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Tested Muse Spark 1.2, Kimi K3, and GLM 5.2 with our FlappyBench 3 models, same prompt with the /design command. Reviewed gameplay features, UX/UI, and cost. 🔹 Kimi K3 → 9.5/10 · $0.0740 🔹 GLM 5.2 → 9/10 · $0.0480 🔹 Muse Spark 1.2 → 8/10 · $0.0187 Muse...

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Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive! We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost
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Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive! We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost

atomic.chat

743,937 просмотров • 1 месяц назад

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.
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Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

atomic.chat

70,490 просмотров • 1 месяц назад

Fable 5 totally crushed our new contest, but it cost 6x more than Opus 4.8! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: — A train derailing off a broken bridge into the water — Two cars jumping off ramps and colliding mid-air over a canyon — A monster truck crushing a row of parked cars Outputs: Fable 5: 62,158 tokens, $3.12 GPT 5.5: 37,753 tokens, $1.14 Opus 4.8: 22,280 tokens, $0.56 GLM 5.2: 36,246 tokens, $0.08 Fable 5 did all three scenes at A+. The crashes looked real, things fell and broke the right way, and nothing went through the ground or floated. GPT 5.5 was the closest to Fable. In the Bigfoot show, we think GPT was even a little better. GLM 5.2 did not win any scene, but it was the cheapest by far. Fable is the best pick for quality, but you pay more for it.
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Fable 5 totally crushed our new contest, but it cost 6x more than Opus 4.8! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: — A train derailing off a broken bridge into the water — Two cars jumping off ramps and colliding mid-air over a canyon — A monster truck crushing a row of parked cars Outputs: Fable 5: 62,158 tokens, $3.12 GPT 5.5: 37,753 tokens, $1.14 Opus 4.8: 22,280 tokens, $0.56 GLM 5.2: 36,246 tokens, $0.08 Fable 5 did all three scenes at A+. The crashes looked real, things fell and broke the right way, and nothing went through the ground or floated. GPT 5.5 was the closest to Fable. In the Bigfoot show, we think GPT was even a little better. GLM 5.2 did not win any scene, but it was the cheapest by far. Fable is the best pick for quality, but you pay more for it.

atomic.chat

2,838,124 просмотров • 1 месяц назад

it took us 3 weeks to get 500k+ views with a fully AI UGC on instagram and it taught me something huge if you create good content for long enough the algorithm is very simple it works to keep finding the type of user that engaged with your post similar to paid ads but this only works if you stay within niche and post consistently great content so stop seeing 1,000-4000 views as bad it’s simply taking the time to learn we’re being extremely aggressive with our marketing and created 40+ content variations each A/B testing a different part of the video - hook, music, transitions, wallpapers, workout, location, CTA, captions every single component is being tested we had a huge set back with Meta randomly banning 8 of our accounts last week we got the content ready and aim to post 20x per day minimum by 6/10 each day breaking down the data and creating more of what works then running it on paid ads both tiktok spark ads and meta ads marketing is definitely takes more time but at least shipping took just 2 weeks using Rork Max + Opus 4.7 at the time i launched, there was no other possible way to natively code in SwiftUI
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it took us 3 weeks to get 500k+ views with a fully AI UGC on instagram and it taught me something huge if you create good content for long enough the algorithm is very simple it works to keep finding the type of user that engaged with your post similar to paid ads but this only works if you stay within niche and post consistently great content so stop seeing 1,000-4000 views as bad it’s simply taking the time to learn we’re being extremely aggressive with our marketing and created 40+ content variations each A/B testing a different part of the video - hook, music, transitions, wallpapers, workout, location, CTA, captions every single component is being tested we had a huge set back with Meta randomly banning 8 of our accounts last week we got the content ready and aim to post 20x per day minimum by 6/10 each day breaking down the data and creating more of what works then running it on paid ads both tiktok spark ads and meta ads marketing is definitely takes more time but at least shipping took just 2 weeks using Rork Max + Opus 4.7 at the time i launched, there was no other possible way to natively code in SwiftUI

Jah Mills

92,243 просмотров • 2 месяцев назад

I experimented a lot with the prompt until I landed on this version😎 Only face reference + exact 15-second audio. Generated in Higgsfield AI 🧩 Seedance 2.0. Small tip: when uploading reference audio, cut it precisely to whole seconds (6, 8, 10, 15…) with no leftover tails 🎵 Prompt: A cinematic 15-second music video shot in a dark modern dance studio with smooth grey reflective floor, black walls and horizontal neon light tubes. Continuous dynamic camera movement, mostly medium shots and close-ups, never too wide. The woman is constantly moving, no freezes or static poses. Main subject: young woman with messy medium-length wavy brown hair with bangs partially covering her face, freckles on nose and cheeks, blue-grey eyes, full glossy lips. She wears a tight black fishnet bodysuit. She is singing the entire time with clear, precise lip-sync, mouth actively moving, intense emotional expression. Lyrics: "Ye! Ye! You keep a box of names / In the drawer by your bed / Polaroids and ticket stubs / Stuffed under the red / You never take one thing / You take the whole last spark / Leave a little thumbprint / On every private heart". 0-2s: Tight medium close-up. She leans her upper body back, head tilting, singing passionately with strong lip-sync, hair falling over her face, body arching, one hand sliding across her chest. 2-4s: Camera slowly pushes in and circles. She comes out of the deep arch, torso still bent forward, hands on her thighs, lifting her head and looking straight into camera while singing with aggressive lip-sync. Three male dancers of different appearances (different ethnicities, hair styles and builds) wearing black tank tops and black wide pants are already close around her, moving with her in low tense postures. The other two men are visible at the edges of the frame, approaching. 4-7s: Medium close-up. She drops lower, body still in constant motion, hair swinging, singing intensely with clear mouth movements, sharp head turns, eyes locked on camera. Male dancers stay close, their hands lightly touching her as they move together. 8-12s: Medium shot with slow camera drift. Exactly five male dancers of completely different appearances, all wearing black tank tops and black wide pants, surround her tightly on the floor in a dense, intertwined formation. She is in the center, body still moving, upper body rising and shifting, singing with strong lip-sync. All five men move subtly with her, never static. The men never fully obscure her body. 13-15s: Dynamic medium shot. The five diverse male dancers lift her into the air in a powerful deep backbend. Her body is fully extended and arched, head thrown back, still singing with clear lip-sync. While holding her they gently rock her up and down in time with the beat. The camera starts from a clear side view of her arched body and smoothly transitions to a frontal view of her face. At the end they lower her smoothly onto her feet; she lands and immediately continues singing as the five men stay low on the floor around her. The men never fully obscure her. High fashion dance energy, sweaty skin, sharp timing, continuous fluid motion of the woman, priority on accurate lip-sync in every frame. Rules: no hand morphing, no body distortions, clean stable anatomy, fingers and hands remain consistent and natural throughout the entire video. #AIvideo #AIMusicVideo #AIFilmmaking
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I experimented a lot with the prompt until I landed on this version😎 Only face reference + exact 15-second audio. Generated in Higgsfield AI 🧩 Seedance 2.0. Small tip: when uploading reference audio, cut it precisely to whole seconds (6, 8, 10, 15…) with no leftover tails 🎵 Prompt: A cinematic 15-second music video shot in a dark modern dance studio with smooth grey reflective floor, black walls and horizontal neon light tubes. Continuous dynamic camera movement, mostly medium shots and close-ups, never too wide. The woman is constantly moving, no freezes or static poses. Main subject: young woman with messy medium-length wavy brown hair with bangs partially covering her face, freckles on nose and cheeks, blue-grey eyes, full glossy lips. She wears a tight black fishnet bodysuit. She is singing the entire time with clear, precise lip-sync, mouth actively moving, intense emotional expression. Lyrics: "Ye! Ye! You keep a box of names / In the drawer by your bed / Polaroids and ticket stubs / Stuffed under the red / You never take one thing / You take the whole last spark / Leave a little thumbprint / On every private heart". 0-2s: Tight medium close-up. She leans her upper body back, head tilting, singing passionately with strong lip-sync, hair falling over her face, body arching, one hand sliding across her chest. 2-4s: Camera slowly pushes in and circles. She comes out of the deep arch, torso still bent forward, hands on her thighs, lifting her head and looking straight into camera while singing with aggressive lip-sync. Three male dancers of different appearances (different ethnicities, hair styles and builds) wearing black tank tops and black wide pants are already close around her, moving with her in low tense postures. The other two men are visible at the edges of the frame, approaching. 4-7s: Medium close-up. She drops lower, body still in constant motion, hair swinging, singing intensely with clear mouth movements, sharp head turns, eyes locked on camera. Male dancers stay close, their hands lightly touching her as they move together. 8-12s: Medium shot with slow camera drift. Exactly five male dancers of completely different appearances, all wearing black tank tops and black wide pants, surround her tightly on the floor in a dense, intertwined formation. She is in the center, body still moving, upper body rising and shifting, singing with strong lip-sync. All five men move subtly with her, never static. The men never fully obscure her body. 13-15s: Dynamic medium shot. The five diverse male dancers lift her into the air in a powerful deep backbend. Her body is fully extended and arched, head thrown back, still singing with clear lip-sync. While holding her they gently rock her up and down in time with the beat. The camera starts from a clear side view of her arched body and smoothly transitions to a frontal view of her face. At the end they lower her smoothly onto her feet; she lands and immediately continues singing as the five men stay low on the floor around her. The men never fully obscure her. High fashion dance energy, sweaty skin, sharp timing, continuous fluid motion of the woman, priority on accurate lip-sync in every frame. Rules: no hand morphing, no body distortions, clean stable anatomy, fingers and hands remain consistent and natural throughout the entire video. #AIvideo #AIMusicVideo #AIFilmmaking

Kiber Alla

65,345 просмотров • 1 месяц назад

AUKUS, Mistakes and Opportunities In 2016, Japan offered Australia state-of-the-art, diesel-electric, ultra-quiet submarines with the option of local production at the Henderson shipyard. The Australian government rejected the proposal, claiming its goal was always nuclear-powered submarines. Instead, Australia decided to spend roughly A$4-5 billion extending the life of its ageing Collins-class fleet until the 2040s . enough money to have bought seven-eight Japanese Taigei-class submarines outright. If that’s really what the government wanted, the Americans and British certainly sent them the bill for AUKUS. Australia is footing almost the entire cost: A$368 billion over three decades. - The United States receives US$3 billion from Australia to expand its industrial base, build more Virginia-class submarines, and then sells 3–5 second-hand boats back to Canberra. - The United Kingdom receives around £2.4 billion from Australia for design and infrastructure work, shares some development costs, and ends up using the exact same SSN-AUKUS design for its own future fleet at essentially no extra cost. I’m genuinely intrigued by how they managed to sell the Australians on this deal. I’d love to meet and congratulate the American and British negotiators – true sales geniuses. Nuclear submarines must have been a childhood dream of that Australian government; there’s no other explanation. But the problems don’t end there. Just as the Americans have cancelled over 300 programmes and thrown away more than US$200 billion in the last 20 years, the British have serious and very recent issues with their own naval projects. It feels like a structural disease in the Western defence industry. - The Astute programme is more than a decade late, costs have tripled, only 5 of the planned 7 boats have been delivered, and engineering problems keep cropping up. - The Dreadnought class (replacement for the Vanguard ballistic-missile submarines) has ballooned by billions and is now delayed well beyond 2030 because of failures integrating propulsion systems and Trident missiles. - And the crown jewels – the Queen Elizabeth and Prince of Wales aircraft carriers – are operational but chronically short of compatible F-35s and cost a staggering £10 billion in overruns. - The Type 45 destroyers suffered catastrophic electrical failures that left them inoperable for years, and the Type 26 frigate programme has been repeatedly cut back, reflecting completely misplaced priorities. And a programme that is supposed to deliver eight submarines to Australia sometime around 2050–2060 is extremely unlikely to proceed as planned, not only because of budgets and operational complications, but because underwater drones are evolving fast and China is leading that race. The Americans and British have a long naval history, but they are also visionaries who understand perfectly well that the future lies in decentralisation: swarms of UUVs, lithium or solid battery submarines, or even small nuclear-powered ones using micro-reactors. These platforms cost 10–20 % of today’s conventional SSNs to maintain, are lighter, and leave far more internal volume for weapons – meaning smaller, cheaper, and more heavily armed submarines. And what does Australia get left with? Far more than just a submarine partnership with Japan – an entire security ecosystem. By 2026-2028, Japan plans to have the HVPG hypersonic glide vehicles fully operational with ranges up to 2,000 km. Their upgraded Type 12 missiles will reach 1,000–1,500 km and can be launched from ships, aircraft, and land batteries. This is enough to cover and protect the entire Australian coastline for thousands of kilometers. And finally, a 3,000 km-range hypersonic missile is being integrated into the Taigei-class and its successor. That arsenal is far beyond anything currently fielded by any Western nation and only Russia and China have comparable systems.
0:17

