USC fr. EDGE Luke Wafle grabbing breakfast before practice 😳

#USC freshman edge Luke Wafle ✌🏼 WeAreSC Luke Wafle #FightOn

4️⃣⭐️ and highly sought after edge-rusher, Luke Wafle, commits... to USC ✌️ Luke Wafleshow more

#USC DE Luke Wafle wrecking plays

Luke Wafle is a true freshman EDGE at USC... and already built like he pays property taxesshow more

#USC DL Luke Wafle brings the POWER USCFootball.com Gerard Martinez

USC true freshman DE Luke Wafle is already wreaking havoc

USC 5-Star DE Luke Wafle is going to be... an INSTANT game-changer year 1 as a Trojan 😳show more

6’-6” 265lb. freshman Luke Wafle vs. 6’-6” 285lb. freshman... Vlad Dyakonov 😳 This is like watching King Kong vs Godzilla… whoa 🤯show more

#NJDevils Luke Hughes absolutely roasting his older brother Jack... Hughes and his newly chipped teeth just because he felt like it in his new post-practice interview: "He's definitely not as pretty... not as good looking as he was before..."show more

Elijah Holyfield looks absolutely JACKED 😳 Elijah is the... son of former heavyweight world champion boxer, Evander Holyfield. Holyfield last played for the Bengals before suffering a significant knee injury at practice. Now, Holyfield is 11 months removed from his ACL surgery. And he looks READY to come back.show more

Observation 2: A closer look at 5 ⭐️ KJ... Green. A part of his game that people don't talk about enough is his ability to contain the edge and be a run stopper. Throughout practice, he was working on beating double teams and sifting through offensive linemen. Another facet of his game, he is fine-tuning before his final high school season. Green is one of the most complete players you will find in the 2027 cycle, regardless of position. He has a very polished game.show more

Notes from today’s #Bengals practice: - Overall, another winning... day for the defense, but not as conclusive as recent practices - Cashius Howell popped in a big way on a couple rushes off the edge against Orlando Brown using both speed and power. Also blew by Andrew Coker for a would-be sack. Probably his best practice to date. The fact Zac Taylor referenced him having the same tenacity as Erick All tells you what you need to know about he’s viewed. - Vet day for Chase meant extra passes for Tee Higgins. Hit some, but still off at times, working to get their connection down (nobody should be worried about this). - Ugly drop by Andrei Iosivas to close practice on a crosser. A lot to like about where Iosivas is at, but he just has to eliminate those moments from his game or he’ll be watching Colbie Young before long. - Offense had been very good operationally this camp, but a delay of game and false start showed up today. -Dax Hill still limited, DJ Ivey and DJ Turner both with PBUs. 📽️ Jalen Davis with a nice play on third down with a PBU -Colbie Young had a drop, but also the recipient of one of Burrow’s best balls of the day running a seam between Knight at Battle.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

