC officially launches in a few days. 2⃣NO ICO, NO Pre-sale. Fair distribution via airdrops and liquidity mining only. 3⃣A portion of all platform fees will be used for continuous Buy-Back & Burn to secure $DXCC value for you. 🪙 Initial LP & Price: After $DXCC launch in a few days, the DXCC/xUSD and DXCC/KAS liquidity pools will be created with an initial price of 1 $DXCC = 1 $KAS. 🔗 Simple Start Guide: 1. Use the in-app cross-chain bridge to bring over WBTC, ETH, or USDT to your wallet. (Tap 'Bridge' on wallet page) 2. Transfer assets from your Wallet account to your DEX Account. (Tap 'Wallet 🔁 DEX' on wallet page) 3. You're all set! Start Swapping or Adding Liquidity. (Go to 'Swap' page, tap into any pool you'd like to 'Add Liquidity') Don't miss out! The future of swapping on Kaspa is here. ⚡ #DeFi #DEX #Airdrop #AMM #Kaspa #DEXcc #DXCC #Kasplex

🚀 AMM Swaps are LIVE on Google Play & Apple App Store! 🚀 To celebrate, we're launching a massive Airdrop Campaign with our platform token $DXCC ! 🎁 📅 Campaign Period: 48 HOURS ONLY (from the time of this tweet) 🎯 How to Participate & Earn $DXCC: ✅ Task 1: Make a Swap Perform any swap in our AMM to receive 77 $DXCC. ✅ Task 2: Provide Liquidity Add any amount of liquidity to a pool to receive 333 $DXCC. Bonus: Get an additional 1 $DXCC for every 1 xUSD TVL you provide. ✅ Mandatory Social Tasks: - Follow DEX.cc 𐤊 DEX.cc Commons 𐤊 - Like & Retweet this announcement - Tag 2 friends - Reply to this tweet confirming which task you completed (1, 2, or both) and paste your wallet address. Note: Tasks 1 and 2 can be combined for maximum rewards! 📌 Important Highlights about $DXCC: 1⃣DXCC officially launches in a few days. 2⃣NO ICO, NO Pre-sale. Fair distribution via airdrops and liquidity mining only. 3⃣A portion of all platform fees will be used for continuous Buy-Back & Burn to secure $DXCC value for you. 🪙 Initial LP & Price: After $DXCC launch in a few days, the DXCC/xUSD and DXCC/KAS liquidity pools will be created with an initial price of 1 $DXCC = 1 $KAS. 🔗 Simple Start Guide: 1. Use the in-app cross-chain bridge to bring over WBTC, ETH, or USDT to your wallet. (Tap 'Bridge' on wallet page) 2. Transfer assets from your Wallet account to your DEX Account. (Tap 'Wallet 🔁 DEX' on wallet page) 3. You're all set! Start Swapping or Adding Liquidity. (Go to 'Swap' page, tap into any pool you'd like to 'Add Liquidity') Don't miss out! The future of swapping on Kaspa is here. ⚡ #DeFi #DEX #Airdrop #AMM #Kaspa #DEXcc #DXCC #Kasplex

DEX.cc 𐤊

15,715 次观看 • 9 个月前

🚨 $BTC IS TRAPPING EVERYONE... For 110+ days, market has stayed inside an “ascending channel”, slowly printing HHs and HLs Main target of this move was CME GAP ( $79.4k-$81k ) - after filling it, BTC already dropped more than ~12% Strong short reaction now signals broader downside ahead Based on my chart, here’s plan for next 30-60 days: 1. Bounce to $78k to make retail think rally continues 2. Liquidity sweep → move into support zone 3. Breakdown of support + dump toward $63k 4. Sideways consolidation around $63k-$65k 5. Drop toward $52k + formation of cyclical bottom Everything happening right now looks like preparation for a larger market dump There’s no sign of a “new Bull Run” yet... Don’t become liquidity for smart money - turn on notifs, I’ll update

