Two Duo Pull Variants. Same concept; different puller based... on front: Odd Front- BST pulls to handle play side edge; QB handles backside edge. Even Front- BSG pulls to handle play side edge; QB handles backside edge.show more

"...take a look at that sharp edge on the... side of the new mclaren front wing...that's why nobody wants to get too close to that mclaren..if they start wheel banging, if max verstappen or anybody gets too close to that, THAT IS PUNCTURE CITY.." - Will Buxton 🔮‼️ #F1 #AustrianGPshow more

5. If you were frustrated by watching UC try... to rush the QB with three defenders over the past two years, that won’t be the case under new DC Nate Woody. “Coach Woody emphasizes sending the blitz,” said edge Brian Simms. “On every play, the offense has to worry about something coming so that’s the good thing about it. It’s never going to be a three-man rush.”show more

Today was the final day with the Mrs. and... our two friends in Scotland… (my trip continues for another 6 days 😬) We finished at Kilspindie today - the perfect course to wrap up our incredible trip. A “wee breeze” as Scots would say… This video on the front nine shows how strong it was. (The green is midway between the white house and left edge of the video). Ball ended up on the left side of the fairway. 🫣 Cc: Jamie Kennedyshow more

Sunderland vs Liverpool predicted lineup. Not starting two true... wingers here wouldn’t make sense to me. Both can play either side but I feel Angulo is more comfortable on the right than Talbi is. However, it’s 6 and 2 3’s. I really like our midfield dynamic with this 3. The rotations are fantastic. We must start front footed and capitalise on what could be a depleted Liverpool team after the Man City game. Thoughts? 🎨 Cam #SAFCshow more

This Polymarket trader has made $1.3M in the past... 2-3 Months and he didn’t do it by predicting the news. He waits for pricing mistakes… When odds drift out of balance, the bot buys Yes and No in the same market at the same time. Here’s an example of a bet/prediction: “Will BTC be above $70k on Friday?” There are only two contracts: •Yes •No Now here’s where the opportunity shows up. Because of emotion, size, or thin liquidity, prices can drift. Let’s say: •Yes = $0.46 •No = $0.50 To own both, you pay $0.96 total. But at settlement, you are guaranteed that one side becomes $1. So you paid $0.96 for something that must return $1.00. 👉 That $0.04 difference is the built-in edge. No matter what happens to BTC. Up, down, nukes, moon mission — doesn’t matter. You’re trading the mispricing, not the outcome. So if both sides together cost less than $1, the difference becomes a built-in edge. Scale the size, repeat the process, and the numbers compound. Checkout his profile:show more

CT always asks where the edge is. It’s not... charts. It’s not indicators. It’s not copy trading. It’s being early. Same way people made fortunes grabbing $ASTEROID early. Same way we positioned on $SI before it moved. Now we’re here, live, hearing the next narrative get named: Superior Intelligence. No media coverage yet. No clips yet (besides this one, badly recorded by me ). Pure insidoor play , approx 200 people were in mar a lago attending Trumps speaking about building not an Artificial Intelligence empire, but a SUPERIOR INTELLIGENCE one. No noise. Just one spark. (A public Trump’s interview, an Elon Musk comment, an Sam Altman quote, a Mark Zuckerberg mention… and it flies.) And that’s all it takes for Superior Intelligence to moon. I already see , NVIDIA , Meta , Claude , Grok posting about SUPERIOR INTELLIGENCE One narrative → one ignition → full explosion. If you wait for confirmation, you lose your hedge ! Front run the next 100 M narrative potential on eth 0x91139d452c144486D49B388509e459AB8748d07Bshow more

This trader gave Claude $44 and one question. Not... “what should I build.” Not “how do bots work.” He asked: what’s the easiest way to consistently extract money from short-term mispricing. No lecture. No disclaimer. No theory. It said: find small windows where the market is off by a few percent. Quietly. Repeatedly. At scale. Then it dropped a wallet. stargate5 $165,618 profit 16,279 trades Joined November 2025 He almost skipped it. Then he opened the activity. No predictions. No narratives. Just micro trades on 5–15 minute windows. Over and over and over. Tiny edges. Constant flow. The entire strategy: scan for mispricing above ~6% enter instantly redeem at $1 repeat. He asked Claude how this keeps working. It said: you don’t need big wins. You need a small edge executed thousands of times at near-even conditions. Volume handles the rest. He asked: what kind of capital this started with. Claude: based on sizing — probably under $1,000. Compounding did the rest. He went through the trades manually. Same pattern every time: short window mispriced odds fast execution instant settlement No randomness. No luck. The same edge, repeated 16,279 times until it turned into $165K. He asked Claude one last thing. What do you call this strategy? It said: capturing the gap between panic pricing and actual probability on repeat. At scale, that gap becomes income. Still running. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word 'Trade' 2. Like and Retweet this post 3. Follow me Marry Evan (so i can DM you)show more