AUKUS, Mistakes and Opportunities In 2016, Japan offered Australia state-of-the-art, diesel-electric, ultra-quiet submarines with the option of local production at the Henderson shipyard. The Australian government rejected the proposal, claiming its goal was always nuclear-powered submarines. Instead, Australia decided to spend roughly A$4-5 billion extending the life of its ageing Collins-class fleet until the 2040s . enough money to have bought seven-eight Japanese Taigei-class submarines outright. If that’s really what the government wanted, the Americans and British certainly sent them the bill for AUKUS. Australia is footing almost the entire cost: A$368 billion over three decades. - The United States receives US$3 billion from Australia to expand its industrial base, build more Virginia-class submarines, and then sells 3–5 second-hand boats back to Canberra. - The United Kingdom receives around £2.4 billion from Australia for design and infrastructure work, shares some development costs, and ends up using the exact same SSN-AUKUS design for its own future fleet at essentially no extra cost. I’m genuinely intrigued by how they managed to sell the Australians on this deal. I’d love to meet and congratulate the American and British negotiators – true sales geniuses. Nuclear submarines must have been a childhood dream of that Australian government; there’s no other explanation. But the problems don’t end there. Just as the Americans have cancelled over 300 programmes and thrown away more than US$200 billion in the last 20 years, the British have serious and very recent issues with their own naval projects. It feels like a structural disease in the Western defence industry. - The Astute programme is more than a decade late, costs have tripled, only 5 of the planned 7 boats have been delivered, and engineering problems keep cropping up. - The Dreadnought class (replacement for the Vanguard ballistic-missile submarines) has ballooned by billions and is now delayed well beyond 2030 because of failures integrating propulsion systems and Trident missiles. - And the crown jewels – the Queen Elizabeth and Prince of Wales aircraft carriers – are operational but chronically short of compatible F-35s and cost a staggering £10 billion in overruns. - The Type 45 destroyers suffered catastrophic electrical failures that left them inoperable for years, and the Type 26 frigate programme has been repeatedly cut back, reflecting completely misplaced priorities. And a programme that is supposed to deliver eight submarines to Australia sometime around 2050–2060 is extremely unlikely to proceed as planned, not only because of budgets and operational complications, but because underwater drones are evolving fast and China is leading that race. The Americans and British have a long naval history, but they are also visionaries who understand perfectly well that the future lies in decentralisation: swarms of UUVs, lithium or solid battery submarines, or even small nuclear-powered ones using micro-reactors. These platforms cost 10–20 % of today’s conventional SSNs to maintain, are lighter, and leave far more internal volume for weapons – meaning smaller, cheaper, and more heavily armed submarines. And what does Australia get left with? Far more than just a submarine partnership with Japan – an entire security ecosystem. By 2026-2028, Japan plans to have the HVPG hypersonic glide vehicles fully operational with ranges up to 2,000 km. Their upgraded Type 12 missiles will reach 1,000–1,500 km and can be launched from ships, aircraft, and land batteries. This is enough to cover and protect the entire Australian coastline for thousands of kilometers. And finally, a 3,000 km-range hypersonic missile is being integrated into the Taigei-class and its successor. That arsenal is far beyond anything currently fielded by any Western nation and only Russia and China have comparable systems.

Patricia Marins

86,068 просмотров • 8 месяцев назад

Just finished a one-week trip to China. I've now "survived" all the major (~20) L2 self-driving and robotaxi vehicles in both the US and China. Some thoughts & observations: ▶️L2 self-driving I tested major brands like $Huawei, $Li, $NIO, $Xpeng, and $Xiaomi. Overall, they exceeded my expectations. The rides were not overly cautious and handled complex situations (yes, road conditions in China are very challenging!) quite well. Nothing compares to $Tsla's approach. I see imitation learning/end-to-end as the only effective approach for self-driving. While Chinese peers perform well on main roads, they struggle on frontage roads due to reliance on high-precision maps and rule-based methods (e.g. cars stopped in the middle of the road where there was no clear white lining). Chinese EVs' self-driving capabilities are far ahead of those from US and EU brands. I doubt any Chinese players can profit from L2 self-driving, not because it’s not useful, but because it’s hard to differentiate, and price wars dominate the market in China. Chinese consumers and regulators seem much more receptive to self-driving. Even with a 5/10 self-driving capability, cars are practically *hands-free(!)* Insurance-wise, for L3+ cars, OEMs bear responsibility for incidents, so OEMs avoid labeling cars as L3+. ▶️Robotaxi I tested major brands like $Didi, and $Bidu. I'd rate equal to $Waymo, and it's ahead of other peers. However, the same issue applies here: user experience is nearly perfect (in Yizhuang, Beijing), but expansion is the real question. Chinese robotaxi companies are very sophisticated. While the rest of the world focuses on technology, Chinese peers treat it as a product, considering unit economics, operations, mass production, etc. Interestingly, most companies expressed a preference NOT to operate fleets themselves. They aim to be asset-light and let fleet managers handle operations. Policy Support: China has a very clear approval process, driven by data (autonomous driving distance, fully driverless distance, intervention rate, passenger ratings, etc.). ▶️Chinese EVs In major cities like Beijing or Shanghai, EV adoption (green license plates vs. gas cars with blue license plates) seems to be 40%+. If 40% of cars on the road are EVs, then EV penetration (defined as the % of new car sales) must already be over 50%. In shopping malls, the ground floor is filled with EV showrooms—easily 10+ brands, many of which are unfamiliar Chinese brands. It appears almost too easy to make an electric car, which is a stark contrast to the US. $Xiaomi, for example, can achieve a 10% gross profit margin in its first year of operation, compared to $RIVN's -45%. Additionally, $Xiaomi cars are priced at 30% of $RIVN's price. It's fascinating to see how China transitioned from "couldn't make their own gas cars at all (only JVs)" to "dominating EVs globally." The government deserves credit for setting the direction and executing effectively. China now controls the entire supply chain, with $CATL holding 40% of the global market share. 🔹How did it happen? The success of the industry Incentives were set just right: the government provided incentives early on to make EVs and gas cars have comparable MSRPs, allowing consumers to choose based on functionality. This approach differs from how the IRA offers incentives... Perfectly competitive market: $TSLA was brought in, and competition was welcomed, unlike the US, which has a 100% import tax on Chinese EVs. Strategic regulations: License plate restrictions were used effectively; for example, taxis and minivans are required to be EVs. 🔹The challenges Despite the success, the industry faces challenges with low-margin companies and struggling stocks. The intense competition shows no sign of ending. Well-funded global OEMs and Chinese state-owned car companies continue to subsidize, leading to new EV brands emerging annually. The natural tendency in China is to race to the bottom. I think this ties back to China's history as the "world’s factory," where manufacturers price products at "cost plus" versus the US and developing countries, which price based on "affordability/value creation." 🔹The wow EV feature >Software features that surprised me the most: - Everything in the car can be voice-controlled. Not just simple tasks like playing music; users can adjust the height of the steering wheel and set the temperature easily. - Self-parking, which $Tsla has yet to release to all FSD users, is already a table stake in China (I'd rate the quality as 10/10). >Other fun hardware features: - Mini fridges in the car - Infotainment systems - IoT: remote access the car/home via cellphone - all connected together - Heads-up displays - UV-protected glass roofs: $Xiaomi took $Tsla's design, but the glass roof of the $Xiaomi car is made of double layers with silver, blocking 99.9% of UV and infrared rays...as a result, heat is no longer a problem inside the car
0:31

Just finished a one-week trip to China. I've now "survived" all the major (~20) L2 self-driving and robotaxi vehicles in both the US and China. Some thoughts & observations: ▶️L2 self-driving I tested major brands like $Huawei, $Li, $NIO, $Xpeng, and $Xiaomi. Overall, they exceeded my expectations. The rides were not overly cautious and handled complex situations (yes, road conditions in China are very challenging!) quite well. Nothing compares to $Tsla's approach. I see imitation learning/end-to-end as the only effective approach for self-driving. While Chinese peers perform well on main roads, they struggle on frontage roads due to reliance on high-precision maps and rule-based methods (e.g. cars stopped in the middle of the road where there was no clear white lining). Chinese EVs' self-driving capabilities are far ahead of those from US and EU brands. I doubt any Chinese players can profit from L2 self-driving, not because it’s not useful, but because it’s hard to differentiate, and price wars dominate the market in China. Chinese consumers and regulators seem much more receptive to self-driving. Even with a 5/10 self-driving capability, cars are practically *hands-free(!)* Insurance-wise, for L3+ cars, OEMs bear responsibility for incidents, so OEMs avoid labeling cars as L3+. ▶️Robotaxi I tested major brands like $Didi, and $Bidu. I'd rate equal to $Waymo, and it's ahead of other peers. However, the same issue applies here: user experience is nearly perfect (in Yizhuang, Beijing), but expansion is the real question. Chinese robotaxi companies are very sophisticated. While the rest of the world focuses on technology, Chinese peers treat it as a product, considering unit economics, operations, mass production, etc. Interestingly, most companies expressed a preference NOT to operate fleets themselves. They aim to be asset-light and let fleet managers handle operations. Policy Support: China has a very clear approval process, driven by data (autonomous driving distance, fully driverless distance, intervention rate, passenger ratings, etc.). ▶️Chinese EVs In major cities like Beijing or Shanghai, EV adoption (green license plates vs. gas cars with blue license plates) seems to be 40%+. If 40% of cars on the road are EVs, then EV penetration (defined as the % of new car sales) must already be over 50%. In shopping malls, the ground floor is filled with EV showrooms—easily 10+ brands, many of which are unfamiliar Chinese brands. It appears almost too easy to make an electric car, which is a stark contrast to the US. $Xiaomi, for example, can achieve a 10% gross profit margin in its first year of operation, compared to $RIVN's -45%. Additionally, $Xiaomi cars are priced at 30% of $RIVN's price. It's fascinating to see how China transitioned from "couldn't make their own gas cars at all (only JVs)" to "dominating EVs globally." The government deserves credit for setting the direction and executing effectively. China now controls the entire supply chain, with $CATL holding 40% of the global market share. 🔹How did it happen? The success of the industry Incentives were set just right: the government provided incentives early on to make EVs and gas cars have comparable MSRPs, allowing consumers to choose based on functionality. This approach differs from how the IRA offers incentives... Perfectly competitive market: $TSLA was brought in, and competition was welcomed, unlike the US, which has a 100% import tax on Chinese EVs. Strategic regulations: License plate restrictions were used effectively; for example, taxis and minivans are required to be EVs. 🔹The challenges Despite the success, the industry faces challenges with low-margin companies and struggling stocks. The intense competition shows no sign of ending. Well-funded global OEMs and Chinese state-owned car companies continue to subsidize, leading to new EV brands emerging annually. The natural tendency in China is to race to the bottom. I think this ties back to China's history as the "world’s factory," where manufacturers price products at "cost plus" versus the US and developing countries, which price based on "affordability/value creation." 🔹The wow EV feature >Software features that surprised me the most: - Everything in the car can be voice-controlled. Not just simple tasks like playing music; users can adjust the height of the steering wheel and set the temperature easily. - Self-parking, which $Tsla has yet to release to all FSD users, is already a table stake in China (I'd rate the quality as 10/10). >Other fun hardware features: - Mini fridges in the car - Infotainment systems - IoT: remote access the car/home via cellphone - all connected together - Heads-up displays - UV-protected glass roofs: $Xiaomi took $Tsla's design, but the glass roof of the $Xiaomi car is made of double layers with silver, blocking 99.9% of UV and infrared rays...as a result, heat is no longer a problem inside the car