🚨 $BTC IS TRAPPING EVERYONE... For 110+ days, market has stayed inside an “ascending channel”, slowly printing HHs and HLs Main target of this move was CME GAP ( $79.4k-$81k ) - after filling it, BTC already dropped more than ~12% Strong short reaction now signals broader downside ahead Based on my chart, here’s plan for next 30-60 days: 1. Bounce to $78k to make retail think rally continues 2. Liquidity sweep → move into support zone 3. Breakdown of support + dump toward $63k 4. Sideways consolidation around $63k-$65k 5. Drop toward $52k + formation of cyclical bottom Everything happening right now looks like preparation for a larger market dump There’s no sign of a “new Bull Run” yet... Don’t become liquidity for smart money - turn on notifs, I’ll update

Aralez 🐕

111,322 次观看 • 1 个月前

🧩When to add to winners? 💡Averaging up in a trade (adding to your winner) is increasing reward value without increasing any risk 💵I like to call it on stream as "using house money" When I catch a nice entry, I keep in mind market conditions, my confidence in the draw on liquidity, time levels, and upcoming PDarrays 🎯I do take partials at major time levels or areas I think price could turn to pay myself and then from there, the remaining contracts are now allowing me to trade with "house money" 💥Once I feel more confident on draw on liquidity, I then wait for pullbacks into 5min PDarray or key time levels 🥊These are the times I look to add (average up) into my winners Anytime I add to my winner, I keep my stop to BE. Adding to a winner will bring your BE closer, but if you are able to strategically time it, many times you can squeeze out more profits after your targets were hit Yes you will hit BE often, but keep in mind this is after my initial targets were hit and I have paid myself. In theory my trade completed, but I leave on enough to then "re-build" a whole new position again This trade from yesterday on stream shows examples of this. Not always easy to do, but when it works it pays off. Again, the key here is no additional risk for more profits

🧩When to add to winners? 💡Averaging up in a trade (adding to your winner) is increasing reward value without increasing any risk 💵I like to call it on stream as "using house money" When I catch a nice entry, I keep in mind market conditions, my confidence in the draw on liquidity, time levels, and upcoming PDarrays 🎯I do take partials at major time levels or areas I think price could turn to pay myself and then from there, the remaining contracts are now allowing me to trade with "house money" 💥Once I feel more confident on draw on liquidity, I then wait for pullbacks into 5min PDarray or key time levels 🥊These are the times I look to add (average up) into my winners Anytime I add to my winner, I keep my stop to BE. Adding to a winner will bring your BE closer, but if you are able to strategically time it, many times you can squeeze out more profits after your targets were hit Yes you will hit BE often, but keep in mind this is after my initial targets were hit and I have paid myself. In theory my trade completed, but I leave on enough to then "re-build" a whole new position again This trade from yesterday on stream shows examples of this. Not always easy to do, but when it works it pays off. Again, the key here is no additional risk for more profits

JD

23,940 次观看 • 1 年前

Once this liquidity sweep hits my target price (TP), I make $85,600 from this trade. This is a clip from a LIVE trade two weeks ago. (it’s not edited, cherry-picked or fabricated) And I’m sharing it with you now to reveal the fundamental principles behind why it was successful that can be applied to ANY trade. The video below is just a 4-minute breakdown from the original 20-minute live recording where I walk the thought process on the liquidity sweep, demand zones and target price in real-time. If you want to watch the full uncut live trade, then I can send it over to you immediately. Just comment “TRADING” for me (and you must be following). Now, why did I set 1.3253 as my TP? I already considered the best AND worse case scenarios: Even if price switched bearish after my entry, it would have to sweep liquidity again before reversing, which means coming up to take out the swing high first. This means either price continues pumping OR it reverses—but both scenarios hit my TP first. Once I mapped out the liquidity sweep + extreme demand zone hold + higher timeframe alignment, I could comfortably set 1.3253 as the optimal TP. We're not doing anything fancy here. Just the fundamentals I've executed 1,000+ times. Once you begin seeing the patterns, you can do this like clockwork. But it takes repetition to wire it into your brain.

