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Current sensitivity settings for Black Ops 6 #BlackOps6 6-6 Horizontal And Vertical 0.75ADS Speed 2 Deadzone Left | 3 Deadzone Right Aim Response Curve: Linear Left stick max threshold 80 | Right stick threshold max 99

Frankie

8,868 subscribers

216,539 views • 1 year ago •via X (Twitter)

Gaming #BlackOps6

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Updated controller settings. #BlackOps6 1.60-1.60 0.90ADS 3-3 deadzone 85 left stick max threshold Dynamic response curve Ads transition speed gradual 103FOV

Updated controller settings. #BlackOps6 1.60-1.60 0.90ADS 3-3 deadzone 85 left stick max threshold Dynamic response curve Ads transition speed gradual 103FOV

Frankie

87,952 views • 1 year ago

A vote for me earns Double XP! Reply below up to 3 times to earn 3 Double XP tokens available for Call of Duty: Black Ops 6 launch! Each reply (max 3) will earn 1 Hour of Double XP for Player, Weapon, and BattlePass at launch. 💥 #BlackOps6 🎮 Activision ID 🔥 Call of Duty

A vote for me earns Double XP! Reply below up to 3 times to earn 3 Double XP tokens available for Call of Duty: Black Ops 6 launch! Each reply (max 3) will earn 1 Hour of Double XP for Player, Weapon, and BattlePass at launch. 💥 #BlackOps6 🎮 Activision ID 🔥 Call of Duty

Call of Duty

4,975,687 views • 1 year ago

𝓡𝓪𝓷𝓴𝓲𝓷𝓰𝓼 𝓡𝓲𝓼𝓮𝓻 + 𝗜𝘀𝗮𝗶𝗮𝗵 𝗦𝗻𝗮𝘃𝗲𝗹𝘆 jumps to No. 2 in this update. - Electric hands from the left side of the dish, complimented by a wiry, 6-foot-3 frame, 6.68 60-yard dash, & a real shot to stick at SS. Must follow for recruiters ➡️ Isaiah Snavely

𝓡𝓪𝓷𝓴𝓲𝓷𝓰𝓼 𝓡𝓲𝓼𝓮𝓻 + 𝗜𝘀𝗮𝗶𝗮𝗵 𝗦𝗻𝗮𝘃𝗲𝗹𝘆 jumps to No. 2 in this update. - Electric hands from the left side of the dish, complimented by a wiry, 6-foot-3 frame, 6.68 60-yard dash, & a real shot to stick at SS. Must follow for recruiters ➡️ Isaiah Snavely

Cooper Trinkle

38,340 views • 1 year ago

🆕 Need for Speed: Undercover - Fusion Fix updated! 🔗 Download: • 🔧 Fixes: • All resolutions available in video options • HUD repositioned and scaled to match any aspect ratio ✨ New options: • Skip intro, borderless/bordered/resizable/borderless fullscreen windowed mode • Configurable sim rate - sync to monitor or 2× • Xbox, PlayStation, or text-only gamepad labels with fixed bindings • Configurable analog deadzones • Custom save directory and registry-free settings (saves to settings.ini instead of registry) • Shadow resolution up to 16384 (default 8192) • Cascade shadow map scale with configurable per-cascade draw distances (near/mid/far) • Adjustable bloom intensity • Motion blur toggle - disable without affecting World FX • Improved scenery LOD in the closest map section • Disable preculler and force bruteforce culling to reduce popping • Xbox 360 gamma curve 🎥 Free-look camera - mouse & right stick, full configurability for sensitivity, timeout, return speed, and axis inversion.

🆕 Need for Speed: Undercover - Fusion Fix updated! 🔗 Download: • 🔧 Fixes: • All resolutions available in video options • HUD repositioned and scaled to match any aspect ratio ✨ New options: • Skip intro, borderless/bordered/resizable/borderless fullscreen windowed mode • Configurable sim rate - sync to monitor or 2× • Xbox, PlayStation, or text-only gamepad labels with fixed bindings • Configurable analog deadzones • Custom save directory and registry-free settings (saves to settings.ini instead of registry) • Shadow resolution up to 16384 (default 8192) • Cascade shadow map scale with configurable per-cascade draw distances (near/mid/far) • Adjustable bloom intensity • Motion blur toggle - disable without affecting World FX • Improved scenery LOD in the closest map section • Disable preculler and force bruteforce culling to reduce popping • Xbox 360 gamma curve 🎥 Free-look camera - mouse & right stick, full configurability for sensitivity, timeout, return speed, and axis inversion.