Freda Duan

399,004 просмотров • 2 лет назад

Not every hero wears a cape. Some just share their bread. Created with Seedance 2.0 + GPT Image 2 on OpenArt Prompt: TITLE The Piece of Bread REFERENCE Use the provided combined character sheet and storyboard board as the main visual reference. Follow the same woman design, stray cat design, bread, sidewalk, low wall, cloth shoulder bag, water bottle, warm sunset lighting, and emotional story beats. Keep the woman and cat visually consistent in every shot. Do not add extra characters. Do not change the core story. SUBJECTS Woman: A young woman in her early 20s with shoulder-length slightly messy dark brown hair loosely tied back, a soft oval face, gentle expressive anime eyes, and a tired but kind expression. She wears a faded oversized hoodie, loose trousers, worn sneakers, and carries a simple cloth shoulder bag. She appears hungry, humble, compassionate, and resilient. Her acting should remain subtle, natural, and emotional. Cat: A small original stray cat with short charcoal-gray fur, a cream-colored chest and paws, one ear with a tiny notch, a long curved tail, and expressive anime-style eyes. The cat feels timid, hungry, hopeful, innocent, and lovable. It begins cautious and hungry, then gradually becomes trusting and comforted. Bread: One small round bread bun. This is the central story object. The woman breaks it into two pieces and shares one half with the cat. ENVIRONMENT Quiet city sidewalk at sunset. Low concrete wall behind them. Soft blurred road and distant buildings in the background. A simple cloth shoulder bag and a small water bottle placed beside the woman. Warm golden-hour light with long shadows. Peaceful, lonely, emotional atmosphere. STYLE 2D Japanese anime short film with a hand-drawn aesthetic. Clean inked outlines, flat-to-soft cel shading, simplified shadow blocks, no CG or 3D rendering. Soft emotional storytelling. Warm golden-hour lighting with painterly anime backgrounds. Expressive anime eyes with subtle, believable facial acting. Gentle cinematic movement with a traditional anime animation feel. No 3D render. No CGI. No Pixar-style shading. No photorealism. No comedy. No chaos. No copyrighted characters. No text. No subtitles. No logos. No social media UI. No background music—only natural ambient sound effects. CAMERA 16:9 cinematic framing. Use close-ups and medium shots to emphasize emotion. End with one wide cinematic shot. Slow push-ins and gentle cuts. Shallow depth of field. Keep both characters clear and expressive. Avoid fast movement or exaggerated actions. TIMELINE 0:00–0:02 Extreme close-up. The woman slowly lifts a small bread bun toward her mouth. She is about to take a bite. Warm sunset light softly illuminates her face and hands. Her expression shows hunger, exhaustion, and quiet resilience. She pauses just before eating. SFX: quiet street ambience, soft breathing, gentle hand movement. --- 0:02–0:04 Medium shot from the woman's side. A small stray cat sits a few feet away on the sidewalk. The cat gazes at the bread with sad, hopeful eyes. It remains still, timid, and cautious. The woman notices the cat and slowly lowers the bread. SFX: soft cat meow, light breeze, distant city ambience. --- 0:04–0:06 Close-up of the woman's hands. She slowly breaks the bread into two pieces. Tiny crumbs fall gently. The moment feels like an important emotional decision. Her hands pause briefly after splitting the bread. SFX: soft bread tearing, tiny crumbs falling. --- 0:06–0:08 Medium side shot. The woman gently extends one half of the bread toward the cat. The cat looks at the bread, then into the woman's eyes. It is nervous but curious. The woman gives a soft, reassuring smile and keeps her hand perfectly still. SFX: gentle hand movement, cat sniffing, quiet breeze. --- 0:08–0:10 Low close shot near the cat. The cat slowly steps forward. It carefully takes the bread from the woman's hand. The woman remains calm and gentle. The cat begins eating, and its expression gradually softens into trust. SFX: tiny paw steps, soft bite, gentle chewing. --- 0:10–0:12 Medium shot. The woman sits comfortably on the sidewalk. The cat comes closer and sits beside her. She gently strokes the cat's head. The cat leans into her hand and relaxes. The moment feels warm, peaceful, and safe. SFX: soft fur brushing, content cat purring, distant street ambience. --- 0:12–0:14 Close emotional shot. The cat rests its head on the woman's lap. She looks down with a warm, slightly bittersweet smile. She still holds her own half of the bread in her other hand. Both appear comforted, no longer feeling completely alone. SFX: quiet breathing, soft breeze, distant city sounds. --- 0:14–0:15 Wide sunset shot from behind. The woman and the cat sit side by side facing the glowing sunset. Their long shadows stretch across the sidewalk. The cloth shoulder bag and water bottle rest nearby. The final frame feels peaceful, hopeful, and heartwarming. SFX: soft wind, distant street ambience, gentle satisfied cat purr. Storyboard Prompt: Create a single horizontal animation pre-production board for an original emotional 2D anime-style short film titled "The Piece of Bread." The output must be one image only and combine: 1. a character design sheet 2. a hand-drawn storyboard page. IMPORTANT Do not make the woman or cat resemble any reference screenshots or existing characters. Keep the same emotional story concept, but create completely original character designs, unique silhouettes, and distinct facial features. No copyrighted characters or close resemblance to any existing animated films or anime. STYLE Professional anime production board. Hand-drawn storyboard style with loose pencil sketch lines, light gray shading, red panel borders, blue motion arrows, and short handwritten production notes. Rendered with anime-inspired linework featuring clean inked outlines, expressive eyes, and simplified shading blocks. It should look like a real animation studio planning sheet, not a polished final illustration. LAYOUT Clean horizontal 16:9 board divided into two sections. SECTION A: CHARACTER SHEET Show both characters consistently. Woman A young woman in her early 20s with shoulder-length slightly messy dark brown hair tied loosely at the back, a soft oval face, gentle expressive anime eyes, and a tired but kind expression. She wears a faded oversized hoodie, loose trousers, worn sneakers, and carries a simple cloth shoulder bag. She should appear humble, exhausted, compassionate, and resilient. Show: front view side view 3/4 view expressions: hungry, thoughtful, soft smile, emotional pose holding a small bread bun pose offering bread Cat A small original stray cat with short charcoal-gray fur, a cream-colored chest and paws, slightly oversized anime-style eyes, one ear with a tiny notch, and a long curved tail. The cat should feel timid, hungry, hopeful, innocent, and lovable. Show: front view side view 3/4 view expressions: sad, shy, hopeful, happy, trusting sitting pose taking bread pose cuddling beside the woman Include tiny handwritten notes and a few small color swatches. SECTION B: STORYBOARD Create 8 cinematic storyboard panels arranged neatly in a grid. Keep character designs consistent throughout. Each panel should include simple handwritten shot notes and blue arrows indicating motion. STORY BEATS 1. Close-up of the woman about to take a bite from a small bread bun during sunset. 2. Medium shot of the hungry stray cat sitting nearby, staring at the bread with sad, hopeful eyes. 3. Close-up of the woman breaking the bread into two pieces as crumbs fall. 4. Medium shot of the woman offering one piece to the cat. 5. Close shot of the cat cautiously stepping forward and taking the bread. 6. Medium shot of the cat sitting beside the woman while she gently pets it. 7. Emotional close-up of the cat resting its head on the woman's lap. 8. Wide sunset shot from behind, showing the woman and cat sitting together with a cloth bag and water bottle beside them. ENVIRONMENT Quiet city sidewalk beside a low wall, soft urban background, cloth shoulder bag, small water bottle, warm golden sunset lighting, peaceful atmosphere, and an emotional mood. FINAL GOAL Create a dense, clean, production-ready anime pre-production board combining a character design sheet and storyboard in a single 16:9 horizontal image, conveying a heartfelt story of compassion and kindness through completely original character designs.
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Not every hero wears a cape. Some just share their bread. Created with Seedance 2.0 + GPT Image 2 on OpenArt Prompt: TITLE The Piece of Bread REFERENCE Use the provided combined character sheet and storyboard board as the main visual reference. Follow the same woman design, stray cat design, bread, sidewalk, low wall, cloth shoulder bag, water bottle, warm sunset lighting, and emotional story beats. Keep the woman and cat visually consistent in every shot. Do not add extra characters. Do not change the core story. SUBJECTS Woman: A young woman in her early 20s with shoulder-length slightly messy dark brown hair loosely tied back, a soft oval face, gentle expressive anime eyes, and a tired but kind expression. She wears a faded oversized hoodie, loose trousers, worn sneakers, and carries a simple cloth shoulder bag. She appears hungry, humble, compassionate, and resilient. Her acting should remain subtle, natural, and emotional. Cat: A small original stray cat with short charcoal-gray fur, a cream-colored chest and paws, one ear with a tiny notch, a long curved tail, and expressive anime-style eyes. The cat feels timid, hungry, hopeful, innocent, and lovable. It begins cautious and hungry, then gradually becomes trusting and comforted. Bread: One small round bread bun. This is the central story object. The woman breaks it into two pieces and shares one half with the cat. ENVIRONMENT Quiet city sidewalk at sunset. Low concrete wall behind them. Soft blurred road and distant buildings in the background. A simple cloth shoulder bag and a small water bottle placed beside the woman. Warm golden-hour light with long shadows. Peaceful, lonely, emotional atmosphere. STYLE 2D Japanese anime short film with a hand-drawn aesthetic. Clean inked outlines, flat-to-soft cel shading, simplified shadow blocks, no CG or 3D rendering. Soft emotional storytelling. Warm golden-hour lighting with painterly anime backgrounds. Expressive anime eyes with subtle, believable facial acting. Gentle cinematic movement with a traditional anime animation feel. No 3D render. No CGI. No Pixar-style shading. No photorealism. No comedy. No chaos. No copyrighted characters. No text. No subtitles. No logos. No social media UI. No background music—only natural ambient sound effects. CAMERA 16:9 cinematic framing. Use close-ups and medium shots to emphasize emotion. End with one wide cinematic shot. Slow push-ins and gentle cuts. Shallow depth of field. Keep both characters clear and expressive. Avoid fast movement or exaggerated actions. TIMELINE 0:00–0:02 Extreme close-up. The woman slowly lifts a small bread bun toward her mouth. She is about to take a bite. Warm sunset light softly illuminates her face and hands. Her expression shows hunger, exhaustion, and quiet resilience. She pauses just before eating. SFX: quiet street ambience, soft breathing, gentle hand movement. --- 0:02–0:04 Medium shot from the woman's side. A small stray cat sits a few feet away on the sidewalk. The cat gazes at the bread with sad, hopeful eyes. It remains still, timid, and cautious. The woman notices the cat and slowly lowers the bread. SFX: soft cat meow, light breeze, distant city ambience. --- 0:04–0:06 Close-up of the woman's hands. She slowly breaks the bread into two pieces. Tiny crumbs fall gently. The moment feels like an important emotional decision. Her hands pause briefly after splitting the bread. SFX: soft bread tearing, tiny crumbs falling. --- 0:06–0:08 Medium side shot. The woman gently extends one half of the bread toward the cat. The cat looks at the bread, then into the woman's eyes. It is nervous but curious. The woman gives a soft, reassuring smile and keeps her hand perfectly still. SFX: gentle hand movement, cat sniffing, quiet breeze. --- 0:08–0:10 Low close shot near the cat. The cat slowly steps forward. It carefully takes the bread from the woman's hand. The woman remains calm and gentle. The cat begins eating, and its expression gradually softens into trust. SFX: tiny paw steps, soft bite, gentle chewing. --- 0:10–0:12 Medium shot. The woman sits comfortably on the sidewalk. The cat comes closer and sits beside her. She gently strokes the cat's head. The cat leans into her hand and relaxes. The moment feels warm, peaceful, and safe. SFX: soft fur brushing, content cat purring, distant street ambience. --- 0:12–0:14 Close emotional shot. The cat rests its head on the woman's lap. She looks down with a warm, slightly bittersweet smile. She still holds her own half of the bread in her other hand. Both appear comforted, no longer feeling completely alone. SFX: quiet breathing, soft breeze, distant city sounds. --- 0:14–0:15 Wide sunset shot from behind. The woman and the cat sit side by side facing the glowing sunset. Their long shadows stretch across the sidewalk. The cloth shoulder bag and water bottle rest nearby. The final frame feels peaceful, hopeful, and heartwarming. SFX: soft wind, distant street ambience, gentle satisfied cat purr. Storyboard Prompt: Create a single horizontal animation pre-production board for an original emotional 2D anime-style short film titled "The Piece of Bread." The output must be one image only and combine: 1. a character design sheet 2. a hand-drawn storyboard page. IMPORTANT Do not make the woman or cat resemble any reference screenshots or existing characters. Keep the same emotional story concept, but create completely original character designs, unique silhouettes, and distinct facial features. No copyrighted characters or close resemblance to any existing animated films or anime. STYLE Professional anime production board. Hand-drawn storyboard style with loose pencil sketch lines, light gray shading, red panel borders, blue motion arrows, and short handwritten production notes. Rendered with anime-inspired linework featuring clean inked outlines, expressive eyes, and simplified shading blocks. It should look like a real animation studio planning sheet, not a polished final illustration. LAYOUT Clean horizontal 16:9 board divided into two sections. SECTION A: CHARACTER SHEET Show both characters consistently. Woman A young woman in her early 20s with shoulder-length slightly messy dark brown hair tied loosely at the back, a soft oval face, gentle expressive anime eyes, and a tired but kind expression. She wears a faded oversized hoodie, loose trousers, worn sneakers, and carries a simple cloth shoulder bag. She should appear humble, exhausted, compassionate, and resilient. Show: front view side view 3/4 view expressions: hungry, thoughtful, soft smile, emotional pose holding a small bread bun pose offering bread Cat A small original stray cat with short charcoal-gray fur, a cream-colored chest and paws, slightly oversized anime-style eyes, one ear with a tiny notch, and a long curved tail. The cat should feel timid, hungry, hopeful, innocent, and lovable. Show: front view side view 3/4 view expressions: sad, shy, hopeful, happy, trusting sitting pose taking bread pose cuddling beside the woman Include tiny handwritten notes and a few small color swatches. SECTION B: STORYBOARD Create 8 cinematic storyboard panels arranged neatly in a grid. Keep character designs consistent throughout. Each panel should include simple handwritten shot notes and blue arrows indicating motion. STORY BEATS 1. Close-up of the woman about to take a bite from a small bread bun during sunset. 2. Medium shot of the hungry stray cat sitting nearby, staring at the bread with sad, hopeful eyes. 3. Close-up of the woman breaking the bread into two pieces as crumbs fall. 4. Medium shot of the woman offering one piece to the cat. 5. Close shot of the cat cautiously stepping forward and taking the bread. 6. Medium shot of the cat sitting beside the woman while she gently pets it. 7. Emotional close-up of the cat resting its head on the woman's lap. 8. Wide sunset shot from behind, showing the woman and cat sitting together with a cloth bag and water bottle beside them. ENVIRONMENT Quiet city sidewalk beside a low wall, soft urban background, cloth shoulder bag, small water bottle, warm golden sunset lighting, peaceful atmosphere, and an emotional mood. FINAL GOAL Create a dense, clean, production-ready anime pre-production board combining a character design sheet and storyboard in a single 16:9 horizontal image, conveying a heartfelt story of compassion and kindness through completely original character designs.