Once this liquidity sweep hits my target price (TP), I make $85,600 from this trade. This is a clip from a LIVE trade two weeks ago. (it’s not edited, cherry-picked or fabricated) And I’m sharing it with you now to reveal the fundamental principles behind why it was successful that can be applied to ANY trade. The video below is just a 4-minute breakdown from the original 20-minute live recording where I walk the thought process on the liquidity sweep, demand zones and target price in real-time. If you want to watch the full uncut live trade, then I can send it over to you immediately. Just comment “TRADING” for me (and you must be following). Now, why did I set 1.3253 as my TP? I already considered the best AND worse case scenarios: Even if price switched bearish after my entry, it would have to sweep liquidity again before reversing, which means coming up to take out the swing high first. This means either price continues pumping OR it reverses—but both scenarios hit my TP first. Once I mapped out the liquidity sweep + extreme demand zone hold + higher timeframe alignment, I could comfortably set 1.3253 as the optimal TP. We're not doing anything fancy here. Just the fundamentals I've executed 1,000+ times. Once you begin seeing the patterns, you can do this like clockwork. But it takes repetition to wire it into your brain.

The Trading Geek (Brad Goh)

48,918 次观看 • 7 个月前

📺 $TSLA BELOW $417 — DOWNSIDE TO $366 NEXT As long as #Tesla stays below $417.42, the bias remains bearish in the 2–5 week timeframe. $417.42 is #TSLA channel resistance (sellable level), while $366.07 is a descending channel support (near-term downside target). If weakness persists, support drifts lower into the $330s–$340s. There is no compelling reason for a swing short to exit unless $TSLA closes above $417.42, or it tests the $366 area (where profits should be taken). TWO TACTICAL SHORT SETUPS: 1. Sell near $417.42, target $390.12; 2. If $TSLA closes below $390.12, the target becomes $373.27 within 3–5 days. On the other side, if $TSLA closes above $417.42, the entire short thesis shifts. Upside is expected: $436.35 within days, $456.31 within 2–3 weeks, and continuation toward the low $500s within 1–2 months. So, above $417.42, $TSLA structure shifts from “sell rallies” to “buy pullbacks.” For now, respect resistance until broken. Take profits into support. Flip bias only on confirmed breakout. If you enjoyed this update, please ❤️like and 🔁retweet The full #TSLA Trading Plan for Feb 24, 2026, in this short video🔽

📺 $TSLA BELOW $417 — DOWNSIDE TO $366 NEXT As long as #Tesla stays below $417.42, the bias remains bearish in the 2–5 week timeframe. $417.42 is #TSLA channel resistance (sellable level), while $366.07 is a descending channel support (near-term downside target). If weakness persists, support drifts lower into the $330s–$340s. There is no compelling reason for a swing short to exit unless $TSLA closes above $417.42, or it tests the $366 area (where profits should be taken). TWO TACTICAL SHORT SETUPS: 1. Sell near $417.42, target $390.12; 2. If $TSLA closes below $390.12, the target becomes $373.27 within 3–5 days. On the other side, if $TSLA closes above $417.42, the entire short thesis shifts. Upside is expected: $436.35 within days, $456.31 within 2–3 weeks, and continuation toward the low $500s within 1–2 months. So, above $417.42, $TSLA structure shifts from “sell rallies” to “buy pullbacks.” For now, respect resistance until broken. Take profits into support. Flip bias only on confirmed breakout. If you enjoyed this update, please ❤️like and 🔁retweet The full #TSLA Trading Plan for Feb 24, 2026, in this short video🔽