Fusion Fix

10,751 views • 3 months ago

When strangers become big sisters in seconds. GPT Image 2 + Seedance 2.0 on Renoise prompt Create a clean, professional Pixar-style 3D character sheet of a 6-year-old girl, highly detailed, vibrant colors, polished family-friendly animation style. Character Description: Adorable 6-year-old girl with soft dark wavy hair tied in a cute high ponytail with a small pink bow. Big sparkling brown eyes, round expressive face, rosy cheeks, small button nose, and a warm friendly smile. She has a healthy, energetic child body type suitable for a 6-year-old. Outfit: Bright yellow t-shirt, light pink shorts, white ankle socks, and colorful small sneakers (blue and pink accents). Character Sheet Layout: A single clean vertical character sheet with multiple views of the same girl arranged neatly: Top Left: Full-body front view, standing naturally with hands on hips, smiling confidently. Top Right: Full-body 3/4 angle view, showing depth and personality. Middle Left: Full-body side profile view (facing right). Middle Right: Full-body back view. Bottom Center: Large close-up of her face showing 4 different expressions in small circles — Happy smile, Surprised, Shy/embarrassed, and Excited/grinning. Bottom Right: Small extra details — close-up of her ponytail with bow, shoe details, and hand poses. Style & Quality: Beautiful 3D Pixar animation style, smooth rounded shapes, expressive eyes, soft realistic textures, vibrant yet soft lighting, clean white background with subtle light shadows, highly polished, professional character design sheet, perfect proportions, studio quality, sharp details, warm and wholesome feel. 16:9 aspect ratio, ultra-detailed, cinematic lighting. #RenoiseCanvas

Sharon Riley

157,146 views • 2 months ago

> he turned himself into Tom Holland's Spider-Man > and it reads as real because of one prompt line 1. higgsfield → create image (nano banana pro) 2. upload your normal photo 3. the prompt does the work: > "ultra-realistic, no smoothing, no CGI look" > "looks like real behind-the-scenes footage" 4. that image → kling motion control 5. your video left, the face right → generate 6. the character copies your every move > everyone chases the model > the realism lives in the prompt

> he turned himself into Tom Holland's Spider-Man > and it reads as real because of one prompt line 1. higgsfield → create image (nano banana pro) 2. upload your normal photo 3. the prompt does the work: > "ultra-realistic, no smoothing, no CGI look" > "looks like real behind-the-scenes footage" 4. that image → kling motion control 5. your video left, the face right → generate 6. the character copies your every move > everyone chases the model > the realism lives in the prompt

RetroChainer

43,451 views • 2 months ago

“Jesus loves you” “But he was pierced for our rebellion, crushed for our sins. He was beaten so we could be whole. He was whipped so we could be healed. All of us, like sheep, have strayed away. We have left God’s paths to follow our own. Yet the Lord laid on him the sins of us all.” Isaiah 53:5-6 “We are made right with God by placing our faith in Jesus Christ. And this is true for everyone who believes, no matter who we are.” Romans 3:22

“Jesus loves you” “But he was pierced for our rebellion, crushed for our sins. He was beaten so we could be whole. He was whipped so we could be healed. All of us, like sheep, have strayed away. We have left God’s paths to follow our own. Yet the Lord laid on him the sins of us all.” Isaiah 53:5-6 “We are made right with God by placing our faith in Jesus Christ. And this is true for everyone who believes, no matter who we are.” Romans 3:22

TreVeyon Henderson

423,302 views • 1 year ago

Did you know AirPods Pro have 4 wireless transmitters? Left bud - 2.45 GHz Bluetooth Right bud - 2.45 GHz Bluetooth Case - 2.45 GHz Bluetooth low energy (BLE) and 6-8 GHz UWB (Ultra-Wideband) 4 separate RF transmitters. In a product marketed as earphones. You put 2 of them directly in your ear canal. Then "store" the other 2 on your nightstand. Where they pulse ALL NIGHT. Looking for your iPhone. Broadcasting to Find My. Staying ready to pair. There is NO OFF. Just... different distances from your head.

Did you know AirPods Pro have 4 wireless transmitters? Left bud - 2.45 GHz Bluetooth Right bud - 2.45 GHz Bluetooth Case - 2.45 GHz Bluetooth low energy (BLE) and 6-8 GHz UWB (Ultra-Wideband) 4 separate RF transmitters. In a product marketed as earphones. You put 2 of them directly in your ear canal. Then "store" the other 2 on your nightstand. Where they pulse ALL NIGHT. Looking for your iPhone. Broadcasting to Find My. Staying ready to pair. There is NO OFF. Just... different distances from your head.

Rusty ⚡️: Solar Powered ☀️

94,939 views • 3 months ago

The X algo is broken Right now quantity over quality is being promoted Fight videos, toxicity, engagement farming spam are winning High quality content and small accounts are being suppressed Luckily I have a really easy fix for you Simple fix to dramatically improve your X: 1. Click Lists on the left hand side 2. Create a new List 3. Add accounts that don't try to poison your brain 4. Add accounts you strive to be like 5. Use this List to replace your home feed 6. Reply guy like crazy to everyone you want to network with Guarantee your X experience becomes 10x better