This trader gave Claude $44 and one question. Not... “what should I build.” Not “how do bots work.” He asked: what’s the easiest way to consistently extract money from short-term mispricing. No lecture. No disclaimer. No theory. It said: find small windows where the market is off by a few percent. Quietly. Repeatedly. At scale. Then it dropped a wallet. stargate5 $165,618 profit 16,279 trades Joined November 2025 He almost skipped it. Then he opened the activity. No predictions. No narratives. Just micro trades on 5–15 minute windows. Over and over and over. Tiny edges. Constant flow. The entire strategy: scan for mispricing above ~6% enter instantly redeem at $1 repeat. He asked Claude how this keeps working. It said: you don’t need big wins. You need a small edge executed thousands of times at near-even conditions. Volume handles the rest. He asked: what kind of capital this started with. Claude: based on sizing — probably under $1,000. Compounding did the rest. He went through the trades manually. Same pattern every time: short window mispriced odds fast execution instant settlement No randomness. No luck. The same edge, repeated 16,279 times until it turned into $165K. He asked Claude one last thing. What do you call this strategy? It said: capturing the gap between panic pricing and actual probability on repeat. At scale, that gap becomes income. Still running. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: Just: 1. Comment the word ‘CLAUDE’ 2. Like and Retweet this post 3. Follow me Marry Evan (so that I can message you)show more

This trader gave Claude $44 and one question. Not... “what should I build.” Not “how do bots work.” He asked: what’s the easiest way to consistently extract money from short-term mispricing. No lecture. No disclaimer. No theory. It said: find small windows where the market is off by a few percent. Quietly. Repeatedly. At scale. Then it dropped a wallet. stargate5 $165,618 profit 16,279 trades Joined November 2025 He almost skipped it. Then he opened the activity. No predictions. No narratives. Just micro trades on 5–15 minute windows. Over and over and over. Tiny edges. Constant flow. The entire strategy: scan for mispricing above ~6% enter instantly redeem at $1 repeat. He asked Claude how this keeps working. It said: you don’t need big wins. You need a small edge executed thousands of times at near-even conditions. Volume handles the rest. He asked: what kind of capital this started with. Claude: based on sizing — probably under $1,000. Compounding did the rest. He went through the trades manually. Same pattern every time: short window mispriced odds fast execution instant settlement No randomness. No luck. The same edge, repeated 16,279 times until it turned into $165K. He asked Claude one last thing. What do you call this strategy? It said: capturing the gap between panic pricing and actual probability on repeat. At scale, that gap becomes income. Still running. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: Just: 1. Comment the word ‘CLAUDE’ 2. Like and Retweet this post 3. Follow me ZAYVEN KNOX (so that I can message you)show more