Zara

22,997 просмотров • 1 месяц назад

i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running
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i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running

Argona

22,355 просмотров • 2 месяцев назад

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!
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Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

19,931 просмотров • 1 месяц назад

Do you want to own part of a AAA game? I know, you hear it all the time. “Triple A game”, you go to play it, it’s crap. This is different, and it’s only possible with Sonic (Sonic) speed, transaction cost, and of-course FeeM. A game that includes talent from Kojima, Ubisoft, EA Sports, Gameloft & more with advisors from NVIDIA. A game that you’ll be able to play on mobile, desktop, and then Xbox and PlayStation (yes really)! YES! A PRETTY BIG DEAL! Before I tell you about the sale, let me at least tell you about this game (being a massive gamer nerd, this excited me), so…. Introducing Animera (Search for Animera): • Fast-paced skill-based PvP in the Nubera galaxy • Compete in real-time space battles for real rewards It will be powered with $STRIKE: • Compete2Earn: win matches, earn tokens • Play2Burn: 5% of $STRIKE used in matches gets burned Oh, and with 8.75% of all game revenue will be used to buy & burn $SWPx, so the SwapX (SwapX) community owns a real stake in this AAA title. Absolutely insane. > Now let me tell you about its beta run quickly: • 16K+ beta signups • 500+ players added weekly • 7.5K+ matches already played • Launching to 500K+ mobile users via Nomina Games > How can you own a piece of Animera? June 5th at 2pm EDT the sale will go live on SwapX, it will go in three phases each lasting 12 hours or until sold out: PHASE 1️⃣: xNFT Holders Early access with exclusive perks and bonuses. These are for xNFT holders only you can get these here on paintswap PHASE 2️⃣ Whitelisted Communities These will be whitelisted from Creo Engine, SFA AGC, derp, and GOGLZ | SONIC 🥽💥. PHASE 3️⃣ Public Round Any remaining allocation will open to the public - only if Phases 1 & 2 don’t sell out. > What is the raise? Token Price & Allocation: • Token: $STRIKE • Currency: USDC • Total tokens for sale: 101.75M Unlock structure: • 50% unlocked at TGE • Remaining 50% claimable in 30 days • Raise cap: Max $100,000 per user, capped at $10,000 per xNFT • Purchase window priority: xNFT holders get early access (see above)! Transparency is key: Why I love working with the team is because transparency is crucial, so I’m going to tell you about its tokenomics, seed, and fully diluted valuation here: Token Symbol: STRIKE Total Supply: 370,000,000 Initial FDV: $1.48M Total Raise: $950,160 Total Initial Unlock: 112,947,501 STRIKE Initial Market Cap (excluding liquidity): $303,790 Token Allocation: • Seed Round: 59.2M tokens (16% allocation), with a 1-month cliff and linear vesting over 9 months. • Private Round: 94.35M tokens (25.5% allocation), with a 1-month cliff and 6-month vesting period. • Crowdsale: 10.75M tokens (2.91% allocation), unlocked 50% at TGE. • xNFT Holders: 10M tokens (2.7% allocation), with a 1-month cliff. • Liquidity: 37M tokens (10% allocation), with no lock or vesting. • Team: 18.5M tokens (5% allocation), with a 6-month cliff and 12-month vesting. • Rewards: 28.6M tokens (8% allocation), vested over 18 months. • Product Growth: 19.6M tokens (5.3% allocation), vested over 24 months. Token Offering: • Seed Round: Priced at $0.0033 per token, raising $195,360 by selling 59.2M tokens. 10% unlocks at TGE, with a 1-month cliff and 9-month vesting. The initial market cap from seed unlock is $234,127. • Private Round: Priced at $0.0037 per token, raising $349,095 for 94.35M tokens. 15% unlocks at TGE, with a 1-month cliff and 6-month vesting. Initial market cap contribution is $262,508. • Crowdsale: Priced at $0.0040 per token, raising $407,000 by selling 10.75M tokens. 50% unlocks at TGE, with no cliff or vesting. Adds $283,790 to the initial market cap. It’s important you had the full information at hand so you can decide whether or not you’d like to participate. I will be, because it’s a low FDV and it looks great. This is not financial advice, I’m helping the team out. Below is real gameplay: Further details: 👇
0:12

Do you want to own part of a AAA game? I know, you hear it all the time. “Triple A game”, you go to play it, it’s crap. This is different, and it’s only possible with Sonic (Sonic) speed, transaction cost, and of-course FeeM. A game that includes talent from Kojima, Ubisoft, EA Sports, Gameloft & more with advisors from NVIDIA. A game that you’ll be able to play on mobile, desktop, and then Xbox and PlayStation (yes really)! YES! A PRETTY BIG DEAL! Before I tell you about the sale, let me at least tell you about this game (being a massive gamer nerd, this excited me), so…. Introducing Animera (Search for Animera): • Fast-paced skill-based PvP in the Nubera galaxy • Compete in real-time space battles for real rewards It will be powered with $STRIKE: • Compete2Earn: win matches, earn tokens • Play2Burn: 5% of $STRIKE used in matches gets burned Oh, and with 8.75% of all game revenue will be used to buy & burn $SWPx, so the SwapX (SwapX) community owns a real stake in this AAA title. Absolutely insane. > Now let me tell you about its beta run quickly: • 16K+ beta signups • 500+ players added weekly • 7.5K+ matches already played • Launching to 500K+ mobile users via Nomina Games > How can you own a piece of Animera? June 5th at 2pm EDT the sale will go live on SwapX, it will go in three phases each lasting 12 hours or until sold out: PHASE 1️⃣: xNFT Holders Early access with exclusive perks and bonuses. These are for xNFT holders only you can get these here on paintswap PHASE 2️⃣ Whitelisted Communities These will be whitelisted from Creo Engine, SFA AGC, derp, and GOGLZ | SONIC 🥽💥. PHASE 3️⃣ Public Round Any remaining allocation will open to the public - only if Phases 1 & 2 don’t sell out. > What is the raise? Token Price & Allocation: • Token: $STRIKE • Currency: USDC • Total tokens for sale: 101.75M Unlock structure: • 50% unlocked at TGE • Remaining 50% claimable in 30 days • Raise cap: Max $100,000 per user, capped at $10,000 per xNFT • Purchase window priority: xNFT holders get early access (see above)! Transparency is key: Why I love working with the team is because transparency is crucial, so I’m going to tell you about its tokenomics, seed, and fully diluted valuation here: Token Symbol: STRIKE Total Supply: 370,000,000 Initial FDV: $1.48M Total Raise: $950,160 Total Initial Unlock: 112,947,501 STRIKE Initial Market Cap (excluding liquidity): $303,790 Token Allocation: • Seed Round: 59.2M tokens (16% allocation), with a 1-month cliff and linear vesting over 9 months. • Private Round: 94.35M tokens (25.5% allocation), with a 1-month cliff and 6-month vesting period. • Crowdsale: 10.75M tokens (2.91% allocation), unlocked 50% at TGE. • xNFT Holders: 10M tokens (2.7% allocation), with a 1-month cliff. • Liquidity: 37M tokens (10% allocation), with no lock or vesting. • Team: 18.5M tokens (5% allocation), with a 6-month cliff and 12-month vesting. • Rewards: 28.6M tokens (8% allocation), vested over 18 months. • Product Growth: 19.6M tokens (5.3% allocation), vested over 24 months. Token Offering: • Seed Round: Priced at $0.0033 per token, raising $195,360 by selling 59.2M tokens. 10% unlocks at TGE, with a 1-month cliff and 9-month vesting. The initial market cap from seed unlock is $234,127. • Private Round: Priced at $0.0037 per token, raising $349,095 for 94.35M tokens. 15% unlocks at TGE, with a 1-month cliff and 6-month vesting. Initial market cap contribution is $262,508. • Crowdsale: Priced at $0.0040 per token, raising $407,000 by selling 10.75M tokens. 50% unlocks at TGE, with no cliff or vesting. Adds $283,790 to the initial market cap. It’s important you had the full information at hand so you can decide whether or not you’d like to participate. I will be, because it’s a low FDV and it looks great. This is not financial advice, I’m helping the team out. Below is real gameplay: Further details: 👇

hoeem

21,634 просмотров • 1 год назад

She doesn't just ink skin, she ink's constellations into flesh. Watch a phoenix shatter into stardust across his back, one needle stroke at a time. From stencil to soul, this is how myths get made. Made with Seedance 2.0 on BudgetPixel AI Prompt: VIDEO PROMPT — 15 SECONDS (1-second cut scenes) Style: Dark painterly cinematic animation, Arcane-inspired, rich shadows, expressive faces, sharp directional lighting, moody tattoo-studio atmosphere, textured brushwork, high-contrast color grading, realistic anatomy, professional concept-art quality. Character note: The tattoo artist shares only the facial likeness of the reference image "Jessy" — same face shape, eyes, and features. Her hairstyle is changed to a sleek, short asymmetric black bob with a shaved undercut side, and her outfit is changed to a fitted black technical vest over a long-sleeve compression top, with fingerless tactical gloves swapped for tight black latex gloves once the session begins. New tattoo subject: Instead of a samurai, the design is a cosmic phoenix made of shattering constellations — black linework forming a phoenix mid-transformation, its wingtips dissolving into scattered stars and fine red ember-like accents, giving it a mythic, otherworldly quality rather than a standard warrior motif. Second 1: Wide establishing shot — neon-lit tattoo studio at night, rain streaking the window; the artist (Jessy-faced, black bob, black vest) steps into frame as the huge bald muscular man lies face down on the table. Second 2: Close-up — she snaps on black latex gloves, quick confident motion, light catching the vest's fabric. Second 3: Close-up — ink caps in a row, deep black and a single vivid ember-red poured with precision. Second 4: Medium shot — she holds up a glowing tablet/reference showing the cosmic phoenix design, studying it against his back. Second 5: Over-the-shoulder — she lays the stencil across his broad back, aligning the phoenix wingspan to his shoulder blades. Second 6: Close-up — gloved hands smooth the stencil down, faint static crackle effect where it presses to skin. Second 7: Medium shot — stencil peels away, revealing the full black-line phoenix-and-shattering-stars outline across his back. Second 8: Close-up — the tattoo machine powers on, needle glinting, her focused eyes reflected in the metal. Second 9: Extreme close-up — first bold black lines forming the phoenix's wing feathers. Second 10: Side angle — the man stays still, muscles tense under lamplight, she works with total concentration. Second 11: Close-up — ember-red ink added to the phoenix's core, glowing against the black linework like embers catching fire. Second 12: Overhead shot — sparks of red bleed outward into the "shattering stars," visual rhythm of the needle in motion. Second 13: Close-up on her face — a flicker of satisfaction, sweat and studio light on her skin. Second 14: Wide dramatic shot — she leans back, revealing the half-finished phoenix glowing under directional light, black smoke/mist styling in the air. Second 15: Final hero shot — slow push-in on the tattoo, black and ember-red phoenix mid-transformation across his back, studio neon reflected in the wet floor, freeze on a cinematic beat.
0:15

She doesn't just ink skin, she ink's constellations into flesh. Watch a phoenix shatter into stardust across his back, one needle stroke at a time. From stencil to soul, this is how myths get made. Made with Seedance 2.0 on BudgetPixel AI Prompt: VIDEO PROMPT — 15 SECONDS (1-second cut scenes) Style: Dark painterly cinematic animation, Arcane-inspired, rich shadows, expressive faces, sharp directional lighting, moody tattoo-studio atmosphere, textured brushwork, high-contrast color grading, realistic anatomy, professional concept-art quality. Character note: The tattoo artist shares only the facial likeness of the reference image "Jessy" — same face shape, eyes, and features. Her hairstyle is changed to a sleek, short asymmetric black bob with a shaved undercut side, and her outfit is changed to a fitted black technical vest over a long-sleeve compression top, with fingerless tactical gloves swapped for tight black latex gloves once the session begins. New tattoo subject: Instead of a samurai, the design is a cosmic phoenix made of shattering constellations — black linework forming a phoenix mid-transformation, its wingtips dissolving into scattered stars and fine red ember-like accents, giving it a mythic, otherworldly quality rather than a standard warrior motif. Second 1: Wide establishing shot — neon-lit tattoo studio at night, rain streaking the window; the artist (Jessy-faced, black bob, black vest) steps into frame as the huge bald muscular man lies face down on the table. Second 2: Close-up — she snaps on black latex gloves, quick confident motion, light catching the vest's fabric. Second 3: Close-up — ink caps in a row, deep black and a single vivid ember-red poured with precision. Second 4: Medium shot — she holds up a glowing tablet/reference showing the cosmic phoenix design, studying it against his back. Second 5: Over-the-shoulder — she lays the stencil across his broad back, aligning the phoenix wingspan to his shoulder blades. Second 6: Close-up — gloved hands smooth the stencil down, faint static crackle effect where it presses to skin. Second 7: Medium shot — stencil peels away, revealing the full black-line phoenix-and-shattering-stars outline across his back. Second 8: Close-up — the tattoo machine powers on, needle glinting, her focused eyes reflected in the metal. Second 9: Extreme close-up — first bold black lines forming the phoenix's wing feathers. Second 10: Side angle — the man stays still, muscles tense under lamplight, she works with total concentration. Second 11: Close-up — ember-red ink added to the phoenix's core, glowing against the black linework like embers catching fire. Second 12: Overhead shot — sparks of red bleed outward into the "shattering stars," visual rhythm of the needle in motion. Second 13: Close-up on her face — a flicker of satisfaction, sweat and studio light on her skin. Second 14: Wide dramatic shot — she leans back, revealing the half-finished phoenix glowing under directional light, black smoke/mist styling in the air. Second 15: Final hero shot — slow push-in on the tattoo, black and ember-red phoenix mid-transformation across his back, studio neon reflected in the wet floor, freeze on a cinematic beat.