The X algo is broken Right now quantity over quality is being promoted Fight videos, toxicity, engagement farming spam are winning High quality content and small accounts are being suppressed Luckily I have a really easy fix for you Simple fix to dramatically improve your X: 1. Click Lists on the left hand side 2. Create a new List 3. Add accounts that don't try to poison your brain 4. Add accounts you strive to be like 5. Use this List to replace your home feed 6. Reply guy like crazy to everyone you want to network with Guarantee your X experience becomes 10x better

Alex Finn

172,480 views • 2 years ago

This happened last Monday at Phoenix Sky Harbor Airport, Terminal 4, 6:45 AM. Technical Sergeant Kevin Harris, 35, Air Force, was going through TSA screening when his former military dog, Riley — a seven-year-old German Shepherd - spotted him through the glass. Max and Kevin served together overseas from 2013 to 2015. When Max retired, Kevin was stationed abroad and couldn't take him right away, so Max entered the adoption system. Kevin searched for two years before finally locating him three weeks ago through a military dog rescue volunteer in Arizona. The plan was simple: volunteer Sandra would bring Max through security to meet Kevin on proper service dog credentials after he cleared TSA. But Max saw Kevin taking off his belt and shoes at the screening area - and everything changed. Security footage shows Max yanking the leash from Sandra's hands, sprinting full speed, and jumping a four-foot "Do Not Enter" barrier straight into the secure screening zone. He hit Kevin before Kevin even got his belt off. Both went down. TSA agents swarmed. Radios crackled. A supervisor reviewed the footage, checked Max's credentials, and questioned everyone involved. After fifteen minutes, he issued a formal warning — no charges. "I thought we were getting arrested," Sandra told us. "But the supervisor watched the footage and understood - it was a reunion, not a threat." Kevin laughed about it afterward: "I'm taking my belt off and Max comes flying over a TSA barrier in the middle of airport security. Worth every second of panic." The two flew home to Texas together that afternoon -twelve years after serving side by side, reunited by a barrier jump. Sometimes a dog's love doesn't wait for protocol. It just jumps the barrier and runs.

This happened last Monday at Phoenix Sky Harbor Airport, Terminal 4, 6:45 AM. Technical Sergeant Kevin Harris, 35, Air Force, was going through TSA screening when his former military dog, Riley — a seven-year-old German Shepherd - spotted him through the glass. Max and Kevin served together overseas from 2013 to 2015. When Max retired, Kevin was stationed abroad and couldn't take him right away, so Max entered the adoption system. Kevin searched for two years before finally locating him three weeks ago through a military dog rescue volunteer in Arizona. The plan was simple: volunteer Sandra would bring Max through security to meet Kevin on proper service dog credentials after he cleared TSA. But Max saw Kevin taking off his belt and shoes at the screening area - and everything changed. Security footage shows Max yanking the leash from Sandra's hands, sprinting full speed, and jumping a four-foot "Do Not Enter" barrier straight into the secure screening zone. He hit Kevin before Kevin even got his belt off. Both went down. TSA agents swarmed. Radios crackled. A supervisor reviewed the footage, checked Max's credentials, and questioned everyone involved. After fifteen minutes, he issued a formal warning — no charges. "I thought we were getting arrested," Sandra told us. "But the supervisor watched the footage and understood - it was a reunion, not a threat." Kevin laughed about it afterward: "I'm taking my belt off and Max comes flying over a TSA barrier in the middle of airport security. Worth every second of panic." The two flew home to Texas together that afternoon -twelve years after serving side by side, reunited by a barrier jump. Sometimes a dog's love doesn't wait for protocol. It just jumps the barrier and runs.

Gabriele Corno

144,289 views • 8 days ago

3 Safety critical interventions in a 6-mile round trip to the post office tonight with FSD 13.2.9. That’s 2 miles per critical intervention: 1. Goes right through a stop sign shown on map and visualization. See below. 2. At the intersection just past the stop sign, it did not yield right-of-way to oncoming traffic going straight as it was turning left. I had to emergency brake to prevent a collision. Reported. 3. After stopping and waiting at a stoplight appropriately, all of a sudden it accelerated through the stoplight for no apparent reason. I had to emergency brake before much of the car had not entered the intersection. (1) and (2) above are regularly reproducible and I have posted them earlier with videos. Anyone thinking there will be nationwide unsupervised FSD or nationwide Robotaxi this year or next year: you better think again. Still long TSLA, but FSD is at an impasse with no real improvements in 8+ months. Are they making a paradigm shift to simulation training. That’s been my hunch for months. AleXandra Merz 🇺🇲 Tesla AI

3 Safety critical interventions in a 6-mile round trip to the post office tonight with FSD 13.2.9. That’s 2 miles per critical intervention: 1. Goes right through a stop sign shown on map and visualization. See below. 2. At the intersection just past the stop sign, it did not yield right-of-way to oncoming traffic going straight as it was turning left. I had to emergency brake to prevent a collision. Reported. 3. After stopping and waiting at a stoplight appropriately, all of a sudden it accelerated through the stoplight for no apparent reason. I had to emergency brake before much of the car had not entered the intersection. (1) and (2) above are regularly reproducible and I have posted them earlier with videos. Anyone thinking there will be nationwide unsupervised FSD or nationwide Robotaxi this year or next year: you better think again. Still long TSLA, but FSD is at an impasse with no real improvements in 8+ months. Are they making a paradigm shift to simulation training. That’s been my hunch for months. AleXandra Merz 🇺🇲 Tesla AI