Created with seedance 2.0 on Pollo AI Duration: 15... Seconds Style: Live-action + ultra-photoreal miniature human composite comedy short film. First-person breakfast POV vlog. 8K Ultra HD, vertical 9:16, handheld camera with subtle natural movement, cinematic shallow depth of field. Every object is completely real: wooden breakfast table, giant fluffy pancakes, maple syrup, butter, blueberries, coffee mug, flowers, plants, and the miniature woman. Everything shares identical photographic lighting, reflections, realistic textures, and contact shadows. Bright outdoor apartment balcony during golden morning sunlight with a soft breeze moving nearby curtains and plants. Character: Aria Lin — a 22-year-old adult East Asian woman, exactly 15 cm tall. Mature facial features, fair skin with realistic pores, deep brown almond eyes, black waist-length hair with fluffy air bangs and one loose braid over her left shoulder. Wearing a fitted white sleeveless tank top, fitted blue jeans, white thick-soled sneakers, and a thin gold bracelet on her right wrist. Same face, hairstyle, outfit, proportions and size throughout. No CGI, no cartoon, no doll, no figurine. ──────────────────────── 00:00–00:03 | Shot 1 – Pancake Summit First-person breakfast POV. A giant stack of fluffy pancakes towers above the breakfast table like a mountain. Aria uses a tiny dessert fork as a climbing tool, carefully climbing the soft pancake edge. She reaches the top, looks around proudly at the giant breakfast landscape, then raises both arms with a victorious smile as warm sunlight illuminates her face. SFX: Morning birds, gentle breeze, soft ambience. ──────────────────────── 00:03–00:06 | Shot 2 – Maple Syrup River A real human gently pours warm maple syrup from above. The syrup slowly flows around her like a beautiful golden river. Aria instantly jumps onto a fresh strawberry slice and rides it like a tiny surfboard, gliding smoothly along the flowing syrup while laughing. Golden reflections shimmer across the syrup. SFX: Maple syrup pouring. Soft flowing liquid. ──────────────────────── 00:06–00:09 | Shot 3 – Butter Ice Slide A giant butter cube slowly slides across the warm pancake. Aria quickly jumps onto the butter and slides across its smooth surface like an ice skater. She spins once playfully before landing perfectly with both arms stretched outward. The butter leaves a glossy trail behind. SFX: Soft sliding. Light laughter. ──────────────────────── 00:09–00:12 | Shot 4 – Blueberry Bounce A giant blueberry gently rolls toward her. Instead of running away, she hops onto the blueberry. The blueberry bounces naturally across the pancake while she balances with both arms and laughs like she's riding a giant adventure ball. The camera follows smoothly with cinematic motion. SFX: Soft bouncing. Happy laughter. ──────────────────────── 00:12–00:15 | Shot 5 – Breakfast Champion A real human gently places a shiny coffee spoon beside the pancake. Aria confidently climbs onto the spoon. She raises her tiny dessert fork like a victory trophy. She waves happily toward the camera. Makes a playful "OK" hand gesture. Smiles brightly. Morning sunlight glows behind her while flowers gently sway in the background. Final 0.3 seconds: Freeze-frame with perfect scale contrast between the tiny chef, giant spoon, pancake mountain, blueberries, maple syrup, and warm golden morning light. SFX: Soft magical chime. Morning ambience fades naturally. ──────────────────────── Strict Requirements • Same adult woman throughout. • Height remains exactly 15 cm. • Same face, hairstyle, outfit and proportions. • Full photorealism only. • Outdoor breakfast balcony environment only. • Realistic sunlight, shadows, reflections and contact shadows. • Smooth cinematic camera transitions.show more