Jessica Collins

11,899 просмотров • 24 дней назад

The upcoming weeks could be a period that requires close attention in terms of the geopolitical direction of East Africa. The overall picture in Addis Ababa indicates that the region is passing through a critical threshold. We are in Addis Ababa as part of President Erdoğan’s visit to Ethiopia. 1. This year marks the 100th anniversary of Ethiopia–Türkiye relations. Türkiye’s first embassy in Sub-Saharan Africa was opened in Addis Ababa in 1926. The main theme of the visit, consistent with this historical continuity, is a “Permanent Partnership.” 2. Ethiopia occupies an extremely sensitive position within the conflict-prone and competitive environment of the Horn of Africa. The leadership seeks to consolidate its position on issues it considers vital. At the same time, it tends to turn any emerging opportunity into a strategic gain. 3. However, growing demographic pressure, economic challenges, the fatigue of internal conflict, and other domestic vulnerabilities-combined with the structural disadvantages of being a landlocked country-are pushing Addis Ababa toward alternative strategic options. 4. In this context, the strategy of “access to the sea” is seen as a form of strategic relief-both for managing internal pressures and deepening regional influence. Creating economic breathing space, reducing logistical dependence, and increasing strategic maneuverability are the core motivations behind this approach. 5. This places Ethiopia in a difficult equation: it must produce policy within a competitive-and at times confrontational-regional environment where its ambitions may clash with the sovereignty sensitivities of its neighbors. The search for maritime access therefore stands out as an issue that could easily harden the region’s fragile balance. 6. Its closer alignment with the UAE in the Sudan civil war reflects the cumulative pressures of multiple security and economic challenges. Recent developments suggest that Ethiopia may seek a more active and visible role in the Sudan file in the near term. 7. At the same time, military movements along the Eritrean border carry risks that could trigger a new confrontation. For Ethiopia, the Eritrea issue is directly linked to its most critical maritime option. Access to the sea through Assab remains central to its strategic thinking. Historical grievances and current security concerns are deeply intertwined in this file. 8. Another critical relationship is with Egypt. The core issue here is the Nile Basin and water security. While the GERD dam represents energy production, development, and strategic leverage for Ethiopia, it is perceived by Egypt as a high-risk scenario. What began as a technical water management issue has evolved into a geopolitical matter directly tied to regional power balances. 9. When regional dynamics and the engagement of external actors are considered together, it becomes clear that Ethiopia is navigating a multilayered but high-risk foreign policy landscape-one that affects fault lines across North Africa, the Middle East, the Gulf, and the Horn of Africa. 10. Within this broader tension environment, Türkiye-Ethiopia relations remain in a positive trajectory. Despite differences in certain areas, the potential for deeper cooperation is high. Today, both leaders delivered strong messages in this direction. 11. President Erdoğan’s visit comes at a time when regional conflict dynamics are becoming increasingly visible. Ankara is seeking to develop a balancing approach aimed at preventing potential conflicts and lowering rising regional tensions. Given that Türkiye has both direct and indirect interests tied to the nature of its relationship with Ethiopia, the process is being managed with careful strategic consideration. 12. From arrival to departure, President Erdoğan was warmly received in Addis Ababa. The key areas of focus included economic cooperation, defense industry partnerships, and critical regional issues. Murat Yeşiltaş SETA
0:20

The upcoming weeks could be a period that requires close attention in terms of the geopolitical direction of East Africa. The overall picture in Addis Ababa indicates that the region is passing through a critical threshold. We are in Addis Ababa as part of President Erdoğan’s visit to Ethiopia. 1. This year marks the 100th anniversary of Ethiopia–Türkiye relations. Türkiye’s first embassy in Sub-Saharan Africa was opened in Addis Ababa in 1926. The main theme of the visit, consistent with this historical continuity, is a “Permanent Partnership.” 2. Ethiopia occupies an extremely sensitive position within the conflict-prone and competitive environment of the Horn of Africa. The leadership seeks to consolidate its position on issues it considers vital. At the same time, it tends to turn any emerging opportunity into a strategic gain. 3. However, growing demographic pressure, economic challenges, the fatigue of internal conflict, and other domestic vulnerabilities-combined with the structural disadvantages of being a landlocked country-are pushing Addis Ababa toward alternative strategic options. 4. In this context, the strategy of “access to the sea” is seen as a form of strategic relief-both for managing internal pressures and deepening regional influence. Creating economic breathing space, reducing logistical dependence, and increasing strategic maneuverability are the core motivations behind this approach. 5. This places Ethiopia in a difficult equation: it must produce policy within a competitive-and at times confrontational-regional environment where its ambitions may clash with the sovereignty sensitivities of its neighbors. The search for maritime access therefore stands out as an issue that could easily harden the region’s fragile balance. 6. Its closer alignment with the UAE in the Sudan civil war reflects the cumulative pressures of multiple security and economic challenges. Recent developments suggest that Ethiopia may seek a more active and visible role in the Sudan file in the near term. 7. At the same time, military movements along the Eritrean border carry risks that could trigger a new confrontation. For Ethiopia, the Eritrea issue is directly linked to its most critical maritime option. Access to the sea through Assab remains central to its strategic thinking. Historical grievances and current security concerns are deeply intertwined in this file. 8. Another critical relationship is with Egypt. The core issue here is the Nile Basin and water security. While the GERD dam represents energy production, development, and strategic leverage for Ethiopia, it is perceived by Egypt as a high-risk scenario. What began as a technical water management issue has evolved into a geopolitical matter directly tied to regional power balances. 9. When regional dynamics and the engagement of external actors are considered together, it becomes clear that Ethiopia is navigating a multilayered but high-risk foreign policy landscape-one that affects fault lines across North Africa, the Middle East, the Gulf, and the Horn of Africa. 10. Within this broader tension environment, Türkiye-Ethiopia relations remain in a positive trajectory. Despite differences in certain areas, the potential for deeper cooperation is high. Today, both leaders delivered strong messages in this direction. 11. President Erdoğan’s visit comes at a time when regional conflict dynamics are becoming increasingly visible. Ankara is seeking to develop a balancing approach aimed at preventing potential conflicts and lowering rising regional tensions. Given that Türkiye has both direct and indirect interests tied to the nature of its relationship with Ethiopia, the process is being managed with careful strategic consideration. 12. From arrival to departure, President Erdoğan was warmly received in Addis Ababa. The key areas of focus included economic cooperation, defense industry partnerships, and critical regional issues. Murat Yeşiltaş SETA

Tunç Demirtaş

14,131 просмотров • 6 месяцев назад

Cool summer, grab a bottle of Coca-Cola🥺 Seedance 2.0 Mini prompt 👇 Referencing the face, hairstyle, and body proportions of image_1 — this exact young Asian woman with shoulder-length black hair with bangs, delicate features, and slender figure must remain 100% identical across all outfit changes. Face never changes. 9:16 vertical, 15 seconds, high-saturation commercial style: photo-realistic character on flat graphic animation backgrounds, red-black-white color palette. [0–1.7s] Pure white studio background. Full-body 35mm shot. Character wears simple grey-black outfit, faces camera, holds a transparent plastic cola bottle filled with deep-brown carbonated liquid, red label, red cap. She leans in and twists the cap open with effort. [1.7–2.2s] Cap pops off with a burst — dense bubbles rush upward, cold mist sprays out. Her face shows delighted surprise. A red flash wipes the frame. [2.2–3.5s] Background instantly transforms: deep crimson flat-design. A giant matte-black cola bottle silhouette dominates center frame. Character shrinks and stands inside the silhouette, tilts head back to drink. Bold red and black geometric shapes slide rapidly behind her. [3.5–4s] A giant condensation-covered cola bottle sweeps horizontally across the full frame at high speed with motion blur — TRANSITION 1. [4–6s] Bottle clears. OUTFIT 1 — sporty sweet: red-black cropped top, black shorts, red cap, black sneakers. Same face. She raises one leg, holds cola bottle, energetic jump pose. Floating props: red headphones, black camera, ice cubes, red bottle caps. [6–8s] Cola bubbles explode across frame — TRANSITION 2. OUTFIT 2 — street casual: black oversized tee with red graphic, black wide shorts, red jacket tied at waist, red-black sneakers. Same face. She spins, lands, turns back to camera and raises cola bottle with cool confidence. [8–10s] Giant red bottle cap spins into frame — TRANSITION 3. OUTFIT 3 — hot athletic: red off-shoulder crop top, black high-waist shorts, red-black wristbands, black ankle boots. Same face. She leaps sideways mid-air, one hand drinking from cola. Floating: skewers, straws, ice cubes, cola bubbles. [10–12s] Dark brown cola liquid splashes across body — TRANSITION 4. OUTFIT 4 — cool summer: black cropped top with red trim, red-black shorts, light summer shoes. Same face. Hair gently blown by air current. She floats sideways, holds cola bottle. Floating: red-black swim ring, sunglasses, walkman, small tote bag. [12–13s] A giant ice-frosted cola bottle rises fast from bottom of frame. Character shrinks, floats beside the bottle. Red-black accessories explode outward in a shockwave. Background: deep red and black diagonal geometric blocks. [13–15s] Cut to product close-up: cola bottle front view, transparent body filled with deep-brown carbonated liquid, dense fine bubbles rising continuously, real condensation droplets coating the glass, red label with clean white 'COLA' text only. Black ice cubes and red bottle cap float around it. Character peeks from behind the bottle, smiling warmly, raises the cola joyfully, and says with accurate lip-sync: '啊——爽！' Camera: fixed frontal composition throughout, 35mm full-body for character shots, 50mm product close-up for final. All outfit transitions use in-frame occlusion only — bottle sweep, bubble burst, cap spin, liquid splash — never direct cut. All action beats hit heavy bass downbeats. Character face, hairstyle, and body proportions are identical in every shot. No subtitles. No music. Sound: bottle pop 'bō', carbonation hiss, ice clink, bubble fizz, bass drum hit on each transition beat, brief whoosh on each change, final Chinese line lip-synced precisely.
0:15

Cool summer, grab a bottle of Coca-Cola🥺 Seedance 2.0 Mini prompt 👇 Referencing the face, hairstyle, and body proportions of image_1 — this exact young Asian woman with shoulder-length black hair with bangs, delicate features, and slender figure must remain 100% identical across all outfit changes. Face never changes. 9:16 vertical, 15 seconds, high-saturation commercial style: photo-realistic character on flat graphic animation backgrounds, red-black-white color palette. [0–1.7s] Pure white studio background. Full-body 35mm shot. Character wears simple grey-black outfit, faces camera, holds a transparent plastic cola bottle filled with deep-brown carbonated liquid, red label, red cap. She leans in and twists the cap open with effort. [1.7–2.2s] Cap pops off with a burst — dense bubbles rush upward, cold mist sprays out. Her face shows delighted surprise. A red flash wipes the frame. [2.2–3.5s] Background instantly transforms: deep crimson flat-design. A giant matte-black cola bottle silhouette dominates center frame. Character shrinks and stands inside the silhouette, tilts head back to drink. Bold red and black geometric shapes slide rapidly behind her. [3.5–4s] A giant condensation-covered cola bottle sweeps horizontally across the full frame at high speed with motion blur — TRANSITION 1. [4–6s] Bottle clears. OUTFIT 1 — sporty sweet: red-black cropped top, black shorts, red cap, black sneakers. Same face. She raises one leg, holds cola bottle, energetic jump pose. Floating props: red headphones, black camera, ice cubes, red bottle caps. [6–8s] Cola bubbles explode across frame — TRANSITION 2. OUTFIT 2 — street casual: black oversized tee with red graphic, black wide shorts, red jacket tied at waist, red-black sneakers. Same face. She spins, lands, turns back to camera and raises cola bottle with cool confidence. [8–10s] Giant red bottle cap spins into frame — TRANSITION 3. OUTFIT 3 — hot athletic: red off-shoulder crop top, black high-waist shorts, red-black wristbands, black ankle boots. Same face. She leaps sideways mid-air, one hand drinking from cola. Floating: skewers, straws, ice cubes, cola bubbles. [10–12s] Dark brown cola liquid splashes across body — TRANSITION 4. OUTFIT 4 — cool summer: black cropped top with red trim, red-black shorts, light summer shoes. Same face. Hair gently blown by air current. She floats sideways, holds cola bottle. Floating: red-black swim ring, sunglasses, walkman, small tote bag. [12–13s] A giant ice-frosted cola bottle rises fast from bottom of frame. Character shrinks, floats beside the bottle. Red-black accessories explode outward in a shockwave. Background: deep red and black diagonal geometric blocks. [13–15s] Cut to product close-up: cola bottle front view, transparent body filled with deep-brown carbonated liquid, dense fine bubbles rising continuously, real condensation droplets coating the glass, red label with clean white 'COLA' text only. Black ice cubes and red bottle cap float around it. Character peeks from behind the bottle, smiling warmly, raises the cola joyfully, and says with accurate lip-sync: '啊——爽！' Camera: fixed frontal composition throughout, 35mm full-body for character shots, 50mm product close-up for final. All outfit transitions use in-frame occlusion only — bottle sweep, bubble burst, cap spin, liquid splash — never direct cut. All action beats hit heavy bass downbeats. Character face, hairstyle, and body proportions are identical in every shot. No subtitles. No music. Sound: bottle pop 'bō', carbonation hiss, ice clink, bubble fizz, bass drum hit on each transition beat, brief whoosh on each change, final Chinese line lip-synced precisely.