A. Joseph Borelli, Jr., MD

168,636 views • 1 year ago

What would you have done? Merge right and let it play out? Hold your ground? Exactly what a Preventable Crash Really Looks Like. You’re driving a semi. Three-lane highway. You're in the center lane. Left lane is ending. Right lane is completely empty. Convex mirror shows no one beside you on the right. Three cars are pacing you in the left lane… and they’re about to run out of road. So Why are you still in the center lane? You’re not boxed in. You’re not blind. And you’ve already seen the sign telling you the left lane ends. You could’ve merged right a half-mile ago and cruised on. Instead, you’re now stuck in the lane of most resistance. That car flying up on your left? It’s doing what 80% of cars do when the lane end, they punch it. They try to get ahead. Not smart. Not courteous. But absolutely predictable. And that’s the point. You’re the professional. They're not. You know how this goes. You should know exactly what to expect. Textbook behavior. So when that car forces a merge in front of your bumper with 20 feet left of asphalt, are they wrong? Yep. But when you don’t lift off the gas, don’t merge right, and don’t anticipate what’s clearly unfolding, that’s no longer just their problem. Now it’s yours. And it’s preventable. What happens next👇 ✅ You plow into the car. ✅ You get to sit roadside for 3 hours explaining it to law enforcement and doing paperwork. ✅ You spend another 3 months Data Q’ing it, defending it in court, or arguing it with claims adjusters. ✅ It’s on your CSA now. ✅ Your insurance premium goes up. ✅ The carrier eats the deductible. ✅ And you get labeled as someone who “could’ve avoided it but didn’t.” Being a truck driver isn’t just turning a wheel. It’s a chess match. It’s anticipating the next three moves. When you choose not to play defense, you’re the one who loses, even when you’re “not at fault.” Some stats to chew on👇 👉Lane change/merge crashes account for nearly 10% of all large truck crashes. 👉FMCSA's Large Truck Crash Causation Study consistently shows "decision error" and "recognition error" as top contributing factors in preventable crashes. 👉The average post-crash litigation process? 3–6 months minimum, and that’s before settlements, audits, or nuclear verdicts.

What would you have done? Merge right and let it play out? Hold your ground? Exactly what a Preventable Crash Really Looks Like. You’re driving a semi. Three-lane highway. You're in the center lane. Left lane is ending. Right lane is completely empty. Convex mirror shows no one beside you on the right. Three cars are pacing you in the left lane… and they’re about to run out of road. So Why are you still in the center lane? You’re not boxed in. You’re not blind. And you’ve already seen the sign telling you the left lane ends. You could’ve merged right a half-mile ago and cruised on. Instead, you’re now stuck in the lane of most resistance. That car flying up on your left? It’s doing what 80% of cars do when the lane end, they punch it. They try to get ahead. Not smart. Not courteous. But absolutely predictable. And that’s the point. You’re the professional. They're not. You know how this goes. You should know exactly what to expect. Textbook behavior. So when that car forces a merge in front of your bumper with 20 feet left of asphalt, are they wrong? Yep. But when you don’t lift off the gas, don’t merge right, and don’t anticipate what’s clearly unfolding, that’s no longer just their problem. Now it’s yours. And it’s preventable. What happens next👇 ✅ You plow into the car. ✅ You get to sit roadside for 3 hours explaining it to law enforcement and doing paperwork. ✅ You spend another 3 months Data Q’ing it, defending it in court, or arguing it with claims adjusters. ✅ It’s on your CSA now. ✅ Your insurance premium goes up. ✅ The carrier eats the deductible. ✅ And you get labeled as someone who “could’ve avoided it but didn’t.” Being a truck driver isn’t just turning a wheel. It’s a chess match. It’s anticipating the next three moves. When you choose not to play defense, you’re the one who loses, even when you’re “not at fault.” Some stats to chew on👇 👉Lane change/merge crashes account for nearly 10% of all large truck crashes. 👉FMCSA's Large Truck Crash Causation Study consistently shows "decision error" and "recognition error" as top contributing factors in preventable crashes. 👉The average post-crash litigation process? 3–6 months minimum, and that’s before settlements, audits, or nuclear verdicts.