EVERYONE PROMPTS THE ACTION. ALMOST NOBODY LOCKS THE IDENTITY... — WHICH IS WHY TWO-CHARACTER SCENES FALL APART. Two freerunners racing across Tokyo rooftops, eight cuts, corkscrews over a rooftop gap at the end. The parkour is the easy part. Keeping them two separate people who never blend into each other is the part that actually breaks. Here's the full prompt built that way. Attach two reference photos as image_1 and image_2, and the same structure works for any multi-character action piece: FORMAT: 15 seconds, 16:9, 1080p, 8-cut cinematic ultra-advanced parkour footage. CHARACTERS: Two realistic individuals from image_1 and image_2. Use the attached images as absolute character references, and fully maintain the facial features, hairstyles, hair colors, skin textures, body types, height differences, outfits, color schemes, and age appearances of each person across all cuts. No altering into different people, face swaps, outfit changes, hairstyle changes, or mixing of the two individuals' features. SETTING: A sunny modern Japanese city reminiscent of Tokyo, Shibuya, and Yokohama — rooftops, alleys, staircases, railings, pipes, concrete walls. The two protagonists, as equals, race through at high speed running side by side, following, crossing paths, and coordinating. CUTS: 1. (00:00–00:01.60) Low-angle rear tracking. Both accelerate side by side and simultaneously kong vault over separate obstacles. 2. (00:01.60–00:03.40) Front low-angle. One wall runs the left wall, the other the right, then tic-tac to cross in midair and land on opposite rooftops. 3. (00:03.40–00:05.20) Lateral tracking. Consecutive precision jumps, then cat leaps to grab and climb a high wall. 4. (00:05.20–00:07.20) Rooftop tracking. The leader dash vaults, the trailer websters over the gap, then they swap front and back positions. 5. (00:07.20–00:09.20) Overhead moving camera. Both dive roll, then run side by side to speed vault a long railing. 6. (00:09.20–00:11.30) Handheld retreating from the front. One underbars, the other side flips, conquering the obstacle simultaneously. 7. (00:11.30–00:13.20) Drone from diagonal rear above. Both palm spin off left and right walls, kong vault, accelerate into the final jump. 8. (00:13.20–00:15.00) Climax. Both leap a large rooftop gap, each doing a corkscrew, camera circling them in midair as they land on separate rooftop edges — then run side by side into the distance. QUALITY: Live-action film quality. World-championship-level smooth freerunning. Realistic center-of-gravity shifts, muscle movement, natural landing impacts, swaying hair and clothing. Sharp background, natural motion blur only during high-speed movement. PROHIBITED: Facial distortion, altering into different people, face or body swaps, outfit changes, hairstyle changes, body type changes, limb multiplication, duplicates, body fusion, penetration, warping, floating, unnatural landings, anime style, CG style. A few things worth noticing about why it's built this way: The character block does identity work three separate times — the reference images, the "fully maintain" list, and the prohibited list at the end. That redundancy isn't padding; each one closes a different door the model tends to walk through. The prohibited list names the exact failure modes — face swaps, body fusion, limb multiplication. Telling the model what not to do is more effective here than describing what you want, because these are the specific ways two-character scenes collapse. Every cut assigns each person a distinct action — one wall runs left, the other right; one underbars, the other side flips. Giving them separate roles keeps them functionally two people, so the model can't average them into one. And the cuts are individually timed and framed. Long continuous motion is where identity drift creeps in — breaking it into eight discrete shots gives the model less room to blend them. Made in Seedance 2.0.show more

Erik Spoelstra on the Five Core Tenants of Miami... "Heat Culture": 1️⃣ Toughest 2️⃣ Nastiest 3️⃣ Best Conditioned 4️⃣ Most Professional 5️⃣ Least Liked 💪 Toughest = You do not need ideal circumstances to compete. You can absorb contact, handle adversity, and keep responding when the game stops being comfortable. 😷 Nastiest = You set the standard with your edge. You do not play entitled, passive, or polite. You compete with force, urgency, and a refusal to be easy to play against. 🍃 Best Conditioned = fatigue cannot become your excuse. When everybody else is negotiating with tired, your habits, preparation, and discipline allow you to keep executing. 👔 Most Professional = your standard does not change with your mood. You show up on time, do your job, accept coaching, tell the truth, and carry yourself like the work matters. 👎 Least Liked = when teams see you on the schedule they feel a wave of frustration, a smidge of anxiety, and that feeling you get before jump into a cold pool that feels like fear mixed with regret. Giannis Antetokounmpo will be indoctrinated into the Miami HEAT identity from day 1. Heat Culture became (in)famous because words showed up in behaviors. Everybody wants the reputation of toughness. Coach Spo and the Heat built it through repetition. ☄️show more

2025 UGA-Texas game has been on SEC Network today.... Had it on in the background while doing some stuff around the house. Couple random thoughts… - Really impressed with the back that Nate Frazier became last year. Doled out a lot of punishment in that 4th quarter - Kirby’s big game experience has allowed him to feel momentum and take his swings (4th&5/onside) before it becomes predictable. As the sport continues to compress, that might be worth a couple wins a year - Gunner’s ability to influence second/third level defenders off RPO/rollout/play-action looks when UGA gets the run going is really good. When gap scheme runs are hitting they become really hard to defend, especially when using the depth of the TE room to offset predictability. - UGA has to fight temptation to go into a shell after an early turnover. Lot of wasted possessions after Gunner’s INT in that game. - Zayden Walker’s bend off the edge is high level. Will be fun to watch in 2026 - Losing Bobo/Harris in those last two games is why Kirby and every other SEC coach are distressed by the CFP selection process and a 9-game SEC schedule - Mike Bobo is the favorite punching bag of some but he was cooking in the fourth quarter of that game. Found ways to freeze defenders and create leverage after Texas keyed in on Branch and did a good job stuffing a lot of zone looks up front.show more

