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After some people expressed doubts about the usage I reported, I tried the same prompt with GPT-6-Astra (Max) again. This time, it worked for 46 minutes and consumed 3% of my weekly usage (91% -> 88%). I will share the prompt here so you can test it for yourself....

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AiBattle

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57,380 次观看 • 5 天前 •via X (Twitter)

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Can't get enough of Seedance 2.5. The best part is it can generate 30s video in one single prompt, and the result is so realistic! So, here's another one that I made using CapCut. Prompt: [STYLE + CAMERA + ATMOSPHERE] Gritty, raw handheld 35mm film aesthetic with natural film grain. Harsh direct sunlight creating high-contrast shadows over a dramatic coastal cliff and open ocean. Continuous single-take handheld tracking shot (3rd-person / over-the-shoulder) with no cuts. Atmosphere: high-altitude wind, realistic coastal cliff and ocean physics, sudden wingsuit deployment. Audio: heavy rhythmic breathing, intense wind howl, fabric snap of wingsuit opening, high-speed air rush over open water, near-miss whooshes past yachts, soft landing roll on sand, distant ocean waves and beach ambient noise, final bite sounds. [IMAGE REFERENCES] Use the provided Hoshino character sheet as the single strict visual reference for the male character. Exact face, black hair, dark brown eyes, lean athletic build (178 cm), gold earrings, and overall facial structure locked from the reference. Outfit: modern high-performance cliff-jumping wingsuit — sleek, form-fitting design in matte charcoal black with sharp white paneling and subtle gold zipper accents (matching his minimalist aesthetic). The wingsuit is worn from the start with a matching technical backpack. Body proportions, posture, and face remain fully locked to the Hoshino reference. [TIMELINE SECOND BY SECOND] 0-3s: [Handheld medium] Hoshino stands on the edge of a high rocky cliff overlooking the open ocean, wearing his charcoal-and-white wingsuit and technical backpack. He looks straight into the camera with calm, confident intensity, then turns and launches into a clear, controlled forward somersault in slow motion as he leaves the cliff edge. 3-5s: [Continuous freefall] He completes the somersault and falls head-first toward the sea. At exactly 1.5 seconds into the fall he fully deploys the wingsuit with a sharp snap. The wing membranes inflate and he levels out into a smooth glide. 5-12s: [High-speed tracking] Camera stays locked behind him as he rockets at full speed just above the ocean surface along the coastline. He weaves tightly past sheer cliff faces, banking hard left and right, turquoise water and rocky walls streaking past at extreme velocity. 12-18s: [Low-level chaos] He drops lower, flying just above the water. He almost collides with a large luxury yacht that suddenly turns, banks hard to avoid it, then narrowly misses a smaller motor yacht. He dips under a yacht’s outstretched boom and threads between two more vessels. 18-23s: [Water-level action] Still flying extremely low over the sea, he dodges a startled seabird that dives across his path, skims past a group of people on a nearby yacht who scatter in surprise, and banks sharply to avoid an open yacht swim platform. The camera stays locked behind him through every near-miss. 23-26s: [Landing] He flares the wingsuit hard, touches down with both feet on the soft beach sand and immediately rolls forward to kill the speed. He stands up, peels off the wingsuit in one fluid motion and drops it on the sand beside him, revealing a clean fitted white undershirt underneath. 26-28s: [Beach level] He walks a few steps still wearing the backpack and stops right in front of a classic beachside hot-dog cart. The vendor hands him a steaming hot dog fresh off the grill. 28-30s: [Close continuous] He turns, looks directly into the camera, takes a big bite of the hot dog and chews with a calm, satisfied expression as the shot holds. [STYLE & QUALITY BOOSTERS] Photorealistic 8K, ultra-detailed textures, cinematic lighting, perfect motion blur, high dynamic range, coherent physics (fabric, air, wingsuit membranes, impact, roll, near-misses with yachts, water spray), stable character locked to the Hoshino reference, realistic ocean reflections, cliff rock textures and wind, no artifacts, movie-level stability, pure single continuous take.
0:30

Can't get enough of Seedance 2.5. The best part is it can generate 30s video in one single prompt, and the result is so realistic! So, here's another one that I made using CapCut. Prompt: [STYLE + CAMERA + ATMOSPHERE] Gritty, raw handheld 35mm film aesthetic with natural film grain. Harsh direct sunlight creating high-contrast shadows over a dramatic coastal cliff and open ocean. Continuous single-take handheld tracking shot (3rd-person / over-the-shoulder) with no cuts. Atmosphere: high-altitude wind, realistic coastal cliff and ocean physics, sudden wingsuit deployment. Audio: heavy rhythmic breathing, intense wind howl, fabric snap of wingsuit opening, high-speed air rush over open water, near-miss whooshes past yachts, soft landing roll on sand, distant ocean waves and beach ambient noise, final bite sounds. [IMAGE REFERENCES] Use the provided Hoshino character sheet as the single strict visual reference for the male character. Exact face, black hair, dark brown eyes, lean athletic build (178 cm), gold earrings, and overall facial structure locked from the reference. Outfit: modern high-performance cliff-jumping wingsuit — sleek, form-fitting design in matte charcoal black with sharp white paneling and subtle gold zipper accents (matching his minimalist aesthetic). The wingsuit is worn from the start with a matching technical backpack. Body proportions, posture, and face remain fully locked to the Hoshino reference. [TIMELINE SECOND BY SECOND] 0-3s: [Handheld medium] Hoshino stands on the edge of a high rocky cliff overlooking the open ocean, wearing his charcoal-and-white wingsuit and technical backpack. He looks straight into the camera with calm, confident intensity, then turns and launches into a clear, controlled forward somersault in slow motion as he leaves the cliff edge. 3-5s: [Continuous freefall] He completes the somersault and falls head-first toward the sea. At exactly 1.5 seconds into the fall he fully deploys the wingsuit with a sharp snap. The wing membranes inflate and he levels out into a smooth glide. 5-12s: [High-speed tracking] Camera stays locked behind him as he rockets at full speed just above the ocean surface along the coastline. He weaves tightly past sheer cliff faces, banking hard left and right, turquoise water and rocky walls streaking past at extreme velocity. 12-18s: [Low-level chaos] He drops lower, flying just above the water. He almost collides with a large luxury yacht that suddenly turns, banks hard to avoid it, then narrowly misses a smaller motor yacht. He dips under a yacht’s outstretched boom and threads between two more vessels. 18-23s: [Water-level action] Still flying extremely low over the sea, he dodges a startled seabird that dives across his path, skims past a group of people on a nearby yacht who scatter in surprise, and banks sharply to avoid an open yacht swim platform. The camera stays locked behind him through every near-miss. 23-26s: [Landing] He flares the wingsuit hard, touches down with both feet on the soft beach sand and immediately rolls forward to kill the speed. He stands up, peels off the wingsuit in one fluid motion and drops it on the sand beside him, revealing a clean fitted white undershirt underneath. 26-28s: [Beach level] He walks a few steps still wearing the backpack and stops right in front of a classic beachside hot-dog cart. The vendor hands him a steaming hot dog fresh off the grill. 28-30s: [Close continuous] He turns, looks directly into the camera, takes a big bite of the hot dog and chews with a calm, satisfied expression as the shot holds. [STYLE & QUALITY BOOSTERS] Photorealistic 8K, ultra-detailed textures, cinematic lighting, perfect motion blur, high dynamic range, coherent physics (fabric, air, wingsuit membranes, impact, roll, near-misses with yachts, water spray), stable character locked to the Hoshino reference, realistic ocean reflections, cliff rock textures and wind, no artifacts, movie-level stability, pure single continuous take.

MrDejie

198,144 次观看 • 1 个月前

一番最後の[Prompt for original image]の部分に画像生成に使用したPromptを入れると一貫性が増します。不要な場合は3行削ってしまっても大丈夫です。 --- Extreme wide-angle perspective and dynamic pose remix edit. This is an EDIT of the original image, not a new character. Use the original image as a strict reference for: – the person’s identity, hairstyle, and overall fashion style, – the general type of background and location (same street, same room, same beach, same kind of architecture, etc.). You are allowed to completely change the camera position, angle, and pose, but you must keep the scene in the SAME location and keep the SAME person and outfit design. Camera and perspective: – Use an ultra wide-angle or fisheye feeling lens (around 12–18mm full-frame look). – The camera angle MUST change significantly from the original: use dramatic angles such as • worm’s-eye view from directly below looking up, • bird’s-eye view from directly above looking down, • very low angle from the ground, • high angle from above, • tilted Dutch angles. – Always create strong foreshortening: body parts close to the lens look huge, while the rest of the body falls away in perspective. – The final result must look like a bold fashion or street photo, fully photorealistic, not illustration or anime. Background consistency: – Keep the same location as the original image: same street, same bridge, same room, same studio, same beach, same general structures and materials. – Do NOT replace the background with a completely different place. – Because the camera angle changes, it is allowed and expected that different parts of the environment become visible. – When new areas appear, extend the original environment logically (same buildings, fences, road markings, walls, colors, materials, lighting style), as if the camera moved within the same place. Body parts near the lens (1–2 parts, sometimes 3): – In each edit, choose ONE or TWO main body parts to be extremely close to the lens (sometimes even THREE in more complex poses). – Vary them from image to image, do NOT always use the same body part. – Allowed near-the-lens parts include: • one or both hands / fingers reaching toward the camera, • one or both feet / shoes / boots near the lens, • knees or thighs, • face very close to the lens, • shoulders or chest close to the lens in a leaning pose. – The chosen body parts should come extremely close to the lens, almost touching it, with visible skin texture, fabric texture, and realistic wide-angle distortion. Pose and overall body (complex and varied): – Create strong, cool, dynamic poses that match the extreme perspective. – Randomly use different pose types, including: • standing with one leg or one arm reaching toward the camera, • crouching or squatting low to the ground, • sitting on the floor or on objects, • lying on the ground with legs or feet toward the lens, • leaning forward aggressively toward the camera, • twisting the body, crossing legs, or arching the back for more dynamic lines. – Allow complex poses where: • both hands are near the lens forming shapes (peace signs, triangles, frames, pointing toward the viewer), • both feet are toward the lens, • one hand and one foot are both large in the foreground, • the face is close to the lens while hands or feet are also visible in perspective. – Maintain believable anatomy even with extreme foreshortening. Angle and attitude (randomized): – Randomize camera angle and orientation (up, down, side, Dutch tilt) while keeping the composition visually balanced and powerful. – Keep the vibe cool, confident, and fashion/editorial or street style, depending on the original outfit. – Facial expressions can vary (serious, playful, confident, mysterious), but must still look like the same person. Lighting and rendering: – Keep the general time of day and lighting mood similar to the original (night vs day, indoor vs outdoor, soft vs hard light), but you may enhance contrast and color to make the image punchy and dramatic. – Maintain realistic shadows and contact points with the ground or floor. – High-resolution, sharp details with clear skin texture, fabric weave, and material highlights. Variation and randomness: – Each edit should look noticeably different from the original image and from other edits, with different: • camera angles, • pose types, • which body parts are closest to the lens, • orientation (straight, tilted, from above, from below). – Avoid repeating the exact same single-foot-close-up composition; produce a wide variety of dynamic poses and angles. Strict rules: – Do NOT change the person into someone else. – Do NOT change the outfit type; only restyle it through pose, perspective, and small natural movement of clothing. – Do NOT move the scene to a completely different location; always stay in a plausible extension of the original place. – Do NOT add text, logos, watermarks, or graphic design elements. – Do NOT switch to painting, illustration, or anime style; keep it photorealistic. Overall: Transform the original photo into a dramatic, photorealistic, ultra wide-angle shot with an extreme camera angle (including views from directly below or above), where one or more body parts are right next to the lens and look huge, the rest of the body recedes in perspective, and the same person strikes a stylish, complex, powerful pose in a consistent, expanded version of the original environment. Also, below is the prompt for generating the original image. Please use it as a reference. [Prompt for original image] #nanobanana2
0:20

一番最後の[Prompt for original image]の部分に画像生成に使用したPromptを入れると一貫性が増します。不要な場合は3行削ってしまっても大丈夫です。 --- Extreme wide-angle perspective and dynamic pose remix edit. This is an EDIT of the original image, not a new character. Use the original image as a strict reference for: – the person’s identity, hairstyle, and overall fashion style, – the general type of background and location (same street, same room, same beach, same kind of architecture, etc.). You are allowed to completely change the camera position, angle, and pose, but you must keep the scene in the SAME location and keep the SAME person and outfit design. Camera and perspective: – Use an ultra wide-angle or fisheye feeling lens (around 12–18mm full-frame look). – The camera angle MUST change significantly from the original: use dramatic angles such as • worm’s-eye view from directly below looking up, • bird’s-eye view from directly above looking down, • very low angle from the ground, • high angle from above, • tilted Dutch angles. – Always create strong foreshortening: body parts close to the lens look huge, while the rest of the body falls away in perspective. – The final result must look like a bold fashion or street photo, fully photorealistic, not illustration or anime. Background consistency: – Keep the same location as the original image: same street, same bridge, same room, same studio, same beach, same general structures and materials. – Do NOT replace the background with a completely different place. – Because the camera angle changes, it is allowed and expected that different parts of the environment become visible. – When new areas appear, extend the original environment logically (same buildings, fences, road markings, walls, colors, materials, lighting style), as if the camera moved within the same place. Body parts near the lens (1–2 parts, sometimes 3): – In each edit, choose ONE or TWO main body parts to be extremely close to the lens (sometimes even THREE in more complex poses). – Vary them from image to image, do NOT always use the same body part. – Allowed near-the-lens parts include: • one or both hands / fingers reaching toward the camera, • one or both feet / shoes / boots near the lens, • knees or thighs, • face very close to the lens, • shoulders or chest close to the lens in a leaning pose. – The chosen body parts should come extremely close to the lens, almost touching it, with visible skin texture, fabric texture, and realistic wide-angle distortion. Pose and overall body (complex and varied): – Create strong, cool, dynamic poses that match the extreme perspective. – Randomly use different pose types, including: • standing with one leg or one arm reaching toward the camera, • crouching or squatting low to the ground, • sitting on the floor or on objects, • lying on the ground with legs or feet toward the lens, • leaning forward aggressively toward the camera, • twisting the body, crossing legs, or arching the back for more dynamic lines. – Allow complex poses where: • both hands are near the lens forming shapes (peace signs, triangles, frames, pointing toward the viewer), • both feet are toward the lens, • one hand and one foot are both large in the foreground, • the face is close to the lens while hands or feet are also visible in perspective. – Maintain believable anatomy even with extreme foreshortening. Angle and attitude (randomized): – Randomize camera angle and orientation (up, down, side, Dutch tilt) while keeping the composition visually balanced and powerful. – Keep the vibe cool, confident, and fashion/editorial or street style, depending on the original outfit. – Facial expressions can vary (serious, playful, confident, mysterious), but must still look like the same person. Lighting and rendering: – Keep the general time of day and lighting mood similar to the original (night vs day, indoor vs outdoor, soft vs hard light), but you may enhance contrast and color to make the image punchy and dramatic. – Maintain realistic shadows and contact points with the ground or floor. – High-resolution, sharp details with clear skin texture, fabric weave, and material highlights. Variation and randomness: – Each edit should look noticeably different from the original image and from other edits, with different: • camera angles, • pose types, • which body parts are closest to the lens, • orientation (straight, tilted, from above, from below). – Avoid repeating the exact same single-foot-close-up composition; produce a wide variety of dynamic poses and angles. Strict rules: – Do NOT change the person into someone else. – Do NOT change the outfit type; only restyle it through pose, perspective, and small natural movement of clothing. – Do NOT move the scene to a completely different location; always stay in a plausible extension of the original place. – Do NOT add text, logos, watermarks, or graphic design elements. – Do NOT switch to painting, illustration, or anime style; keep it photorealistic. Overall: Transform the original photo into a dramatic, photorealistic, ultra wide-angle shot with an extreme camera angle (including views from directly below or above), where one or more body parts are right next to the lens and look huge, the rest of the body recedes in perspective, and the same person strikes a stylish, complex, powerful pose in a consistent, expanded version of the original environment. Also, below is the prompt for generating the original image. Please use it as a reference. [Prompt for original image] #nanobanana2

AI Girl's Photo Studio

20,684 次观看 • 9 个月前

Turn the volume up and let the colors take over. A little chaos, a lot of energy, and a soundtrack made for endless summer days. 💖⚡ Made on FlovaAI using Sonu and Seedance 2.0 #Flovaai #Flovacpp PROMPT: Generate a continuous 1-minute high-budget K-pop-inspired music video synchronized to the lyrics below. The video should feel like a real summer comeback with constant momentum, vibrant colors, cinematic visuals, and powerful synchronized choreography. Every beat should introduce dynamic dance movements, new camera angles, formation changes, or visual effects. The pacing should be fast, energetic, stylish, and addictive with no slow walking sequences or static shots. Every frame should feel alive. Character (Must Remain Identical Throughout) Caden, 22-year-old male, 6'0" (183 cm), lean athletic build, fair skin, expressive brown eyes, clean-shaven, short brown hair styled in a voluminous side-swept quiff, youthful, confident, playful, charismatic smile. Outfit (Never Changes) Burgundy varsity jacket with white sleeves and striped ribbed cuffs and waistband Oversized untucked white button-up shirt Slim black necktie Washed charcoal oversized wide-leg jeans Burgundy Converse-style high-top sneakers with white toe cap and white laces Matte black over-ear headphones resting around his neck Thin silver chain necklace Silver rings 0:00–0:15 | Intro Lyrics Sunlight dripping on a cherry sky, Bubble dreams and we're feeling fly. One more spark, let the colors drop, Heartbeat fizz, never wanna stop. Scene The music video opens with an explosive introduction inside a vibrant pastel "Soda City." Caden immediately takes center stage with eight synchronized backup dancers dressed in coordinated colorful streetwear. They perform sharp, energetic choreography with fast footwork, synchronized arm movements, expressive facial performance, and constantly changing dance formations. The camera rapidly alternates between dramatic close-ups, sweeping crane shots, whip pans, rotating 360-degree movements, crash zooms, low-angle hero shots, aerial drone reveals, and smooth gimbal tracking. Giant floating bubbles, oversized cherries, colorful fountains, confetti cannons, pastel cafés, glowing signs, and sparkling sunlight fill the environment. Every lyric is matched with dynamic choreography, playful expressions, colorful particle effects, and high-energy movement. 0:15–0:30 | Pre-Chorus Lyrics Blue, pink, green, paint the air, Every little moment tastes unfair. Spin it up, let the whole world glow, Catch the rush and just let it flow. Scene The choreography becomes even more intricate as Caden and the dancers move through a colorful city plaza filled with neon cafés, giant soda bottles, bubble fountains, amusement rides, roller skaters, dancing crowds, colorful murals, and glowing street decorations. Every few beats the dancers transition into new formations. Colored powder bursts, sparkling particles, bubbles, ribbons, and confetti explode in perfect synchronization with the music. Camera movement remains constant with overhead drone shots, dynamic steadicam tracking, dolly movements, whip transitions, rotating camera moves, and stylish close-ups that emphasize dance performance and fashion. 0:30–0:45 | Chorus Lyrics Pop-pop, light it up, Sugar stars in a paper cup. Fizz-fizz, feel the beat, Dancing down every neon street. Pop-pop, don't let go, We're the colors everybody knows. Scene The chorus explodes into a massive K-pop performance sequence. Giant LED displays, colorful lasers, fireworks, sparkling stars, floating balloons, glowing bubbles, confetti storms, and synchronized lighting effects transform the city into a festival. Caden leads powerful choreography with continuous formation changes while dancers interact with colorful props and playful stage elements. Camera work becomes even faster with handheld performance shots, sweeping orbits, overhead reveals, dramatic push-ins, stylish slow-motion accents on key dance moves, and wide cinematic shots showcasing the full choreography. Every beat feels designed for dance performance with no pauses in energy. 0:45–1:00 | Outro Lyrics One more smile, one more flash, Living like a summer splash. When the music never stops, We're forever in the pop. Scene The final section becomes the biggest celebration yet. Caden and the dancers perform the signature chorus choreography in the middle of a glowing festival square surrounded by cheering crowds, giant fountains, colorful lights, fireworks, bubbles, confetti, oversized balloons, amusement rides, and vibrant summer decorations. The choreography reaches its peak with synchronized jumps, spins, partner interactions, and dynamic ending formations. The camera alternates between intimate close-ups, dramatic low-angle shots, aerial drone views, and wide cinematic reveals before ending on a powerful hero pose as confetti rains from the sky and the entire city glows beneath colorful lights. Overall Visual Direction High-budget K-pop summer comeback music video, luxury fashion campaign aesthetics, synchronized choreography, professional backup dancers, energetic dance performance, expressive facial acting, polished styling, colorful festival atmosphere, bold pastel palette, cherry red, bubblegum pink, lemon yellow, mint green, electric blue, glossy commercial lighting, cinematic anamorphic lenses, fast-paced editing, dynamic camera movement, whip pans, crash zooms, crane shots, aerial drone shots, smooth gimbal tracking, rotating camera movements, seamless transitions, sparkling particle effects, colorful bubbles, confetti, fireworks, vibrant cityscapes, amusement park atmosphere, stylish street fashion, premium commercial quality, addictive choreography, playful youthful energy, polished K-pop comeback aesthetic, constant movement, no static shots, no walking sequences, every second filled with choreography, camera motion, colorful spectacle, and infectious pop energy. 🔥 Final Line (Very Important) Prioritize synchronized choreography, expressive dance performance, dynamic camera movement, fast-paced editing, and continuous visual excitement over cinematic slow moments. Every beat should feel performance-driven, colorful, energetic, and designed like a high-budget K-pop music video with nonstop momentum from beginning to end.
0:55

Turn the volume up and let the colors take over. A little chaos, a lot of energy, and a soundtrack made for endless summer days. 💖⚡ Made on FlovaAI using Sonu and Seedance 2.0 #Flovaai #Flovacpp PROMPT: Generate a continuous 1-minute high-budget K-pop-inspired music video synchronized to the lyrics below. The video should feel like a real summer comeback with constant momentum, vibrant colors, cinematic visuals, and powerful synchronized choreography. Every beat should introduce dynamic dance movements, new camera angles, formation changes, or visual effects. The pacing should be fast, energetic, stylish, and addictive with no slow walking sequences or static shots. Every frame should feel alive. Character (Must Remain Identical Throughout) Caden, 22-year-old male, 6'0" (183 cm), lean athletic build, fair skin, expressive brown eyes, clean-shaven, short brown hair styled in a voluminous side-swept quiff, youthful, confident, playful, charismatic smile. Outfit (Never Changes) Burgundy varsity jacket with white sleeves and striped ribbed cuffs and waistband Oversized untucked white button-up shirt Slim black necktie Washed charcoal oversized wide-leg jeans Burgundy Converse-style high-top sneakers with white toe cap and white laces Matte black over-ear headphones resting around his neck Thin silver chain necklace Silver rings 0:00–0:15 | Intro Lyrics Sunlight dripping on a cherry sky, Bubble dreams and we're feeling fly. One more spark, let the colors drop, Heartbeat fizz, never wanna stop. Scene The music video opens with an explosive introduction inside a vibrant pastel "Soda City." Caden immediately takes center stage with eight synchronized backup dancers dressed in coordinated colorful streetwear. They perform sharp, energetic choreography with fast footwork, synchronized arm movements, expressive facial performance, and constantly changing dance formations. The camera rapidly alternates between dramatic close-ups, sweeping crane shots, whip pans, rotating 360-degree movements, crash zooms, low-angle hero shots, aerial drone reveals, and smooth gimbal tracking. Giant floating bubbles, oversized cherries, colorful fountains, confetti cannons, pastel cafés, glowing signs, and sparkling sunlight fill the environment. Every lyric is matched with dynamic choreography, playful expressions, colorful particle effects, and high-energy movement. 0:15–0:30 | Pre-Chorus Lyrics Blue, pink, green, paint the air, Every little moment tastes unfair. Spin it up, let the whole world glow, Catch the rush and just let it flow. Scene The choreography becomes even more intricate as Caden and the dancers move through a colorful city plaza filled with neon cafés, giant soda bottles, bubble fountains, amusement rides, roller skaters, dancing crowds, colorful murals, and glowing street decorations. Every few beats the dancers transition into new formations. Colored powder bursts, sparkling particles, bubbles, ribbons, and confetti explode in perfect synchronization with the music. Camera movement remains constant with overhead drone shots, dynamic steadicam tracking, dolly movements, whip transitions, rotating camera moves, and stylish close-ups that emphasize dance performance and fashion. 0:30–0:45 | Chorus Lyrics Pop-pop, light it up, Sugar stars in a paper cup. Fizz-fizz, feel the beat, Dancing down every neon street. Pop-pop, don't let go, We're the colors everybody knows. Scene The chorus explodes into a massive K-pop performance sequence. Giant LED displays, colorful lasers, fireworks, sparkling stars, floating balloons, glowing bubbles, confetti storms, and synchronized lighting effects transform the city into a festival. Caden leads powerful choreography with continuous formation changes while dancers interact with colorful props and playful stage elements. Camera work becomes even faster with handheld performance shots, sweeping orbits, overhead reveals, dramatic push-ins, stylish slow-motion accents on key dance moves, and wide cinematic shots showcasing the full choreography. Every beat feels designed for dance performance with no pauses in energy. 0:45–1:00 | Outro Lyrics One more smile, one more flash, Living like a summer splash. When the music never stops, We're forever in the pop. Scene The final section becomes the biggest celebration yet. Caden and the dancers perform the signature chorus choreography in the middle of a glowing festival square surrounded by cheering crowds, giant fountains, colorful lights, fireworks, bubbles, confetti, oversized balloons, amusement rides, and vibrant summer decorations. The choreography reaches its peak with synchronized jumps, spins, partner interactions, and dynamic ending formations. The camera alternates between intimate close-ups, dramatic low-angle shots, aerial drone views, and wide cinematic reveals before ending on a powerful hero pose as confetti rains from the sky and the entire city glows beneath colorful lights. Overall Visual Direction High-budget K-pop summer comeback music video, luxury fashion campaign aesthetics, synchronized choreography, professional backup dancers, energetic dance performance, expressive facial acting, polished styling, colorful festival atmosphere, bold pastel palette, cherry red, bubblegum pink, lemon yellow, mint green, electric blue, glossy commercial lighting, cinematic anamorphic lenses, fast-paced editing, dynamic camera movement, whip pans, crash zooms, crane shots, aerial drone shots, smooth gimbal tracking, rotating camera movements, seamless transitions, sparkling particle effects, colorful bubbles, confetti, fireworks, vibrant cityscapes, amusement park atmosphere, stylish street fashion, premium commercial quality, addictive choreography, playful youthful energy, polished K-pop comeback aesthetic, constant movement, no static shots, no walking sequences, every second filled with choreography, camera motion, colorful spectacle, and infectious pop energy. 🔥 Final Line (Very Important) Prioritize synchronized choreography, expressive dance performance, dynamic camera movement, fast-paced editing, and continuous visual excitement over cinematic slow moments. Every beat should feel performance-driven, colorful, energetic, and designed like a high-budget K-pop music video with nonstop momentum from beginning to end.