John

22,860 просмотров • 28 дней назад

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto—focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( – $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > ( - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > ( — Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules >Tropical Tidbits ( - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high >Climate Reanalyzer ( - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities >Windy ( - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online ( - 100+ years of historical location data NOAA Weather Prediction ? >Center ( - short forecasts for precipitation, anomalies. Climate Prediction Center ( - long-term ENSO, droughts >Open-Meteo ( - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. >OpenWeatherMap ( = Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. >Visual Crossing Weather ( - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities >WeatherAPI. com ( - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information
0:14

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto—focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( – $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > ( - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > ( — Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules >Tropical Tidbits ( - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high >Climate Reanalyzer ( - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities >Windy ( - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online ( - 100+ years of historical location data NOAA Weather Prediction ? >Center ( - short forecasts for precipitation, anomalies. Climate Prediction Center ( - long-term ENSO, droughts >Open-Meteo ( - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. >OpenWeatherMap ( = Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. >Visual Crossing Weather ( - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities >WeatherAPI. com ( - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information

Aleiah

77,397 просмотров • 6 месяцев назад

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto-focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( - $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > - Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules > - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high > - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities > - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online - 100+ years of historical location data NOAA Weather Prediction > - short forecasts for precipitation, anomalies. Climate Prediction Center - long-term ENSO, droughts > - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. > - Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. > - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities > - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information
0:14

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto-focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( - $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > - Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules > - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high > - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities > - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online - 100+ years of historical location data NOAA Weather Prediction > - short forecasts for precipitation, anomalies. Climate Prediction Center - long-term ENSO, droughts > - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. > - Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. > - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities > - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information

Valentin

17,081 просмотров • 3 месяцев назад

You Can't Vibe-Code Trust Avishai Abrahami, Co-Founder & CEO of Wix , interviewed by Harry Stebbings (kevin andres) Summary: Wix trades at a $2.8B market cap on $2.1B of revenue while the market ascribes roughly zero value to a business throwing off $400M a year in free cash flow. Wix CEO Avishai Abrahami's argument is that the market can't yet price what AI actually threatens: the moat is trust and business logic, and neither gets vibe-coded away. His response is to own the disruptor (Base44), train his own narrow models, and stay committed through a storm he insists always arrives on a random Wednesday. 1. Trust is the moat. The real value of Salesforce is trust: JP Morgan and huge banks let it hold all their customer data, and the CRM itself is a small part of that. "What other platform will JP Morgan trust for their customers' data? None." That trust took years to build and can't be reconstructed by an agent scraping a database, so the companies whose value lives in trust survive the SaaS apocalypse while the ones reduced to piping get commoditized. 2. The business-logic wall. "You're not going to vibe-code Shopify no matter how good you are. The business logic is too hard." Wix tested this directly: they asked a team of professional developers to build the operating logic for a single hairdresser in Base44, gave up after a week, brought in a stronger team, and still failed two weeks later. Complex operational software is far harder than a demo suggests, which is why the pizza shop and the hairdresser stay Wix customers rather than build their own stack. 3. Own the disruptor. Wix bought Base44, a one-person company, for $80M, and it now does over $150M in ARR, roughly double what they paid. Abrahami frames the future as three buckets: owners who never want to build, owners who vibe-code everything themselves, and a mix in the middle over the next five or six years. Rather than bet on which wins, Wix owns the tool customers would defect to, so a customer who switches platforms still switches to Wix. 4. Trading on someone else's news. "Today we are trading on other companies' news. We're not trading on Wix news. We're trading on what OpenAI or Anthropic or Google are saying." Base44 alone, valued on vibe-coding peer multiples, should be worth around $8B, which means the market assigns less than zero to Wix's core. Abrahami's response is to detach: he doesn't wake up checking whether the stock moved 20%, because the only thing he can influence is the business. 5. The narrow model. Wix fine-tuned and combined its own models and now matches top-tier frontier quality on Base44 tasks at far lower cost. The logic: they sit on a huge stream of training data from watching what users try and where they fail, so a model built for Base44 can skip what frontier models carry, like knowledge of Chinese poetry, and go deep on what someone means when they say "build me a task manager to tell my boyfriend where he's wrong." A narrow target is easier to hit than a frontier model, and Wix already runs a trained model on website generation that's faster, cheaper, and makes fewer errors, retrained weekly on a live feedback loop. 6. Quality before cost. When Harry cites Chamath's claim that open source runs 14-16x cheaper, Abrahami pushes back: that holds for small tasks, but for something as complex as Base44 the savings land at 5-10%, and his own model runs 1-30% cheaper than frontier, not the order of magnitude people assume. More to the point, this is the wrong time to chase cost: "20% more quality, 20% less cost, I'll go for the quality." It's a brand-new market that's just starting, and the job now is to make the product better. 7. The but is very big. "We all give too much credit for AI. It's amazing, it's incredible, it's super powerful, but the but is pretty big." He asked Claude to write a safety protocol and got six mandatory gates, then pushed back on each one and watched the model cave until only one survived, downgrading the rest from "must test" to "might want to look at later." We over-trust these systems, and that reflex, treating a Reddit post as equivalent to research published in Nature, is where the danger lives. 8. Customer support still breaks. Wix has 3,500 people and its single biggest department is customer support, serving 192 countries. They tried hard not to build their own AI support agent, tested many off-the-shelf products, and concluded flatly: "It doesn't work. We tried, we tried again, it didn't work." The gap between hyped AI support startups and what actually ships in production is the tell that the technology is earlier than the marketing, maybe five years from being different. 9. Buybacks as dividends. Wix had $1.5B sitting in the bank it couldn't put into a major acquisition because it was focused on the new product and Base44, so it bought back stock at a low price, with admittedly terrible short-term timing. Abrahami is unbothered: "The big question is where it's going to be in three years, not what happened in the last three months." He argues buybacks are a fantastic, underused tool, essentially a dividend to every shareholder, and companies should lean on them to balance stock-based compensation instead of endlessly diluting. 10. Execution, not finance. A low stock price makes M&A currency less valuable, but Abrahami says that's not his real constraint. Base44 was a one-person company; Wix had to build an entire company around it, staffing it with people pulled from the core. "I don't know how to do another one of those at the same time and have the same quality." The bottleneck on the next acquisition is execution capacity, not the balance sheet. 11. Chosen to be here. The one thing money buys beyond food security is freedom, and the deepest form of that freedom is knowing you're here by choice. "I'm here because I've chosen to be here. Nobody made me." He could move to Costa Rica or dance carnival in Brazil, and choosing to stay and run a public company through a crashing stock is where he finds his power. Money also made him more impatient and a bit lazier, and more rational because he's no longer deciding from fear. 12. The random Wednesday. Resilience starts with accepting the storm will come, because we assume that if yesterday was easy tomorrow will be too, and reality doesn't move in gentle slopes. "The worst thing that happens is probably some random thing on some random Wednesday. It's not something you get a lot of warning for." His anchor, borrowed from Babylon 5, is that you get there when you get there and the weapons you have are the weapons you have, so the only real question is whether you're doing the best you can with what you control.
0:15

You Can't Vibe-Code Trust Avishai Abrahami, Co-Founder & CEO of Wix , interviewed by Harry Stebbings (kevin andres) Summary: Wix trades at a $2.8B market cap on $2.1B of revenue while the market ascribes roughly zero value to a business throwing off $400M a year in free cash flow. Wix CEO Avishai Abrahami's argument is that the market can't yet price what AI actually threatens: the moat is trust and business logic, and neither gets vibe-coded away. His response is to own the disruptor (Base44), train his own narrow models, and stay committed through a storm he insists always arrives on a random Wednesday. 1. Trust is the moat. The real value of Salesforce is trust: JP Morgan and huge banks let it hold all their customer data, and the CRM itself is a small part of that. "What other platform will JP Morgan trust for their customers' data? None." That trust took years to build and can't be reconstructed by an agent scraping a database, so the companies whose value lives in trust survive the SaaS apocalypse while the ones reduced to piping get commoditized. 2. The business-logic wall. "You're not going to vibe-code Shopify no matter how good you are. The business logic is too hard." Wix tested this directly: they asked a team of professional developers to build the operating logic for a single hairdresser in Base44, gave up after a week, brought in a stronger team, and still failed two weeks later. Complex operational software is far harder than a demo suggests, which is why the pizza shop and the hairdresser stay Wix customers rather than build their own stack. 3. Own the disruptor. Wix bought Base44, a one-person company, for $80M, and it now does over $150M in ARR, roughly double what they paid. Abrahami frames the future as three buckets: owners who never want to build, owners who vibe-code everything themselves, and a mix in the middle over the next five or six years. Rather than bet on which wins, Wix owns the tool customers would defect to, so a customer who switches platforms still switches to Wix. 4. Trading on someone else's news. "Today we are trading on other companies' news. We're not trading on Wix news. We're trading on what OpenAI or Anthropic or Google are saying." Base44 alone, valued on vibe-coding peer multiples, should be worth around $8B, which means the market assigns less than zero to Wix's core. Abrahami's response is to detach: he doesn't wake up checking whether the stock moved 20%, because the only thing he can influence is the business. 5. The narrow model. Wix fine-tuned and combined its own models and now matches top-tier frontier quality on Base44 tasks at far lower cost. The logic: they sit on a huge stream of training data from watching what users try and where they fail, so a model built for Base44 can skip what frontier models carry, like knowledge of Chinese poetry, and go deep on what someone means when they say "build me a task manager to tell my boyfriend where he's wrong." A narrow target is easier to hit than a frontier model, and Wix already runs a trained model on website generation that's faster, cheaper, and makes fewer errors, retrained weekly on a live feedback loop. 6. Quality before cost. When Harry cites Chamath's claim that open source runs 14-16x cheaper, Abrahami pushes back: that holds for small tasks, but for something as complex as Base44 the savings land at 5-10%, and his own model runs 1-30% cheaper than frontier, not the order of magnitude people assume. More to the point, this is the wrong time to chase cost: "20% more quality, 20% less cost, I'll go for the quality." It's a brand-new market that's just starting, and the job now is to make the product better. 7. The but is very big. "We all give too much credit for AI. It's amazing, it's incredible, it's super powerful, but the but is pretty big." He asked Claude to write a safety protocol and got six mandatory gates, then pushed back on each one and watched the model cave until only one survived, downgrading the rest from "must test" to "might want to look at later." We over-trust these systems, and that reflex, treating a Reddit post as equivalent to research published in Nature, is where the danger lives. 8. Customer support still breaks. Wix has 3,500 people and its single biggest department is customer support, serving 192 countries. They tried hard not to build their own AI support agent, tested many off-the-shelf products, and concluded flatly: "It doesn't work. We tried, we tried again, it didn't work." The gap between hyped AI support startups and what actually ships in production is the tell that the technology is earlier than the marketing, maybe five years from being different. 9. Buybacks as dividends. Wix had $1.5B sitting in the bank it couldn't put into a major acquisition because it was focused on the new product and Base44, so it bought back stock at a low price, with admittedly terrible short-term timing. Abrahami is unbothered: "The big question is where it's going to be in three years, not what happened in the last three months." He argues buybacks are a fantastic, underused tool, essentially a dividend to every shareholder, and companies should lean on them to balance stock-based compensation instead of endlessly diluting. 10. Execution, not finance. A low stock price makes M&A currency less valuable, but Abrahami says that's not his real constraint. Base44 was a one-person company; Wix had to build an entire company around it, staffing it with people pulled from the core. "I don't know how to do another one of those at the same time and have the same quality." The bottleneck on the next acquisition is execution capacity, not the balance sheet. 11. Chosen to be here. The one thing money buys beyond food security is freedom, and the deepest form of that freedom is knowing you're here by choice. "I'm here because I've chosen to be here. Nobody made me." He could move to Costa Rica or dance carnival in Brazil, and choosing to stay and run a public company through a crashing stock is where he finds his power. Money also made him more impatient and a bit lazier, and more rational because he's no longer deciding from fear. 12. The random Wednesday. Resilience starts with accepting the storm will come, because we assume that if yesterday was easy tomorrow will be too, and reality doesn't move in gentle slopes. "The worst thing that happens is probably some random thing on some random Wednesday. It's not something you get a lot of warning for." His anchor, borrowed from Babylon 5, is that you get there when you get there and the weapons you have are the weapons you have, so the only real question is whether you're doing the best you can with what you control.