What happens when you stop guessing and let machine... learning read the market for you? $2.2M in 4 months. ilovecircle built something different on Polymarket. Not a speed bot. Not a spread farmer. An AI system that actually thinks. 1,347 predictions. 74% win rate. Biggest single hit: $258.4K. Current positions: basically zero he extracted everything. The setup: 10 machine learning models running in parallel, each trained on news feeds and social media data. They don't predict events they predict when the crowd is wrong about probabilities. Market prices an outcome at 50 cents. His ensemble says the real odds are 60%. That gap is the trade. Every week the models retrain themselves on fresh data. The edge evolves because the system never stops learning. Most traders react to headlines. This wallet front runs the market's understanding of what headlines actually mean. 51K people watching now. Most still think AI trading is a scam until they see a curve like this. → Following wallets that run AI-powered probability models is simpler with PMX.show more

🚀 Introducing: Travel to Earn (P1) 🌍 What is... it? Ever wondered what sets Depinsim apart from the usual tap to earn, invite to earn, and task to earn clichés? It's time to unveil Travel to Earn! 🌟 Get rewarded for your roaming distance—simply travel (triggering a change of location on your eSIM or physical SIM) and earn rewards. 🏆✈️ Why Travel to Earn? In the world of airdrop-based tokenomics, bot accounts are a hassle. But with Depinsim's Travel to Earn, controlling numerous bots becomes nearly impossible. You can only have a max of two (e)SIMs active on one mobile device. Want thousands of accounts to earn travel rewards? You’d need to carry thousands of phones! 📱💼 Worried about not traveling much? No worries! Our system is designed to reward real users and curb bots. Even if your longest journey is to grab takeout from your front door, you can still earn rewards and airdrops. Just remember to take your phone with you! 📲🍔 Plus, we’ve capped the daily travel rewards to ensure even Ironman and Doctor Strange cannot cheat with technology or magic. What's P1? P1 stands for Phase One. Get ready for more exciting location-based play modes coming soon to our Travel to Earn system! 🌐 When is it live? P1 is now live for all early users. 🔑Here are some invite codes if you don’t have one: lyx8vovfie lyejrvno0E lz74plenL8 Ready to earn while you roam? 🌍🚀 #TravelToEarn #Depinsim #RewardsOnTheGoshow more

the house always priced weather off a real model.... you had a weather app. that gap was the whole game. the desks settling temperature markets ran physics. you ran "feels like 78°." they were never guessing. you always were. today that ends with minmax pro — and the edge is already live. denver: a thunderstorm 200 km west. our outflow detector has flagged the cold pool — about −5°F landing in an hour. the crowd is still pricing yesterday's number. the model already moved. new york, same day: public forecast says 78°. our model says 81°. the 81° bucket sits at 0.05 because nobody sees it coming. it settles 81°. 0.05 → 1.00 = 20×. illustrative — but the temperature matched our model, not the market. on temperature markets the best forecast wins. sharper than the crowd = mispriced buckets you can take. that is the whole game. here is what is reading the sky for you. say it plainly: a hybrid ml system forecasting temperature at the 11 largest us airports — chicago, atlanta, miami, houston, denver, seattle, san francisco, los angeles, new york, dallas, austin — every horizon from 1 to 24 hours out. not one model. a stack. · base ensemble → 3 lightgbm models (synoptic · convective · base) blended with per-hour-per-horizon weights · detector-boosters → 24 specialist experts per airport, each trained on one phenomenon — front (the sharp swing) · cloud (solar heating blocked) · fog (the morning stall) · outflow (a distant storm's cold pool crashing in) · heuristic physical correctors on the short horizons h+1..h+6 24 detector-experts. per airport. one knows a front passing. one knows shadow cooling as cloud builds. one knows fog and the minute it burns off. one watches a storm 200 km west and knows its outflow lands in an hour and pulls the temp down ~5°F. minmaxshow more