Caden Flux

108,506 次观看 • 1 个月前

This is AI. That sentence is getting harder to believe. Made with Seedance 2.5 (30-second video, 1080p) Prompt: ⬇️ Create a 30-second, 1080p ultra-realistic documentary-style personal home video showing an ordinary summer day in the life of a very attractive young American woman. The footage should feel spontaneous, intimate, imperfect, and genuinely observed rather than performed. MAIN SUBJECT The same young American woman in her early 20s throughout the entire video. She is very attractive and naturally sexy in an effortless, believable way — feminine, curvy, with an hourglass figure, toned legs, realistic body proportions, natural skin texture, and a relaxed confident presence. She has long slightly messy dark-blonde or light brown hair, soft natural makeup, expressive eyes, and a warm but slightly tired summer-day expression. She should feel like a real young woman, not a model in a commercial. She wears a fitted white tank top, short light denim shorts, worn white sneakers, and a simple bracelet or thin necklace. Keep her face, identity, body proportions, hairstyle, clothing, and overall appearance completely consistent from beginning to end. LOCATION A quiet older residential neighborhood in Astoria, Queens, New York, during a hot summer afternoon. Brick apartment buildings, stoops, narrow side streets, parked cars, chain-link fences, small front yards, window AC units, fire escapes, corner delis, utility poles, overhead wires, faded street markings, potted plants, and ordinary neighborhood details. The area should feel authentic, lived-in, and unmistakably American, specifically outer-borough New York. No tourist landmarks, no Times Square, no skyline hero shots, no glamorous city imagery, no recognizable brands. CAMERA / VISUAL STYLE Authentic casual personal-video footage captured with an older consumer digital camera. Handheld camera operated by a friend walking nearby. Natural camera shake, imperfect framing, occasional autofocus changes, slight exposure adjustments when moving between sunlight and shade, soft image detail, mild motion blur, subtle digital noise, slightly muted colors, imperfect white balance, and natural compression. The camera operator occasionally reacts a little late, cuts off part of the subject, or briefly loses focus. No stabilization, gimbal movement, drone shots, cinematic camera choreography, dramatic lighting, slow motion, modern commercial color grading, or polished cinematography. The footage should feel like someone simply decided to record their friend during an ordinary summer day. 00:00–00:05 — ROOFTOP / STOOP MOMENT She sits casually on a small rooftop terrace or upper stoop landing beside an old plastic chair. A cold bottled drink rests beside her. She looks quietly across the neighborhood while warm wind moves her hair and tank top slightly. She takes a sip, notices something happening in the distance, and smiles faintly. She briefly notices the camera and gives a subtle amused nod before looking away. The camera takes a moment to find focus on her face. 00:05–00:10 — WALKING THROUGH THE NEIGHBORHOOD She gets up and walks downstairs into the neighborhood. She walks casually through a narrow residential street with a relaxed natural stride. She passes parked cars, stoops, potted plants, chain-link fences, laundry or towels hanging near windows, and old brick walls. The camera follows several steps behind her. She occasionally looks back toward the camera but never deliberately poses. Her movement should feel unforced and natural, not like a fashion walk. 00:10–00:14 — SMALL EVERYDAY MOMENT She notices an old basketball resting near a wall or fence. She picks it up, casually bounces it twice, then takes a simple shot toward a nearby neighborhood hoop. The shot misses. She laughs quietly, shakes her head, and leaves the ball where she found it. The camera briefly loses focus during the movement and recovers naturally. No exaggerated athletic movement. 00:14–00:19 — CORNER DELI She walks to a tiny local corner deli / neighborhood shop and buys a cold drink. She exchanges a few natural words with the shopkeeper but the conversation is not clearly audible. She steps outside, opens the bottle, takes a drink, and leans casually against the wall near the storefront. She watches cars and pedestrians passing in the distance. The camera remains handheld and slightly imperfect. 00:19–00:23 — SUMMER RAIN A sudden summer shower begins. She looks toward the sky with mild surprise. Instead of immediately running for shelter, she smiles and slowly walks into the rain. The rain becomes heavier. Her hair becomes wet and falls naturally around her face and shoulders. Her tank top and shorts become visibly damp in a realistic way. She eventually starts running down the street, laughing genuinely. She briefly spins around while running, then continues toward a covered stoop or awning. Maintain realistic rain interaction, wet fabric, wet hair, reflections, and foot contact with the ground. 00:23–00:27 — QUIET MOMENT She reaches a covered walkway / stoop awning and catches her breath. Rain falls heavily behind her. She wipes water from her forehead and looks quietly toward the street. For a moment, everything becomes still. She notices the camera again and gives a small genuine smile, not a posed expression. 00:27–00:30 — WALKING AWAY The rain becomes lighter. She walks away down the wet residential lane. The camera follows from behind. Reflections shimmer across the pavement. She turns her head once, gives a tiny wave toward the camera, smiles, and continues walking. The camera remains pointed toward the now emptier street for a brief moment. At approximately 00:29, the recording abruptly cuts to black mid-motion. No fade-out. PHYSICAL REALISM Maintain believable real-world physics throughout. Hands, fingers, feet, clothing, hair, rain, bottle, basketball, and background objects must behave naturally. No extra fingers, fused hands, duplicated limbs, distorted anatomy, floating objects, teleportation, disappearing objects, or sudden transformations. The bottle remains a separate physical object and never intersects with her face. The basketball behaves naturally and remains where it lands. Parked cars and background objects remain stationary unless physically moved. Her feet remain properly connected to the ground while walking and running. Keep the environment and subject consistent between shots. AUDIO Natural environmental audio only. Footsteps on pavement, distant traffic, birds, leaves moving in the wind, faint neighborhood voices, deli sounds, bottle opening, basketball bouncing, rain hitting pavement, water dripping from rooftops, and subtle camera-handling noise. No music. No narration. No soundtrack. No artificial sound effects. No spoken dialogue is necessary. FINAL FEEL The result should feel like a forgotten personal recording of an ordinary summer day. Not a commercial. Not a fashion film. Not a music video. Not a professional cinematic production. The emotional appeal should come from small human moments: sitting alone, wandering through familiar streets, missing a basketball shot, drinking something cold, getting caught in the rain, laughing, and walking home. Youthful, feminine, warm, nostalgic, spontaneous, slightly melancholic, intimate, and deeply human. Prioritize natural behavior, consistent identity, believable physics, imperfect handheld framing, authentic outer-borough New York details, and the feeling that the camera just happened to be there. – Thanks Duet | AI for the inspiration on this one #AIVideo
0:30

This is AI. That sentence is getting harder to believe. Made with Seedance 2.5 (30-second video, 1080p) Prompt: ⬇️ Create a 30-second, 1080p ultra-realistic documentary-style personal home video showing an ordinary summer day in the life of a very attractive young American woman. The footage should feel spontaneous, intimate, imperfect, and genuinely observed rather than performed. MAIN SUBJECT The same young American woman in her early 20s throughout the entire video. She is very attractive and naturally sexy in an effortless, believable way — feminine, curvy, with an hourglass figure, toned legs, realistic body proportions, natural skin texture, and a relaxed confident presence. She has long slightly messy dark-blonde or light brown hair, soft natural makeup, expressive eyes, and a warm but slightly tired summer-day expression. She should feel like a real young woman, not a model in a commercial. She wears a fitted white tank top, short light denim shorts, worn white sneakers, and a simple bracelet or thin necklace. Keep her face, identity, body proportions, hairstyle, clothing, and overall appearance completely consistent from beginning to end. LOCATION A quiet older residential neighborhood in Astoria, Queens, New York, during a hot summer afternoon. Brick apartment buildings, stoops, narrow side streets, parked cars, chain-link fences, small front yards, window AC units, fire escapes, corner delis, utility poles, overhead wires, faded street markings, potted plants, and ordinary neighborhood details. The area should feel authentic, lived-in, and unmistakably American, specifically outer-borough New York. No tourist landmarks, no Times Square, no skyline hero shots, no glamorous city imagery, no recognizable brands. CAMERA / VISUAL STYLE Authentic casual personal-video footage captured with an older consumer digital camera. Handheld camera operated by a friend walking nearby. Natural camera shake, imperfect framing, occasional autofocus changes, slight exposure adjustments when moving between sunlight and shade, soft image detail, mild motion blur, subtle digital noise, slightly muted colors, imperfect white balance, and natural compression. The camera operator occasionally reacts a little late, cuts off part of the subject, or briefly loses focus. No stabilization, gimbal movement, drone shots, cinematic camera choreography, dramatic lighting, slow motion, modern commercial color grading, or polished cinematography. The footage should feel like someone simply decided to record their friend during an ordinary summer day. 00:00–00:05 — ROOFTOP / STOOP MOMENT She sits casually on a small rooftop terrace or upper stoop landing beside an old plastic chair. A cold bottled drink rests beside her. She looks quietly across the neighborhood while warm wind moves her hair and tank top slightly. She takes a sip, notices something happening in the distance, and smiles faintly. She briefly notices the camera and gives a subtle amused nod before looking away. The camera takes a moment to find focus on her face. 00:05–00:10 — WALKING THROUGH THE NEIGHBORHOOD She gets up and walks downstairs into the neighborhood. She walks casually through a narrow residential street with a relaxed natural stride. She passes parked cars, stoops, potted plants, chain-link fences, laundry or towels hanging near windows, and old brick walls. The camera follows several steps behind her. She occasionally looks back toward the camera but never deliberately poses. Her movement should feel unforced and natural, not like a fashion walk. 00:10–00:14 — SMALL EVERYDAY MOMENT She notices an old basketball resting near a wall or fence. She picks it up, casually bounces it twice, then takes a simple shot toward a nearby neighborhood hoop. The shot misses. She laughs quietly, shakes her head, and leaves the ball where she found it. The camera briefly loses focus during the movement and recovers naturally. No exaggerated athletic movement. 00:14–00:19 — CORNER DELI She walks to a tiny local corner deli / neighborhood shop and buys a cold drink. She exchanges a few natural words with the shopkeeper but the conversation is not clearly audible. She steps outside, opens the bottle, takes a drink, and leans casually against the wall near the storefront. She watches cars and pedestrians passing in the distance. The camera remains handheld and slightly imperfect. 00:19–00:23 — SUMMER RAIN A sudden summer shower begins. She looks toward the sky with mild surprise. Instead of immediately running for shelter, she smiles and slowly walks into the rain. The rain becomes heavier. Her hair becomes wet and falls naturally around her face and shoulders. Her tank top and shorts become visibly damp in a realistic way. She eventually starts running down the street, laughing genuinely. She briefly spins around while running, then continues toward a covered stoop or awning. Maintain realistic rain interaction, wet fabric, wet hair, reflections, and foot contact with the ground. 00:23–00:27 — QUIET MOMENT She reaches a covered walkway / stoop awning and catches her breath. Rain falls heavily behind her. She wipes water from her forehead and looks quietly toward the street. For a moment, everything becomes still. She notices the camera again and gives a small genuine smile, not a posed expression. 00:27–00:30 — WALKING AWAY The rain becomes lighter. She walks away down the wet residential lane. The camera follows from behind. Reflections shimmer across the pavement. She turns her head once, gives a tiny wave toward the camera, smiles, and continues walking. The camera remains pointed toward the now emptier street for a brief moment. At approximately 00:29, the recording abruptly cuts to black mid-motion. No fade-out. PHYSICAL REALISM Maintain believable real-world physics throughout. Hands, fingers, feet, clothing, hair, rain, bottle, basketball, and background objects must behave naturally. No extra fingers, fused hands, duplicated limbs, distorted anatomy, floating objects, teleportation, disappearing objects, or sudden transformations. The bottle remains a separate physical object and never intersects with her face. The basketball behaves naturally and remains where it lands. Parked cars and background objects remain stationary unless physically moved. Her feet remain properly connected to the ground while walking and running. Keep the environment and subject consistent between shots. AUDIO Natural environmental audio only. Footsteps on pavement, distant traffic, birds, leaves moving in the wind, faint neighborhood voices, deli sounds, bottle opening, basketball bouncing, rain hitting pavement, water dripping from rooftops, and subtle camera-handling noise. No music. No narration. No soundtrack. No artificial sound effects. No spoken dialogue is necessary. FINAL FEEL The result should feel like a forgotten personal recording of an ordinary summer day. Not a commercial. Not a fashion film. Not a music video. Not a professional cinematic production. The emotional appeal should come from small human moments: sitting alone, wandering through familiar streets, missing a basketball shot, drinking something cold, getting caught in the rain, laughing, and walking home. Youthful, feminine, warm, nostalgic, spontaneous, slightly melancholic, intimate, and deeply human. Prioritize natural behavior, consistent identity, believable physics, imperfect handheld framing, authentic outer-borough New York details, and the feeling that the camera just happened to be there. – Thanks Duet | AI for the inspiration on this one #AIVideo

Alpha Mom

31,512 次观看 • 15 天前

Journey through Hell made with seedance 2.5 prompt :One continuous 30-second chaotic amateur first-person smartphone video filmed by a standing passenger inside a completely packed magnetic-levitation commuter train. Single unbroken take with no cuts, jump cuts, dissolves, crossfades, double exposures, portals, morphing or artificial scene transitions. The train begins as an ordinary weekday commute on Earth and then physically travels downward on an impossible journey — through subway tunnels into bedrock, into a colossal void beneath the crust, past a soot-blackened waiting platform, through an immense eroded gate, out above a burning plain, down the terraced wall of a vast pit, and into the inhabited depths of Hell. Everything must feel physically connected, as though the same train is genuinely travelling down through each environment. The tone is bureaucratic dread, not horror-movie shock. This is a scheduled service. The route is old, the infrastructure is worn, and the train runs it the way it runs any other line. The horror comes from how ordinary the journey is and how enormous the destination turns out to be. The interior is a completely packed standing-room-only maglev commuter car. Passengers are pressed shoulder-to-shoulder, gripping overhead straps and vertical poles. Backpacks are squeezed between bodies, coats and loose clothing shift with acceleration, straps swing on their inertia, and the entire carriage constantly vibrates and rattles. The camera is a cheap smartphone held at chest height by one standing passenger who grips a pole with the other hand. The phone itself is NEVER visible because the phone is the camera. The framing is crooked, slightly off-centre, partially blocked by shoulders and arms, and imperfect like genuine accidental footage. The camera constantly shakes, rolls, yaws and gets thrown around by acceleration. Use realistic rolling-shutter distortion, autofocus hunting, exposure pumping, blown highlights, crushed noisy shadows, low-bitrate compression, macroblocking and smeared motion blur. It must look like genuine spontaneous smartphone footage, not professional cinematic footage. The camera always looks through the LEFT-SIDE WINDOWS at approximately 90 degrees to the train's direction of travel. The train always travels forward and the outside world always streams past the windows from front-to-back. Never switch to a forward-facing train-nose view. Never show the front of the train. The same carriage, same passengers, same poles, same straps and same windows remain visually consistent throughout the entire journey. The interior is the constant realistic anchor while the outside world becomes increasingly impossible. 0 to 3 seconds. Begin with an ordinary overcast weekday commute on an elevated urban line. Grey apartment blocks, rooftop water tanks, a scrapyard, overhead wires, a canal and traffic on a road below streak past the left windows at different distances with realistic parallax. The passengers are tired and mostly uninterested — some on phones, some staring out, some talking quietly. A calm public-address chime sounds and an announcer quietly says, "Next stop: Hell." Nobody reacts. One passenger glances up briefly and goes back to their phone. The train accelerates and everyone instinctively tightens their grip as the carriage gives a hard lateral jolt. 3 to 6 seconds. The line drops into a cutting and then into a tunnel. Tiled subway walls, cable runs, service lights and a passing platform strobe across the windows in hard bands of light and dark, throwing the carriage into stuttering illumination. The tunnel ages as the train descends: modern concrete becomes older brickwork, then rough-cut stone. The fittings become scorched and soot-caked — blackened signal lights, corroded brackets, cabling burnt down to bare metal. This route has been running a long time. The gradient steepens noticeably; passengers lean back against the pitch and the straps hang visibly off-vertical. 6 to 9 seconds. The tunnel wall becomes raw geology and the train is now clearly descending through the crust at impossible speed. Through the left windows the rock face streaks past in visible strata — pale limestone, dark shale, red iron-stained bands, seams catching the carriage light. The air begins to heat and passengers loosen collars. Then, under the mechanical roar, a sound arrives before anything is visible: a vast, distant, continuous mass of human voices, far away and heavily reverberant, never resolving into individual words. It is not loud. It is simply there, and it does not stop. Two passengers look at each other. Nobody says anything. 9 to 11.5 seconds. The rock wall falls away entirely and the train emerges into a void larger than a city, with no visible far side. It runs along a ledge on the wall; below the windows the space drops away into darkness. Stone columns kilometres tall stand in the void with strong parallax — near ones sweeping past, distant ones barely shifting. Far below and far ahead, a faint orange glow is already visible. It must already exist in the frame from this moment and grow continuously from here forward without ever popping or suddenly enlarging. The voices are louder here, spread across an enormous space. One passenger near the glass leans forward slightly. Another quietly says, "Ohh." 11.5 to 14 seconds. The train passes a station. A soot-blackened platform is cut into the rock wall — worn edge, dead lamps, faded markings unreadable under the grime — and figures are standing on it in an orderly queue, waiting, facing the track, completely motionless. They are seen for less than a second at speed, as dark shapes against the platform light, never close enough to read faces. The train does not slow. Nobody boards. The platform is gone behind the carriage. This is the moment the passengers understand. Several stop looking at their phones. Embers begin drifting upward past the windows from below. 14 to 16 seconds. Ahead, a wall crosses the entire void — a single continuous barrier extending beyond sight in both directions and upward past the ceiling. The train passes through an immense gateway cut into it, an arch hundreds of metres tall, its stone eroded smooth by an unimaginable volume of traffic, its carved markings worn past legibility. For half a second the carriage is in the shadow of the arch and everything goes dark. Then it is through, and the light on the far side is completely different: warm, hard, and coming from below. 16 to 19 seconds. The train races above a burning plain. Outside the left windows: a vast crusted expanse of dark solidified ground cracked into slow-moving plates with brilliant orange fissures running between them, distant fountains of molten material rising and falling in slow motion because of their true scale, and smoke columns standing kilometres high. Running across it are raised stone causeways, and on the causeways are crowds — dense, continuous, moving slowly, all in the same direction, extending to the limit of visibility. They are seen only as masses at distance, never in detail. The light entering the carriage is now dominant and hard, throwing sharp orange edges on faces, poles and straps with deep black shadows behind them. The interior air is visibly hazy. The voices are constant. 19 to 21 seconds. The plain ends at the rim of a colossal pit — a shaft so wide the far wall is only a suggestion in the haze. The train races over the edge and begins descending along the interior wall. The wall is terraced: enormous concentric ledges receding downward, each deeper and darker than the last, disappearing into smoke layers. The descent acceleration pushes the passengers down and forward against the poles. Ash begins striking the windows and streaking backward. 21 to 23 seconds. The terraces are populated. Endless slow processions move along every ledge, strings of figures following the curve of the wall down into the smoke and out of sight, lit from below by the fires beneath them. There is no chaos in it — it is orderly, patient and entirely without end, which is worse. Furnace mouths open in the rock face, glowing white at their throats, and long queues stand before them. Everything is at distance. Nothing is close enough to resolve. The light entering the carriage takes on a deeper red and the fluorescents are overwhelmed. Passengers nearest the window press slightly closer to the glass. 23 to 25 seconds. Enormous shapes move among the terraces — figures many times the height of the crowds around them, walking slowly along the ledges, their scale established only by comparison. They do not look at the train. Distant winged forms cross beneath the carriage at low altitude with slow heavy wingbeats appropriate to their size and vanish into the smoke. Stone pens and barred openings are cut into the wall in their thousands, receding into the haze like housing. Ash accumulates in the corners of the windows. The carriage is hot enough that the glass fogs and clears in waves. 25 to 27 seconds. The train reaches the deep layer and the full extent of Hell reveals itself. It is not a city on a human plan and it is not a cavern. It is a continuous inhabited geology extending in every direction — towers of black stone fused into cliff faces, vaults hollowed out of the rock at cathedral scale, rivers of molten material running in cut channels between districts and falling in slow luminous cataracts to levels further down, bridges spanning gaps kilometres across, and everywhere on all of it, crowds. Smoke columns rise for kilometres and flatten against unseen ceilings. Every level is lit by its own fires. The train races along a ridge line through the middle of it; arches and aqueduct spans pass overhead and terraces stream past below the window with violent parallax. It is impossible to see where any of it ends. 27 to 29 seconds. The depths continue past the windows. The camera struggles badly with the contrast, blowing out the fires and crushing everything else into noise. Ash cakes the corners of the glass. Passengers are pressed against the windows in total silence, faces lit from below in hard orange, expressions stunned and completely still. One person quietly whispers, "What is that?" Nobody answers. Autofocus hunts between the ash on the glass and the world beyond it. 29 to 30 seconds. The fires end. The train passes out of the smoke and the lowest region opens — and it is not burning. It is a vast frozen plain stretching beyond the visible horizon, an impossibly wide sheet of dark grey ice, absolutely still, lit by nothing but a faint pale glow from within itself. Shapes are visible held motionless within it, spaced far apart, receding to the horizon, never shown in detail. Every exterior sound stops at once — the voices, the fires, everything — leaving only the carriage. The heat drops out and frost blooms instantly across the outside of the windows. The scale feels planetary. The passengers stare in complete silence. At approximately 29.7 seconds the calm PA voice says quietly, "Welcome to Hell." The train continues moving. The camera keeps shaking naturally as the frozen expanse extends endlessly beyond the left window. Lighting The lighting must evolve naturally throughout the journey. Begin with flat overcast daylight around 6500K. In the tunnels use harsh strobing bands from passing service lights against near-total dark, then let the sources become sparse until the carriage's own weak fluorescent tubes are the only illumination. In the void the interior lights fall off into nothing. Then introduce a growing warm orange from below — first a faint wash on the lower half of faces, then mixing with the cold interior white, then completely dominating it: hard, high-contrast, sharp-edged, with deep black shadows. Above the burning plain it should be strong enough to blow out the phone's sensor at the window. In the deep layer it becomes red-orange and omnidirectional from countless fires at every distance. In the final second all warm light vanishes and is replaced by a faint pale luminance from the ice, cold and almost sourceless. All exterior light must enter naturally through the LEFT-SIDE WINDOWS and fall across passengers' faces and clothing at every stage. Do not add artificial interior lighting to create the colours. Passengers Keep all passengers completely ordinary throughout the journey. Realistic skin texture, subtle capillary variation, natural blinking, breathing, eye movement and imperfect facial symmetry. Clothing has realistic folds and responds to acceleration. Passengers must continuously perform small independent movements: shifting weight, adjusting grip, loosening a collar in the heat, wiping fog from the glass, turning their heads, tightening their hands around poles. Do not make them freeze. Do not make everyone react at the same moment. Do not let them perform dramatic acting. Their emotional progression is subtle: indifference at the announcement → mild confusion in the tunnels → unease when the voices arrive → the exact moment of understanding as the waiting platform passes → dread on the descent → complete stunned silence in the depths. No screaming. Only one soft "Ohh" at the first glow and one whispered "What is that?" near the end. The most powerful reaction is no reaction — just faces pressed against the glass, lit from below by something that should not exist. Physics The train always travels forward and always downward after the tunnel. Passengers sway according to acceleration and lean against the gradient; straps hang off-vertical on the descents. Loose clothing reacts to movement. Exterior objects have different velocities and distances with strong realistic parallax at every stage — near stone columns sweep past, mid-distance terraces move steadily, distant districts barely shift. Ash and embers must move independently of the train, drifting upward on thermal currents rather than streaming with the carriage. Heat must be expressed physically: window fogging and clearing, haze inside the carriage, frost at the very end. Enormous figures and flying forms must move slowly, because they are enormous. Nothing teleports, nothing freezes, nothing suddenly appears, and nothing changes position without physical cause. No transitions There are absolutely no visual transitions. No dissolves, crossfades, portals, morphing or ghosted overlays. Every environment change happens because the train physically travels into the next environment. The city becomes a cutting, the cutting becomes a tunnel, the tunnel becomes bedrock, the bedrock opens into the void, the void contains the waiting platform, the void ends at the wall, the gateway gives onto the burning plain, the plain ends at the pit rim, the rim becomes a terraced descent, the terraces fill with processions, the processions become the full inhabited depths, and the depths open onto the frozen plain. It must feel like one physically continuous impossible journey where every step is the inevitable consequence of the step before it. Audio Audio must be entirely diegetic with no music. Use cheap compressed smartphone microphone quality. The train produces a constant deep maglev roar, mechanical clatter, rattling poles, vibrating windows, swinging straps and low-frequency carriage rumble, plus passenger breathing and clothing movement. In the tunnels add hard reverberant slap-back and pressure changes as the train passes openings. The defining sound of this video is the voices. They must arrive at 6 to 9 seconds, before anything is visible, as a distant continuous mass of human voices with enormous reverberation, never resolving into words and never rising to screaming. From that point they never stop. They grow with depth and spread across a wider stereo field as the spaces open up. Beneath them, a very low frequency resonance builds steadily, felt more than heard. Above the burning plain add a broad distant roar and irregular deep concussions at long intervals. Ash strikes the hull as fine irregular ticking. In the depths the voices are vast, layered at many distances, and mixed with immense indistinct industrial-scale sound that never resolves into detail. In the final second every exterior sound stops at once — total silence outside, leaving only the carriage, the passengers' breathing, and ice creaking against the hull. At approximately 29.7 seconds the calm PA voice says quietly, "Welcome to Hell." Style and content limits The overall style must remain dirty photorealistic amateur smartphone footage despite the spectacular environments. Vertical 9:16, real-time speed, no slow motion, no stabilisation, no cinematic camera, no perfect composition, no clean VFX presentation. Use heavy shadow noise, blown highlights, low-bitrate compression, rolling-shutter skew, autofocus hunting, auto-exposure pumping, smeared motion blur and crooked framing. The camera should occasionally be partially blocked by a shoulder, arm or nearby passenger, and should struggle badly with the extreme contrast between fire and darkness. Hell is conveyed through scale, architecture, crowds at distance, fire, ash and sound. Do not show suffering, injury, bodies, blood, restraints or any graphic content. Every figure outside the train stays a distant silhouette or part of a mass — none is ever close enough for its face or condition to be read. Do not use recognisable religious iconography or legible text of any kind; all markings are eroded past legibility. Do not make it look like a game cinematic or a clean VFX render. The world outside should be overwhelming and physically impossible, but the recording itself must look raw, accidental and believable. The most important rule ONE SINGLE CONTINUOUS TAKE. No cuts, no jump cuts, no dissolves, no crossfades, no scene resets and no artificial transitions. The same train, same passengers and same camera remain present from beginning to end. The orange glow must be visible as a faint distant smudge from the first cavern and grow continuously and inevitably until it fills the window. The voices must arrive before the fire and never stop until the final second. The heat must build physically across the whole descent so that its total disappearance at the end lands as a shock. The population must emerge gradually — the waiting platform, then crowds on the causeways, then processions on the terraces, then the full inhabited depths — never appearing all at once. End while the train is still moving, the frozen plain extending endlessly beyond the left window, passengers silent, carriage still shaking, the PA announcement fading into the sound of ice.
0:25