Seedance 2 on TapNow Prompt: **CAMERA:** DV 16mm tape... camcorder handheld feel. POV of CHASE holding the camera herself throughout each location, occasionally propped briefly for hands-free moments. Hand shake, misaligned framing, delayed focus pulls, clumsy zooms, occasional face cut-off framing, imperfect shots. Camcorder never appears on screen. **LOOK:** Soft, slightly blurry tape quality, faint tape noise, bloomed highlights, flickering auto-exposure, muted contrast, realistic skin tones — lighting shifts naturally per location (warm dorm light → cool van light → studio fluorescents → bright stage lights). **STYLE:** Fast-paced time-lapse montage feel — quick cuts stitched together across locations, sped-up transitional movement between them. Instead of synced dialogue, her voice plays as reflective voiceover narration over the visuals, tying the day together. Energy builds progressively from sleepy morning to high-adrenaline stage finish. **Character** CHASE — Korean idol, 20s. Long straight black hair, elegant yet lovely Korean features, dewy glass skin, coral pink lips, big eyes. Outfit changes naturally per location: cozy loungewear at the dorm, casual comfortable clothes in the van, modest athletic wear in the practice room, and a stage outfit for the final segment — each fully covering arms and torso. **Setting Progression** Dorm room (morning) → van interior (daytime) → practice room (afternoon) → backstage/stage (night). **Storyboard (voiceover narration over visuals)** 1. *(~2s, dorm, propped camera, sleepy morning light)* She stretches, rubbing her eyes, hair messy. VOICEOVER (CHASE): "Every day starts the same way — way too early." 2. *(~2s, dorm, handheld, quick motion)* She moves through her morning routine in fast, sped-up cuts — brushing hair, grabbing a bag. VOICEOVER (CHASE): "Get ready, grab everything, and go." 3. *(~2s, van interior, handheld, window light)* She sits by the window, phone in hand, soft daylight passing over her face. VOICEOVER (CHASE): "The van's basically my second home at this point." 4. *(~1.5s, van, macro insert)* Close-up on her hand adjusting a playlist on her phone, light flickering through the window. No narration — ambient road sound only. 5. *(~2s, practice room, handheld, energetic)* She's mid-movement rehearsing choreography, camera catching quick glimpses of the mirror wall. VOICEOVER (CHASE): "Then it's hours of practice till my legs give out." 6. *(~2s, practice room, propped camera, water break)* She wipes sweat, drinks water, breathless but smiling. VOICEOVER (CHASE): "But somehow I never get tired of this part." 7. *(~2s, backstage, handheld, quick transition)* Hair and makeup blur past in quick cuts, staff moving around her, adrenaline building. VOICEOVER (CHASE): "And then suddenly, it's showtime." 8. *(~2s, stage, wide-to-close, high energy finish)* Bright stage lights, her silhouette stepping out as the camera catches a final glimpse before cutting to black. VOICEOVER (CHASE): "This is the part that makes all of it worth it."show more

Seedance 2.5 is astonishing when it comes to realism... 😱 Prompt: **CAMERA:** DV 16mm tape camcorder handheld feel. POV of CHASE holding the camera herself throughout each location, occasionally propped briefly for hands-free moments. Hand shake, misaligned framing, delayed focus pulls, clumsy zooms, occasional face cut-off framing, imperfect shots. Camcorder never appears on screen. **LOOK:** Soft, slightly blurry tape quality, faint tape noise, bloomed highlights, flickering auto-exposure, muted contrast, realistic skin tones — lighting shifts naturally per location (warm dorm light → cool van light → studio fluorescents → bright stage lights). **STYLE:** Fast-paced time-lapse montage feel — quick cuts stitched together across locations, sped-up transitional movement between them. Instead of synced dialogue, her voice plays as reflective voiceover narration over the visuals, tying the day together. Energy builds progressively from sleepy morning to high-adrenaline stage finish. **Character** CHASE — Korean idol , 20s. Long straight black hair, elegant yet lovely Korean features, dewy glass skin, coral pink lips, big eyes. Outfit changes naturally per location: cozy loungewear at the dorm, casual comfortable clothes in the van, modest athletic wear in the practice room, and a stage outfit for the final segment — each fully covering arms and torso. **Setting Progression** Dorm room (morning) → van interior (daytime) → practice room (afternoon) → backstage/stage (night). **Storyboard (voiceover narration over visuals)** 1. *(~2s, dorm, propped camera, sleepy morning light)* She stretches, rubbing her eyes, hair messy. VOICEOVER (CHASE): "Every day starts the same way — way too early." 2. *(~2s, dorm, handheld, quick motion)* She moves through her morning routine in fast, sped-up cuts — brushing hair, grabbing a bag. VOICEOVER (CHASE): "Get ready, grab everything, and go." 3. *(~2s, van interior, handheld, window light)* She sits by the window, phone in hand, soft daylight passing over her face. VOICEOVER (CHASE): "The van's basically my second home at this point." 4. *(~1.5s, van, macro insert)* Close-up on her hand adjusting a playlist on her phone, light flickering through the window. No narration — ambient road sound only. 5. *(~2s, practice room, handheld, energetic)* She's mid-movement rehearsing choreography, camera catching quick glimpses of the mirror wall. VOICEOVER (CHASE): "Then it's hours of practice till my legs give out." 6. *(~2s, practice room, propped camera, water break)* She wipes sweat, drinks water, breathless but smiling. VOICEOVER (CHASE): "But somehow I never get tired of this part." 7. *(~2s, backstage, handheld, quick transition)* Hair and makeup blur past in quick cuts, staff moving around her, adrenaline building. VOICEOVER (CHASE): "And then suddenly, it's showtime." 8. *(~2s, stage, wide-to-close, high energy finish)* Bright stage lights, her silhouette stepping out as the camera catches a final glimpse before cutting to black. VOICEOVER (CHASE): "This is the part that makes all of it worth it."show more