Gokul Rajaram

22,657 просмотров • 16 дней назад

oh wow I'm literally enjoying the match made with GPT Image 2 + Seedance 2.0 on Yapper prompt Character Description Young woman in her early 20s with the exact same facial features as the reference image, realistic appearance, soft oval face, fair warm skin tone, expressive almond-shaped hazel eyes, naturally thick brows, delicate nose, soft pink lips, gentle elegant smile, realistic skin texture, loosely tied brunette hair with soft face-framing strands. Wearing a stylish red-and-black football jersey layered over a gray oversized hoodie, casual streetwear vibe, slim build, slightly shy personality, authentic candid facial expressions, seated among football supporters inside a packed stadium. No celebrity look, no model styling, completely ordinary realistic sports fan energy. Updated Version (Football Stadium + English Commentary) Second 1 Wide shot of a massive professional indoor football stadium in South Korea. A giant hanging jumbotron dominates the center of the arena. Bright stadium floodlights, realistic crowd movement, subtle audience chatter, distant referee whistles, and authentic football match atmosphere. The jumbotron displays a young female audience member wearing a red-and-black football jersey layered over a gray oversized hoodie, casually sitting in the stands. Faint English sports commentary can be heard naturally in the background: “Looks like we’ve found one of the fans enjoying tonight’s match…” Second 2 Camera remains focused on the jumbotron screen. Subtle Korean live-TV graphics appear naturally in the corner: minimalist Korean broadcast interface, translucent scoreboard overlay, small Korean text, tiny red LIVE icon. The young woman still does not realize she is being shown on the giant stadium screen. English commentator: “She has absolutely no idea she’s on camera right now.” Second 3 People sitting nearby begin noticing her on the screen. Some spectators laugh softly and point toward her. The young woman starts looking around in confusion, trying to understand what is happening. English commentator chuckles lightly: “And there it is… realization is slowly kicking in.” Second 4 The broadcast camera smoothly tilts downward from the hanging jumbotron toward the real audience seating area. Realistic long-lens sports camera movement with subtle handheld micro-shake from a live TV operator. Crowd noise swells naturally. Second 5 The camera finally locates the young woman in the crowd. Autofocus briefly adjusts and locks onto her face. Slow zoom-in begins naturally like a real football broadcast reaction cam. English commentator: “We found her. Front row of embarrassment tonight.” Second 6 The zoom becomes tighter and slightly faster. The young woman suddenly realizes the live camera is focused on her and that her face is being displayed on the stadium screen. Her expression instantly becomes awkward and embarrassed. Nearby fans laugh naturally. English commentator laughing softly: “There it is! She knows now.” Second 7 Medium close-up shot. She gives a nervous laugh, briefly looks away from the camera, shoulders slightly tense. Her hands move awkwardly as if unsure how to react. Nearby audience members smile and laugh naturally. English commentator: “You’ve got to wave now. Stadium rules.” Second 8 The camera zooms even closer in authentic live-TV fan-cam style. Background spectators become softly blurred due to telephoto lens compression and shallow depth of field. The young woman forces a shy smile while partially covering her face. Subtle compression artifacts and realistic sports-broadcast sharpness. Second 9 Tight realistic close-up. She avoids direct eye contact, laughs awkwardly, then gives a small embarrassed wave toward the camera. Crowd reactions remain subtle and natural. English commentator: “There we go! She survived the fan cam.” Second 10 The camera slightly shakes as if the live-TV operator is adjusting the shot while staying locked onto her awkward expression. Korean LIVE broadcast overlays remain visible. The moment feels spontaneous, candid, and completely authentic like a rea
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oh wow I'm literally enjoying the match made with GPT Image 2 + Seedance 2.0 on Yapper prompt Character Description Young woman in her early 20s with the exact same facial features as the reference image, realistic appearance, soft oval face, fair warm skin tone, expressive almond-shaped hazel eyes, naturally thick brows, delicate nose, soft pink lips, gentle elegant smile, realistic skin texture, loosely tied brunette hair with soft face-framing strands. Wearing a stylish red-and-black football jersey layered over a gray oversized hoodie, casual streetwear vibe, slim build, slightly shy personality, authentic candid facial expressions, seated among football supporters inside a packed stadium. No celebrity look, no model styling, completely ordinary realistic sports fan energy. Updated Version (Football Stadium + English Commentary) Second 1 Wide shot of a massive professional indoor football stadium in South Korea. A giant hanging jumbotron dominates the center of the arena. Bright stadium floodlights, realistic crowd movement, subtle audience chatter, distant referee whistles, and authentic football match atmosphere. The jumbotron displays a young female audience member wearing a red-and-black football jersey layered over a gray oversized hoodie, casually sitting in the stands. Faint English sports commentary can be heard naturally in the background: “Looks like we’ve found one of the fans enjoying tonight’s match…” Second 2 Camera remains focused on the jumbotron screen. Subtle Korean live-TV graphics appear naturally in the corner: minimalist Korean broadcast interface, translucent scoreboard overlay, small Korean text, tiny red LIVE icon. The young woman still does not realize she is being shown on the giant stadium screen. English commentator: “She has absolutely no idea she’s on camera right now.” Second 3 People sitting nearby begin noticing her on the screen. Some spectators laugh softly and point toward her. The young woman starts looking around in confusion, trying to understand what is happening. English commentator chuckles lightly: “And there it is… realization is slowly kicking in.” Second 4 The broadcast camera smoothly tilts downward from the hanging jumbotron toward the real audience seating area. Realistic long-lens sports camera movement with subtle handheld micro-shake from a live TV operator. Crowd noise swells naturally. Second 5 The camera finally locates the young woman in the crowd. Autofocus briefly adjusts and locks onto her face. Slow zoom-in begins naturally like a real football broadcast reaction cam. English commentator: “We found her. Front row of embarrassment tonight.” Second 6 The zoom becomes tighter and slightly faster. The young woman suddenly realizes the live camera is focused on her and that her face is being displayed on the stadium screen. Her expression instantly becomes awkward and embarrassed. Nearby fans laugh naturally. English commentator laughing softly: “There it is! She knows now.” Second 7 Medium close-up shot. She gives a nervous laugh, briefly looks away from the camera, shoulders slightly tense. Her hands move awkwardly as if unsure how to react. Nearby audience members smile and laugh naturally. English commentator: “You’ve got to wave now. Stadium rules.” Second 8 The camera zooms even closer in authentic live-TV fan-cam style. Background spectators become softly blurred due to telephoto lens compression and shallow depth of field. The young woman forces a shy smile while partially covering her face. Subtle compression artifacts and realistic sports-broadcast sharpness. Second 9 Tight realistic close-up. She avoids direct eye contact, laughs awkwardly, then gives a small embarrassed wave toward the camera. Crowd reactions remain subtle and natural. English commentator: “There we go! She survived the fan cam.” Second 10 The camera slightly shakes as if the live-TV operator is adjusting the shot while staying locked onto her awkward expression. Korean LIVE broadcast overlays remain visible. The moment feels spontaneous, candid, and completely authentic like a rea

Aaliya

19,207 просмотров • 3 месяцев назад

HERMES AGENT NOW RUNS CLAUDE OPUS 5. NEAR FABLE 5 INTELLIGENCE. HALF THE PRICE. SELF-VERIFIES ITS OWN WORK. AVAILABLE TODAY VIA NOUS PORTAL (20% OFF ALL MODELS). Anthropic shipped Opus 5 on July 24, 2026. same $5/$25 per million tokens as Opus 4.8. but the benchmarks tell a different story. WHAT CHANGED FROM OPUS 4.8: FrontierBench v0.1: Opus 5: 43.3%. Opus 4.8: 18.7%. 2.3x jump on the same test. ARC-AGI-3: Opus 5: 30.2%. 3x better than the next closest model. beat Fable 5 on 8 out of 13 benchmarks. at half the cost ($5/$25 vs $10/$50). same price as Opus 4.8. twice the intelligence. no reason to stay on 4.8. THE SPECS: model ID: claude-opus-5 context: 1M tokens (default and maximum) max output: 128K tokens thinking: on by default effort toggle: low / medium / high per request fast mode: $10/$50, 2.5x faster knowledge cutoff: May 2026 minimum cacheable prompt: 512 tokens (was 1,024) SELF-VERIFICATION (the biggest change): Opus 5 checks its own work automatically. Anthropic says: delete your verification prompts. "include a final verification step" now causes OVER-verification because the model already does it. for Hermes /goal tasks this is a direct upgrade. the judge checks evidence. the model also checks evidence. double layer of verification without extra tokens. EFFORT TOGGLE: low: fast, cheap, routine work. medium: balanced, daily tasks. high: full reasoning, complex problems. set per request. not a global switch. matches Hermes /reasoning command: /reasoning low (routine) /reasoning high (complex) Opus 5 effort toggle + Hermes reasoning control = precise cost management per turn. WHERE OPUS 5 FITS IN HERMES: DAILY DRIVER (replaces Opus 4.8): same price. 2.3x better benchmarks. set as your main model: Desktop app / Dashboard: Models → claude-opus-5 CHIEF OF STAFF: synthesis across multiple agents. reads Kanban, prioritizes, routes tasks. self-verification catches routing errors before they cascade. COMPLEX CODING: SOTA on agentic coding benchmarks. FrontierBench 43.3% = best public model for coding. set as coder profile model. /GOAL TASKS: self-verification + completion contracts = the model proves its work AND double-checks the proof. long-horizon goals finish correctly more often. MoA AGGREGATOR: strongest synthesis model at $5/$25. pair with GPT-5.6 and Grok 4.5 as references. Opus 5 aggregates. best quality at mid-range price. presets: max-quality: reference_models: - provider: openai-codex model: gpt-5.6-sol - provider: xai model: grok-4.5 aggregator: provider: anthropic model: claude-opus-5 COMPUTER USE: near-Fable 5 quality for browser automation. at half the token cost per session. computer_use tasks burn lots of vision tokens. Opus 5 halves that bill vs Fable 5. WHAT TO KEEP OPUS 5 AWAY FROM: cron monitoring: too expensive. use DeepSeek or no_agent mode. sub-agent grunt work: use GPT-5.6 Luna ($1/$6) or DeepSeek. auxiliary tasks: use Gemini Flash. routine web extraction: use a cheap model. Opus 5 is for the turns where quality compounds. planning, synthesis, verification, complex reasoning. budget models handle everything else. NOUS PORTAL: 20% OFF ALL MODELS Nous Portal currently runs a 20% discount on all models including Opus 5. $5/$25 official → $4/$20 through Nous Portal. the cheapest way to run Opus 5 right now. hermes setup --portal select claude-opus-5 as your model. discount applies automatically. Opus 5 replaces Opus 4.8 everywhere. same price. better at everything. no tradeoff. straight upgrade. hermes update /model claude-opus-5
0:10

HERMES AGENT NOW RUNS CLAUDE OPUS 5. NEAR FABLE 5 INTELLIGENCE. HALF THE PRICE. SELF-VERIFIES ITS OWN WORK. AVAILABLE TODAY VIA NOUS PORTAL (20% OFF ALL MODELS). Anthropic shipped Opus 5 on July 24, 2026. same $5/$25 per million tokens as Opus 4.8. but the benchmarks tell a different story. WHAT CHANGED FROM OPUS 4.8: FrontierBench v0.1: Opus 5: 43.3%. Opus 4.8: 18.7%. 2.3x jump on the same test. ARC-AGI-3: Opus 5: 30.2%. 3x better than the next closest model. beat Fable 5 on 8 out of 13 benchmarks. at half the cost ($5/$25 vs $10/$50). same price as Opus 4.8. twice the intelligence. no reason to stay on 4.8. THE SPECS: model ID: claude-opus-5 context: 1M tokens (default and maximum) max output: 128K tokens thinking: on by default effort toggle: low / medium / high per request fast mode: $10/$50, 2.5x faster knowledge cutoff: May 2026 minimum cacheable prompt: 512 tokens (was 1,024) SELF-VERIFICATION (the biggest change): Opus 5 checks its own work automatically. Anthropic says: delete your verification prompts. "include a final verification step" now causes OVER-verification because the model already does it. for Hermes /goal tasks this is a direct upgrade. the judge checks evidence. the model also checks evidence. double layer of verification without extra tokens. EFFORT TOGGLE: low: fast, cheap, routine work. medium: balanced, daily tasks. high: full reasoning, complex problems. set per request. not a global switch. matches Hermes /reasoning command: /reasoning low (routine) /reasoning high (complex) Opus 5 effort toggle + Hermes reasoning control = precise cost management per turn. WHERE OPUS 5 FITS IN HERMES: DAILY DRIVER (replaces Opus 4.8): same price. 2.3x better benchmarks. set as your main model: Desktop app / Dashboard: Models → claude-opus-5 CHIEF OF STAFF: synthesis across multiple agents. reads Kanban, prioritizes, routes tasks. self-verification catches routing errors before they cascade. COMPLEX CODING: SOTA on agentic coding benchmarks. FrontierBench 43.3% = best public model for coding. set as coder profile model. /GOAL TASKS: self-verification + completion contracts = the model proves its work AND double-checks the proof. long-horizon goals finish correctly more often. MoA AGGREGATOR: strongest synthesis model at $5/$25. pair with GPT-5.6 and Grok 4.5 as references. Opus 5 aggregates. best quality at mid-range price. presets: max-quality: reference_models: - provider: openai-codex model: gpt-5.6-sol - provider: xai model: grok-4.5 aggregator: provider: anthropic model: claude-opus-5 COMPUTER USE: near-Fable 5 quality for browser automation. at half the token cost per session. computer_use tasks burn lots of vision tokens. Opus 5 halves that bill vs Fable 5. WHAT TO KEEP OPUS 5 AWAY FROM: cron monitoring: too expensive. use DeepSeek or no_agent mode. sub-agent grunt work: use GPT-5.6 Luna ($1/$6) or DeepSeek. auxiliary tasks: use Gemini Flash. routine web extraction: use a cheap model. Opus 5 is for the turns where quality compounds. planning, synthesis, verification, complex reasoning. budget models handle everything else. NOUS PORTAL: 20% OFF ALL MODELS Nous Portal currently runs a 20% discount on all models including Opus 5. $5/$25 official → $4/$20 through Nous Portal. the cheapest way to run Opus 5 right now. hermes setup --portal select claude-opus-5 as your model. discount applies automatically. Opus 5 replaces Opus 4.8 everywhere. same price. better at everything. no tradeoff. straight upgrade. hermes update /model claude-opus-5