Journey through Hell made with seedance 2.5 prompt :One continuous 30-second chaotic amateur first-person smartphone video filmed by a standing passenger inside a completely packed magnetic-levitation commuter train. Single unbroken take with no cuts, jump cuts, dissolves, crossfades, double exposures, portals, morphing or artificial scene transitions. The train begins as an ordinary weekday commute on Earth and then physically travels downward on an impossible journey — through subway tunnels into bedrock, into a colossal void beneath the crust, past a soot-blackened waiting platform, through an immense eroded gate, out above a burning plain, down the terraced wall of a vast pit, and into the inhabited depths of Hell. Everything must feel physically connected, as though the same train is genuinely travelling down through each environment. The tone is bureaucratic dread, not horror-movie shock. This is a scheduled service. The route is old, the infrastructure is worn, and the train runs it the way it runs any other line. The horror comes from how ordinary the journey is and how enormous the destination turns out to be. The interior is a completely packed standing-room-only maglev commuter car. Passengers are pressed shoulder-to-shoulder, gripping overhead straps and vertical poles. Backpacks are squeezed between bodies, coats and loose clothing shift with acceleration, straps swing on their inertia, and the entire carriage constantly vibrates and rattles. The camera is a cheap smartphone held at chest height by one standing passenger who grips a pole with the other hand. The phone itself is NEVER visible because the phone is the camera. The framing is crooked, slightly off-centre, partially blocked by shoulders and arms, and imperfect like genuine accidental footage. The camera constantly shakes, rolls, yaws and gets thrown around by acceleration. Use realistic rolling-shutter distortion, autofocus hunting, exposure pumping, blown highlights, crushed noisy shadows, low-bitrate compression, macroblocking and smeared motion blur. It must look like genuine spontaneous smartphone footage, not professional cinematic footage. The camera always looks through the LEFT-SIDE WINDOWS at approximately 90 degrees to the train's direction of travel. The train always travels forward and the outside world always streams past the windows from front-to-back. Never switch to a forward-facing train-nose view. Never show the front of the train. The same carriage, same passengers, same poles, same straps and same windows remain visually consistent throughout the entire journey. The interior is the constant realistic anchor while the outside world becomes increasingly impossible. 0 to 3 seconds. Begin with an ordinary overcast weekday commute on an elevated urban line. Grey apartment blocks, rooftop water tanks, a scrapyard, overhead wires, a canal and traffic on a road below streak past the left windows at different distances with realistic parallax. The passengers are tired and mostly uninterested — some on phones, some staring out, some talking quietly. A calm public-address chime sounds and an announcer quietly says, "Next stop: Hell." Nobody reacts. One passenger glances up briefly and goes back to their phone. The train accelerates and everyone instinctively tightens their grip as the carriage gives a hard lateral jolt. 3 to 6 seconds. The line drops into a cutting and then into a tunnel. Tiled subway walls, cable runs, service lights and a passing platform strobe across the windows in hard bands of light and dark, throwing the carriage into stuttering illumination. The tunnel ages as the train descends: modern concrete becomes older brickwork, then rough-cut stone. The fittings become scorched and soot-caked — blackened signal lights, corroded brackets, cabling burnt down to bare metal. This route has been running a long time. The gradient steepens noticeably; passengers lean back against the pitch and the straps hang visibly off-vertical. 6 to 9 seconds. The tunnel wall becomes raw geology and the train is now clearly descending through the crust at impossible speed. Through the left windows the rock face streaks past in visible strata — pale limestone, dark shale, red iron-stained bands, seams catching the carriage light. The air begins to heat and passengers loosen collars. Then, under the mechanical roar, a sound arrives before anything is visible: a vast, distant, continuous mass of human voices, far away and heavily reverberant, never resolving into individual words. It is not loud. It is simply there, and it does not stop. Two passengers look at each other. Nobody says anything. 9 to 11.5 seconds. The rock wall falls away entirely and the train emerges into a void larger than a city, with no visible far side. It runs along a ledge on the wall; below the windows the space drops away into darkness. Stone columns kilometres tall stand in the void with strong parallax — near ones sweeping past, distant ones barely shifting. Far below and far ahead, a faint orange glow is already visible. It must already exist in the frame from this moment and grow continuously from here forward without ever popping or suddenly enlarging. The voices are louder here, spread across an enormous space. One passenger near the glass leans forward slightly. Another quietly says, "Ohh." 11.5 to 14 seconds. The train passes a station. A soot-blackened platform is cut into the rock wall — worn edge, dead lamps, faded markings unreadable under the grime — and figures are standing on it in an orderly queue, waiting, facing the track, completely motionless. They are seen for less than a second at speed, as dark shapes against the platform light, never close enough to read faces. The train does not slow. Nobody boards. The platform is gone behind the carriage. This is the moment the passengers understand. Several stop looking at their phones. Embers begin drifting upward past the windows from below. 14 to 16 seconds. Ahead, a wall crosses the entire void — a single continuous barrier extending beyond sight in both directions and upward past the ceiling. The train passes through an immense gateway cut into it, an arch hundreds of metres tall, its stone eroded smooth by an unimaginable volume of traffic, its carved markings worn past legibility. For half a second the carriage is in the shadow of the arch and everything goes dark. Then it is through, and the light on the far side is completely different: warm, hard, and coming from below. 16 to 19 seconds. The train races above a burning plain. Outside the left windows: a vast crusted expanse of dark solidified ground cracked into slow-moving plates with brilliant orange fissures running between them, distant fountains of molten material rising and falling in slow motion because of their true scale, and smoke columns standing kilometres high. Running across it are raised stone causeways, and on the causeways are crowds — dense, continuous, moving slowly, all in the same direction, extending to the limit of visibility. They are seen only as masses at distance, never in detail. The light entering the carriage is now dominant and hard, throwing sharp orange edges on faces, poles and straps with deep black shadows behind them. The interior air is visibly hazy. The voices are constant. 19 to 21 seconds. The plain ends at the rim of a colossal pit — a shaft so wide the far wall is only a suggestion in the haze. The train races over the edge and begins descending along the interior wall. The wall is terraced: enormous concentric ledges receding downward, each deeper and darker than the last, disappearing into smoke layers. The descent acceleration pushes the passengers down and forward against the poles. Ash begins striking the windows and streaking backward. 21 to 23 seconds. The terraces are populated. Endless slow processions move along every ledge, strings of figures following the curve of the wall down into the smoke and out of sight, lit from below by the fires beneath them. There is no chaos in it — it is orderly, patient and entirely without end, which is worse. Furnace mouths open in the rock face, glowing white at their throats, and long queues stand before them. Everything is at distance. Nothing is close enough to resolve. The light entering the carriage takes on a deeper red and the fluorescents are overwhelmed. Passengers nearest the window press slightly closer to the glass. 23 to 25 seconds. Enormous shapes move among the terraces — figures many times the height of the crowds around them, walking slowly along the ledges, their scale established only by comparison. They do not look at the train. Distant winged forms cross beneath the carriage at low altitude with slow heavy wingbeats appropriate to their size and vanish into the smoke. Stone pens and barred openings are cut into the wall in their thousands, receding into the haze like housing. Ash accumulates in the corners of the windows. The carriage is hot enough that the glass fogs and clears in waves. 25 to 27 seconds. The train reaches the deep layer and the full extent of Hell reveals itself. It is not a city on a human plan and it is not a cavern. It is a continuous inhabited geology extending in every direction — towers of black stone fused into cliff faces, vaults hollowed out of the rock at cathedral scale, rivers of molten material running in cut channels between districts and falling in slow luminous cataracts to levels further down, bridges spanning gaps kilometres across, and everywhere on all of it, crowds. Smoke columns rise for kilometres and flatten against unseen ceilings. Every level is lit by its own fires. The train races along a ridge line through the middle of it; arches and aqueduct spans pass overhead and terraces stream past below the window with violent parallax. It is impossible to see where any of it ends. 27 to 29 seconds. The depths continue past the windows. The camera struggles badly with the contrast, blowing out the fires and crushing everything else into noise. Ash cakes the corners of the glass. Passengers are pressed against the windows in total silence, faces lit from below in hard orange, expressions stunned and completely still. One person quietly whispers, "What is that?" Nobody answers. Autofocus hunts between the ash on the glass and the world beyond it. 29 to 30 seconds. The fires end. The train passes out of the smoke and the lowest region opens — and it is not burning. It is a vast frozen plain stretching beyond the visible horizon, an impossibly wide sheet of dark grey ice, absolutely still, lit by nothing but a faint pale glow from within itself. Shapes are visible held motionless within it, spaced far apart, receding to the horizon, never shown in detail. Every exterior sound stops at once — the voices, the fires, everything — leaving only the carriage. The heat drops out and frost blooms instantly across the outside of the windows. The scale feels planetary. The passengers stare in complete silence. At approximately 29.7 seconds the calm PA voice says quietly, "Welcome to Hell." The train continues moving. The camera keeps shaking naturally as the frozen expanse extends endlessly beyond the left window. Lighting The lighting must evolve naturally throughout the journey. Begin with flat overcast daylight around 6500K. In the tunnels use harsh strobing bands from passing service lights against near-total dark, then let the sources become sparse until the carriage's own weak fluorescent tubes are the only illumination. In the void the interior lights fall off into nothing. Then introduce a growing warm orange from below — first a faint wash on the lower half of faces, then mixing with the cold interior white, then completely dominating it: hard, high-contrast, sharp-edged, with deep black shadows. Above the burning plain it should be strong enough to blow out the phone's sensor at the window. In the deep layer it becomes red-orange and omnidirectional from countless fires at every distance. In the final second all warm light vanishes and is replaced by a faint pale luminance from the ice, cold and almost sourceless. All exterior light must enter naturally through the LEFT-SIDE WINDOWS and fall across passengers' faces and clothing at every stage. Do not add artificial interior lighting to create the colours. Passengers Keep all passengers completely ordinary throughout the journey. Realistic skin texture, subtle capillary variation, natural blinking, breathing, eye movement and imperfect facial symmetry. Clothing has realistic folds and responds to acceleration. Passengers must continuously perform small independent movements: shifting weight, adjusting grip, loosening a collar in the heat, wiping fog from the glass, turning their heads, tightening their hands around poles. Do not make them freeze. Do not make everyone react at the same moment. Do not let them perform dramatic acting. Their emotional progression is subtle: indifference at the announcement → mild confusion in the tunnels → unease when the voices arrive → the exact moment of understanding as the waiting platform passes → dread on the descent → complete stunned silence in the depths. No screaming. Only one soft "Ohh" at the first glow and one whispered "What is that?" near the end. The most powerful reaction is no reaction — just faces pressed against the glass, lit from below by something that should not exist. Physics The train always travels forward and always downward after the tunnel. Passengers sway according to acceleration and lean against the gradient; straps hang off-vertical on the descents. Loose clothing reacts to movement. Exterior objects have different velocities and distances with strong realistic parallax at every stage — near stone columns sweep past, mid-distance terraces move steadily, distant districts barely shift. Ash and embers must move independently of the train, drifting upward on thermal currents rather than streaming with the carriage. Heat must be expressed physically: window fogging and clearing, haze inside the carriage, frost at the very end. Enormous figures and flying forms must move slowly, because they are enormous. Nothing teleports, nothing freezes, nothing suddenly appears, and nothing changes position without physical cause. No transitions There are absolutely no visual transitions. No dissolves, crossfades, portals, morphing or ghosted overlays. Every environment change happens because the train physically travels into the next environment. The city becomes a cutting, the cutting becomes a tunnel, the tunnel becomes bedrock, the bedrock opens into the void, the void contains the waiting platform, the void ends at the wall, the gateway gives onto the burning plain, the plain ends at the pit rim, the rim becomes a terraced descent, the terraces fill with processions, the processions become the full inhabited depths, and the depths open onto the frozen plain. It must feel like one physically continuous impossible journey where every step is the inevitable consequence of the step before it. Audio Audio must be entirely diegetic with no music. Use cheap compressed smartphone microphone quality. The train produces a constant deep maglev roar, mechanical clatter, rattling poles, vibrating windows, swinging straps and low-frequency carriage rumble, plus passenger breathing and clothing movement. In the tunnels add hard reverberant slap-back and pressure changes as the train passes openings. The defining sound of this video is the voices. They must arrive at 6 to 9 seconds, before anything is visible, as a distant continuous mass of human voices with enormous reverberation, never resolving into words and never rising to screaming. From that point they never stop. They grow with depth and spread across a wider stereo field as the spaces open up. Beneath them, a very low frequency resonance builds steadily, felt more than heard. Above the burning plain add a broad distant roar and irregular deep concussions at long intervals. Ash strikes the hull as fine irregular ticking. In the depths the voices are vast, layered at many distances, and mixed with immense indistinct industrial-scale sound that never resolves into detail. In the final second every exterior sound stops at once — total silence outside, leaving only the carriage, the passengers' breathing, and ice creaking against the hull. At approximately 29.7 seconds the calm PA voice says quietly, "Welcome to Hell." Style and content limits The overall style must remain dirty photorealistic amateur smartphone footage despite the spectacular environments. Vertical 9:16, real-time speed, no slow motion, no stabilisation, no cinematic camera, no perfect composition, no clean VFX presentation. Use heavy shadow noise, blown highlights, low-bitrate compression, rolling-shutter skew, autofocus hunting, auto-exposure pumping, smeared motion blur and crooked framing. The camera should occasionally be partially blocked by a shoulder, arm or nearby passenger, and should struggle badly with the extreme contrast between fire and darkness. Hell is conveyed through scale, architecture, crowds at distance, fire, ash and sound. Do not show suffering, injury, bodies, blood, restraints or any graphic content. Every figure outside the train stays a distant silhouette or part of a mass — none is ever close enough for its face or condition to be read. Do not use recognisable religious iconography or legible text of any kind; all markings are eroded past legibility. Do not make it look like a game cinematic or a clean VFX render. The world outside should be overwhelming and physically impossible, but the recording itself must look raw, accidental and believable. The most important rule ONE SINGLE CONTINUOUS TAKE. No cuts, no jump cuts, no dissolves, no crossfades, no scene resets and no artificial transitions. The same train, same passengers and same camera remain present from beginning to end. The orange glow must be visible as a faint distant smudge from the first cavern and grow continuously and inevitably until it fills the window. The voices must arrive before the fire and never stop until the final second. The heat must build physically across the whole descent so that its total disappearance at the end lands as a shock. The population must emerge gradually — the waiting platform, then crowds on the causeways, then processions on the terraces, then the full inhabited depths — never appearing all at once. End while the train is still moving, the frozen plain extending endlessly beyond the left window, passengers silent, carriage still shaking, the PA announcement fading into the sound of ice.