Rob Carpenter

16,777 views • 6 months ago

$🔥 Late June 2026: The Great Selective Rotation is Accelerating $KAS $0.45–4.5 $HBAR $1–8 $FIL $35–150 $ICP $45–250 $AR $80–400 $STX $8–40 $QNT $400–2000 $RIVER $6–60+ (abstraction + stablecoin plays gaining steam) $AKT $18–80 $HNT $30–150 $PYTH $1.5–12 $WIF $7–35 $BONK $0.00007–0.0007 $FLOKI $0.0015–0.015 $BRETT $0.8–6 $PENDLE $9–45 $LDO $12–45 $GMX $80–300 $CRV $2–10 $DYDX $20–80 $MANTRA $6–30 $CFG $1.5–10 $GRT $1.3–8 $MINA $2.5–14 $KSM $100–400 $GLMR $1.4–7 $CFX $1.2–6 $AXS $16–70 $SAND $2–12 $VET $0.14–0.7 $ALGO $0.7–4 $TRX $0.65–3 $DOGE $1.3–7 $PIXEL $2–15 $ILV $400–2000 $BEAM $0.12–1.2 Not the chaotic 2021 flood — this is precision capital flowing into projects delivering real utility, on-chain activity, and infrastructure moats. BTC dominance holding firm \~58-59% but showing fatigue. Altseason Index in that measured "build quietly" zone. Smart money isn’t chasing noise; it’s stacking narratives with actual traction: DePIN compute demand, decentralized intelligence, high-performance trading infra, and BTC-aligned ecosystems. My Core Conviction Basket for this cycle (long-term targets, NFA, DYOR — position size responsibly): High-Momentum Plays Heating Up Right Now: $HYPE $60–200+ — Perp DEX king with insane volume, staking mechanics, and ecosystem flywheel $TAO $250–2500 — Decentralized AI subnets exploding with real compute demand and agent growth $RENDER $12–90 — GPU workloads going parabolic as AI infra demand surges This rotation rewards conviction in builders who ship, not hype cycles. Quality narratives + patient capital = asymmetric upside. What’s your strongest conviction bag heading into Q3? Top 3 plays or hidden gems? Drop them below — let’s crowdsource the alpha 👇 Steady rotation. Data over delusion. Builders win. #Altseason3 #DePIN #DeAI #RIVER River River4FUN 🐝$

🔥 Late June 2026: The Great Selective Rotation is Accelerating $KAS $0.45–4.5 $HBAR $1–8 $FIL $35–150 $ICP $45–250 $AR $80–400 $STX $8–40 $QNT $400–2000 $RIVER $6–60+ (abstraction + stablecoin plays gaining steam) $AKT $18–80 $HNT $30–150 $PYTH $1.5–12 $WIF $7–35 $BONK $0.00007–0.0007 $FLOKI $0.0015–0.015 $BRETT $0.8–6 $PENDLE $9–45 $LDO $12–45 $GMX $80–300 $CRV $2–10 $DYDX $20–80 $MANTRA $6–30 $CFG $1.5–10 $GRT $1.3–8 $MINA $2.5–14 $KSM $100–400 $GLMR $1.4–7 $CFX $1.2–6 $AXS $16–70 $SAND $2–12 $VET $0.14–0.7 $ALGO $0.7–4 $TRX $0.65–3 $DOGE $1.3–7 $PIXEL $2–15 $ILV $400–2000 $BEAM $0.12–1.2 Not the chaotic 2021 flood — this is precision capital flowing into projects delivering real utility, on-chain activity, and infrastructure moats. BTC dominance holding firm \~58-59% but showing fatigue. Altseason Index in that measured "build quietly" zone. Smart money isn’t chasing noise; it’s stacking narratives with actual traction: DePIN compute demand, decentralized intelligence, high-performance trading infra, and BTC-aligned ecosystems. My Core Conviction Basket for this cycle (long-term targets, NFA, DYOR — position size responsibly): High-Momentum Plays Heating Up Right Now: $HYPE $60–200+ — Perp DEX king with insane volume, staking mechanics, and ecosystem flywheel $TAO $250–2500 — Decentralized AI subnets exploding with real compute demand and agent growth $RENDER $12–90 — GPU workloads going parabolic as AI infra demand surges This rotation rewards conviction in builders who ship, not hype cycles. Quality narratives + patient capital = asymmetric upside. What’s your strongest conviction bag heading into Q3? Top 3 plays or hidden gems? Drop them below — let’s crowdsource the alpha 👇 Steady rotation. Data over delusion. Builders win. #Altseason3 #DePIN #DeAI #RIVER River River4FUN 🐝

Đecentralized Člub ©

11,943 views • 1 month ago

While working on a new video with solutions to the previous one, I found ChatGPT's new UI struggles even more with concurrent updates: entries lose state and stick around for too long (see video). If this was a LiveView app, we would be getting so much flak.😅 --- I believe part of the problem here is having separate mutate and fetch requests on every deletion. The first fetch is cancelled when the second one comes up, causing items to stick around for longer. Many said yesterday that you could do the mutation and fetch as a single request, but that leads to other problems, such zombie entries. For example, imagine you delete link1 and link2 within a brief period of time. There is no guarantee the deletion order in the database will match the order the client receives the response, so you may end up with this: 1. (client) request to delete link1 sent 2. (client) request to delete link2 sent 3. (server) deletes link1 and loads a new list (includes link2) 4. (server) deletes link2 and loads a new list (no link1 or link2) 5. (client) receives link2 response 6. (client) receives link1 response So if you choose to use the latest response (link1), you brought link2 back to life. If you say you will use the response from the last request, events 3-4 can be swapped, and now you bring link1 back to life. Another way to solve this is by basically not allowing concurrent requests at all but that can affect the user experience drastically in other ways. Next week I should publish a video explaining how LiveView tackles this. Stay tuned!