Wicked Stocks

10,135 次观看 • 5 个月前

Russian young guy used free source Claude Code and Open Claw and turned $13 into $195,000 in one month He is from a small town near Moscow He had no starting capital He just used free tools When limits appeared on his account he simply created new ones and continued building His account is guh123 Result: $13 → $190,800 17,805 trades Win rate 77% The biggest win $3,286 on a single bet Profile and copytrading guh123: How it bots work: Random forest 100+ models voting at once. Not one indicator. Not a feeling. A consensus. Each model sees: - price - volume - momentum - liquidity - min to expiry. Final answer = majority vote across all 100+. Entry: buy if: market_price ≤ model_prob × 0.5 Market says 28%. Model says 65%. You buy at half the real value. Even if the model is off by 20% you're still in profit. Exit: sell if: market_price ≥ model_prob × 0.9 or: min_to_expiry ≤ 8 Validation: SR = (returns − risk_free) / std(returns) Sharpe above 2 = the edge is real, not luck. 4 rules. 100+ models. No emotions. Insert these formulas and build your own bot

AdiiX

57,519 次观看 • 4 个月前

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

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

Blaze

93,219 次观看 • 1 个月前

📣BREAKING TEXAS BREAKING: Williamson County Elections Administrator Wilco TX Elections Caught Destroying Evidence of Surveillance Gaps A digital clock in the Early Voting Ballot Board room exposed 2+ HOURS of missing surveillance video on 2/20/26. What did EA Bridgette Escobedo do? She unplugged the clock. 🧵 1/ Dr. Laura Pressley was monitoring the legally required surveillance feed (Tex. Elec. Code §127.1232(b)) and noticed the clock on the EVBB table jumped from 5:18pm→6:24pm, then again from 6:47pm→7:47pm. Over 2 hours of video — gone. 2/ AFTER those 2+ hours of missing footage, Escobedo personally delivered 4 early voting ballot boxes to the vault — alone. No Republican designee. No watchers. No chain of custody. Then at 8:28pm she unplugged the clock. 3/ The clock stayed off for nearly 5 DAYS — eliminating the only independent timestamp that could expose future video gaps. 4/ TODAY at noon, the Republican Presiding Judge plugged the clock back in. At 4:53pm, Escobedo marched into the EVBB room, shook the clock at the judge, and physically REMOVED it from the room. It's all on video. The ballot board members laughed at her after she left. 5/ Why would an elections administrator remove a clock from a room with legally required 24/7 surveillance? Because that clock is the only way the public can verify the video feed hasn't been spliced. Williamson County, Texas. March 2026 Republican Primary. This is happening RIGHT NOW.

📣BREAKING TEXAS BREAKING: Williamson County Elections Administrator Wilco TX Elections Caught Destroying Evidence of Surveillance Gaps A digital clock in the Early Voting Ballot Board room exposed 2+ HOURS of missing surveillance video on 2/20/26. What did EA Bridgette Escobedo do? She unplugged the clock. 🧵 1/ Dr. Laura Pressley was monitoring the legally required surveillance feed (Tex. Elec. Code §127.1232(b)) and noticed the clock on the EVBB table jumped from 5:18pm→6:24pm, then again from 6:47pm→7:47pm. Over 2 hours of video — gone. 2/ AFTER those 2+ hours of missing footage, Escobedo personally delivered 4 early voting ballot boxes to the vault — alone. No Republican designee. No watchers. No chain of custody. Then at 8:28pm she unplugged the clock. 3/ The clock stayed off for nearly 5 DAYS — eliminating the only independent timestamp that could expose future video gaps. 4/ TODAY at noon, the Republican Presiding Judge plugged the clock back in. At 4:53pm, Escobedo marched into the EVBB room, shook the clock at the judge, and physically REMOVED it from the room. It's all on video. The ballot board members laughed at her after she left. 5/ Why would an elections administrator remove a clock from a room with legally required 24/7 surveillance? Because that clock is the only way the public can verify the video feed hasn't been spliced. Williamson County, Texas. March 2026 Republican Primary. This is happening RIGHT NOW.