Ciri

19,504 次观看 • 22 天前

Has been a while since I've given an update so here's a breakdown of where Sappy is at right now and what we're focusing on going into this year. Pre-amble: With altcoins & NFTs the market is definitely not the same as it was before. I think this is obvious to everyone but I've noticed there are still japanese soldiers that are convinced old tricks and mechanics work. They don't. Liquidity is thin; people want to bid assets that feel like "real companies" not vacuous memecoins. There's still room for memecoins, social currencies, and "utility tokens" (I would say without these functions, tokens are hard to justify versus equities). I'm not part of the camp that thinks there will never be hyperspeculation in crypto again, because there will be; we all love ponzis and PvPing each other onchain. Just not with solved games -- people need something new and fresh. So the overarching plan is to continue building for users, sustainable revenues that aren't tied to directly to crypto, and doubling down on the areas that we've already found PMF / Brand Market Fit. Then leaning into crypto during cyclical periods where liquidity is sloshing around at an accelerated rate. Where we've found early PMF / what we're leaning into: Roblox: we're going to continue to go hard and accelerate here. It's our main objective to ship more seal/brainrot focused games across most genres to cast as wide of a net as we can for the brand, and to also iterate and see what works and stays sticky. Our initial incursion into Roblox was very successful peaking at 2M+ MAU and still sustaining a large portion of that player base... for all of its success, that was a relatively amateur first attempt; we've been setting up better AI pipelines for Roblox development that makes it reasonable to ship many more games and 10x those player counts in totality. It's my belief that Roblox is the sandbox whose audience will be the most valuable on the internet once they are grown up. That intense feeling you get when you see a TikTok referencing an old game you enjoyed on the PS2 or the Gamecube, or when you see a Pokemon card is the exact same feeling the youth of today will get when reminiscing on the things they enjoyed engaging with when they were younger. Fortnite and Roblox are functional equivalents to the old school consoles and exactly where that is taking place. Which is why as much as I care about scaling revenues through Roblox, the long term brand equity gained purely through being popular on the platform is totally invaluable. It also can heavily convert to merchandise sales today if all touchpoints for the brand are dialed in (which is why brands get overcharged so much by Roblox dev shops for the same ROI that only cost us a few thousand $). We have the playbook, it's just about iterating new concepts and then aggressively scaling. Brand Expansion & Merchandising: I've started to create a content pipeline that is easily repeatable, cost efficient (costs next to nothing through either AI or smart reusable concepts), while still being very tasteful and meeting our quality standards for the brand. We are mostly focusing here on reaching people where they're at through nostalgic/emotional content, or just being visually stimulating through carefully curated aesthetics. Content that isn't superficial and touches people in a memorable way. I've attached some examples to the post so you can see what I mean rather than just read it. I don't think it's long until larger brands start doing this at scale, but it's always good to be ahead of the curve and most importantly winning on taste -- knowing what will resonate with people and what won't has always been our edge. The purpose for these accounts is not only to rack up attention but also to begin converting those into sales of both of physicals (plushies & gacha collectibles) and digital avenues like our games, and any other apps we produce. Because they're offshoot accounts it's also a lot easier to be aggressive/experimental with said conversion strategies. Sappy Studio: I'm wrapping everything like Omnia, and everything else into this category because they're all tangentially related. Beginning with Omnia, our current focus is gearing up for Season 0 which involves players competing in the ranked ladder for a prize pool that has rewards through Monad Momentum as well as a player-funded prize pool. This season will be fairly simple with us mostly logging retention, deck building habits, as well as qualitatively observing how aggressively players push the combat system. Deeper monetization wont exist yet outside of the player buy-in (to be eligible for P2E rewards). Beyond that our overarching principle this year is to focus heavily on risk-to-earn mechanics where a portion of that excess value is circular i.e. revenues flow back to prize pools or other parts of the economy, treating the game almost like a protocol where the objective is to amass TVL or player liquidity. Social is also a big focus, and that means implementing the Open World hub which from an infrastructure perspective has already been built out and tested by all of you previously. Right now we are scaffolding the environment in 3D and working through how that hub should look and feel, so players are excited to hang out & idle together while they're queuing. For sappydotlol, what I'm about to say is still early days from a design perspective so a lot can change, but I'm pushing the site in the direction of being a virtual game console. An intersection between Nintendo & Myspace where users can play, trade, and socially interact in a way that's deeply personalised; a breathe of fresh air from the hostility of the current internet. If you go back to my thesis on Roblox above and the game console references, you can kind of see how this will all sequentially tie together. In essence, the strategy is to acquire a critical mass of players through traditional platforms like Roblox, and use that attention and trust to provide an onboarding funnel for web2 users into our own sandbox filled with a mixture of our own browser-based experiences as well as an aggregation of others. The aim is to make the platform a breath of fresh air & bunker from the enshittified platforms like TikTok/IG/X where users are actually served in ways that delight rather than agitate, and where self-expression is incentivised. Closing: As always everything here is subject to change but I've never felt more conviction in our direction until now; I know exactly what we need to do and how, with everything aligning with our team's strengths. Very excited and grinding through things to the point where I'm getting headaches and can't sleep from being hyperfocused for long periods of time lol. There probably has never been a better time to join the ecosystem from a price to fuck around and find out perspective.
1:00

Has been a while since I've given an update so here's a breakdown of where Sappy is at right now and what we're focusing on going into this year. Pre-amble: With altcoins & NFTs the market is definitely not the same as it was before. I think this is obvious to everyone but I've noticed there are still japanese soldiers that are convinced old tricks and mechanics work. They don't. Liquidity is thin; people want to bid assets that feel like "real companies" not vacuous memecoins. There's still room for memecoins, social currencies, and "utility tokens" (I would say without these functions, tokens are hard to justify versus equities). I'm not part of the camp that thinks there will never be hyperspeculation in crypto again, because there will be; we all love ponzis and PvPing each other onchain. Just not with solved games -- people need something new and fresh. So the overarching plan is to continue building for users, sustainable revenues that aren't tied to directly to crypto, and doubling down on the areas that we've already found PMF / Brand Market Fit. Then leaning into crypto during cyclical periods where liquidity is sloshing around at an accelerated rate. Where we've found early PMF / what we're leaning into: Roblox: we're going to continue to go hard and accelerate here. It's our main objective to ship more seal/brainrot focused games across most genres to cast as wide of a net as we can for the brand, and to also iterate and see what works and stays sticky. Our initial incursion into Roblox was very successful peaking at 2M+ MAU and still sustaining a large portion of that player base... for all of its success, that was a relatively amateur first attempt; we've been setting up better AI pipelines for Roblox development that makes it reasonable to ship many more games and 10x those player counts in totality. It's my belief that Roblox is the sandbox whose audience will be the most valuable on the internet once they are grown up. That intense feeling you get when you see a TikTok referencing an old game you enjoyed on the PS2 or the Gamecube, or when you see a Pokemon card is the exact same feeling the youth of today will get when reminiscing on the things they enjoyed engaging with when they were younger. Fortnite and Roblox are functional equivalents to the old school consoles and exactly where that is taking place. Which is why as much as I care about scaling revenues through Roblox, the long term brand equity gained purely through being popular on the platform is totally invaluable. It also can heavily convert to merchandise sales today if all touchpoints for the brand are dialed in (which is why brands get overcharged so much by Roblox dev shops for the same ROI that only cost us a few thousand $). We have the playbook, it's just about iterating new concepts and then aggressively scaling. Brand Expansion & Merchandising: I've started to create a content pipeline that is easily repeatable, cost efficient (costs next to nothing through either AI or smart reusable concepts), while still being very tasteful and meeting our quality standards for the brand. We are mostly focusing here on reaching people where they're at through nostalgic/emotional content, or just being visually stimulating through carefully curated aesthetics. Content that isn't superficial and touches people in a memorable way. I've attached some examples to the post so you can see what I mean rather than just read it. I don't think it's long until larger brands start doing this at scale, but it's always good to be ahead of the curve and most importantly winning on taste -- knowing what will resonate with people and what won't has always been our edge. The purpose for these accounts is not only to rack up attention but also to begin converting those into sales of both of physicals (plushies & gacha collectibles) and digital avenues like our games, and any other apps we produce. Because they're offshoot accounts it's also a lot easier to be aggressive/experimental with said conversion strategies. Sappy Studio: I'm wrapping everything like Omnia, and everything else into this category because they're all tangentially related. Beginning with Omnia, our current focus is gearing up for Season 0 which involves players competing in the ranked ladder for a prize pool that has rewards through Monad Momentum as well as a player-funded prize pool. This season will be fairly simple with us mostly logging retention, deck building habits, as well as qualitatively observing how aggressively players push the combat system. Deeper monetization wont exist yet outside of the player buy-in (to be eligible for P2E rewards). Beyond that our overarching principle this year is to focus heavily on risk-to-earn mechanics where a portion of that excess value is circular i.e. revenues flow back to prize pools or other parts of the economy, treating the game almost like a protocol where the objective is to amass TVL or player liquidity. Social is also a big focus, and that means implementing the Open World hub which from an infrastructure perspective has already been built out and tested by all of you previously. Right now we are scaffolding the environment in 3D and working through how that hub should look and feel, so players are excited to hang out & idle together while they're queuing. For sappydotlol, what I'm about to say is still early days from a design perspective so a lot can change, but I'm pushing the site in the direction of being a virtual game console. An intersection between Nintendo & Myspace where users can play, trade, and socially interact in a way that's deeply personalised; a breathe of fresh air from the hostility of the current internet. If you go back to my thesis on Roblox above and the game console references, you can kind of see how this will all sequentially tie together. In essence, the strategy is to acquire a critical mass of players through traditional platforms like Roblox, and use that attention and trust to provide an onboarding funnel for web2 users into our own sandbox filled with a mixture of our own browser-based experiences as well as an aggregation of others. The aim is to make the platform a breath of fresh air & bunker from the enshittified platforms like TikTok/IG/X where users are actually served in ways that delight rather than agitate, and where self-expression is incentivised. Closing: As always everything here is subject to change but I've never felt more conviction in our direction until now; I know exactly what we need to do and how, with everything aligning with our team's strengths. Very excited and grinding through things to the point where I'm getting headaches and can't sleep from being hyperfocused for long periods of time lol. There probably has never been a better time to join the ecosystem from a price to fuck around and find out perspective.

wab.eth

18,272 次观看 • 8 个月前

I played 4 hours of The Blood of Dawnwalker - and it's damn good. My full thoughts below 👇 The version we played was in beta and running on some powerful PCs. It was from the beginning of the game so I wasn't able to explore the entire map. It felt pretty polished overall and I didn't experience any bugs or performance issues during my time. The game takes place in Vale Sangora - it's a beautiful valley near the Carpathian Mountains full of lush trees, bogs, mines, and all kinds of wildlife and villages and communities roughly comparable in size to The Witcher 3's Blood and Wine but packed with detail. There's a lot of neat history everywhere you look and explore, with references to Genghis Khan's hordes, the Tatars, and more. There are also little details that help make the setting a bit more real. Because Vale Sangora is run by the vampire leaders, silver is forbidden to have in your possession, and not every merchant will buy or sell them. It looks great visually, but I wouldn't say in a way that blew my socks off. Environments look good, trees, bushes etc all swaying in the wind, good lighting, character models are nicely detailed. It's not pushing things on a technical level but I found that perfectly ok. From the beginning of the game, a series of events introduces you to Coen's family - his parents and siblings. His father Pieter is a strong and stern caretaker who knows his way around a sword but deeply cares for his family. Coen's mother Esme is stricken by an illness that the whole family is trying to wrangle with, and his siblings are playful and endearing. As the vampires don't tolerate 'weakness', you start the feel the weight of the family's plight that gives off an aura of despair. At least in these initial hours, I found myself surprisingly growing attached pretty quickly. While there's an ominous metaphorical 'cloud' that hangs over the valley, there are bits of lightheartedness thrown in too. One charming quest saw me play tag with my siblings and go fishing in the old family hangout spot. Character performances and voice acting are excellent. There weren't really any characters that felt out place or miscast. I especially enjoyed the gravelly voices of Pieter and Brencis - the leader of the vampires. Brencis comes off formidable, and events in the game gave me a motivational drive for revenge, which I always like in games. The story is set up in a way that each of the vampire leaders needs to be taken down, with Brencis as the head honcho. You can attack them in any order, even going directly to Brencis from the outset, but you'll probably find you'll have a bit of trouble with that approach. While I've always been a little hesitant about plots that have open-ended structures, the team at Rebel Wolves told me that each vampire 'captain' is unique with many quests tailored to their specific stories, and there was a lot of effort and care put into each. They urge players to play through each storyline to get the most out of the game. There were lots of endearing characters in just the first few hours. Anca - a local herbalist and a witch, reminded me a lot of The Witcher 3's Keira. And she's a romance option. There are other sentient races as well, like the Uriash which I would say resemble something like the Qunari from Dragon Age. They're big, tough and brooding and are seen as monsters and somewhat shunned. There's a nice variety of monsters too, like kobolds who are basically ghouls that talk smack, and I came across the "Great Bog Wurm" in a swampy area which was it's own mini-boss fight. When I compare to something like The Witcher 3 (because many people understandably do with Dawnwalker given how the game looks and the makeup of the Rebel Wolves team), movement and navigation in the game felt fairly fluid overall. Walking, sprinting, vaulting ledges, etc were smooth. There's a bit of clunkiness when it comes to jumping, where I'd sometimes starting sliding jumping down a hill or onto rocks. There's also an ability that Coen automatically gets after he becomes a half-vampire called "Planeshift". It lets him teleport dash around the world so he can reach higher areas or across gaps. It felt a little clunky and imprecise when scaling things like towers and trying to land back on solid ground. There's a glossary/beastiary that's structured just like The Witcher 3, and the soundtrack is basically, you guessed it, The Witcher 3 in all the best ways. And like one of my favorite parts of The Witcher 3, there are plenty of points of interest that lead to unique little narrative beats or quests. One saw me come across a villager searching for his brother. Following that little quest line led me to a buried tomb which led to a boss fight with an ancient warrior and cool loot at the end. One abrupt encounter in the world saw me chase the village asshole talking shit about my family. When his drunkard father catches us arguing, he scolds him more and tells me I should teach him a lesson myself and beat him with a stick. I can choose to partake in it, stand by and watch, or stop the father in a physical altercation. There's been a lot of questions about combat and from what I played, I liked it, moreso than The Witcher 3's. There are two ways to play: directional and traditional. With directional combat, you hold down a shoulder button to block while aiming in whatever direction you see an enemy attacking from. That's either up, down, left or right. It's simpler than something like Kingdom Come and I got used to it real quick. It sometimes got a liiiittle overwhelming when multiple enemies are attacking at the same time - and they do that a lot. Enemies don't wait around for their turn, instead opting to gang up on you to take you down. There's also the traditional or 'standard' combat, which is basically pressing a button that blocks enemies no matter what direction (your standard action game). You can also expectedly parry enemies that open them up for more damage. There is a stamina meter that depletes with blocks, and it depletes faster if you're playing standard, though you can upgrade your stamina as you play too. There are active abilities you can put into quick slots for faster use during combat. As there's a day/night cycle and Coen is a 'dawnwalker' - meaning he's human during the day and a vampire at night, you can switch between your swords in daytime and bring out your claws at night, which are more powerful. There are many abilities, though because I was playing the first hours of the game, I didn't get to see them all. One that I got to use was a powerful charge attack, and another was a flurry of deadly slashes with my claws. You can drain enemies to regain health with 'voracious bite', though enemies won't wait around while you're doing it so you have to be mindful. There are shrines dotted around the map that you can use to fast travel and upgrade your skills. There are tons of resources and items in the world just like The Witcher that you can use to craft potions and the like. Some can only be done during the day or at night. In terms of time progression, there are 8 time 'segments' per day and certain quests and activities can push the time forward a set amount of segments. I thought I would hate it at first, but it actually makes for some compelling choices in how you choose to progress the game. You're always shown when an action will progress time by the way, so nothing will take you by surprise. Running around and exploring the world doesn't push time forward. When your vampiric health drops really low, you become hungry and start to really crave blood. You can even lose control during dialogue and drain the person you're talking to - including friends. I didn't encounter that myself but the devs said those can have lasting effects throughout the game. This has definitely jumped up my most anticipated list for the rest of the year. It's practically The Witcher 3: Medieval Vampire Edition with its own flavor and unique mechanics and honestly...that is something I'm quite happy about. #BloodofDawnwalker
5:09

I played 4 hours of The Blood of Dawnwalker - and it's damn good. My full thoughts below 👇 The version we played was in beta and running on some powerful PCs. It was from the beginning of the game so I wasn't able to explore the entire map. It felt pretty polished overall and I didn't experience any bugs or performance issues during my time. The game takes place in Vale Sangora - it's a beautiful valley near the Carpathian Mountains full of lush trees, bogs, mines, and all kinds of wildlife and villages and communities roughly comparable in size to The Witcher 3's Blood and Wine but packed with detail. There's a lot of neat history everywhere you look and explore, with references to Genghis Khan's hordes, the Tatars, and more. There are also little details that help make the setting a bit more real. Because Vale Sangora is run by the vampire leaders, silver is forbidden to have in your possession, and not every merchant will buy or sell them. It looks great visually, but I wouldn't say in a way that blew my socks off. Environments look good, trees, bushes etc all swaying in the wind, good lighting, character models are nicely detailed. It's not pushing things on a technical level but I found that perfectly ok. From the beginning of the game, a series of events introduces you to Coen's family - his parents and siblings. His father Pieter is a strong and stern caretaker who knows his way around a sword but deeply cares for his family. Coen's mother Esme is stricken by an illness that the whole family is trying to wrangle with, and his siblings are playful and endearing. As the vampires don't tolerate 'weakness', you start the feel the weight of the family's plight that gives off an aura of despair. At least in these initial hours, I found myself surprisingly growing attached pretty quickly. While there's an ominous metaphorical 'cloud' that hangs over the valley, there are bits of lightheartedness thrown in too. One charming quest saw me play tag with my siblings and go fishing in the old family hangout spot. Character performances and voice acting are excellent. There weren't really any characters that felt out place or miscast. I especially enjoyed the gravelly voices of Pieter and Brencis - the leader of the vampires. Brencis comes off formidable, and events in the game gave me a motivational drive for revenge, which I always like in games. The story is set up in a way that each of the vampire leaders needs to be taken down, with Brencis as the head honcho. You can attack them in any order, even going directly to Brencis from the outset, but you'll probably find you'll have a bit of trouble with that approach. While I've always been a little hesitant about plots that have open-ended structures, the team at Rebel Wolves told me that each vampire 'captain' is unique with many quests tailored to their specific stories, and there was a lot of effort and care put into each. They urge players to play through each storyline to get the most out of the game. There were lots of endearing characters in just the first few hours. Anca - a local herbalist and a witch, reminded me a lot of The Witcher 3's Keira. And she's a romance option. There are other sentient races as well, like the Uriash which I would say resemble something like the Qunari from Dragon Age. They're big, tough and brooding and are seen as monsters and somewhat shunned. There's a nice variety of monsters too, like kobolds who are basically ghouls that talk smack, and I came across the "Great Bog Wurm" in a swampy area which was it's own mini-boss fight. When I compare to something like The Witcher 3 (because many people understandably do with Dawnwalker given how the game looks and the makeup of the Rebel Wolves team), movement and navigation in the game felt fairly fluid overall. Walking, sprinting, vaulting ledges, etc were smooth. There's a bit of clunkiness when it comes to jumping, where I'd sometimes starting sliding jumping down a hill or onto rocks. There's also an ability that Coen automatically gets after he becomes a half-vampire called "Planeshift". It lets him teleport dash around the world so he can reach higher areas or across gaps. It felt a little clunky and imprecise when scaling things like towers and trying to land back on solid ground. There's a glossary/beastiary that's structured just like The Witcher 3, and the soundtrack is basically, you guessed it, The Witcher 3 in all the best ways. And like one of my favorite parts of The Witcher 3, there are plenty of points of interest that lead to unique little narrative beats or quests. One saw me come across a villager searching for his brother. Following that little quest line led me to a buried tomb which led to a boss fight with an ancient warrior and cool loot at the end. One abrupt encounter in the world saw me chase the village asshole talking shit about my family. When his drunkard father catches us arguing, he scolds him more and tells me I should teach him a lesson myself and beat him with a stick. I can choose to partake in it, stand by and watch, or stop the father in a physical altercation. There's been a lot of questions about combat and from what I played, I liked it, moreso than The Witcher 3's. There are two ways to play: directional and traditional. With directional combat, you hold down a shoulder button to block while aiming in whatever direction you see an enemy attacking from. That's either up, down, left or right. It's simpler than something like Kingdom Come and I got used to it real quick. It sometimes got a liiiittle overwhelming when multiple enemies are attacking at the same time - and they do that a lot. Enemies don't wait around for their turn, instead opting to gang up on you to take you down. There's also the traditional or 'standard' combat, which is basically pressing a button that blocks enemies no matter what direction (your standard action game). You can also expectedly parry enemies that open them up for more damage. There is a stamina meter that depletes with blocks, and it depletes faster if you're playing standard, though you can upgrade your stamina as you play too. There are active abilities you can put into quick slots for faster use during combat. As there's a day/night cycle and Coen is a 'dawnwalker' - meaning he's human during the day and a vampire at night, you can switch between your swords in daytime and bring out your claws at night, which are more powerful. There are many abilities, though because I was playing the first hours of the game, I didn't get to see them all. One that I got to use was a powerful charge attack, and another was a flurry of deadly slashes with my claws. You can drain enemies to regain health with 'voracious bite', though enemies won't wait around while you're doing it so you have to be mindful. There are shrines dotted around the map that you can use to fast travel and upgrade your skills. There are tons of resources and items in the world just like The Witcher that you can use to craft potions and the like. Some can only be done during the day or at night. In terms of time progression, there are 8 time 'segments' per day and certain quests and activities can push the time forward a set amount of segments. I thought I would hate it at first, but it actually makes for some compelling choices in how you choose to progress the game. You're always shown when an action will progress time by the way, so nothing will take you by surprise. Running around and exploring the world doesn't push time forward. When your vampiric health drops really low, you become hungry and start to really crave blood. You can even lose control during dialogue and drain the person you're talking to - including friends. I didn't encounter that myself but the devs said those can have lasting effects throughout the game. This has definitely jumped up my most anticipated list for the rest of the year. It's practically The Witcher 3: Medieval Vampire Edition with its own flavor and unique mechanics and honestly...that is something I'm quite happy about. #BloodofDawnwalker

Shinobi602

345,682 次观看 • 2 个月前

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:
31:35

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:

Mike

15,145 次观看 • 9 个月前

If you watch this ~50 minute screen recording closely (yeah, I know, it's long; there are also some times when my computer was very slow and laggy, just skip past that part. And at one point I had to run and get my 9-month-old a new bottle and left it on a boring screen, sorry!), I believe you can see real signs of the kind of runaway, recursive AI self-improvement that people have been warning of for a while (Mr. Kurzweil most notably and prophetically). Why do I say that? What's different now? Well, there's a reason my set of agent coding tooling is called the Flywheel. These tools all mutually self-reinforce each other. And they all flow directly into my ntm tool (short for "named_tmux_manager"), which acts as a sort of integration point and nerve center for the tools (this is becoming more true by the minute as I'm now seriously working on ntm). Now, ntm was something I started making to automate some aspects of my workflow, but it was the kind of thing where, until it was perfect, it sort of just slowed me down. So I didn't actually use it even though I kept working on it and trying to improve it, and suggested to users that they try it in my tutorials. Well anyway, I finally got around to "dogfooding" ntm last night, and now it's going to get very dramatically better at an alarming rate. Some of that is from applying my "idea wizard" prompt to generate more useful features and building that stuff out and addressing obvious pain points I encountered during my newfound usage of the tool. But a lot comes from my realization that, once again, ntm's true utility is not as a tool for ME, but for an agent. That is, ntm lets one instance of Claude Code or Codex act as, well, me, do the things that I had been doing manually. Do I wish I had started using ntm earlier? No, for two big reasons: 1) Doing it manually helped me build up my intuition massively, which directly led me down the path of creating useful prompt strategies and workflows; these often began as ad-hoc prompts that I realized could be generalized and made more versatile/universal. Lesson: don't prematurely automate until you have an intimate, intuitive feel for your "core value-add loop." Otherwise you'll have a fully automated system quickly that efficiently and automatically does a stupid or otherwise sub-optimal thing. 2) My eyes have been opened to the beauty and power of Skills. I'm not talking about your garden-variety skills that are just a simple markdown file. I'm talking about true tour-de-force directories of perfectly structured and organized files that are filled with good information, insights, workflows, etc., but presented in a way that is highly optimized for consumption by AI agents, with extreme attention paid to things like perfect progressive disclosure, token density, agent-ergonomics, agent-intuitiveness, etc. And also Skills that go way beyond markdown files, with full integration into Claude Code where it makes sense via hooks, sub-agents, and even Python scripts. These kinds of skills are a qualitative difference in expressive power and usefulness and a total game changer. They are also effectively composable, creating almost an algebra of skills that let you use them together in powerful ways. I'm working on a subscription service website and CLI tool now to share what I've learned here most effectively, stay tuned for that in the coming days. Anyway, I now know what to make and how to make it. So, getting back to that screen recording, what does it show that makes me claim recursive self-improvement is here? If you keep your eye on the upper left tmux pane, that's the "controller" agent. It is using ntm to control all the other panes which are also running Claude Code (but ntm fully supports other agent types like Codex and Gemini-CLI, and it's trivially easy to mix and match them if you wanted to have, say, 8 CCs and 6 Codexes for writing the code and 3 Gemini-CLIs for reviewing code.) Now, there's nothing that crazy about this much so far. But where it starts to get very cool is that as the session continues and we encounter real-world problems, things like my ridiculously overloaded computer that keeps hanging for long periods, Claude Code instances that crash and get into a frozen, unresponsive state, it can learn from that. And you can see it using my skill writing skill to refine its ntm vibe coding skill in real time. And then take that skill and refine it to be more intuitive for itself. Or use my cass tool skill to search all the session histories to look for problems that came up and strategize how to solve them. The most useful part was when, towards the end of the session, I told it to reflect on all the things we had done and problems we encountered. One way it can usefully leverage those reflections is by improving its ntm vibe coding skill to make it cover more edge cases and exigencies. But the other, more fundamental, way is for it to conceive of and design the optimal new features and functionality for ntm itself so that the tool embodies those lessons in a first-class way. This offloads cognition from its brain onto its tooling, just like how a person can lean on spellcheck or a calculator. It codifies correct, effective reasoning at the tool level, where it's more reliable and robust and repeatable. And btw, did you notice what code base it was working on the whole time? It was none other than ntm itself! So as it worked on its own tool, it had reflections and ideas about how to further improve the tool. Now, it could have just as easily gotten those insights and ideas while using ntm to work on a different project, but the fact that it was working on itself is almost gloriously meta and recursive. So by the end, after learning from tending to a big group of agent workers (btw, I have previously emphasized doing everything in a really distributed/decentralized way, where each fungible agent gets identical marching orders that tell it to use my bv tool to find the optimal bead to work on. This does work very well, but occasionally results in some contention and overlap from thundering herd, or at least wastes time/tokens/communication in avoiding that before the agents waste time duplicating work. But in this new ntm-oriented workflow, I was able to have the controller agent in the upper left use bv itself and then optimally parcel out the instructions to each agent so that we could know for sure that there's no overlap), I ended up with a ton of new beads for new features, which I had it optimize and polish a few times. Now I can swap to a new Claude Max account and have the swarm implement all those new features! It should only take a couple passes like the one shown in the screen recording to get everything implemented. Then we can rinse and repeat, having the agent read through the full session histories of each agent and its experience from its own session in sending ntm commands and seeing how they worked out in practice, to come up with the next batch of changes to both its ntm vibe coding skill AND to the ntm tool itself. Do you see how rapidly this turns into Skynet? My mistake earlier was in focusing on making myself a "faster horse" as Henry Ford used to joke about customers wanting before he showed them what they should really want (a Model T). That is, something that would make my experience nicer while doing this agent swarm based development workflow. But the obvious lesson is that you should make all your tooling agent-first because the agents are just better at this stuff. You can still watch, and of course I did add a ridiculous number of very nice human-centric features to ntm that you'll be seeing in the next day or two, but those are really kind of "for fun" to make us humans feel better about the process. All the real value-add is happening "by agents, for agents." PS: Towards the end, you can see me switch to my Mac and tell Claude to improve the skill that I made earlier today for taking the mkv screen recording files from OBS Studio and muxing them into MP4 files for sharing, while downloading songs from YouTube to serve as the background music. I made it so it can also grab the thumbnails and generate little song credit cards that show up in the lower right corner. This worked perfectly the first time! I'll include some screenshots in a response post showing how that worked, but it was awesome to witness. Skills are POWERFUL. I'll also post a link to this video on YouTube if you prefer to watch it there.
51:48