While working on a new video with solutions to the previous one, I found ChatGPT's new UI struggles even more with concurrent updates: entries lose state and stick around for too long (see video). If this was a LiveView app, we would be getting so much flak.😅 --- I believe part of the problem here is having separate mutate and fetch requests on every deletion. The first fetch is cancelled when the second one comes up, causing items to stick around for longer. Many said yesterday that you could do the mutation and fetch as a single request, but that leads to other problems, such zombie entries. For example, imagine you delete link1 and link2 within a brief period of time. There is no guarantee the deletion order in the database will match the order the client receives the response, so you may end up with this: 1. (client) request to delete link1 sent 2. (client) request to delete link2 sent 3. (server) deletes link1 and loads a new list (includes link2) 4. (server) deletes link2 and loads a new list (no link1 or link2) 5. (client) receives link2 response 6. (client) receives link1 response So if you choose to use the latest response (link1), you brought link2 back to life. If you say you will use the response from the last request, events 3-4 can be swapped, and now you bring link1 back to life. Another way to solve this is by basically not allowing concurrent requests at all but that can affect the user experience drastically in other ways. Next week I should publish a video explaining how LiveView tackles this. Stay tuned!

José Valim

22,976 views • 1 year ago

MLP in PyTorch by hand ✍️ ~ 7 steps walkthrough below Goal: fill in every blank in the PyTorch code to build a multi-layer perceptron. 1. Given Let us start with a code template on the left and the network it is supposed to build on the right. Every blank in the code can be worked out from the picture. 2. Linear layer We count: 3 features in, 4 features out. So the weight matrix is 4 by 3. There is an extra column for the biases, which means bias = T. 3. ReLU Let us apply the activation. ReLU crosses out the negatives, so -1 becomes 0. 4. Linear layer The input size is 4, because that is what the previous layer put out. The output size is 2. A 2 by 4 weight matrix, and this time no extra column, so bias = F. 5. ReLU We cross out the negatives again. 6. Linear layer Two features in, five out. A 5 by 2 weight matrix, with a bias column, so bias = T. 7. Sigmoid Let us finish. Sigmoid squashes the raw scores (3, 0, -2, 5, -5) into probabilities between 0 and 1. You have just implemented a three-layer deep neural network by hand. ✍️ == Story == Three years ago I gave this exercise to my students, to connect the code to the math. They found it odd. Every other AI course they were taking lived inside a Jupyter notebook, and here I was handing out paper. Three years later, my colleagues are the ones rushing to move their materials to paper. The exercise has not changed. Paper still asks the one thing a notebook lets you skip: do you actually understand what the code is doing? If you can tell me why the weight matrix is 4 by 3, and why bias is F on the second layer, you understand nn.Linear better than someone who has been copy-pasting it for a year. 💾 Save this post! #AIbyHand #PyTorch #DeepLearning

MLP in PyTorch by hand ✍️ ~ 7 steps walkthrough below Goal: fill in every blank in the PyTorch code to build a multi-layer perceptron. 1. Given Let us start with a code template on the left and the network it is supposed to build on the right. Every blank in the code can be worked out from the picture. 2. Linear layer We count: 3 features in, 4 features out. So the weight matrix is 4 by 3. There is an extra column for the biases, which means bias = T. 3. ReLU Let us apply the activation. ReLU crosses out the negatives, so -1 becomes 0. 4. Linear layer The input size is 4, because that is what the previous layer put out. The output size is 2. A 2 by 4 weight matrix, and this time no extra column, so bias = F. 5. ReLU We cross out the negatives again. 6. Linear layer Two features in, five out. A 5 by 2 weight matrix, with a bias column, so bias = T. 7. Sigmoid Let us finish. Sigmoid squashes the raw scores (3, 0, -2, 5, -5) into probabilities between 0 and 1. You have just implemented a three-layer deep neural network by hand. ✍️ == Story == Three years ago I gave this exercise to my students, to connect the code to the math. They found it odd. Every other AI course they were taking lived inside a Jupyter notebook, and here I was handing out paper. Three years later, my colleagues are the ones rushing to move their materials to paper. The exercise has not changed. Paper still asks the one thing a notebook lets you skip: do you actually understand what the code is doing? If you can tell me why the weight matrix is 4 by 3, and why bias is F on the second layer, you understand nn.Linear better than someone who has been copy-pasting it for a year. 💾 Save this post! #AIbyHand #PyTorch #DeepLearning

Tom Yeh

13,318 views • 17 days ago

🚨 Roles RUINED FC25 FIFA15 KICK-OFF ATTACKING AI 1. The ST does a get in behind for a possible cross. 2. The fullback tries to get in behind after the pass with a DIAGONAL RUN. 3. The ST understands that it will be recycled and turns into a target man. 4. The CM understands that the opponent CM is on the wing and moves into that space. 5. The RM understands that the ST left his position and moves to the ST position. 6. The CAM understands that the CM is going into his place, and the RM is moving into ST, so he goes RM. How is it possible that 10 years ago we had better AI than we have right now, with all the technology advancement? The old custom tactic with organized/free roam is better than FC IQ. We need this attacking AI in FC26, EASPORTS!