YanXbt

16,744 просмотров • 24 дней назад

I love that Higgsfield is making its best AI commercial workflows completely open-source. Seedance 2.0 on Higgsfield AI Full open-sourced prompts & assets below. Prompts : Style: 8K IMAX. Photorealistic — no 3D render, no game engine, no game-cutscene aesthetic. Cinematography: Emmanuel Lubezki × Roger Deakins. Lighting: Natural light only — contre-jour backlight, camera on shadow side, atmospheric haze throughout. Key light from sky and windows only. Color: 60:30:10 — dominant / secondary / accent. Camera: Physical cine lens. 180° shutter motion blur. Skin: Pore-level realism — vellus hair, asymmetric moles, capillary flush, pore-shadow matching on-set light. Acting: Hollywood — micro-pauses before reactions, precise eye-line, wet living eyes with catch-lights, visible breath and chest rise. Physics: Gravity and inertia respected — mass has real weight, correct contact shadows. No floating props. Composition: Rule of thirds + golden ratio. Every person moving from frame one. Continuity: Characters, props, environment identical across every cut. No identity drift. Technical: 24fps smooth motion. 8K detail. No jitter. Audio: Environmental SFX only. No music. No subtitles. SUBJECT — >>, character matches input 100%, >> can in right hand. By end fully transformed into >> — armor assembled mid-air in real-time, >> matches input 100%. WB 6500K. MULTISHOT — fast cinematic cutting, escalating energy across the full 15s. LOCATION — >> is a STYLE REFERENCE ONLY, not a fixed keyframe. Use it for the look, mood, architecture and lighting of the environment — a deserted NYC-type avenue in hard midday sun. Seedance should freely generate and extend the world: new buildings, new stretches of avenue, fresh camera angles. The character TRAVELS through space across the shots — he is NOT pinned to the exact spot or framing of the input image. Do not reproduce the reference frame 1:1. ACTION — SHOT 1 (0:00–0:02) — EXTREME CLOSE, chin to forehead. >> raises >> to lips, deep pull. Adam's apple descends then rises twice. Hard cut. SHOT 2 (0:02–0:04) — Same close framing. Can lowers fast. Eyes snap open, sharp and alert (natural skin and iris detail, no glow). Zygomaticus major fires — wide genuine grin builds in under a second, 2mm to 8mm at mouth corners. Sternum rises with a full deep inhale. He is electric. Hard cut. SHOT 3 (0:04–0:06) — WIDE, low. >> EXPLODES forward into a sprint at 30 km/h, continuing in the SAME direction he was already facing — straight ahead down the avenue, NOT turning around. First strides — sneakers slam cracked asphalt, dust puffs eject 10cm. Hard cut. SHOT 4 (0:06–0:08) — Tracking the sprint. Arms drive at 90°, track jacket trails 15cm, grin held, brow raised, jaw relaxed open. Stride extends, ground contacts shorten. Building speed. Hard cut. SHOT 5 (0:08–0:10) — Final two strides, knees compress to 110°. Right foot drives into asphalt — surface fractures 20cm radius. He LAUNCHES upward at 65 km/h. Hard cut. SHOT 6 (0:10–0:13) — Mid-air in the >> building canyon, 24fps FULL SPEED, NO slow-motion. He is NOT frozen — his whole body keeps moving through the jump: torso rotates, legs cycle and tuck, arms swing out and reach, head turns to track the incoming armor. As he flies and twists through the air, armor pieces rip in from the city geometry and converge onto his moving body — chest plate LOCKS mid-rotation, pauldrons CLAMP as his shoulders swing, gauntlets SNAP onto forearms while his arms extend, shin plates clamp onto cycling legs, helmet descends and visor SLAMS shut as his head turns forward — one violent fluid sequence, body in constant motion throughout. Sharp metallic CRACK at visor seal. Hard cut. SHOT 7 (0:13–0:15) — >> SLAMS into >> in a hard crouch, both armored feet hit asphalt simultaneously, dust ring erupts 80cm. He rises, snaps gauntleted fists open and shut — left then right — glances down at the chest plate. Muffled laugh from behind the sealed visor. Hard cut to black. CAMERA — SUPER-DYNAMIC HANDHELD, never static, never locked off. Strong aggressive operator shake on every shot — 4–8cm bounce, organic human jitter, no stabilization. Pronounced DUTCH ANGLE throughout — frame canted 12–20° and shifting between shots, tilting harder as energy builds. SHOT 1: FOV 18° extreme close, hard dutch 15°, violent micro-shake. SHOT 2: FOV 18°, dutch flips to opposite side ~18°, jittery. SHOT 3: FOV 84° low, dutch 12°, whip into the sprint. SHOT 4: FOV 84°, camera physically chasing the run, dutch 16°, heavy stride bounce, motion blur on pans. SHOT 5: FOV 63°, dutch 20°, snap-tilt up tracking the launch. SHOT 6: FOV 63°, AGGRESSIVE HANDHELD CAMERA MOVEMENT — operator arcs and swings around the airborne body, 6–8cm bounce, fast reframes chasing each armor piece as it locks onto his moving form, strong DUTCH ANGLE swinging 18°→8°, no slow-mo. SHOT 7: FOV 63°, hard jolt on landing impact, dutch 14°, camera recoils 8cm then keeps breathing on the suit inspection. STYLE — Dominant: bleached asphalt grey and building canyon 60%. Secondary: navy/cream outfit transitioning to chrome armor 30%. Accent: grin, dust, chrome flash and visor LED blue on landing 10%. WB 6500K. NO glowing eyes — eyes lit by natural sunlight only. CONSTRAINTS — Cut to black at end. 16:9. MULTISHOT with hard cuts per the timecodes above. NO slow-motion anywhere — everything 24fps full speed, especially the transformation. Camera always handheld with strong shake and dutch angle — NEVER static or locked. NO eye glow. >> is a reference for style only — do NOT lock to the input frame or keep him in the same spot; the world extends and he moves through it. Grin must be fully established before the sprint begins.
0:15

I love that Higgsfield is making its best AI commercial workflows completely open-source. Seedance 2.0 on Higgsfield AI Full open-sourced prompts & assets below. Prompts : Style: 8K IMAX. Photorealistic — no 3D render, no game engine, no game-cutscene aesthetic. Cinematography: Emmanuel Lubezki × Roger Deakins. Lighting: Natural light only — contre-jour backlight, camera on shadow side, atmospheric haze throughout. Key light from sky and windows only. Color: 60:30:10 — dominant / secondary / accent. Camera: Physical cine lens. 180° shutter motion blur. Skin: Pore-level realism — vellus hair, asymmetric moles, capillary flush, pore-shadow matching on-set light. Acting: Hollywood — micro-pauses before reactions, precise eye-line, wet living eyes with catch-lights, visible breath and chest rise. Physics: Gravity and inertia respected — mass has real weight, correct contact shadows. No floating props. Composition: Rule of thirds + golden ratio. Every person moving from frame one. Continuity: Characters, props, environment identical across every cut. No identity drift. Technical: 24fps smooth motion. 8K detail. No jitter. Audio: Environmental SFX only. No music. No subtitles. SUBJECT — >>, character matches input 100%, >> can in right hand. By end fully transformed into >> — armor assembled mid-air in real-time, >> matches input 100%. WB 6500K. MULTISHOT — fast cinematic cutting, escalating energy across the full 15s. LOCATION — >> is a STYLE REFERENCE ONLY, not a fixed keyframe. Use it for the look, mood, architecture and lighting of the environment — a deserted NYC-type avenue in hard midday sun. Seedance should freely generate and extend the world: new buildings, new stretches of avenue, fresh camera angles. The character TRAVELS through space across the shots — he is NOT pinned to the exact spot or framing of the input image. Do not reproduce the reference frame 1:1. ACTION — SHOT 1 (0:00–0:02) — EXTREME CLOSE, chin to forehead. >> raises >> to lips, deep pull. Adam's apple descends then rises twice. Hard cut. SHOT 2 (0:02–0:04) — Same close framing. Can lowers fast. Eyes snap open, sharp and alert (natural skin and iris detail, no glow). Zygomaticus major fires — wide genuine grin builds in under a second, 2mm to 8mm at mouth corners. Sternum rises with a full deep inhale. He is electric. Hard cut. SHOT 3 (0:04–0:06) — WIDE, low. >> EXPLODES forward into a sprint at 30 km/h, continuing in the SAME direction he was already facing — straight ahead down the avenue, NOT turning around. First strides — sneakers slam cracked asphalt, dust puffs eject 10cm. Hard cut. SHOT 4 (0:06–0:08) — Tracking the sprint. Arms drive at 90°, track jacket trails 15cm, grin held, brow raised, jaw relaxed open. Stride extends, ground contacts shorten. Building speed. Hard cut. SHOT 5 (0:08–0:10) — Final two strides, knees compress to 110°. Right foot drives into asphalt — surface fractures 20cm radius. He LAUNCHES upward at 65 km/h. Hard cut. SHOT 6 (0:10–0:13) — Mid-air in the >> building canyon, 24fps FULL SPEED, NO slow-motion. He is NOT frozen — his whole body keeps moving through the jump: torso rotates, legs cycle and tuck, arms swing out and reach, head turns to track the incoming armor. As he flies and twists through the air, armor pieces rip in from the city geometry and converge onto his moving body — chest plate LOCKS mid-rotation, pauldrons CLAMP as his shoulders swing, gauntlets SNAP onto forearms while his arms extend, shin plates clamp onto cycling legs, helmet descends and visor SLAMS shut as his head turns forward — one violent fluid sequence, body in constant motion throughout. Sharp metallic CRACK at visor seal. Hard cut. SHOT 7 (0:13–0:15) — >> SLAMS into >> in a hard crouch, both armored feet hit asphalt simultaneously, dust ring erupts 80cm. He rises, snaps gauntleted fists open and shut — left then right — glances down at the chest plate. Muffled laugh from behind the sealed visor. Hard cut to black. CAMERA — SUPER-DYNAMIC HANDHELD, never static, never locked off. Strong aggressive operator shake on every shot — 4–8cm bounce, organic human jitter, no stabilization. Pronounced DUTCH ANGLE throughout — frame canted 12–20° and shifting between shots, tilting harder as energy builds. SHOT 1: FOV 18° extreme close, hard dutch 15°, violent micro-shake. SHOT 2: FOV 18°, dutch flips to opposite side ~18°, jittery. SHOT 3: FOV 84° low, dutch 12°, whip into the sprint. SHOT 4: FOV 84°, camera physically chasing the run, dutch 16°, heavy stride bounce, motion blur on pans. SHOT 5: FOV 63°, dutch 20°, snap-tilt up tracking the launch. SHOT 6: FOV 63°, AGGRESSIVE HANDHELD CAMERA MOVEMENT — operator arcs and swings around the airborne body, 6–8cm bounce, fast reframes chasing each armor piece as it locks onto his moving form, strong DUTCH ANGLE swinging 18°→8°, no slow-mo. SHOT 7: FOV 63°, hard jolt on landing impact, dutch 14°, camera recoils 8cm then keeps breathing on the suit inspection. STYLE — Dominant: bleached asphalt grey and building canyon 60%. Secondary: navy/cream outfit transitioning to chrome armor 30%. Accent: grin, dust, chrome flash and visor LED blue on landing 10%. WB 6500K. NO glowing eyes — eyes lit by natural sunlight only. CONSTRAINTS — Cut to black at end. 16:9. MULTISHOT with hard cuts per the timecodes above. NO slow-motion anywhere — everything 24fps full speed, especially the transformation. Camera always handheld with strong shake and dutch angle — NEVER static or locked. NO eye glow. >> is a reference for style only — do NOT lock to the input frame or keep him in the same spot; the world extends and he moves through it. Grin must be fully established before the sprint begins.

yopiwhs

11,743 просмотров • 1 месяц назад