If you watch this ~50 minute screen recording closely (yeah, I know, it's long; there are also some times when my computer was very slow and laggy, just skip past that part. And at one point I had to run and get my 9-month-old a new bottle and left it on a boring screen, sorry!), I believe you can see real signs of the kind of runaway, recursive AI self-improvement that people have been warning of for a while (Mr. Kurzweil most notably and prophetically). Why do I say that? What's different now? Well, there's a reason my set of agent coding tooling is called the Flywheel. These tools all mutually self-reinforce each other. And they all flow directly into my ntm tool (short for "named_tmux_manager"), which acts as a sort of integration point and nerve center for the tools (this is becoming more true by the minute as I'm now seriously working on ntm). Now, ntm was something I started making to automate some aspects of my workflow, but it was the kind of thing where, until it was perfect, it sort of just slowed me down. So I didn't actually use it even though I kept working on it and trying to improve it, and suggested to users that they try it in my tutorials. Well anyway, I finally got around to "dogfooding" ntm last night, and now it's going to get very dramatically better at an alarming rate. Some of that is from applying my "idea wizard" prompt to generate more useful features and building that stuff out and addressing obvious pain points I encountered during my newfound usage of the tool. But a lot comes from my realization that, once again, ntm's true utility is not as a tool for ME, but for an agent. That is, ntm lets one instance of Claude Code or Codex act as, well, me, do the things that I had been doing manually. Do I wish I had started using ntm earlier? No, for two big reasons: 1) Doing it manually helped me build up my intuition massively, which directly led me down the path of creating useful prompt strategies and workflows; these often began as ad-hoc prompts that I realized could be generalized and made more versatile/universal. Lesson: don't prematurely automate until you have an intimate, intuitive feel for your "core value-add loop." Otherwise you'll have a fully automated system quickly that efficiently and automatically does a stupid or otherwise sub-optimal thing. 2) My eyes have been opened to the beauty and power of Skills. I'm not talking about your garden-variety skills that are just a simple markdown file. I'm talking about true tour-de-force directories of perfectly structured and organized files that are filled with good information, insights, workflows, etc., but presented in a way that is highly optimized for consumption by AI agents, with extreme attention paid to things like perfect progressive disclosure, token density, agent-ergonomics, agent-intuitiveness, etc. And also Skills that go way beyond markdown files, with full integration into Claude Code where it makes sense via hooks, sub-agents, and even Python scripts. These kinds of skills are a qualitative difference in expressive power and usefulness and a total game changer. They are also effectively composable, creating almost an algebra of skills that let you use them together in powerful ways. I'm working on a subscription service website and CLI tool now to share what I've learned here most effectively, stay tuned for that in the coming days. Anyway, I now know what to make and how to make it. So, getting back to that screen recording, what does it show that makes me claim recursive self-improvement is here? If you keep your eye on the upper left tmux pane, that's the "controller" agent. It is using ntm to control all the other panes which are also running Claude Code (but ntm fully supports other agent types like Codex and Gemini-CLI, and it's trivially easy to mix and match them if you wanted to have, say, 8 CCs and 6 Codexes for writing the code and 3 Gemini-CLIs for reviewing code.) Now, there's nothing that crazy about this much so far. But where it starts to get very cool is that as the session continues and we encounter real-world problems, things like my ridiculously overloaded computer that keeps hanging for long periods, Claude Code instances that crash and get into a frozen, unresponsive state, it can learn from that. And you can see it using my skill writing skill to refine its ntm vibe coding skill in real time. And then take that skill and refine it to be more intuitive for itself. Or use my cass tool skill to search all the session histories to look for problems that came up and strategize how to solve them. The most useful part was when, towards the end of the session, I told it to reflect on all the things we had done and problems we encountered. One way it can usefully leverage those reflections is by improving its ntm vibe coding skill to make it cover more edge cases and exigencies. But the other, more fundamental, way is for it to conceive of and design the optimal new features and functionality for ntm itself so that the tool embodies those lessons in a first-class way. This offloads cognition from its brain onto its tooling, just like how a person can lean on spellcheck or a calculator. It codifies correct, effective reasoning at the tool level, where it's more reliable and robust and repeatable. And btw, did you notice what code base it was working on the whole time? It was none other than ntm itself! So as it worked on its own tool, it had reflections and ideas about how to further improve the tool. Now, it could have just as easily gotten those insights and ideas while using ntm to work on a different project, but the fact that it was working on itself is almost gloriously meta and recursive. So by the end, after learning from tending to a big group of agent workers (btw, I have previously emphasized doing everything in a really distributed/decentralized way, where each fungible agent gets identical marching orders that tell it to use my bv tool to find the optimal bead to work on. This does work very well, but occasionally results in some contention and overlap from thundering herd, or at least wastes time/tokens/communication in avoiding that before the agents waste time duplicating work. But in this new ntm-oriented workflow, I was able to have the controller agent in the upper left use bv itself and then optimally parcel out the instructions to each agent so that we could know for sure that there's no overlap), I ended up with a ton of new beads for new features, which I had it optimize and polish a few times. Now I can swap to a new Claude Max account and have the swarm implement all those new features! It should only take a couple passes like the one shown in the screen recording to get everything implemented. Then we can rinse and repeat, having the agent read through the full session histories of each agent and its experience from its own session in sending ntm commands and seeing how they worked out in practice, to come up with the next batch of changes to both its ntm vibe coding skill AND to the ntm tool itself. Do you see how rapidly this turns into Skynet? My mistake earlier was in focusing on making myself a "faster horse" as Henry Ford used to joke about customers wanting before he showed them what they should really want (a Model T). That is, something that would make my experience nicer while doing this agent swarm based development workflow. But the obvious lesson is that you should make all your tooling agent-first because the agents are just better at this stuff. You can still watch, and of course I did add a ridiculous number of very nice human-centric features to ntm that you'll be seeing in the next day or two, but those are really kind of "for fun" to make us humans feel better about the process. All the real value-add is happening "by agents, for agents." PS: Towards the end, you can see me switch to my Mac and tell Claude to improve the skill that I made earlier today for taking the mkv screen recording files from OBS Studio and muxing them into MP4 files for sharing, while downloading songs from YouTube to serve as the background music. I made it so it can also grab the thumbnails and generate little song credit cards that show up in the lower right corner. This worked perfectly the first time! I'll include some screenshots in a response post showing how that worked, but it was awesome to witness. Skills are POWERFUL. I'll also post a link to this video on YouTube if you prefer to watch it there.

Jeffrey Emanuel

25,483 次观看 • 7 个月前

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen
49:49

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

Moon Dev

26,242 次观看 • 6 个月前

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

308,176 次观看 • 4 个月前

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for
2:40:29

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for

Moon Dev

11,196 次观看 • 6 个月前

Recently I got some hands-on time with Crimson Desert and below are my first impressions as well as some of the gameplay I was able to capture. Crimson Desert is a good game, but it won’t be for everyone. I know the devs claim this isn’t an RPG, but I don’t know any other way to describe this game other than a HARDCORE action RPG. If you need the yellow paint to know where to climb this game isn’t for you. But if you love getting lost in a whimsical world with a boat load of content this game is going to be right up your alley. I think what impressed me most is the attention to detail. There’s so many little things the dev team took into consideration that I think people who enjoy being immersed into a world are going to appreciate. Even if you aren’t that person; on a basic level I think most will enjoy the game's combat. It’s fast, fluid and provides a ton of player expression with its deep skill tree. The world of Pywel is vibrant, large in scale and full of life. It’s easy to get lost off the main quest line as there’s always something to do and someone to speak to. An example being I was wandering through the open world and encountered a distressed woman seeking help. I agreed to follow her only to find out moments later she was with a gang and they were trying to back door me. That had me cracking up. I think if the open world is consistently full of fun, unique side content like that & the main quest line is fire this game has a lot of potential to impress. It’s just a shame that I didn’t get to spend more time with the main quests as I kept getting side tracked with cool stuff to do in the open world. So I can’t give you much insight into that. What I can say is after the opening section there’s NEXT TO NO tutorials in this game, the puzzles are hard & the default controls are a bit clunky. You will be getting lost and I can see that frustrating some people who aren’t interested in a challenge. That’s why I mentioned earlier that this is a hardcore RPG. It does not do a lot of hand holding. Because of that I predict you and your friends will be sharing tips and tricks similar to when Elden Ring first launched and nobody knew what they were doing. If you are a patient person and take the time to learn the game's systems I promise you will be able to put together some awesome combos that will make you feel like the main character. My biggest fear for this game is that I won't finish it. Not because it’s a bad game, but I can just tell from my brief time with it that it’s next level massive. As someone who's been gaming for 30+ years it’s very rare you’ll hear me say a game was overwhelming, but this game is. For people who lack a ton of free time I can see that being a turn off because once again the game doesn’t give much direction or tutorials outside the opening area. Not to mention this game could be big just for the sake of being big. I was curious to know how much of the content was engaging versus just open world bloat? Hard to tell because I only got a few hours with the game. I also fear that the Ai isn’t the best in this game. The Ai issues I encountered zapped all immersion away for those moments. I’m not sure if the final build will differ from the vertical slice we played, but what I can tell you is that on the build we played I wasn’t impressed by the Ai. Several times I attacked enemy camps and they never reacted to me attacking them. They just stood there and took it which made the world feel less alive. There were also times where enemies were looking dead at me just standing still as the battle music played threatening to beat me up, but they never did anything. It was 3 or 4 times I encountered this poor Ai which is a red flag for me because I only got two hours of hands on time with the game. Mind you in those two hours a good portion of it was just me working my way through the prologue and the early quests, so I didn't spend a ton of time in the open world. What I'm trying to get at is the janky ai was very noticeable. It wasn't something that took long to find. I will say when the game works it's great, but when I tell you the Ai was bad at times it was bad. It reminded me of the dumb NPC’s often found in Ubisoft open world games. I’m not looking for this to be a Souls game but I want some level of challenge in the combat. Hopefully that stuff gets patched out. That being said, I’m confident in saying this game is good. I just didn’t have enough time with it to determine if it’s good, or GREAT. Only time will tell when Crimson Desert drops on March 19th, 2026 for the PC, PS5 and Xbox Series. Pros —---------- - Combat makes you feel like demon - Deep Skill tree - Vibrant world - Solid voice acting - Fire OST - The little details (trust system, you can commit crimes ect.) - No Fall damage - Puzzles are creative & challenging - Game doesn’t hold you hand (some people will hate this) - You can swap in and out of 3rd and 1st person at will. Wasn’t able to explore much of how that changes the game, but it’s nice that it's an option. - EASILY over 100hrs of content (some will hate this though) Cons —---------- - Clunky controls (Default controls take some time adjusting too. I hope there’s other control schemes at launch) - Inconsistent Ai (Ubisoft bad at times. sometimes the enemies wouldn’t attack during combat or act like they never saw you) - Long load times (we were playing on PC’s, but idk the specs) - Your horse can faint & when they do traveling the large world wasn’t as fun (and I couldn’t figure out how to get him back - most likely a skill issue) - Early stamina management is OD. Early game it’s easy to drown & get tired running. I’d imagine it gets better late game, but early game it’s frustrating trying to explore. - Camera takes some getting used to in combat. Sometimes its too close and others too far. - Early arrows have no impact. Felt useless. Hoping later upgrades fix that
3:17

Recently I got some hands-on time with Crimson Desert and below are my first impressions as well as some of the gameplay I was able to capture. Crimson Desert is a good game, but it won’t be for everyone. I know the devs claim this isn’t an RPG, but I don’t know any other way to describe this game other than a HARDCORE action RPG. If you need the yellow paint to know where to climb this game isn’t for you. But if you love getting lost in a whimsical world with a boat load of content this game is going to be right up your alley. I think what impressed me most is the attention to detail. There’s so many little things the dev team took into consideration that I think people who enjoy being immersed into a world are going to appreciate. Even if you aren’t that person; on a basic level I think most will enjoy the game's combat. It’s fast, fluid and provides a ton of player expression with its deep skill tree. The world of Pywel is vibrant, large in scale and full of life. It’s easy to get lost off the main quest line as there’s always something to do and someone to speak to. An example being I was wandering through the open world and encountered a distressed woman seeking help. I agreed to follow her only to find out moments later she was with a gang and they were trying to back door me. That had me cracking up. I think if the open world is consistently full of fun, unique side content like that & the main quest line is fire this game has a lot of potential to impress. It’s just a shame that I didn’t get to spend more time with the main quests as I kept getting side tracked with cool stuff to do in the open world. So I can’t give you much insight into that. What I can say is after the opening section there’s NEXT TO NO tutorials in this game, the puzzles are hard & the default controls are a bit clunky. You will be getting lost and I can see that frustrating some people who aren’t interested in a challenge. That’s why I mentioned earlier that this is a hardcore RPG. It does not do a lot of hand holding. Because of that I predict you and your friends will be sharing tips and tricks similar to when Elden Ring first launched and nobody knew what they were doing. If you are a patient person and take the time to learn the game's systems I promise you will be able to put together some awesome combos that will make you feel like the main character. My biggest fear for this game is that I won't finish it. Not because it’s a bad game, but I can just tell from my brief time with it that it’s next level massive. As someone who's been gaming for 30+ years it’s very rare you’ll hear me say a game was overwhelming, but this game is. For people who lack a ton of free time I can see that being a turn off because once again the game doesn’t give much direction or tutorials outside the opening area. Not to mention this game could be big just for the sake of being big. I was curious to know how much of the content was engaging versus just open world bloat? Hard to tell because I only got a few hours with the game. I also fear that the Ai isn’t the best in this game. The Ai issues I encountered zapped all immersion away for those moments. I’m not sure if the final build will differ from the vertical slice we played, but what I can tell you is that on the build we played I wasn’t impressed by the Ai. Several times I attacked enemy camps and they never reacted to me attacking them. They just stood there and took it which made the world feel less alive. There were also times where enemies were looking dead at me just standing still as the battle music played threatening to beat me up, but they never did anything. It was 3 or 4 times I encountered this poor Ai which is a red flag for me because I only got two hours of hands on time with the game. Mind you in those two hours a good portion of it was just me working my way through the prologue and the early quests, so I didn't spend a ton of time in the open world. What I'm trying to get at is the janky ai was very noticeable. It wasn't something that took long to find. I will say when the game works it's great, but when I tell you the Ai was bad at times it was bad. It reminded me of the dumb NPC’s often found in Ubisoft open world games. I’m not looking for this to be a Souls game but I want some level of challenge in the combat. Hopefully that stuff gets patched out. That being said, I’m confident in saying this game is good. I just didn’t have enough time with it to determine if it’s good, or GREAT. Only time will tell when Crimson Desert drops on March 19th, 2026 for the PC, PS5 and Xbox Series. Pros —---------- - Combat makes you feel like demon - Deep Skill tree - Vibrant world - Solid voice acting - Fire OST - The little details (trust system, you can commit crimes ect.) - No Fall damage - Puzzles are creative & challenging - Game doesn’t hold you hand (some people will hate this) - You can swap in and out of 3rd and 1st person at will. Wasn’t able to explore much of how that changes the game, but it’s nice that it's an option. - EASILY over 100hrs of content (some will hate this though) Cons —---------- - Clunky controls (Default controls take some time adjusting too. I hope there’s other control schemes at launch) - Inconsistent Ai (Ubisoft bad at times. sometimes the enemies wouldn’t attack during combat or act like they never saw you) - Long load times (we were playing on PC’s, but idk the specs) - Your horse can faint & when they do traveling the large world wasn’t as fun (and I couldn’t figure out how to get him back - most likely a skill issue) - Early stamina management is OD. Early game it’s easy to drown & get tired running. I’d imagine it gets better late game, but early game it’s frustrating trying to explore. - Camera takes some getting used to in combat. Sometimes its too close and others too far. - Early arrows have no impact. Felt useless. Hoping later upgrades fix that