1/7 Built a Polymarket trading bot over 3 months.... Here are the biggest mistakes that cost me real money > Went from v1 to v61. Every version fixed something painful. --- 2/7 Stop Loss killed more money than it saved. > Binary markets need room to breathe - fluctuations are normal. > Stop Loss was cutting positions on random noise and locking in losses right before the market flipped. > Removed it in v61. Immediately better. --- 3/7 Martingale + Stop Loss = a loss cascade. > Seemed logical: lost $5 -> bet $8, lost again -> bet $10. > In practice: a losing streak plus early exits = a hole in your balance in a single day. > Killed it. For good. --- 4/7 Smart Exit without Force Exit is a trap. > Token hits 90c (+75% profit), but the bot was waiting for a "BTC reversal" signal. > Market closes, token drops, profit gone. > Fix: hard Force Exit at 85c. No conditions, no waiting. --- 5/7 Blocking the 5:30-10:30 PM ET window felt safe. It wasn't. > NYSE open = sharp spikes = bad signals. Made sense to block it. > But the full block was also killing clean entries at 8-10:30 PM. > Had to split the zone into segments with different edge/move thresholds. --- 6/7 The Gamma API lies about market start time. > Start price ("price to beat") is the core input for every signal. > Gamma was returning stale data. Had to pull prices directly from Chainlink on-chain on Polygon. > That's its own adventure - polling a smart contract every 2 seconds at 2 AM. --- 7/7 The real lesson: don't overcomplicate what works. > v1: complex system, 10 indicators -> -$200/day > v61: "buy the expensive token for $5, exit at +30%" -> consistently green > Simpler logic = fewer failure points. > The bot runs 96 intervals a day. Every mistake shows up fast.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

On the banks of the Danube River in Budapest... 🇭🇺, not far from the Hungarian Parliament building, sit sixty pairs of old-fashioned shoes, the type people wore in the 1940s. There are women's shoes, there are men's shoes and there are children's shoes. They sit at the edge of the water, scattered and abandoned, as though their owners had just stepped out of them and left them there. If you look more closely, you see that the shoes are rusted, made of iron and set into the concrete of the embankment. They are a memorial and a monument to the Hungarian Jews who, in the winter of 1944-1945, were shot on the banks of the Danube River by the members of the Arrow Cross Party. Known as "The Shoes on the Danube Promenade", the memorial was conceptualized by film director Can Togay, and was created by Togay together with the sculptor Gyula Pauer. It was installed on the Pest bank of the Danube River in Budapest in 2005. At three separate places on the memorial, cast iron signs read in Hungarian, English and Hebrew: "To the memory of victims shot into the Danube by Arrow Cross militiamen in 1944-45." During that autumn and winter, after the Germans had toppled the government of Miklos Horthy bringing Ferenc Szálasi and his fascist, violently antisemitic Arrow Cross Party to power, the Arrow Cross introduced a reign of terror in Budapest. The Arrow Cross militiamen ran amok in the streets of Budapest, beating, plundering, and killing Jews publicly. Thousands of Jews were murdered all over the city. Shooting the Jews into the Danube was convenient because the river carried the bodies away. Often, the Arrow Cross murderers would force their terrified Jewish victims to remove their shoes before shooting them into the Danube. Shoes, after all, were a valuable commodity during World War II. The killers could use them, or trade them on the black market. This, then, is the historical reality behind the monument. Sometimes, though, the victims' shoes were so worn-out and useless, that the militiamen killed the Jews with their shoes still on. And sometimes, the Arrow Cross pulled the shoestrings out of children's shoes, and used them to tie the helpless Jewish victims' hands together before they were shot. Sometimes they used rope instead. The killers faced their victims without mercy; the victims faced the killers without blindfolds. In some cases the Arrow Cross men tied together the hands of two or three Jews – adults or children. Then they would shoot only one of the people who were tied together. When they did their work properly and positioned their victims at the edge of the water, all three would fall into the Danube, the dead body pulling the still-living victims with it. All the bodies, tied together by shoelaces or rope or fate, would either sink or float away down the river. If the militiamen noticed that Jews were still alive, they used them for target practice. However, most of the Jews – especially the children – died immediately because the water was freezing cold. During the days of horror in the winter of 1944-1945, the Danube was known as "the Jewish Cemetery." A firsthand account of the horrific scenes along the Danube was presented by Zsuzsanna Ozsváth, a Hungarian survivor who was saved by her nursemaid, Erzsi Fajo. “…I heard a series of popping sounds. Thinking the Russians had arrived, I slunk to the window. But what I saw was worse than anything I had ever seen before, worse than the most frightening accounts I had ever witnessed. Two Arrow Cross men were standing on the embankment of the river, aiming at and shooting a group of men, women and children into the Danube – one after the other, on their coats the Yellow Star. I looked at the Danube. It was neither blue nor gray but red. With a throbbing heart, I ran back to the room in the middle of the apartment and sat on the floor, gasping for air." #archaeohistoriesshow more