Lori Gallagher

46,791,557 次观看 • 5 个月前

We MUST pass the Save America Act Dead people found submitting their applications to vote 2 days AFTER THEY LEGALLY DIED They successfully registered AND VOTED In Swing State Pennsylvania: “Concrete example of what we believe to be valid voter fraud in the state of Pennsylvania. I draw your attention to an obituary listed for 1 Denise Ondish in Allegheny County, born 9/10/1946, deceased 10/22/2020. Her application to vote was received on 10/23, the day after she died. It was then mailed by the county back to her on 10/24/2020, 2 days after she had legally passed away. And the ballot was then received back at the county office on November 2, 2020. And when you go to the secretary of state's website today, it says that she voted in this election effective November 2, 2020, a full 9 days after miss Andish of Allegheny County passed away. This is not excuse me. This is not empirical this is not anecdotal. This is hard evidence” This only means one thing, Democrats are rigging our elections. We all know this is true The Senate GOP has to ban mail in ballots and mandatory ID before midterms. We are always one election away from Democrats regaining power and when they get the Presidency they’ll re-open the border and it’s over We can’t let that happen

We MUST pass the Save America Act Dead people found submitting their applications to vote 2 days AFTER THEY LEGALLY DIED They successfully registered AND VOTED In Swing State Pennsylvania: “Concrete example of what we believe to be valid voter fraud in the state of Pennsylvania. I draw your attention to an obituary listed for 1 Denise Ondish in Allegheny County, born 9/10/1946, deceased 10/22/2020. Her application to vote was received on 10/23, the day after she died. It was then mailed by the county back to her on 10/24/2020, 2 days after she had legally passed away. And the ballot was then received back at the county office on November 2, 2020. And when you go to the secretary of state's website today, it says that she voted in this election effective November 2, 2020, a full 9 days after miss Andish of Allegheny County passed away. This is not excuse me. This is not empirical this is not anecdotal. This is hard evidence” This only means one thing, Democrats are rigging our elections. We all know this is true The Senate GOP has to ban mail in ballots and mandatory ID before midterms. We are always one election away from Democrats regaining power and when they get the Presidency they’ll re-open the border and it’s over We can’t let that happen

Wall Street Apes

157,732 次观看 • 2 个月前

🚨 BREAKING… the most optimized 5m & 15m Polymarket Clawdbot model is no longer private Sounds unreal? ABSOLUTELY. Fake? NOT even close. If you’re trading on Polymarket this deserves YOUR attention. A new wallet just appeared with 17,546 settled predictions and $906,000 in monthly PNL It operates exclusively in NEW 5-minute & 15-minute crypto markets Profile → COPYTRADE → Weekly profit is hovering around ~$200,000 And the engine behind it is almost offensively minimal I went through the entire system No advanced ML frameworks No institutional infrastructure No heavy data engineering Just automated execution + statistical edge His COMPLETE playbook: 1. Ultra-short timeframes Strictly 5-min Up/Down contracts. Focused on 5 & 15m markets only. Clawdbot-style execution logic. Pure intraday momentum capture. No swing exposure. No narratives. Only speed and repetition 2. Order splitting + micro sizing Each trade is fragmented into multiple small fills. Predefined fixed sizing per entry. Size increases only when conviction is stronger. This compresses drawdowns and stabilizes the curve 3. Probability farming The system doesn’t chase moonshots. It compounds small mathematical advantages. Across thousands of executions = that turns into six figures per month Largest trade so far: “Bitcoin Up or Down - January 19, 5-6AM ET” $13,320 → $36,887 (+176%) Now the important part: This is replicable cursor + py-clob-client + python install the SDK generate Polymarket API keys define the Clawdbot strategy prompt AI generates the Clawdbot execution logic paper test with $1 You don’t need elite discretionary skill Bottom line Why are you still executing manually while automated systems operate 24/7?