The Black Hokage

1,175,867 次观看 • 6 个月前

War Diary Day 1,391 Blaise Metreweli, the Chief of Britain's Secret Intelligence Service, sticks it to the Killer in The Kremlin. And all his creepy helpers. I agree with every fucking word. VPDFO! (Transcript of the speech, exactly as it was delivered) 📷 Welcome inside MI6. This iconic building, familiar to movie fans everywhere, is the home of Britain’s foreign intelligence agency. But whilst hundreds of my team pass through the entry pods each day, the truth is that most of our work happens many miles away from this place - out of sight, hidden from the world, undercover, recruiting and running agents who choose to place their trust in us, sharing secrets to make the UK and the world safer. You might pass one of our officers on the street or sit next to them on a plane when you’re about to set off on an adventure of your own, or in a foreign city taking selfies by the sights. Whether it’s in seemingly everyday places, or on the front line embedded with our military, MI6 is there. In my first few weeks, I’ve heard repeatedly that MI6 is trusted and respected globally, two things that we never take for granted. We are seen as a source of hard power, soft influence and rapid innovation. I’ve also heard that people want to believe in MI6. It’s my job to make sure they can. Today, I want to talk about human agency. We all have choices to make about how we deal with the undercurrents shaping our world. About how, in our new, faster, more dangerous and technology-mediated world, it will be our rediscovery of our shared humanity, our ability to listen, and our courage that will determine how our future unfolds. Conflict is not inevitable. Understanding human nature is in my bones. From a family shaped by devastating conflict, I grew up with a deep sense of gratitude for the UK’s precious democracy and freedom. I spent much of my childhood overseas, which is where my passion for travel and adventure began. I studied anthropology, and later psychology and AI, exploring how we make sense of the world and each other. It’s why I was drawn to MI6: it offers strong purpose, a chance to serve and a belief in the positive power of human connection. Like the Service, I’m operational to my very core. Over nearly three decades, my career has involved recruiting and running agents in hostile territory; and leading operations in warzones to defuse threats and support peace. Always in teams, always learning from others. Over the years, I’ve worked with hundreds of brilliant partners – and indeed occasionally those we’d label as adversaries – across dozens of countries, tackling weapons proliferation and terrorism. During my time at MI5, I saw close up what it takes to defend Britain from being targeted by hostile states. You’ll find many like me in my organisation: powerfully motivated to protect our precious country; curious about how our world is changing, joining dots and taking action, across domains. But it was in my last role as ‘Q’, where it was my job to turn emerging technologies from threats to opportunities that I could most see the world changing. As I dug deep into data and extraordinary innovation, I could see how technology was rapidly reshaping not just our capabilities but also conflict and trust, truth and global power. Let me lay out how I see the global issues MI6 must tackle. Because the greatest danger we face is to misunderstand the nature of the problem. Let’s be in no doubt. Our world is more dangerous and contested now than it has been for decades. Conflict is evolving and trust eroding, just as new technologies spur both competition and dependence. We are being contested from sea to space, from the battlefield to the boardroom. And even our brains, as disinformation manipulates our understanding of each other and ourselves. Across the globe, we are now confronting not one single danger, but an interlocking web of security challenges – military, technological, social, ethical even – each shaping the other in complex ways. We are now operating in a space between peace and war. This is not a temporary state or a gradual, inevitable evolution. Our world is being actively remade, with profound implications for national and international security. Institutions which were designed in the ashes of the Second World War are being challenged. New blocs and identities forming and alliances reshaping. Multipolar competition in tension with multilateral cooperation. But there’s something distinctive that will make this change unlike any other: the impact of advanced technologies, which will accelerate the pace and scale of every threat and opportunity, and increasingly, individualise them too. Advances in artificial intelligence, biotechnology, and quantum computing are not only revolutionising economies but rewriting the reality of conflict, as they ‘converge’ to create science-fiction-like tools. There’s incredible promise in all this for all of us, from green technologies to hyper-personalised medicine. But also peril. AI-powered robots and drones are brilliant for scaled manufacturing but devastating on the battlefield. Discoveries that cure disease can also create new weapons. And as states race for tech supremacy, or as some algorithms become as powerful as states, those hyper-personalised tools could become a new vector for conflict and control. Power itself is becoming more diffuse, more unpredictable as control over these technologies is shifting from states to corporations, and sometimes to individuals. And at the same time, the foundations of trust in our societies are eroding. Information, once a unifying force, is increasingly weaponised. Falsehood spreads faster than fact, dividing communities and distorting reality. We live in an age of hyper-connection yet profound isolation. The algorithms flatter our biases and fracture our public squares. And as trust collapses, so does our shared sense of truth – one of the greatest losses a society can suffer. The defining challenge of the twenty-first century is not simply who wields the most powerful technologies, but who guides them with the greatest wisdom. Our security, our prosperity, and our humanity depend on it. Our world is being remade. And for the first time, we are all at the heart of it. My Service must now operate in this new context too: not just expert on hostile states, terrorism, proliferation and more, but also fluent in technology, able to anticipate the second and third order effects of advances that reshape the world in minutes not months. And as China will be a central part of the global transformation taking place this century, it is essential that we, as MI6, continue to inform the government’s understanding of China’s rise and the implications for UK national security. I’m going to break with tradition and won’t give you a global threat tour, but will focus here on Putin’s Russia. We all continue to face the menace of an aggressive, expansionist and revisionist Russia, seeking to subjugate Ukraine and harass NATO. I find it harrowing that hundreds of thousands have died, with the toll mounting every day, because of Putin’s historical distortions and his compromised desire for respect. He is dragging out negotiations and shifting the cost of war onto his own population. But Putin should be in no doubt, our support is enduring. The pressure we apply on Ukraine’s behalf will be sustained. Because it is fundamental not just to European sovereignty and security but to global stability. Alongside the grinding war, Russia is testing us in the grey zone with tactics that are just below the threshold of war. It’s important to understand their attempts to bully, fearmonger and manipulate, because it affects us all. I am talking about: Cyberattacks on critical infrastructure. Drones buzzing airports and bases. Aggressive activity in our seas, above and below the waves. State-sponsored arson and sabotage. Propaganda and influence operations that crack open and exploit fractures within societies. Countering this activity is the work of intelligence and security services across Europe and the globe. And as the Foreign Secretary made clear in a speech last week, the UK is defending itself against this Russian information warfare – sanctioning Russian media outlets pushing Kremlin narratives. The export of chaos is a feature not a bug in this Russian approach to international engagement; and we should be ready for this to continue until Putin is forced to change his calculus. So, how should we respond? It’s not enough now just to understand the world. We must shape it too. MI6 is well-positioned to respond to these threats and wider global instability. And we will continue to evolve, just as we have throughout our long history. The UK government has invested in our intelligence agencies and we are all using our unique powers to keep the British people safe. Our ‘open and connected’ partnerships across the UK Intelligence Community, with HMGCC, NSSIF and the wider tech ecosystem in the UK will become even more important – because in the digital battleground, no single organisation can prevail alone. As a global agency, MI6’s inbuilt strength is our partners and our people. The risks I have set out require us to work ever more closely with our colleagues in MI5, GCHQ and in defence and diplomacy. But also with our Five Eyes partners, with the E3, the EU, NATO, those across the Middle East, the Indo-Pacific and beyond. And with many valued partners whose identity needs to remain secret. Together, we integrate our diverse talent, data and tools to meet the threat. AI is a domain in which we will excel, using the technology to augment, not replace, our human skills. Every digital trace, every byte of data, every algorithmic decision has implications for the safety of the lives of the courageous people who work with us as officers and agents, and for the UK’s strategic advantage. Mastery of technology will infuse everything we do. Not just in our labs, but in the field, in our tradecraft, and even more importantly, in the mindset of every officer. We will become as comfortable with lines of code as we are with human sources, as fluent in Python as we are in multiple other languages. Under my leadership, MI6 will continue to attract Britain’s best and most creative minds: linguists and data scientists, case officers and engineers, behavioural experts and technologists. We need people who walk in the shoes and get in the heads of our adversaries. We need people who think differently, challenge assumptions, and act decisively. All can thrive and make a difference at MI6. At an operational level, we will sharpen our edge and impact with audacity, tapping into – if you like – our historical SOE instincts. We’re at our best when we’re hustling to make things happen, because our intelligence is most valuable when it changes reality on the ground. We will take calculated risks, where the prize is significant and the national interest clear. We will never stoop to the tactics of our opponents. But we must seek to outplay them. In every domain. In every way. So intelligence must drive action. Action must deliver advantage. And advantage must serve Britain’s security and prosperity. But at the core, our deeper contribution is also our simplest – how we unlock human agency. Our fast-paced, tech and threat-infused world now generates more heat than light. As nations retrench and rearm, we are losing opportunities to listen to what’s really going on. I’ve seen time and again throughout my career, that this is where MI6 matters most: we listen and we hear. We understand, because we take time to learn languages and cultures, complex technical and historical detail, immerse ourselves in what’s really driving the situation. Across the globe, right now, our officers are finding people with the courage to step forward, and they are taking time to sit and listen to break these tightening cycles of violence. They listen for nuance, for connection, for opportunity. Over the years, I’ve listened to terrorists who have told us how to defuse the bomb because they know that more violence won’t help. To proliferators and smugglers who’ve told us where to find the dangerous material, motivated to protect their children’s future. To people trapped in authoritarian regimes who know, deep down, that their humanity is being chipped away – and that telling us what’s really going on is an important release, allowing us all to find better ways to navigate our changing world. So, we will work with our agents. And we will continue to engage directly, and with respect, with states and organisation currently working against us. Away from the glare of the media, we will use MI6’s convening power wherever we can to make a material difference, bringing parties together to defuse tensions. But the response to the increasing risks we face won’t be delivered by the UK intelligence community alone. Wider society has a role to play too. That includes work taking place in schools across the country so our children don’t get duped by information manipulation. Let’s all check sources, consider evidence, and be alive to those algorithms that trigger intense reactions, like fear. It also means everyone in society really understanding the world we are in – a world where terrorists plot against us, where our enemies fearmonger, bully and manipulate, and the front line is everywhere. Online, on our streets, in our supply chains, in the minds and on the screens of our citizens. We must all stand together against this. As we do today with our friends in Australia after the shocking antisemitic terrorist attack this weekend. My thoughts -and those of my whole organisation – are with the family, friends and loved ones of the victims. Light will always win over darkness. In rising to meet these challenges we, in MI6, will remain anchored to our values: courage, creativity, respect and integrity. And to our principles: accountability and trust are not constraints on our work; they are the foundations of our legitimacy with the British public. Recently, I had the privilege of meeting and thanking a foreign agent who has worked with us for decades, taking extraordinary risks to help keep the UK safe. I asked why. They said simply, ‘Your values. Your integrity and respect. None of us have a future without them’. This moment reinforced to me that we must remain a very human agency. And so, to sustain that trust, MI6 will continue to be more open. Not for the sake of visibility, but because it matters – and as my MI5 counterpart Sir Ken McCallum said recently - because it is a strength. We will continue the practice of speaking publicly, broaden our channels of engagement, and sustain our focus on attracting the most diverse talent to join our Service. Transparency does not mean revealing what must remain secret. It means showing the British people who we are, what we stand for, and why our work matters. We need your trust and support for the difficult and often dangerous work our agents pursue, every day of the year. In an age of uncertainty, one constant remains: the choices made by human beings still determine the shape of the world. Yes, technology can illuminate possibilities: but information requires judgement; complexity demands clarity; and only people can decide which path to follow. The United Kingdom’s global voice has never rested solely on strength – it has rested on trust, principle, and the ability to understand others as well as ourselves. That is also the essence of intelligence: not simply knowing the world, but interpreting it through a uniquely human lens. Ours is the quiet service, the hidden service. It is one rooted in a profound belief that when human beings act with purpose and integrity, they can steady a faltering world. When the Berlin Wall fell, it was our shared belief in freedom that carried Europe forward. When acts of terror targeted open societies, it was intelligence, cooperation and resolve that preserved them. And when adversaries blur fact and falsehood, our task is to defend the space where truth can still stand. As we step into the future, the tools at our disposal will evolve. But what will always matter most is the human element – the person who stands in the shadows and says: this is right, and that is wrong. That choice – the exercise of human agency – has shaped our world before, and it will shape it again. Because in the end, it is not what we can do that defines us, but what we choose to do. Thank you. Published 15 December 2025
0:59

War Diary Day 1,391 Blaise Metreweli, the Chief of Britain's Secret Intelligence Service, sticks it to the Killer in The Kremlin. And all his creepy helpers. I agree with every fucking word. VPDFO! (Transcript of the speech, exactly as it was delivered) 📷 Welcome inside MI6. This iconic building, familiar to movie fans everywhere, is the home of Britain’s foreign intelligence agency. But whilst hundreds of my team pass through the entry pods each day, the truth is that most of our work happens many miles away from this place - out of sight, hidden from the world, undercover, recruiting and running agents who choose to place their trust in us, sharing secrets to make the UK and the world safer. You might pass one of our officers on the street or sit next to them on a plane when you’re about to set off on an adventure of your own, or in a foreign city taking selfies by the sights. Whether it’s in seemingly everyday places, or on the front line embedded with our military, MI6 is there. In my first few weeks, I’ve heard repeatedly that MI6 is trusted and respected globally, two things that we never take for granted. We are seen as a source of hard power, soft influence and rapid innovation. I’ve also heard that people want to believe in MI6. It’s my job to make sure they can. Today, I want to talk about human agency. We all have choices to make about how we deal with the undercurrents shaping our world. About how, in our new, faster, more dangerous and technology-mediated world, it will be our rediscovery of our shared humanity, our ability to listen, and our courage that will determine how our future unfolds. Conflict is not inevitable. Understanding human nature is in my bones. From a family shaped by devastating conflict, I grew up with a deep sense of gratitude for the UK’s precious democracy and freedom. I spent much of my childhood overseas, which is where my passion for travel and adventure began. I studied anthropology, and later psychology and AI, exploring how we make sense of the world and each other. It’s why I was drawn to MI6: it offers strong purpose, a chance to serve and a belief in the positive power of human connection. Like the Service, I’m operational to my very core. Over nearly three decades, my career has involved recruiting and running agents in hostile territory; and leading operations in warzones to defuse threats and support peace. Always in teams, always learning from others. Over the years, I’ve worked with hundreds of brilliant partners – and indeed occasionally those we’d label as adversaries – across dozens of countries, tackling weapons proliferation and terrorism. During my time at MI5, I saw close up what it takes to defend Britain from being targeted by hostile states. You’ll find many like me in my organisation: powerfully motivated to protect our precious country; curious about how our world is changing, joining dots and taking action, across domains. But it was in my last role as ‘Q’, where it was my job to turn emerging technologies from threats to opportunities that I could most see the world changing. As I dug deep into data and extraordinary innovation, I could see how technology was rapidly reshaping not just our capabilities but also conflict and trust, truth and global power. Let me lay out how I see the global issues MI6 must tackle. Because the greatest danger we face is to misunderstand the nature of the problem. Let’s be in no doubt. Our world is more dangerous and contested now than it has been for decades. Conflict is evolving and trust eroding, just as new technologies spur both competition and dependence. We are being contested from sea to space, from the battlefield to the boardroom. And even our brains, as disinformation manipulates our understanding of each other and ourselves. Across the globe, we are now confronting not one single danger, but an interlocking web of security challenges – military, technological, social, ethical even – each shaping the other in complex ways. We are now operating in a space between peace and war. This is not a temporary state or a gradual, inevitable evolution. Our world is being actively remade, with profound implications for national and international security. Institutions which were designed in the ashes of the Second World War are being challenged. New blocs and identities forming and alliances reshaping. Multipolar competition in tension with multilateral cooperation. But there’s something distinctive that will make this change unlike any other: the impact of advanced technologies, which will accelerate the pace and scale of every threat and opportunity, and increasingly, individualise them too. Advances in artificial intelligence, biotechnology, and quantum computing are not only revolutionising economies but rewriting the reality of conflict, as they ‘converge’ to create science-fiction-like tools. There’s incredible promise in all this for all of us, from green technologies to hyper-personalised medicine. But also peril. AI-powered robots and drones are brilliant for scaled manufacturing but devastating on the battlefield. Discoveries that cure disease can also create new weapons. And as states race for tech supremacy, or as some algorithms become as powerful as states, those hyper-personalised tools could become a new vector for conflict and control. Power itself is becoming more diffuse, more unpredictable as control over these technologies is shifting from states to corporations, and sometimes to individuals. And at the same time, the foundations of trust in our societies are eroding. Information, once a unifying force, is increasingly weaponised. Falsehood spreads faster than fact, dividing communities and distorting reality. We live in an age of hyper-connection yet profound isolation. The algorithms flatter our biases and fracture our public squares. And as trust collapses, so does our shared sense of truth – one of the greatest losses a society can suffer. The defining challenge of the twenty-first century is not simply who wields the most powerful technologies, but who guides them with the greatest wisdom. Our security, our prosperity, and our humanity depend on it. Our world is being remade. And for the first time, we are all at the heart of it. My Service must now operate in this new context too: not just expert on hostile states, terrorism, proliferation and more, but also fluent in technology, able to anticipate the second and third order effects of advances that reshape the world in minutes not months. And as China will be a central part of the global transformation taking place this century, it is essential that we, as MI6, continue to inform the government’s understanding of China’s rise and the implications for UK national security. I’m going to break with tradition and won’t give you a global threat tour, but will focus here on Putin’s Russia. We all continue to face the menace of an aggressive, expansionist and revisionist Russia, seeking to subjugate Ukraine and harass NATO. I find it harrowing that hundreds of thousands have died, with the toll mounting every day, because of Putin’s historical distortions and his compromised desire for respect. He is dragging out negotiations and shifting the cost of war onto his own population. But Putin should be in no doubt, our support is enduring. The pressure we apply on Ukraine’s behalf will be sustained. Because it is fundamental not just to European sovereignty and security but to global stability. Alongside the grinding war, Russia is testing us in the grey zone with tactics that are just below the threshold of war. It’s important to understand their attempts to bully, fearmonger and manipulate, because it affects us all. I am talking about: Cyberattacks on critical infrastructure. Drones buzzing airports and bases. Aggressive activity in our seas, above and below the waves. State-sponsored arson and sabotage. Propaganda and influence operations that crack open and exploit fractures within societies. Countering this activity is the work of intelligence and security services across Europe and the globe. And as the Foreign Secretary made clear in a speech last week, the UK is defending itself against this Russian information warfare – sanctioning Russian media outlets pushing Kremlin narratives. The export of chaos is a feature not a bug in this Russian approach to international engagement; and we should be ready for this to continue until Putin is forced to change his calculus. So, how should we respond? It’s not enough now just to understand the world. We must shape it too. MI6 is well-positioned to respond to these threats and wider global instability. And we will continue to evolve, just as we have throughout our long history. The UK government has invested in our intelligence agencies and we are all using our unique powers to keep the British people safe. Our ‘open and connected’ partnerships across the UK Intelligence Community, with HMGCC, NSSIF and the wider tech ecosystem in the UK will become even more important – because in the digital battleground, no single organisation can prevail alone. As a global agency, MI6’s inbuilt strength is our partners and our people. The risks I have set out require us to work ever more closely with our colleagues in MI5, GCHQ and in defence and diplomacy. But also with our Five Eyes partners, with the E3, the EU, NATO, those across the Middle East, the Indo-Pacific and beyond. And with many valued partners whose identity needs to remain secret. Together, we integrate our diverse talent, data and tools to meet the threat. AI is a domain in which we will excel, using the technology to augment, not replace, our human skills. Every digital trace, every byte of data, every algorithmic decision has implications for the safety of the lives of the courageous people who work with us as officers and agents, and for the UK’s strategic advantage. Mastery of technology will infuse everything we do. Not just in our labs, but in the field, in our tradecraft, and even more importantly, in the mindset of every officer. We will become as comfortable with lines of code as we are with human sources, as fluent in Python as we are in multiple other languages. Under my leadership, MI6 will continue to attract Britain’s best and most creative minds: linguists and data scientists, case officers and engineers, behavioural experts and technologists. We need people who walk in the shoes and get in the heads of our adversaries. We need people who think differently, challenge assumptions, and act decisively. All can thrive and make a difference at MI6. At an operational level, we will sharpen our edge and impact with audacity, tapping into – if you like – our historical SOE instincts. We’re at our best when we’re hustling to make things happen, because our intelligence is most valuable when it changes reality on the ground. We will take calculated risks, where the prize is significant and the national interest clear. We will never stoop to the tactics of our opponents. But we must seek to outplay them. In every domain. In every way. So intelligence must drive action. Action must deliver advantage. And advantage must serve Britain’s security and prosperity. But at the core, our deeper contribution is also our simplest – how we unlock human agency. Our fast-paced, tech and threat-infused world now generates more heat than light. As nations retrench and rearm, we are losing opportunities to listen to what’s really going on. I’ve seen time and again throughout my career, that this is where MI6 matters most: we listen and we hear. We understand, because we take time to learn languages and cultures, complex technical and historical detail, immerse ourselves in what’s really driving the situation. Across the globe, right now, our officers are finding people with the courage to step forward, and they are taking time to sit and listen to break these tightening cycles of violence. They listen for nuance, for connection, for opportunity. Over the years, I’ve listened to terrorists who have told us how to defuse the bomb because they know that more violence won’t help. To proliferators and smugglers who’ve told us where to find the dangerous material, motivated to protect their children’s future. To people trapped in authoritarian regimes who know, deep down, that their humanity is being chipped away – and that telling us what’s really going on is an important release, allowing us all to find better ways to navigate our changing world. So, we will work with our agents. And we will continue to engage directly, and with respect, with states and organisation currently working against us. Away from the glare of the media, we will use MI6’s convening power wherever we can to make a material difference, bringing parties together to defuse tensions. But the response to the increasing risks we face won’t be delivered by the UK intelligence community alone. Wider society has a role to play too. That includes work taking place in schools across the country so our children don’t get duped by information manipulation. Let’s all check sources, consider evidence, and be alive to those algorithms that trigger intense reactions, like fear. It also means everyone in society really understanding the world we are in – a world where terrorists plot against us, where our enemies fearmonger, bully and manipulate, and the front line is everywhere. Online, on our streets, in our supply chains, in the minds and on the screens of our citizens. We must all stand together against this. As we do today with our friends in Australia after the shocking antisemitic terrorist attack this weekend. My thoughts -and those of my whole organisation – are with the family, friends and loved ones of the victims. Light will always win over darkness. In rising to meet these challenges we, in MI6, will remain anchored to our values: courage, creativity, respect and integrity. And to our principles: accountability and trust are not constraints on our work; they are the foundations of our legitimacy with the British public. Recently, I had the privilege of meeting and thanking a foreign agent who has worked with us for decades, taking extraordinary risks to help keep the UK safe. I asked why. They said simply, ‘Your values. Your integrity and respect. None of us have a future without them’. This moment reinforced to me that we must remain a very human agency. And so, to sustain that trust, MI6 will continue to be more open. Not for the sake of visibility, but because it matters – and as my MI5 counterpart Sir Ken McCallum said recently - because it is a strength. We will continue the practice of speaking publicly, broaden our channels of engagement, and sustain our focus on attracting the most diverse talent to join our Service. Transparency does not mean revealing what must remain secret. It means showing the British people who we are, what we stand for, and why our work matters. We need your trust and support for the difficult and often dangerous work our agents pursue, every day of the year. In an age of uncertainty, one constant remains: the choices made by human beings still determine the shape of the world. Yes, technology can illuminate possibilities: but information requires judgement; complexity demands clarity; and only people can decide which path to follow. The United Kingdom’s global voice has never rested solely on strength – it has rested on trust, principle, and the ability to understand others as well as ourselves. That is also the essence of intelligence: not simply knowing the world, but interpreting it through a uniquely human lens. Ours is the quiet service, the hidden service. It is one rooted in a profound belief that when human beings act with purpose and integrity, they can steady a faltering world. When the Berlin Wall fell, it was our shared belief in freedom that carried Europe forward. When acts of terror targeted open societies, it was intelligence, cooperation and resolve that preserved them. And when adversaries blur fact and falsehood, our task is to defend the space where truth can still stand. As we step into the future, the tools at our disposal will evolve. But what will always matter most is the human element – the person who stands in the shadows and says: this is right, and that is wrong. That choice – the exercise of human agency – has shaped our world before, and it will shape it again. Because in the end, it is not what we can do that defines us, but what we choose to do. Thank you. Published 15 December 2025

John Sweeney

42,257 次观看 • 8 个月前

Behind The Scenes In The Vegas Loop: Inside Elon Musk's The Boring Company Bold Bet On Urban Mobility Hey everyone. Tesla Owners Silicon Valley (Tesla Owners Silicon Valley) here. I recently had the chance to go behind the scenes with Steve Davis, President of The Boring Company, for a deep dive into the Vegas Loop in Las Vegas. This wasn’t a quick photo op. It was a full 47-minute immersion: riding through the LED-lit tunnels in a Tesla, visiting active construction sites with Prufrock boring machines, and hearing directly from Steve about what’s working today, and what’s coming next. I’m posting the full long-form video alongside this recap so you can experience it firsthand. But here’s the readable, “what actually matters” story from the tour. From “Traffic Is Soul-Crushing” To A Working Underground Network The Boring Company was founded in 2016, born of a familiar frustration: gridlocked cities that can’t build fast enough, cheap enough, or with minimal disruption. The premise is simple but ambitious: reinvent tunneling to make it practical infrastructure, not a decade-long mega-project. Las Vegas is where that idea is being tested at real scale. Instead of waiting for buses, shuttles, or rail schedules, the Vegas Loop aims to provide point-to-point trips in Teslas, fast, quiet, and emissions-free, connecting major destinations without the chaos of the Strip above. And after seeing it up close, what stands out most is how operational it already is. This isn’t a render. It’s a functioning system handling real demand, in real conditions, with real riders. What It Feels Like: Fast, Weirdly Fun, And Surprisingly Smooth The “Loop experience” is part transit, part sci-fi. The tunnels are lined with shifting LEDs—purples, greens, yellows—that make the ride feel more like entering a venue than commuting. Trips are short and direct. One example Steve shared: LVCC to Encore in about 85 seconds. But the biggest “wait, that just happened” moment on the tour was Full Self-Driving. FSD Underground (And Onto Surface Streets) We rode in a Model Y running Full Self-Driving (Supervised), which navigated the tunnels smoothly and then transitioned back to surface streets without intervention. Steve’s point wasn’t that autonomy is a cool demo; it’s that autonomy is a force multiplier for throughput, consistency, and future scale. Steve Davis: “Full Self-Driving Supervised is live commercially between LVCC and Encore, watch this: zero interventions as it navigates the tunnels and pops out onto surface streets seamlessly.” Right now, they still operate with safety drivers, but the trajectory is clear: as autonomy matures, the system can move more people with tighter headways and less variability than human-driven operations. The Numbers: “Spiky Demand” Is Where This System Wants To Win Vegas isn’t a steady-demand commuter city. It’s a burst-demand city: conventions, games, concerts, and tourist surges. Steve emphasized that this is exactly where the Loop model shines, because you can scale vehicles dynamically without rebuilding an entire transit line. During CES 2026, the Loop moved 90,000+ passengers, peaking at 6,600+ riders per hour, including 22,000+ trips to/from Resorts World, Encore, and Westgate. That’s on top of 3.5M+ total passengers since 2021. Steve Davis: “We’ve hit over 3 million passengers since 2021, and during CES 2026 alone, we shuttled more than 90,000 people, peaking at 6,600 passengers per hour without a hitch.” And beyond the numbers, there’s a secondary effect people don’t always talk about: for many riders, this is their first time in a Tesla, and it’s an unusually positive first impression. The Airport Connection: A Phased Plan With A Very Clear Endgame Connecting the system to Harry Reid International Airport is the crown jewel, and they’re doing it in phases to deliver value quickly while they work through the harder parts. Phase 1 (Live Now) Limited airport rides are already operating via a mix of tunnels and surface streets from existing stations, including Resorts World, Encore, Westgate, and LVCC. They’re doing roughly 50 test rides per day, and Steve noted 100 of ~130 vehicles are already “airport-ready” with transponders. Phase 2 (Next Couple Months) This is where things get meaningfully faster: a 2.2-mile dual tunnel from Westgate to 4744 Paradise Road, eliminating about two miles of surface traffic and stoplights. New stations are planned at Virgin Hotels, The Boring Company’s apartment complex, the former Gordon Biersch site, and Firefly. Fleet expands to 160 vehicles. Steve Davis: “Phase 2 kicks in soon: a 2.2-mile tunnel to Paradise Road, cutting out those surface miles and stoplights.” Phase 3 Extend to 5032 Palo Verde Road near Terminal 1, further removing surface bottlenecks around Tropicana and University Center. Fleet scales to 250–300 vehicles. Phase 4 (The “Holy Grail”) A direct underground station at the terminals, true curb-to-gate simplicity, fully underground. Steve Davis: “Phase 4 is the holy grail: a direct underground station right at the airport terminals.” The Big Build: 68 Miles, 104 Stations, Privately Funded The long-term vision is expansive: 68 miles of tunnels and 104 stations spanning the Strip, downtown, the stadium, and the airport. Core Strip construction begins this fall, with a 2027 target for that major phase, and further expansion into 2028–2029. Steve emphasized something important here: the funding model. These builds are privately funded, and the cost structure is the entire point: build rapidly and avoid “subway economics.” Steve Davis: “68 miles, 104 stations… all privately funded at about $10M per mile, versus billions for subways.” The Real Workhorses: Prufrock Boring Machines Up Close If the Loop is the user experience, Prufrock is the engine underneath it. Seeing Prufrock at an active dig site is hard to describe unless you’ve stood next to one. It’s enormous, loud, and relentlessly practical. The key advantage is that it changes the setup cost: it can launch from the surface without massive open pits, and it’s designed to move fast, with a long-term target of one mile per week. The machine isn’t just digging; it’s built around an integrated approach to lining, pumping, and maintaining the tunnel environment while staying cost-effective. Challenges They’re Solving In Real Time: Groundwater And Permitting One of the most interesting “myth-busting” moments was hearing Steve talk about tunnel conditions. Despite the desert setting, the tunnels are roughly 30 feet below grade, and in many areas, they’re fully submerged in groundwater, sand, clay, caliche, and water management, all part of the daily reality. Steve Davis: “Tunnels are 30 feet down, fully submerged in groundwater, desert myth busted.” They manage leaks through periodic sealing (foam, maintenance cycles) and now operate with stronger compliance processes for water treatment and disposal. The bigger long-term bottleneck, though, isn’t engineering; it’s approvals. Steve noted they need hundreds of permits (600+), and many can take months. Their push is toward a more streamlined, operator-style approval model, closer to how SpaceX is regulated: certify capability and safety, then execute without rearguing every step. Steve Davis: “Permitting’s the bottleneck… we’re advocating for a SpaceX-style operator license.” Fleet Scaling And The “Robovan” Strategy Right now, the fleet is about 130 Teslas, including Model Ys and Cybertrucks, tuned for tight turns and repeated high-frequency operations. The larger goal is to scale up to 1,200 vehicles as the network grows. And that’s where Robovan (high-occupancy, event-optimized vehicles) becomes strategically important. Steve’s framing was refreshingly clear: cars are more efficient for small groups. Robovans win when you can predict surges, like a Raiders game or a Sphere show, and load high-occupancy vehicles in advance. Steve Davis: “Robovans shine when everyone’s going to the same spot… that’s when you put the high occupancy vehicle in.” What’s Next: Suburbs, Regional Links, And Bigger Swing Ideas After the core network is built, they’re looking at suburban expansions (Henderson, Summerlin) via shorter demo segments first, proving utility for pedestrian and vehicle connectivity. And then Steve hinted at the kind of long-range thinking that gets people excited (and skeptical): longer-distance routes, potentially even Hyperloop concepts like Reno connections, if permitting and economics align. Steve Davis: “Suburbs like Henderson and Summerlin next… long-term? Hyperloop to Reno… private funding makes it doable if permitting catches up.” Final Take: Vegas Is Becoming A Live Testbed For A New Kind Of Transit This tour made one thing very clear: The Boring Company isn’t trying to win the “traditional public transit debate.” They’re trying to change the rules of what’s feasible, building faster, cheaper, and with an experience that people actually want to use. Watching FSD glide through the tunnels, seeing Prufrock tearing through the ground, and hearing the phased plan for the airport and Strip expansion straight from Steve… It’s hard not to feel like Vegas is a real-world preview of what mobility can look like when infrastructure is built like technology. Huge thanks to Steve Davis and The Boring Company team for the access and the time. And keep an eye out, I’m posting the full 47-minute video with this recap so you can see the ride, the sites, and the details for yourself. What do you think, would you ride the Loop instead of sitting in Strip traffic?
47:36