HIGGSFIELD + FABLE 5 BUILT A FULL CLIENT WEBSITE.... MY PART WAS 35 MINUTES. the old studio needed a designer, a dev, and someone for media. every hire ate the margin. now you personally touch exactly two stages - the models handle the heavy middle: → INTAKE (you · ~15 min) turn the client request into a tight spec: pages, brand, edge cases. judgment work - the part actually worth paying for. → DESIGN (Fable 5) brief in → design system out: layouts, components, responsive states. the week-in-Figma part, gone. → BUILD + MEDIA (in parallel) Claude Code writes the site - components, CSS, animations, CMS, deploy. Higgsfield MCP generates every visual - hero video, product shots, motion - from prompts, in the same chat. → QA + HANDOFF (you · ~20 min) review against the spec, deploy, notify. templated after your first few clients. two human stages, both fast. the slow, labor-heavy middle is the one you removed yourself from. you went from laborer to orchestrator. CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: - mcp_servers: - higgsfield: - url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what you sell: a productized site + a monthly retainer → what it costs you to deliver: ~$750/month across every client → the margin isn't clever pricing - the cost of delivery fell through the floor while the value stayed the same. you pocket the spread. one operator, three tools, the whole studio. Follow me, reply "MCP" and I'll send you the full step-by-step playbook. full breakdown in the article 👇show more

I really like those 2.5D characters here's my Seedance2... prompt: MANGA READER TURNAROUND CHARACTER: Young Japanese man sitting cross-legged on the floor reading a colorful manga magazine, spiky messy black hair sticking up in chunky angular shapes, thick eyebrows, small round glasses, slight stubble, cross pendant necklace on a thin chain, wearing a loose open dark navy pinstripe yukata robe showing his chest, bare feet tucked under his legs, small ceramic ashtray with cigarette butts on the floor to his left, round ceramic pot to his right, focused slightly annoyed expression looking down at the magazine. 3D CG model with 2D hand-drawn overlay, scribble hatching in all shadow areas boiling every frame, thick ink outlines on silhouette edges redrawing with wobble, flat painted color textures on 3D geometry, color bleeding outside contour lines. SEQUENCE [0s–5s] Character sits cross-legged holding the manga open in both hands, not moving, just reading. Camera begins a smooth continuous 360 degree orbit around him at his eye level. As the camera moves from front to three-quarter to side profile, the 3D model rotates cleanly revealing his full volume but the 2D ink layer on top lives and breathes, scribble hatching in the shadows under his jaw and inside the yukata folds and between his crossed legs redraws every frame with jittery boiling energy, thick ink outlines on his silhouette wobble and shift in thickness as the angle changes, the flat painted navy yukata wraps around his torso showing new pinstripe ink lines and fold marks at each new angle, his spiky hair reveals its 3D chunky volume from new angles, the manga magazine pages catch the light differently as the camera passes, cigarette smoke curls upward as loose scribbled white lines drifting gently [5s–10s] Camera continues the smooth unbroken orbit past his side revealing the cross necklace hanging from his neck in profile, through his back showing the yukata fabric draped across his shoulders and the spiky hair from behind, past his left side and returning to front, the entire rotation fluid and steady, throughout the full 360 the boiling ink outlines never stop trembling on every edge, the scribble hatching in shadows keeps redrawing in slightly different positions each frame giving the whole image a living sketchbook vibration even though he holds perfectly still reading, the flat painted color planes on his skin shift between warm peach and darker shadow tones as new surfaces catch the light, the ashtray and ceramic pot on the floor rotate into and out of view naturally, white background STYLE: 2.5D painterly CG. 3D CG geometry underneath for volume and smooth rotation. 2D Grease Pencil layer on top, thick wobbly ink outlines, scribble hatching in all shadows boiling every frame, flat painted color planes not smooth gradients. No photorealism. No smooth CG rendering. Muted palette dark navy warm peach black white. Film grain. Clean white background. Smooth continuous camera orbit.show more