Shelpid.WI3M

312,450 次观看 • 5 个月前

Executive Thesis - Ripple Bank 2025 If Ripple secures bank-like permissions (U.S. national bank charter or state ILC plus key foreign licenses) and runs RL-stablecoins and XRPL rails under a Basel-caliber risk, capital, and compliance stack, it can become a regulated global settlement and asset-services platform. That platform could let central banks, sovereign treasuries, and regulated financial institutions issue, custody, trade, and settle stablecoins and tokenized RWAs (stocks, bonds, commodities, derivatives) with ISO 20022 native messaging, BSA/AML–FATF controls, and Basel III capital/liquidity governance—collapsing today’s slow correspondent chains into a single, high-compliance operating layer. The “Boom” Implications With the right charter(s), prudential regime, and partnerships, Ripple can become a compliance-first global neo-banking platform that (1) absorbs cross-border payment flows from correspondent networks, (2) powers CBDC and sovereign tokenized markets, and (3) monetizes issuance, custody, settlement, and compliance at scale—all inside Basel III, BSA/AML, FATF, and ISO 20022 guardrails. Impact on XRP If Ripple Bank were formally approved and XRP became the primary liquidity and settlement token across its’ regulated ecosystem, the economic demand for XRP would expand exponentially - transforming it from a speculative asset into regulated financial infrastructure. Structural Shift in XRP Demand From Speculative to Utility-backed Demand • XRP’s value today is primarily market-driven by speculation on future adoption. • Under a Ripple Bank framework, XRP becomes a mandatory utility asset — required for: • Settlement liquidity between all tokenized assets on XRPL (CBDCs, stablecoins, RWAs, derivatives). • Transaction fees and compliance verification across billions of high-value financial messages. • Collateral in interbank, treasury, and derivative clearing functions. This converts XRP from “optional” to “indispensable” in regulated settlement flows - similar to how SWIFT messaging depends on correspondent Nostro/Vostro liquidity BUT executed on a frictionless, tokenized rail. Volume & Velocity Effects Token velocity decreases, float demand increases • Basel III and liquidity regulations require prefunded, high-quality settlement collateral. • As banks, sovereigns, and institutions hold XRP as a liquidity reserve (like Tier-1 capital equivalents for tokenized payments), circulating supply falls while volume increases—driving scarcity-driven price appreciation. An Example of Flow Scale • Global wholesale payments ≈ $250T/year. • If 10% settles through Ripple’s bank-backed network using XRP at a 3-day velocity (roughly 120 settlement turns per year): • Required float ≈ $2.1T equivalent demand. • Even at $100/XRP, that implies 20B XRP locked in active liquidity operations. • At today’s 15B non-escrowed supply, value equilibrium could theoretically exceed $140–$200 per token, depending on velocity and collateral requirements. From today’s ~$3 price, this implies a 46x to 66x price surge. Are we ready? Ripple Treasury Department OCC Comptroller Jonathan Gould