Behind The Scenes In The Vegas Loop: Inside Elon Musk's The Boring Company Bold Bet On Urban Mobility Hey everyone. Tesla Owners Silicon Valley (Tesla Owners Silicon Valley) here. I recently had the chance to go behind the scenes with Steve Davis, President of The Boring Company, for a deep dive into the Vegas Loop in Las Vegas. This wasn’t a quick photo op. It was a full 47-minute immersion: riding through the LED-lit tunnels in a Tesla, visiting active construction sites with Prufrock boring machines, and hearing directly from Steve about what’s working today, and what’s coming next. I’m posting the full long-form video alongside this recap so you can experience it firsthand. But here’s the readable, “what actually matters” story from the tour. From “Traffic Is Soul-Crushing” To A Working Underground Network The Boring Company was founded in 2016, born of a familiar frustration: gridlocked cities that can’t build fast enough, cheap enough, or with minimal disruption. The premise is simple but ambitious: reinvent tunneling to make it practical infrastructure, not a decade-long mega-project. Las Vegas is where that idea is being tested at real scale. Instead of waiting for buses, shuttles, or rail schedules, the Vegas Loop aims to provide point-to-point trips in Teslas, fast, quiet, and emissions-free, connecting major destinations without the chaos of the Strip above. And after seeing it up close, what stands out most is how operational it already is. This isn’t a render. It’s a functioning system handling real demand, in real conditions, with real riders. What It Feels Like: Fast, Weirdly Fun, And Surprisingly Smooth The “Loop experience” is part transit, part sci-fi. The tunnels are lined with shifting LEDs—purples, greens, yellows—that make the ride feel more like entering a venue than commuting. Trips are short and direct. One example Steve shared: LVCC to Encore in about 85 seconds. But the biggest “wait, that just happened” moment on the tour was Full Self-Driving. FSD Underground (And Onto Surface Streets) We rode in a Model Y running Full Self-Driving (Supervised), which navigated the tunnels smoothly and then transitioned back to surface streets without intervention. Steve’s point wasn’t that autonomy is a cool demo; it’s that autonomy is a force multiplier for throughput, consistency, and future scale. Steve Davis: “Full Self-Driving Supervised is live commercially between LVCC and Encore, watch this: zero interventions as it navigates the tunnels and pops out onto surface streets seamlessly.” Right now, they still operate with safety drivers, but the trajectory is clear: as autonomy matures, the system can move more people with tighter headways and less variability than human-driven operations. The Numbers: “Spiky Demand” Is Where This System Wants To Win Vegas isn’t a steady-demand commuter city. It’s a burst-demand city: conventions, games, concerts, and tourist surges. Steve emphasized that this is exactly where the Loop model shines, because you can scale vehicles dynamically without rebuilding an entire transit line. During CES 2026, the Loop moved 90,000+ passengers, peaking at 6,600+ riders per hour, including 22,000+ trips to/from Resorts World, Encore, and Westgate. That’s on top of 3.5M+ total passengers since 2021. Steve Davis: “We’ve hit over 3 million passengers since 2021, and during CES 2026 alone, we shuttled more than 90,000 people, peaking at 6,600 passengers per hour without a hitch.” And beyond the numbers, there’s a secondary effect people don’t always talk about: for many riders, this is their first time in a Tesla, and it’s an unusually positive first impression. The Airport Connection: A Phased Plan With A Very Clear Endgame Connecting the system to Harry Reid International Airport is the crown jewel, and they’re doing it in phases to deliver value quickly while they work through the harder parts. Phase 1 (Live Now) Limited airport rides are already operating via a mix of tunnels and surface streets from existing stations, including Resorts World, Encore, Westgate, and LVCC. They’re doing roughly 50 test rides per day, and Steve noted 100 of ~130 vehicles are already “airport-ready” with transponders. Phase 2 (Next Couple Months) This is where things get meaningfully faster: a 2.2-mile dual tunnel from Westgate to 4744 Paradise Road, eliminating about two miles of surface traffic and stoplights. New stations are planned at Virgin Hotels, The Boring Company’s apartment complex, the former Gordon Biersch site, and Firefly. Fleet expands to 160 vehicles. Steve Davis: “Phase 2 kicks in soon: a 2.2-mile tunnel to Paradise Road, cutting out those surface miles and stoplights.” Phase 3 Extend to 5032 Palo Verde Road near Terminal 1, further removing surface bottlenecks around Tropicana and University Center. Fleet scales to 250–300 vehicles. Phase 4 (The “Holy Grail”) A direct underground station at the terminals, true curb-to-gate simplicity, fully underground. Steve Davis: “Phase 4 is the holy grail: a direct underground station right at the airport terminals.” The Big Build: 68 Miles, 104 Stations, Privately Funded The long-term vision is expansive: 68 miles of tunnels and 104 stations spanning the Strip, downtown, the stadium, and the airport. Core Strip construction begins this fall, with a 2027 target for that major phase, and further expansion into 2028–2029. Steve emphasized something important here: the funding model. These builds are privately funded, and the cost structure is the entire point: build rapidly and avoid “subway economics.” Steve Davis: “68 miles, 104 stations… all privately funded at about $10M per mile, versus billions for subways.” The Real Workhorses: Prufrock Boring Machines Up Close If the Loop is the user experience, Prufrock is the engine underneath it. Seeing Prufrock at an active dig site is hard to describe unless you’ve stood next to one. It’s enormous, loud, and relentlessly practical. The key advantage is that it changes the setup cost: it can launch from the surface without massive open pits, and it’s designed to move fast, with a long-term target of one mile per week. The machine isn’t just digging; it’s built around an integrated approach to lining, pumping, and maintaining the tunnel environment while staying cost-effective. Challenges They’re Solving In Real Time: Groundwater And Permitting One of the most interesting “myth-busting” moments was hearing Steve talk about tunnel conditions. Despite the desert setting, the tunnels are roughly 30 feet below grade, and in many areas, they’re fully submerged in groundwater, sand, clay, caliche, and water management, all part of the daily reality. Steve Davis: “Tunnels are 30 feet down, fully submerged in groundwater, desert myth busted.” They manage leaks through periodic sealing (foam, maintenance cycles) and now operate with stronger compliance processes for water treatment and disposal. The bigger long-term bottleneck, though, isn’t engineering; it’s approvals. Steve noted they need hundreds of permits (600+), and many can take months. Their push is toward a more streamlined, operator-style approval model, closer to how SpaceX is regulated: certify capability and safety, then execute without rearguing every step. Steve Davis: “Permitting’s the bottleneck… we’re advocating for a SpaceX-style operator license.” Fleet Scaling And The “Robovan” Strategy Right now, the fleet is about 130 Teslas, including Model Ys and Cybertrucks, tuned for tight turns and repeated high-frequency operations. The larger goal is to scale up to 1,200 vehicles as the network grows. And that’s where Robovan (high-occupancy, event-optimized vehicles) becomes strategically important. Steve’s framing was refreshingly clear: cars are more efficient for small groups. Robovans win when you can predict surges, like a Raiders game or a Sphere show, and load high-occupancy vehicles in advance. Steve Davis: “Robovans shine when everyone’s going to the same spot… that’s when you put the high occupancy vehicle in.” What’s Next: Suburbs, Regional Links, And Bigger Swing Ideas After the core network is built, they’re looking at suburban expansions (Henderson, Summerlin) via shorter demo segments first, proving utility for pedestrian and vehicle connectivity. And then Steve hinted at the kind of long-range thinking that gets people excited (and skeptical): longer-distance routes, potentially even Hyperloop concepts like Reno connections, if permitting and economics align. Steve Davis: “Suburbs like Henderson and Summerlin next… long-term? Hyperloop to Reno… private funding makes it doable if permitting catches up.” Final Take: Vegas Is Becoming A Live Testbed For A New Kind Of Transit This tour made one thing very clear: The Boring Company isn’t trying to win the “traditional public transit debate.” They’re trying to change the rules of what’s feasible, building faster, cheaper, and with an experience that people actually want to use. Watching FSD glide through the tunnels, seeing Prufrock tearing through the ground, and hearing the phased plan for the airport and Strip expansion straight from Steve… It’s hard not to feel like Vegas is a real-world preview of what mobility can look like when infrastructure is built like technology. Huge thanks to Steve Davis and The Boring Company team for the access and the time. And keep an eye out, I’m posting the full 47-minute video with this recap so you can see the ride, the sites, and the details for yourself. What do you think, would you ride the Loop instead of sitting in Strip traffic?

Tesla Owners Silicon Valley

447,027 次观看 • 7 个月前

Like seemingly everyone on this app I have plenty of opinions about Twitter > X and figure now is a good time to open up a bit about my experience at the company. I tweeted for years into the void for the love of it like many of you, but after selling my startup to Twitter in 2020 I finally got to see it from the inside. Up close it was both amazing and terrible, like so many other companies and things in life. As someone with a maniacal sense of urgency built into me, Twitter often felt siloed and bureaucratic. Dumb power plays, reorgs and team name changes for the sake of someone’s ego were distractions that occurred too regularly. You couldn’t just be a builder — you also needed to be a politician. I was shocked by how old and bespoke the infrastructure was, but there was little will to think beyond quarterly earnings calls because we were all beholden to the masters of mDAU and revenue growth as a public company. It often felt like things were held together with duct tape and glue, and that many people had just accepted that a small product change could take months or quarters to build. Management had become bloated to accommodate career growth and the company culture felt too soft and entitled for my own taste. Healthy debate and criticism was replaced by a default refrain of “no, that can’t be done” or “another team owns that so don’t touch it”. Teams could spend months building a feature and then some last-minute kerfuffle meant it’d get killed for being too risky. Just talking directly to customers could turn into a turf war and create deadlocks between functions. I recall one such episode where a teammate spent a month trying to get clearance to reach out to some creators. He went through 3 layers of management and 6 different functional teams. In the end 4 executives were involved in the approval. It was insanity, and unfortunately I saw several top performers get burnt out and demoralized after exhausting experiences like that. Most people were good at their jobs but it was nearly impossible to fire poor performers — instead they got shuffled around to other teams because few managers had the will or resources to figure out how to get them out. A high performance culture pulls everyone up, but the opposite weighs everyone down. Twitter often felt like a place that kept squandering its own potential, which was sad and frustrating to see. The person who was best at cutting through the BS and inspiring a vision during my tenure was Kayvon Beykpour, but he wasn’t fully empowered to run the company since he wasn’t the CEO. Despite those real issues, I was lucky enough to work with some of the most talented people in the business at Twitter in product, design, engineering, research, legal, BD, trust & safety, marketing, PR and more. Often it was a small cross-functional team of intrinsically motivated people who made the biggest impact by challenging some core assumption. Those teams were very fun to be on but they felt like the exception rather than the rule. The months of waiting for the deal to close in 2022 were particularly slow and painful; it felt like leadership hid behind lawyers and legal language as all answers about the company’s future notoriously included the phrase “fiduciary duty”. Colleagues openly talked about how Twitter was being sold because leadership didn’t have conviction in their own plan or ability to fix longstanding problems. Although I didn’t know much about Elon I was cautiously optimistic – I saw him as the guy who built incredible and enduring companies like Tesla and SpaceX, so perhaps his private ownership could shake things up and breathe new life into the company. My take on what’s happened since then is full of lived nuance. When people ask why I stayed it’s easy to answer: optimism, curiosity, personal growth and money. From the beginning I saw that some changes Elon was going to make were smart and others were stupid, but when I’m on a team I uphold the philosophy of “praise in public and criticize in private”. I was far from a silent wallflower. I shared my opinions openly and pushed back often, both before and after the acquisition. I made peace with the fact that I didn’t have psychological safety at Twitter 2.0 and that meant I could be fired at any moment, and for no reason at all. I watched it happen repeatedly and saw how negatively it impacted team morale. Although I couldn’t change the situation I did my best to shine a light on folks who were doing important work while being an emotionally supportive leader for those who were struggling to adapt to the more brutalist and hardcore culture. In person Elon is oddly charming and he’s genuinely funny. He also has personality quirks like telling the same stories and jokes over and over. The challenge is his personality and demeanor can turn on a dime going from excited to angry. Since it was hard to read what mood he might be in and what his reaction would be to any given thing, people quickly became afraid of being called into meetings or having to share negative news with him. At times it felt like the inner circle was too zealous and fanatical in their unwavering support of everything he said. When individuals encouraged me to be careful about what I said I politely thanked them and said I would not be taking their advice. I had no interest in adding to a culture of fear or walking on eggshells around Elon. Either he would respect me for being real or he could fire me. Either outcome was okay. I quickly learned that product and business decisions were nearly always the result of him following his gut instinct, and he didn’t seem compelled to seek out or rely on a lot of data or expertise to inform it. That was particularly frustrating for me since I believed I had useful institutional knowledge that could help him make better decisions. Instead he'd poll Twitter, ask a friend, or even ask his biographer for product advice. At times it seemed he trusted random feedback more than the people in the room who spent their lives dedicated to tackling the problem at hand. I never figured out why and remain puzzled by it. I don’t think things had to be as difficult or dramatic as they turned out to be but I can’t say I’d bet against Elon or count him out. He’s smart and has enough money to make a lot of mistakes and then course correct when things go awry. As the largest shareholder he can tank the value in the short-term, but eventually he’ll need things to turn around. His focus on speed is incredible and he’s obviously not afraid of blowing things up, but now the real measure will be how it get reconstructed and if enough people want the new everything app he is building. I learned a ton from watching Elon up close – the good, the bad and the ugly. His boldness, passion and storytelling is inspiring, but his lack of process and empathy is painful. Elon has an exceptional talent for tackling hard physics-based problems but products that facilitate human connection and communication require a different type of social-emotional intelligence. Social networks are hard to kill but they’re not immune from death spirals. Only time will tell what the outcome will be but I hope X finds its footing because competition is good for consumers. In the meantime, I have a lot of empathy for the employees who are working tirelessly behind the scenes, the advertisers who want a stable platform to sell their stuff on, and the customers who are experiencing chaotic updates. It’s been a madhouse. Twitter moved at the speed of molasses and suffered from bureaucracy but now X is run by a mercurial leader whose instinct is driven by the unique and undoubtedly weird experience of being the biggest voice on the platform. Many of you know me from the sleeping bag incident where I slept on a conference room floor, so I figure, let’s talk about that too. Going viral was an odd and interesting experience. I was attacked by people on the left and called a billionaire bootlicker, while simultaneously being attacked by people on the right for being a working mom who was demonized as an example of a woman choosing her career over her family. Thankfully I can laugh at myself and I don’t take armchair keyboard ideologues too seriously. Being the main character on the timeline, even for a few minutes, requires a thick skin and a strong sense of self. The real story is pretty simple. I was given a nearly impossible deadline for his first project and as the product lead I would never ask anyone to do anything I wasn’t willing to do myself. So I worked round the clock alongside an amazing team spanning many timezones, and we delivered it on schedule – truly against the odds. It was intense but also fun. Those first few months were wildly crazy but I wanted to be there and I have no regrets. Showing up and giving it your all should, in most cases, be celebrated. Obviously you can’t work at that pace forever but there are moments where bursts are mission critical. I’ve pulled many all-nighters in my career and also when I was a student for something that mattered to me. I don’t regret putting in long hours or being ambitious, and feel proud of how far I’ve come from where I started thanks in part to that type of work ethic. I think of life as a game, and being at Twitter after the acquisition was like playing life at Level 10 on Hard Mode. Since I like taking on difficult challenges I found it interesting and rewarding because I was growing and learning so rapidly. I realize our society today trends toward polarization but when it comes to this app, its owner, and its future, I am neither a fangirl nor a hater — I’m an optimistic pragmatist. This may really irritate the internet but you cannot pigeonhole me into some radical position of either loving or hating every change that’s occurred. I escaped my fundamentalist upbringing and am a free thinker these days. Everyone can be seen as both a hero or a villain, depending on who is telling what angle of the story. Elon doesn’t deserve to be venerated or vilified. He’s a complicated person with an unfathomable amount of financial and geopolitical power which is why humanity needs him to err on the side of goodness, rather than political divisiveness and pettiness. I disagree with many of his decisions and am surprised by his willingness to burn so much down, but with enough money and time, something new & innovative may emerge. I hope it does. Sometimes I get asked about how I felt when I got laid off, and the truth is it was the best gift I’ve ever received. Sure the headlines and punchlines wrote themselves but I was battle hardened by then. I knew that I’d worked in a way where I could walk out with my head held high. I have no bitterness about the Product Management team being dismantled, and it made sense for me to exit as nearly all of the remaining PMs were let go. Going on a sabbatical afterward has been exactly what I needed to decompress and I’m finally feeling rested and relaxed. I’m a creative and a builder, so sooner than later I’ll jump back into a high intensity company but I’m grateful for this season of thinking, reading, traveling and being with people I love. After having time to reflect I believe more than ever that the very best outcomes flow from great leadership that combines the head and the heart. I’d be remiss if I didn’t note that in all of this there is also a cautionary tale for anyone who succeeds at something — which is that the higher you climb, the smaller your world becomes. It’s a strange paradox but the richest and most powerful people are also some of the most isolated. I found myself frequently looking at Elon and seeing a person who seemed quite alone because his time and energy was so purely devoted to work, which is not the model of a life I want to live. Money and fame can create psychological prisons which may worsen mental health conditions. We’ve all seen high profile cases of celebrities who end up with some combination of depression, paranoia, delusions of grandeur, mania and/or erratic behavior. Living in an echo chamber is dangerous and being at the top makes a person even more susceptible to being surrounded by yes people when nearly everyone around you is on the payroll and somehow stands to benefit from being in your orbit. Figuring out how to keep “better angels” around in the form of family, friends, and teammates is critical to staying on the rails and enduring intense ups and downs. Everyone needs to hear hard truths sometimes and if you fire all the people who speak up then the reality distortion field may just turn into a vortex. I was drawn to Twitter because I’m obsessed with the problem of loneliness and connection between people. I find it fascinating & troubling that humans are getting lonelier as we simultaneously create a world that’s both safer and wealthier. I don’t believe that trade-off has to exist, which is why I keep returning to that theme in my personal and professional life. I realize this is too long of a tweet but Twitter was a weird and special place on the internet, and I’m grateful to have played a teeny tiny role in its story and evolution. I’m here for whatever comes next — on this app and in new places. Consumer social is very much alive and at a fascinating juncture, so I’ll be watching and participating and sharing hot takes because I don’t want to, and probably can’t, turn that part of me off. Perhaps X becomes a resounding success. Or it fails epically. Either way, I expect it will continue to be a very entertaining ride. 🫡
14:49