🚨 Roles RUINED FC25 FIFA15 KICK-OFF ATTACKING AI 1. The ST does a get in behind for a possible cross. 2. The fullback tries to get in behind after the pass with a DIAGONAL RUN. 3. The ST understands that it will be recycled and turns into a target man. 4. The CM understands that the opponent CM is on the wing and moves into that space. 5. The RM understands that the ST left his position and moves to the ST position. 6. The CAM understands that the CM is going into his place, and the RM is moving into ST, so he goes RM. How is it possible that 10 years ago we had better AI than we have right now, with all the technology advancement? The old custom tactic with organized/free roam is better than FC IQ. We need this attacking AI in FC26, EASPORTS!

FUT POLICE LEAKS

203,091 views • 1 year ago

I created a dance movement sheet by using a reference image to animate every 16 panels from the reference image I provided. GPT Image 2 + See Dance 2.0 on Yapper Tutorial Below Prompt Here’s every step with the text under each heading: 1. Basic Stance Stand with your feet shoulder-width apart. Relax your knees. Keep your upper body relaxed. Get ready. 2. Step to the Right Step to the right. Move your body in the direction of the step. Keep your knees soft. Keep your gaze forward. 3. Step to the Left Step to the left. Move your body in the direction of the step. Keep your knees soft. Return to the starting position. 4. Two Steps Take two steps in sequence. Step to the right first. Then step to the left. Connect smoothly. 5. Body Wave Start the wave from your chest. Move through your ribs and hips. Finish with your lower body. Make the motion smooth like a wave. 6. Hip Sway Move your hips side to side. Shift your weight with the motion. Use your body naturally. Keep your upper body relaxed. 7. Arm Swing Swing your arms wide. Step to beat with your feet. Coordinate your arms and steps. Return your arms to center. 8. Turn Preparation Prepare for a turn in place. Cross one foot over the other. Use your core to maintain balance. Spot in the direction of the turn. 9. Right Turn Turn to the right. Keep your core engaged. Pivot on the ball of your foot. Spot forward after the turn. 10. Left Turn Turn to the left. Keep your core engaged. Pivot quickly. Keep your posture upright. 11. Jump Up Bend your knees and jump up. Reach your arms overhead. Land softly on your feet. Connect to the next move. 12. Kick Pose Kick your leg forward. Keep your supporting leg stable. Engage your core for balance. Control your landing. 13. Side Lunge Step wide to one side. Bend one knee and lower your body. Keep the other leg straight. Show the direction of your strength. 14. Freeze Pose Hit a strong pose on the beat. Freeze your body momentarily. Create a powerful shape. Show your presence. 15. Finishing Pose Finish the dance gracefully. Keep your balance. Show your confidence. End with a clean, sharp pose. 16. Whole Flow Connect from the basic stance to the finishing pose. The steps, waves, and turns flow naturally together. Express your own style.

I created a dance movement sheet by using a reference image to animate every 16 panels from the reference image I provided. GPT Image 2 + See Dance 2.0 on Yapper Tutorial Below Prompt Here’s every step with the text under each heading: 1. Basic Stance Stand with your feet shoulder-width apart. Relax your knees. Keep your upper body relaxed. Get ready. 2. Step to the Right Step to the right. Move your body in the direction of the step. Keep your knees soft. Keep your gaze forward. 3. Step to the Left Step to the left. Move your body in the direction of the step. Keep your knees soft. Return to the starting position. 4. Two Steps Take two steps in sequence. Step to the right first. Then step to the left. Connect smoothly. 5. Body Wave Start the wave from your chest. Move through your ribs and hips. Finish with your lower body. Make the motion smooth like a wave. 6. Hip Sway Move your hips side to side. Shift your weight with the motion. Use your body naturally. Keep your upper body relaxed. 7. Arm Swing Swing your arms wide. Step to beat with your feet. Coordinate your arms and steps. Return your arms to center. 8. Turn Preparation Prepare for a turn in place. Cross one foot over the other. Use your core to maintain balance. Spot in the direction of the turn. 9. Right Turn Turn to the right. Keep your core engaged. Pivot on the ball of your foot. Spot forward after the turn. 10. Left Turn Turn to the left. Keep your core engaged. Pivot quickly. Keep your posture upright. 11. Jump Up Bend your knees and jump up. Reach your arms overhead. Land softly on your feet. Connect to the next move. 12. Kick Pose Kick your leg forward. Keep your supporting leg stable. Engage your core for balance. Control your landing. 13. Side Lunge Step wide to one side. Bend one knee and lower your body. Keep the other leg straight. Show the direction of your strength. 14. Freeze Pose Hit a strong pose on the beat. Freeze your body momentarily. Create a powerful shape. Show your presence. 15. Finishing Pose Finish the dance gracefully. Keep your balance. Show your confidence. End with a clean, sharp pose. 16. Whole Flow Connect from the basic stance to the finishing pose. The steps, waves, and turns flow naturally together. Express your own style.