Facial reconstruction of a 7,000-year-old man from Monjukli Depe,... Turkmenistan Monjukli Depe is an ancient settlement in southern Turkmenistan, located at the northern edge of the Kopet Dag mountains. Excavations have shown that the site was inhabited from approximately 6200 BC and remained occupied until the beginning of the Copper Age. The inhabitants of the settlement were herders and farmers who raised sheep and goats. Regular communal feasts appear to have been an important part of their lives, as evidenced by the large quantities of animal bones recovered from the central plaza of the settlement. The dwellings consisted of small square mudbrick houses with central pillars supporting the roofs. During five excavation seasons at Monjukli Depe, archaeologists uncovered 15 burials, along with numerous isolated human bones and teeth scattered throughout the settlement. Some burials were located directly beneath houses; in certain cases, they appear to have been placed before construction began, while others were inserted into abandoned but still-standing buildings. This practice was reserved for children and adolescents. Adults, by contrast, were buried in open areas, usually near the walls of structures or beneath the remains of houses that had already collapsed. One elderly woman, for example, was found buried beneath a former doorway. These burials were likely connected to the "life cycle" of houses, either as foundation sacrifices preceding construction or as part of rituals marking the abandonment and closure of a dwelling. The deceased were buried in a flexed position on their right side, accompanied by minimal grave goods and covered with red ochre. The male skull from Burial 4 (the reconstructed individual) at Monjukli Depe is characterized as a Europoid mesocranic type (cranial index 78.0; cranial length 182 mm; cranial breadth 142 mm; cranial height 137 mm) with a very low and broad face (facial height 66 mm; bizygomatic breadth approximately 141 mm; facial index 46.8). Based on the overall facial angle, the skull is mesognathous (83°). The horizontal facial profile is notably flattened (zygomaxillary angle 143.5°), while the nasal bones appear to have projected very strongly. Morphologically, this skull displays similarities to Cro-Magnon-like populations and can be compared to skulls of the Mazandaran cultural horizon from Hotu Cave in northeastern Iran, as well as other Cro-Magnon-like Mesolithic and Upper Paleolithic skulls from the Levant and North Africa. (Ginzburg and Trofimova, 1972) Genetically, the individuals from Monjukli Depe/Anau culture occupied a cline between Iran_N and ANE ancestry. This cluster made a major contribution to the DNA of BMAC and IVC. A sampled individual from the site belonged to Y-chromosome haplogroup L1a2 and mitochondrial haplogroup H14. (Allentoft et al 2024)show more