Executive Thesis - Ripple Bank 2025 If Ripple secures bank-like permissions (U.S. national bank charter or state ILC plus key foreign licenses) and runs RL-stablecoins and XRPL rails under a Basel-caliber risk, capital, and compliance stack, it can become a regulated global settlement and asset-services platform. That platform could let central banks, sovereign treasuries, and regulated financial institutions issue, custody, trade, and settle stablecoins and tokenized RWAs (stocks, bonds, commodities, derivatives) with ISO 20022 native messaging, BSA/AML–FATF controls, and Basel III capital/liquidity governance—collapsing today’s slow correspondent chains into a single, high-compliance operating layer. The “Boom” Implications With the right charter(s), prudential regime, and partnerships, Ripple can become a compliance-first global neo-banking platform that (1) absorbs cross-border payment flows from correspondent networks, (2) powers CBDC and sovereign tokenized markets, and (3) monetizes issuance, custody, settlement, and compliance at scale—all inside Basel III, BSA/AML, FATF, and ISO 20022 guardrails. Impact on XRP If Ripple Bank were formally approved and XRP became the primary liquidity and settlement token across its’ regulated ecosystem, the economic demand for XRP would expand exponentially - transforming it from a speculative asset into regulated financial infrastructure. Structural Shift in XRP Demand From Speculative to Utility-backed Demand • XRP’s value today is primarily market-driven by speculation on future adoption. • Under a Ripple Bank framework, XRP becomes a mandatory utility asset — required for: • Settlement liquidity between all tokenized assets on XRPL (CBDCs, stablecoins, RWAs, derivatives). • Transaction fees and compliance verification across billions of high-value financial messages. • Collateral in interbank, treasury, and derivative clearing functions. This converts XRP from “optional” to “indispensable” in regulated settlement flows - similar to how SWIFT messaging depends on correspondent Nostro/Vostro liquidity BUT executed on a frictionless, tokenized rail. Volume & Velocity Effects Token velocity decreases, float demand increases • Basel III and liquidity regulations require prefunded, high-quality settlement collateral. • As banks, sovereigns, and institutions hold XRP as a liquidity reserve (like Tier-1 capital equivalents for tokenized payments), circulating supply falls while volume increases—driving scarcity-driven price appreciation. An Example of Flow Scale • Global wholesale payments ≈ $250T/year. • If 10% settles through Ripple’s bank-backed network using XRP at a 3-day velocity (roughly 120 settlement turns per year): • Required float ≈ $2.1T equivalent demand. • Even at $100/XRP, that implies 20B XRP locked in active liquidity operations. • At today’s 15B non-escrowed supply, value equilibrium could theoretically exceed $140–$200 per token, depending on velocity and collateral requirements. From today’s ~$3 price, this implies a 46x to 66x price surge. Are we ready? Ripple Treasury Department OCC Comptroller Jonathan Gould

Rob Cunningham

10,911 次观看 • 9 个月前

.Marc Benioff's somewhat contrarian take on AI budgets: 👉"If you have a standalone 'AI budget,' you're doing AI wrong." After 2+ years of GenAI hype, most companies have still failed with AI initiatives. Why? They're treating AI as a separate technology project instead of embedding it into line-of-business accountability. Real example: RBS had incredible AI team + PhD researchers but wasn't getting value. Breakthrough came when they stopped funding "AI projects" and started holding their wealth management and call center teams accountable for AI-driven business outcomes. Marc's insight: AI budgets should live where the impact happens—sales teams measured on AI productivity gains, service teams on efficiency improvements, ops teams on cost reductions. Standalone AI budgets = no business accountability = science experiments that go nowhere. Winners embed AI so deep into operations it becomes invisible infrastructure for superior performance. The great AI budget migration is underway.

.Marc Benioff's somewhat contrarian take on AI budgets: 👉"If you have a standalone 'AI budget,' you're doing AI wrong." After 2+ years of GenAI hype, most companies have still failed with AI initiatives. Why? They're treating AI as a separate technology project instead of embedding it into line-of-business accountability. Real example: RBS had incredible AI team + PhD researchers but wasn't getting value. Breakthrough came when they stopped funding "AI projects" and started holding their wealth management and call center teams accountable for AI-driven business outcomes. Marc's insight: AI budgets should live where the impact happens—sales teams measured on AI productivity gains, service teams on efficiency improvements, ops teams on cost reductions. Standalone AI budgets = no business accountability = science experiments that go nowhere. Winners embed AI so deep into operations it becomes invisible infrastructure for superior performance. The great AI budget migration is underway.

Jason ✨👾SaaStr.Ai✨ Lemkin

27,164 次观看 • 1 年前

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

Milk Road AI

36,711 次观看 • 7 天前