Sharon Riley

54,571 views • 3 months ago

🔥 Nebius AI R&D is hiring AI Research Interns for short, high-impact RL projects. Exclusive to X right now — no LinkedIn mass postings yet. In 2019, I was a fresh dental grad with 3 months of runway left, begging for an AI shot. I know the grind. We’re looking for sharp early-career folks (students, grads, career-switchers) to join us and work on: > Agent trajectories analysis at scale > Long-horizon tasks for coding agents > Pushing open RL environments > Any other data / RL env / eval project that will benefit open-source community What you get: 💰 Fully paid internship (3-6 month) 📦 100% open-source shipping 📄 Co-author research papers ⚡️ Access to Nebius compute infra 🌍 Remote-friendly (EU/US) or Amsterdam/London/other office. If you’ve done any cool AI/ML/RL stuff, dm me with your most impressive project + 1-sentence summary + cv Sharing appreciated!🤝

🔥 Nebius AI R&D is hiring AI Research Interns for short, high-impact RL projects. Exclusive to X right now — no LinkedIn mass postings yet. In 2019, I was a fresh dental grad with 3 months of runway left, begging for an AI shot. I know the grind. We’re looking for sharp early-career folks (students, grads, career-switchers) to join us and work on: > Agent trajectories analysis at scale > Long-horizon tasks for coding agents > Pushing open RL environments > Any other data / RL env / eval project that will benefit open-source community What you get: 💰 Fully paid internship (3-6 month) 📦 100% open-source shipping 📄 Co-author research papers ⚡️ Access to Nebius compute infra 🌍 Remote-friendly (EU/US) or Amsterdam/London/other office. If you’ve done any cool AI/ML/RL stuff, dm me with your most impressive project + 1-sentence summary + cv Sharing appreciated!🤝

Ibragim

33,427 views • 3 months ago

How to Make Homemade Mayo. It’s creamy, delicious, and avoids the usual risks of raw eggs. Ingredients (makes about 1–1.5 cups) 🥚 4–5 boiled eggs (peeled) 💧 Water (a splash, to help blending — roughly 2–4 tbsp) 🍋 Lemon juice (fresh, about 1–2 tbsp or to taste) 🫒 Olive oil (several tablespoons — added gradually, maybe ¼–⅓ cup total) 🧂 Pinch of salt (to taste) Optional add-ins (common for flavor): mustard, garlic powder, black pepper, or a touch of vinegar. Step-by-Step Instructions 1. Place the peeled boiled eggs in a wide-mouth jar or blending container. 2. Add a splash of water to loosen things up. 3. Pour in lemon juice and a pinch of salt. 4. Add olive oil (start with less and add more as needed for richness and emulsification). 5. Insert an immersion blender (stick blender) and blend everything until completely smooth and creamy. It transforms quickly into thick, classic-looking mayonnaise. 6. Taste and adjust seasoning (more salt, lemon, or oil if needed). Done! Store in the fridge for up to a week. Pro Tip: Use a narrow jar for the immersion blender so everything blends evenly without splatter. If it’s too thick, add a tiny bit more water or lemon juice.

How to Make Homemade Mayo. It’s creamy, delicious, and avoids the usual risks of raw eggs. Ingredients (makes about 1–1.5 cups) 🥚 4–5 boiled eggs (peeled) 💧 Water (a splash, to help blending — roughly 2–4 tbsp) 🍋 Lemon juice (fresh, about 1–2 tbsp or to taste) 🫒 Olive oil (several tablespoons — added gradually, maybe ¼–⅓ cup total) 🧂 Pinch of salt (to taste) Optional add-ins (common for flavor): mustard, garlic powder, black pepper, or a touch of vinegar. Step-by-Step Instructions 1. Place the peeled boiled eggs in a wide-mouth jar or blending container. 2. Add a splash of water to loosen things up. 3. Pour in lemon juice and a pinch of salt. 4. Add olive oil (start with less and add more as needed for richness and emulsification). 5. Insert an immersion blender (stick blender) and blend everything until completely smooth and creamy. It transforms quickly into thick, classic-looking mayonnaise. 6. Taste and adjust seasoning (more salt, lemon, or oil if needed). Done! Store in the fridge for up to a week. Pro Tip: Use a narrow jar for the immersion blender so everything blends evenly without splatter. If it’s too thick, add a tiny bit more water or lemon juice.

🦅 Eagle Wings 🦅

32,448 views • 2 months ago