A good technical LLM interview question: Your LLM chatbot... takes 12s before it generates the first token, and the users are complaining. So you move the model onto a GPU with 3x the computing power. The time to first token barely improves. Why did this happen? (answer below) Latency in an LLM app is a placement problem disguised as a model problem. If you profile the 12 seconds, the model's prefill itself may only account for around 1.5 seconds of it. So halving the prefill step saves just 750ms out of 12000, which is under 7%. The rest is spread across stages that never touch the GPU. The request first travels to whatever region the app runs in, and a cross-continent round trip could cost over a second before any code executes. Then the request handler starts. On a container-based serverless platform under load, this adds several seconds of cold start, paid before auth, rate limiting, or prompt assembly even begins. Retrieval adds its own hop, and the response streams back across the same distance. Optimizing a stage that was already fast cannot alter the latency that's majorly affected by other stages. Those other stages are slow for a structural reason. An LLM app runs two workloads that want opposite machines. - The request path is short, spiky, and needs to sit close to users - Inference is long-running, GPU-bound, and billed hourly, whether requests arrive or not. So the actual decision is not which model to run, but where each of these two workloads runs. There are three options, each with its own tradeoffs: > A dedicated GPU box removes inference cold starts, but it bills around the clock and lives in one location, so distant users wait out the round trip on every request > Container-based serverless scales to zero, but the request path pays a cold start, and most of these platforms have no GPU behind them. > Edge runtimes start in under a millisecond, because a WebAssembly module carries no OS or container image to boot. They handle the request path well and cannot hold a model. So the answer is not to pick one, but to split the app across two of them. The request path runs close to users, and inference runs on a dedicated GPU it calls into. That also explains the failed upgrade. More compute made a stage that was already fast faster, and left the 10.5 seconds around it untouched. To actually learn how it's done in practice, Akamai's GitHub has a reference implementation for each half. - vllm-on-lke serves Qwen2.5-7B-Instruct behind an OpenAI-compatible endpoint on one RTX 4000 Ada GPU in Linode Kubernetes Engine, with Terraform creating the cluster, both firewalls, and the GPU operator in one apply. - akamai-functions-llm-chatbot covers the front, where a WebAssembly API checks a KV cache and only calls the GPU-backed instance on a miss. Both are available on Akamai’s new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post treats generation as a single 1.5s block, but that block has its own structure, and knowing it well tells you whether a model is slow to start or slow to stream. I wrote a first-principles walkthrough of it, covering the prefill and decode split, KV caching, and where the time actually goes inside each one. Read it below. Thanks to Akamai Cloud for partnering today!show more

Prediction markets are creating some really strange incentives. As... I was watching the “Will Ronaldo cry” market after Spain beat Portugal, my first thought was *What if someone was at the game, at a seat with a relatively decent view and close to the pitch, with their phone [phone with top end camera capabilities for arguments sake] and remained zoomed in on Ronaldo’s face while he remained on the pitch [yes he turns around but then I thought maybe you could have 2/3 of you at the game, in on it in different areas of the stadium] ****don’t take this market so literally, volumes weren’t massive [$1.4m+], it’s a stretch some of these arguments, but in lay man market terms, you’re creating edge. $ wise, r/r of actually trying the idea, just don’t look at that, rather the new dynamic of “gamblification” specific prediction market markets have created**** As we know, being there is 2-5 seconds quicker than live broadcast, so that’s the edge you’re trying to attack. On top of that, you can also try to “influence” the outcome. There are numerous examples that came to mind, the main theme still remains that you need *access*, but all are possible: 1. You’re the pitch-side presenter. Turn the interview into a tribute to their career instead of talking about the match - Ronaldo is still on the pitch, bet is valid. Takes 1 minute to research a game for Portugal that might trigger Ronaldo to cry. 2. You’re the stadium announcer. Give an unexpected speech thanking Ronaldo for everything he’s done before he leaves the pitch. Sure, requires official approval, logistically tougher, but possible. 3. You’re the stadium DJ. Play a song closely associated with his career while he’s still on the field. Like in 1., takes 1-2 mins to ask an LLM if there is a song or something closely associated with Ronaldo. 4. You’re a TV producer. Keep the cameras on his family and replay emotional moments while he’s waiting for the interview. 5. You have access to his family. Encourage them to come onto the pitch immediately after the final whistle rather than waiting in the tunnel. This requires a lot of access, but what’s stopping someone pushing the kids/wife onto the pitch a little more aggressively, even if they don’t want to. [mostly preplanned, but again, possible] 6. You have premium seats beside the tunnel or family section. Hold up a personal sign where he’s actually likely to see it before leaving the field [if in premium seats, given this market, unlikely it’s fully worth it - you get the idea] 7. You’re a former teammate or coach with pitch access. Make a point of finding him after the final whistle and saying something personal before he walks off. Requires immense access, participant likely doesn’t bother, but again, it’s possible There are loads more. None of these guarantee an outcome, and most probably have little effect. But, like the recurring theme here: it’s all possible. As always, waiting for the retarded comments, but just think. As always with some long form posts, just rambling. This is just the YES side, there’s also another whole story for the NO side.show more