Like seemingly everyone on this app I have plenty of opinions about Twitter > X and figure now is a good time to open up a bit about my experience at the company. I tweeted for years into the void for the love of it like many of you, but after selling my startup to Twitter in 2020 I finally got to see it from the inside. Up close it was both amazing and terrible, like so many other companies and things in life. As someone with a maniacal sense of urgency built into me, Twitter often felt siloed and bureaucratic. Dumb power plays, reorgs and team name changes for the sake of someone’s ego were distractions that occurred too regularly. You couldn’t just be a builder — you also needed to be a politician. I was shocked by how old and bespoke the infrastructure was, but there was little will to think beyond quarterly earnings calls because we were all beholden to the masters of mDAU and revenue growth as a public company. It often felt like things were held together with duct tape and glue, and that many people had just accepted that a small product change could take months or quarters to build. Management had become bloated to accommodate career growth and the company culture felt too soft and entitled for my own taste. Healthy debate and criticism was replaced by a default refrain of “no, that can’t be done” or “another team owns that so don’t touch it”. Teams could spend months building a feature and then some last-minute kerfuffle meant it’d get killed for being too risky. Just talking directly to customers could turn into a turf war and create deadlocks between functions. I recall one such episode where a teammate spent a month trying to get clearance to reach out to some creators. He went through 3 layers of management and 6 different functional teams. In the end 4 executives were involved in the approval. It was insanity, and unfortunately I saw several top performers get burnt out and demoralized after exhausting experiences like that. Most people were good at their jobs but it was nearly impossible to fire poor performers — instead they got shuffled around to other teams because few managers had the will or resources to figure out how to get them out. A high performance culture pulls everyone up, but the opposite weighs everyone down. Twitter often felt like a place that kept squandering its own potential, which was sad and frustrating to see. The person who was best at cutting through the BS and inspiring a vision during my tenure was Kayvon Beykpour, but he wasn’t fully empowered to run the company since he wasn’t the CEO. Despite those real issues, I was lucky enough to work with some of the most talented people in the business at Twitter in product, design, engineering, research, legal, BD, trust & safety, marketing, PR and more. Often it was a small cross-functional team of intrinsically motivated people who made the biggest impact by challenging some core assumption. Those teams were very fun to be on but they felt like the exception rather than the rule. The months of waiting for the deal to close in 2022 were particularly slow and painful; it felt like leadership hid behind lawyers and legal language as all answers about the company’s future notoriously included the phrase “fiduciary duty”. Colleagues openly talked about how Twitter was being sold because leadership didn’t have conviction in their own plan or ability to fix longstanding problems. Although I didn’t know much about Elon I was cautiously optimistic – I saw him as the guy who built incredible and enduring companies like Tesla and SpaceX, so perhaps his private ownership could shake things up and breathe new life into the company. My take on what’s happened since then is full of lived nuance. When people ask why I stayed it’s easy to answer: optimism, curiosity, personal growth and money. From the beginning I saw that some changes Elon was going to make were smart and others were stupid, but when I’m on a team I uphold the philosophy of “praise in public and criticize in private”. I was far from a silent wallflower. I shared my opinions openly and pushed back often, both before and after the acquisition. I made peace with the fact that I didn’t have psychological safety at Twitter 2.0 and that meant I could be fired at any moment, and for no reason at all. I watched it happen repeatedly and saw how negatively it impacted team morale. Although I couldn’t change the situation I did my best to shine a light on folks who were doing important work while being an emotionally supportive leader for those who were struggling to adapt to the more brutalist and hardcore culture. In person Elon is oddly charming and he’s genuinely funny. He also has personality quirks like telling the same stories and jokes over and over. The challenge is his personality and demeanor can turn on a dime going from excited to angry. Since it was hard to read what mood he might be in and what his reaction would be to any given thing, people quickly became afraid of being called into meetings or having to share negative news with him. At times it felt like the inner circle was too zealous and fanatical in their unwavering support of everything he said. When individuals encouraged me to be careful about what I said I politely thanked them and said I would not be taking their advice. I had no interest in adding to a culture of fear or walking on eggshells around Elon. Either he would respect me for being real or he could fire me. Either outcome was okay. I quickly learned that product and business decisions were nearly always the result of him following his gut instinct, and he didn’t seem compelled to seek out or rely on a lot of data or expertise to inform it. That was particularly frustrating for me since I believed I had useful institutional knowledge that could help him make better decisions. Instead he'd poll Twitter, ask a friend, or even ask his biographer for product advice. At times it seemed he trusted random feedback more than the people in the room who spent their lives dedicated to tackling the problem at hand. I never figured out why and remain puzzled by it. I don’t think things had to be as difficult or dramatic as they turned out to be but I can’t say I’d bet against Elon or count him out. He’s smart and has enough money to make a lot of mistakes and then course correct when things go awry. As the largest shareholder he can tank the value in the short-term, but eventually he’ll need things to turn around. His focus on speed is incredible and he’s obviously not afraid of blowing things up, but now the real measure will be how it get reconstructed and if enough people want the new everything app he is building. I learned a ton from watching Elon up close – the good, the bad and the ugly. His boldness, passion and storytelling is inspiring, but his lack of process and empathy is painful. Elon has an exceptional talent for tackling hard physics-based problems but products that facilitate human connection and communication require a different type of social-emotional intelligence. Social networks are hard to kill but they’re not immune from death spirals. Only time will tell what the outcome will be but I hope X finds its footing because competition is good for consumers. In the meantime, I have a lot of empathy for the employees who are working tirelessly behind the scenes, the advertisers who want a stable platform to sell their stuff on, and the customers who are experiencing chaotic updates. It’s been a madhouse. Twitter moved at the speed of molasses and suffered from bureaucracy but now X is run by a mercurial leader whose instinct is driven by the unique and undoubtedly weird experience of being the biggest voice on the platform. Many of you know me from the sleeping bag incident where I slept on a conference room floor, so I figure, let’s talk about that too. Going viral was an odd and interesting experience. I was attacked by people on the left and called a billionaire bootlicker, while simultaneously being attacked by people on the right for being a working mom who was demonized as an example of a woman choosing her career over her family. Thankfully I can laugh at myself and I don’t take armchair keyboard ideologues too seriously. Being the main character on the timeline, even for a few minutes, requires a thick skin and a strong sense of self. The real story is pretty simple. I was given a nearly impossible deadline for his first project and as the product lead I would never ask anyone to do anything I wasn’t willing to do myself. So I worked round the clock alongside an amazing team spanning many timezones, and we delivered it on schedule – truly against the odds. It was intense but also fun. Those first few months were wildly crazy but I wanted to be there and I have no regrets. Showing up and giving it your all should, in most cases, be celebrated. Obviously you can’t work at that pace forever but there are moments where bursts are mission critical. I’ve pulled many all-nighters in my career and also when I was a student for something that mattered to me. I don’t regret putting in long hours or being ambitious, and feel proud of how far I’ve come from where I started thanks in part to that type of work ethic. I think of life as a game, and being at Twitter after the acquisition was like playing life at Level 10 on Hard Mode. Since I like taking on difficult challenges I found it interesting and rewarding because I was growing and learning so rapidly. I realize our society today trends toward polarization but when it comes to this app, its owner, and its future, I am neither a fangirl nor a hater — I’m an optimistic pragmatist. This may really irritate the internet but you cannot pigeonhole me into some radical position of either loving or hating every change that’s occurred. I escaped my fundamentalist upbringing and am a free thinker these days. Everyone can be seen as both a hero or a villain, depending on who is telling what angle of the story. Elon doesn’t deserve to be venerated or vilified. He’s a complicated person with an unfathomable amount of financial and geopolitical power which is why humanity needs him to err on the side of goodness, rather than political divisiveness and pettiness. I disagree with many of his decisions and am surprised by his willingness to burn so much down, but with enough money and time, something new & innovative may emerge. I hope it does. Sometimes I get asked about how I felt when I got laid off, and the truth is it was the best gift I’ve ever received. Sure the headlines and punchlines wrote themselves but I was battle hardened by then. I knew that I’d worked in a way where I could walk out with my head held high. I have no bitterness about the Product Management team being dismantled, and it made sense for me to exit as nearly all of the remaining PMs were let go. Going on a sabbatical afterward has been exactly what I needed to decompress and I’m finally feeling rested and relaxed. I’m a creative and a builder, so sooner than later I’ll jump back into a high intensity company but I’m grateful for this season of thinking, reading, traveling and being with people I love. After having time to reflect I believe more than ever that the very best outcomes flow from great leadership that combines the head and the heart. I’d be remiss if I didn’t note that in all of this there is also a cautionary tale for anyone who succeeds at something — which is that the higher you climb, the smaller your world becomes. It’s a strange paradox but the richest and most powerful people are also some of the most isolated. I found myself frequently looking at Elon and seeing a person who seemed quite alone because his time and energy was so purely devoted to work, which is not the model of a life I want to live. Money and fame can create psychological prisons which may worsen mental health conditions. We’ve all seen high profile cases of celebrities who end up with some combination of depression, paranoia, delusions of grandeur, mania and/or erratic behavior. Living in an echo chamber is dangerous and being at the top makes a person even more susceptible to being surrounded by yes people when nearly everyone around you is on the payroll and somehow stands to benefit from being in your orbit. Figuring out how to keep “better angels” around in the form of family, friends, and teammates is critical to staying on the rails and enduring intense ups and downs. Everyone needs to hear hard truths sometimes and if you fire all the people who speak up then the reality distortion field may just turn into a vortex. I was drawn to Twitter because I’m obsessed with the problem of loneliness and connection between people. I find it fascinating & troubling that humans are getting lonelier as we simultaneously create a world that’s both safer and wealthier. I don’t believe that trade-off has to exist, which is why I keep returning to that theme in my personal and professional life. I realize this is too long of a tweet but Twitter was a weird and special place on the internet, and I’m grateful to have played a teeny tiny role in its story and evolution. I’m here for whatever comes next — on this app and in new places. Consumer social is very much alive and at a fascinating juncture, so I’ll be watching and participating and sharing hot takes because I don’t want to, and probably can’t, turn that part of me off. Perhaps X becomes a resounding success. Or it fails epically. Either way, I expect it will continue to be a very entertaining ride. 🫡

Esther Crawford ✨

5,504,004 次观看 • 3 年前

Moneytaur study blueprint 🗺️ The process I used to go from not knowing what an order block is to pulling cash from the crypto markets in under 6 months using 🎯 Master concepts. Proof of performance, past 120 days👇 Start date: 09/03/2025 Requirements: - A PC/laptop - Wifi - A basic understanding of trading. ( What candlesticks are, how to actually place trades , etc ) - A free mind - Time or the ability to free up time. Starting: - Structure and routine - Stick to that routine + Pre mortem plan. - Notion / Obsidian setup. The first thing you need to create is a clear routine moulded around how you intend to approach this very large and complex task. This will not be linear and you will naturally adapt it as you progress but especially in the beginning some resemblance of structure each day is vital. This is an individual process but it is important to understand from the beginning that this will require a majority of your free time assuming you work a full time Job or study as a student. For me in the beginning this looked like: - Wake up at 6:30. - Shower - Study/work for 1h 45m before leaving for work. - 09:00 -> 17:00 work - 17:30 Exercise / Train - Eat - 19:00 resume study/work - 22:30 Start to wind down and get ready to sleep. It changed several times over the months and especially now I am full time but this is irrelevant, the only thing that matters is sticking with what you choose. Whatever your own routine may look like, it is important to understand it will inevitably require sacrifice. --- The next thing once you have established a draft framework of your routine is ensuring you will actually stick to that routine. Something I implemented which I found particularly beneficial was the concept of a Pre-Mortem plan. This involves creating several scenarios of a future in which you have failed and working backwards from each of these to find where it went wrong. Here is a video which explains it fully: When I did this I came up with 3 scenarios as well as prevention and cure for each. In the 6 months that followed each scenario presented at some point but I was able to catch them early due to having done this. The last thing is to not over complicate this, don't hyper focus on systems and loose momentum optimizing each detail. Just ensure you do the fucking work. I was a little guilty of the above at times, trying to craft the perfect routine. In reality the person who just gets up, drinks too much coffee and works his ass off out performs the workflow perfectionist who visualizes and repeats affirmations, any day of the week. --- Next you need somewhere to store your notes, journal your trades and build your knowledge. For me this was Obsidian but I have also used Notion before and it is an equally viable option. Whichever one of these you choose be warned you will inevitably want to bang your head against a wall trying to use them for the first few days, but they will both click pretty quick and are 100% better options the word document or paper alternative. Here is my full obsidian setup tutorial: Here is a link to MisterPA 's notion Journal: Here is how I create "Meta-Notes" using obsidian: The process: - How I did it. - How I would do it if doing it again. Now I did things the "hard way" and manually worked my way back through each of MT's tweets starting in 2021, reading every one and logging those that I felt where relevant. You can see in my first post: the very first system I used to do this. I quickly adapted though after about a week and focused less on just logging each relevant tweet but trying to find and focusing on those which contained the most information. There where a lot of charts I looked at then skipped over because especially at the start of his timeline they contained little useful information and my time was better spent finding those where there was something to decode. Now this does not mean skip out on "work" just use your time efficiently. -- If however if I was to start from the beginning again with the goal of levelling up technical understanding as quickly as possible I would take a different approach. To start with I would familiarise myself with all relevant SMC concepts, I have linked the best free recourses for this below 👇 CryptoChase beginner friendly index: Barncore's "The Moneytaur Way" series: Gian's Trading bootcamp playlist: Following this I would then work through all of Taur's subscription posts working backwards, recreating his charts and taking notes on his logic. The subscription feed has the highest value density and least noise. Video example of my notes from his subscription posts 👇: --- Okay so now once you have a basic understanding of concepts and can re-recreate them on charts of your own it is time to put this in to practice. The next step is vigorous backtesting, you can use the trading view tool but I think trade Zella offers a more use friendly option if you pay for the subscription. Especially as it allows you to change timeframes without skipping ahead to candle close time of the timeframe you change too ( like Trading view does ) *my only note would be that their LTF/Micro TF data feed with be different to brokerage charts you will use on Trading view, to start with though you should not be going low enough that this is an issue. When you backtest in this context, treat it like real trading. That means journal and logging like you would if real cash was on the line. Take time, do not rush and focus on quality. Stick to BTC, ETH, Major FX pairs or indices as these assets are less reliant on confluence, backtesting a shitcoin is near useless as whether levels work or not will be highly dependent on Majors PA. Go on HTF, scroll back a couple years and try not too look at chart while doing so and then begin. Start with HTF analysis and work down to 2H or wherever you feel comfortable, chart it fully and then identify setups. Make rough notes / plans and then press play, execute the setups as they hit, log and journal trade management as well as observations and key notes. It is very important to not cheat when you do this, do not skip back and adjust your stoploss because it hit by 0.1%, do not skip back and adjust plan because you missed a block and your TP got frontrun. Instead these are the things you journal, embrace these mistakes because they are the cheapest mistakes you are going to make. Grind this, do it for hours, put some music on and enjoy. To start with focus on HTF's, as you get better and start netting $ on paper you can drop the timeframes and increase the difficulty. HTF = Normal, MTF = Medium, LTF = Hard. Even if you do not intend to day trade, learning how to read the lower TF's that force you to think faster, harder and prepare you for lower win rates / loss streaks can greatly improve your ability on higher TF's. While you are doing this as you start to have concepts click you now want to build up your real trading experience, take a sum of money that you care about but will be okay loosing and dedicate this to live trading. Start taking real trades and expect net losses in the beginning. This is where you will make you 2nd cheapest mistakes. This is also where you can begin to learn about your psychology. You may encounter some elements already in backtesting but the real market is where true colours really start to show. Mental issues are inevitable and part of the game, get used to them and start working to identify and fix them. Reading and applying books like Trading in the Zone and Mental Game of Trading are important and will help a lot but there is no easy fix, for some stuff you I believe you just have to get used to it and it goes away with experience. Losses suck at the beginning but after you loose 100 times you starting getting pretty numb to it, same goes for the winners. To accelerate the learning process, build connections and get advice there is also always the option of private groups, while I never personally chose this route and committed to learning everything through my own endeavours there is no denying that having nearly all the information you need structured and compiled in one place is valuable and can save time. Beyond this having access to real time thoughts and opinions of profitable traders can accelerate performance, however it carries the risk of being a double edged sword if not used properly, if relying on it like a crutch and using it as a substitute for real work you will not succeed. With that said if you take it for what it is, a learning opportunity then I believe it can be very beneficial. I am not a member of, nor affiliated with any paid group. There are now many options available within the community, all run by different people with different styles, tailored to different needs. If I was to make a recommendation though, as a non-member, it would be Albert & Co's 618'ers simply due to the diversity in styles of the traders running it and results I have seen from members I know personally. It is important that as you start to trade with real capital you reduce noise in your social feeds or eliminate it all together. You do not need 5 different opinions, you also do not need 2 people telling you the same thing in their own way so you feel re-assured. What you do need is to develop your independent thinking as a trader and be comfortable making different decisions to others, even traders ahead of yourself if it fits with your system or understanding of market. Taur here is perhaps an exception as this is who you are learning from but down the line a real test of your own ability and independence will be being able to stick with your own plan even when it differs from his. Don't get me wrong, counter trading him is retarded but you must learn to adapt his gift to your own style. This will make sense at some point. The next stage is taking your understanding of specific concepts to higher level as you simultaneously snowball experience. Look back through your journal and review where you lost money and made money, do not over extrapolate from a small sample but start to take notes and observe if trends in performance emerge. This is the beginning of the transition to self reliance, you now understand the strategy but must learn for yourself when and where it works. Here you can also learn more nuanced secondary concepts such as VSA, orderflow etc and add these to your game where appropriate. Do NOT get lost in the sauce though and remember mastery of basics is key. IMO a big focus should be understanding correlation thoroughly but especially on HTF's this is the most important thing and what triggers the majority of large swings where most of your cash will be made and losses recovered. Some people will disagree with me here but IMO you should also not be *focusing* on Odd TF's. These are secondary at best and most people overweight their significance leading to avoidable losses while wondering why price did not care about their 327minute Breaker Block which they think is the key to the market. Study Taurs feed and take note of how he mostly uses: 3M, 1M, 3W, 2W, 1W, 5D, 4D, 3D, 2D, 1D, 12H, 8H, 6H, 4H, 2H, 1H, 30m, 15m + micro time frames. The only thing left is time and repetition, you must show up each day and really do this, for months. Maybe you start to see result's, you catch your first key swing and where able to trade where others froze. Congratulations. Learn from these winners and repeat the actions. Find what assets work best for you, find your style, refine and grow. --- The last thing I will include is a short list of tools or links that can be helpful. - Trading view tutorial: - Dictionary: - Market news Calendar: --- Thank you too all those who have read this, I hope this has been helpful for the beginners who want to start but are just not sure how. 🫶 Don't just bookmark this and move on, start 🙃
0:38

Moneytaur study blueprint 🗺️ The process I used to go from not knowing what an order block is to pulling cash from the crypto markets in under 6 months using 🎯 Master concepts. Proof of performance, past 120 days👇 Start date: 09/03/2025 Requirements: - A PC/laptop - Wifi - A basic understanding of trading. ( What candlesticks are, how to actually place trades , etc ) - A free mind - Time or the ability to free up time. Starting: - Structure and routine - Stick to that routine + Pre mortem plan. - Notion / Obsidian setup. The first thing you need to create is a clear routine moulded around how you intend to approach this very large and complex task. This will not be linear and you will naturally adapt it as you progress but especially in the beginning some resemblance of structure each day is vital. This is an individual process but it is important to understand from the beginning that this will require a majority of your free time assuming you work a full time Job or study as a student. For me in the beginning this looked like: - Wake up at 6:30. - Shower - Study/work for 1h 45m before leaving for work. - 09:00 -> 17:00 work - 17:30 Exercise / Train - Eat - 19:00 resume study/work - 22:30 Start to wind down and get ready to sleep. It changed several times over the months and especially now I am full time but this is irrelevant, the only thing that matters is sticking with what you choose. Whatever your own routine may look like, it is important to understand it will inevitably require sacrifice. --- The next thing once you have established a draft framework of your routine is ensuring you will actually stick to that routine. Something I implemented which I found particularly beneficial was the concept of a Pre-Mortem plan. This involves creating several scenarios of a future in which you have failed and working backwards from each of these to find where it went wrong. Here is a video which explains it fully: When I did this I came up with 3 scenarios as well as prevention and cure for each. In the 6 months that followed each scenario presented at some point but I was able to catch them early due to having done this. The last thing is to not over complicate this, don't hyper focus on systems and loose momentum optimizing each detail. Just ensure you do the fucking work. I was a little guilty of the above at times, trying to craft the perfect routine. In reality the person who just gets up, drinks too much coffee and works his ass off out performs the workflow perfectionist who visualizes and repeats affirmations, any day of the week. --- Next you need somewhere to store your notes, journal your trades and build your knowledge. For me this was Obsidian but I have also used Notion before and it is an equally viable option. Whichever one of these you choose be warned you will inevitably want to bang your head against a wall trying to use them for the first few days, but they will both click pretty quick and are 100% better options the word document or paper alternative. Here is my full obsidian setup tutorial: Here is a link to MisterPA 's notion Journal: Here is how I create "Meta-Notes" using obsidian: The process: - How I did it. - How I would do it if doing it again. Now I did things the "hard way" and manually worked my way back through each of MT's tweets starting in 2021, reading every one and logging those that I felt where relevant. You can see in my first post: the very first system I used to do this. I quickly adapted though after about a week and focused less on just logging each relevant tweet but trying to find and focusing on those which contained the most information. There where a lot of charts I looked at then skipped over because especially at the start of his timeline they contained little useful information and my time was better spent finding those where there was something to decode. Now this does not mean skip out on "work" just use your time efficiently. -- If however if I was to start from the beginning again with the goal of levelling up technical understanding as quickly as possible I would take a different approach. To start with I would familiarise myself with all relevant SMC concepts, I have linked the best free recourses for this below 👇 CryptoChase beginner friendly index: Barncore's "The Moneytaur Way" series: Gian's Trading bootcamp playlist: Following this I would then work through all of Taur's subscription posts working backwards, recreating his charts and taking notes on his logic. The subscription feed has the highest value density and least noise. Video example of my notes from his subscription posts 👇: --- Okay so now once you have a basic understanding of concepts and can re-recreate them on charts of your own it is time to put this in to practice. The next step is vigorous backtesting, you can use the trading view tool but I think trade Zella offers a more use friendly option if you pay for the subscription. Especially as it allows you to change timeframes without skipping ahead to candle close time of the timeframe you change too ( like Trading view does ) *my only note would be that their LTF/Micro TF data feed with be different to brokerage charts you will use on Trading view, to start with though you should not be going low enough that this is an issue. When you backtest in this context, treat it like real trading. That means journal and logging like you would if real cash was on the line. Take time, do not rush and focus on quality. Stick to BTC, ETH, Major FX pairs or indices as these assets are less reliant on confluence, backtesting a shitcoin is near useless as whether levels work or not will be highly dependent on Majors PA. Go on HTF, scroll back a couple years and try not too look at chart while doing so and then begin. Start with HTF analysis and work down to 2H or wherever you feel comfortable, chart it fully and then identify setups. Make rough notes / plans and then press play, execute the setups as they hit, log and journal trade management as well as observations and key notes. It is very important to not cheat when you do this, do not skip back and adjust your stoploss because it hit by 0.1%, do not skip back and adjust plan because you missed a block and your TP got frontrun. Instead these are the things you journal, embrace these mistakes because they are the cheapest mistakes you are going to make. Grind this, do it for hours, put some music on and enjoy. To start with focus on HTF's, as you get better and start netting $ on paper you can drop the timeframes and increase the difficulty. HTF = Normal, MTF = Medium, LTF = Hard. Even if you do not intend to day trade, learning how to read the lower TF's that force you to think faster, harder and prepare you for lower win rates / loss streaks can greatly improve your ability on higher TF's. While you are doing this as you start to have concepts click you now want to build up your real trading experience, take a sum of money that you care about but will be okay loosing and dedicate this to live trading. Start taking real trades and expect net losses in the beginning. This is where you will make you 2nd cheapest mistakes. This is also where you can begin to learn about your psychology. You may encounter some elements already in backtesting but the real market is where true colours really start to show. Mental issues are inevitable and part of the game, get used to them and start working to identify and fix them. Reading and applying books like Trading in the Zone and Mental Game of Trading are important and will help a lot but there is no easy fix, for some stuff you I believe you just have to get used to it and it goes away with experience. Losses suck at the beginning but after you loose 100 times you starting getting pretty numb to it, same goes for the winners. To accelerate the learning process, build connections and get advice there is also always the option of private groups, while I never personally chose this route and committed to learning everything through my own endeavours there is no denying that having nearly all the information you need structured and compiled in one place is valuable and can save time. Beyond this having access to real time thoughts and opinions of profitable traders can accelerate performance, however it carries the risk of being a double edged sword if not used properly, if relying on it like a crutch and using it as a substitute for real work you will not succeed. With that said if you take it for what it is, a learning opportunity then I believe it can be very beneficial. I am not a member of, nor affiliated with any paid group. There are now many options available within the community, all run by different people with different styles, tailored to different needs. If I was to make a recommendation though, as a non-member, it would be Albert & Co's 618'ers simply due to the diversity in styles of the traders running it and results I have seen from members I know personally. It is important that as you start to trade with real capital you reduce noise in your social feeds or eliminate it all together. You do not need 5 different opinions, you also do not need 2 people telling you the same thing in their own way so you feel re-assured. What you do need is to develop your independent thinking as a trader and be comfortable making different decisions to others, even traders ahead of yourself if it fits with your system or understanding of market. Taur here is perhaps an exception as this is who you are learning from but down the line a real test of your own ability and independence will be being able to stick with your own plan even when it differs from his. Don't get me wrong, counter trading him is retarded but you must learn to adapt his gift to your own style. This will make sense at some point. The next stage is taking your understanding of specific concepts to higher level as you simultaneously snowball experience. Look back through your journal and review where you lost money and made money, do not over extrapolate from a small sample but start to take notes and observe if trends in performance emerge. This is the beginning of the transition to self reliance, you now understand the strategy but must learn for yourself when and where it works. Here you can also learn more nuanced secondary concepts such as VSA, orderflow etc and add these to your game where appropriate. Do NOT get lost in the sauce though and remember mastery of basics is key. IMO a big focus should be understanding correlation thoroughly but especially on HTF's this is the most important thing and what triggers the majority of large swings where most of your cash will be made and losses recovered. Some people will disagree with me here but IMO you should also not be *focusing* on Odd TF's. These are secondary at best and most people overweight their significance leading to avoidable losses while wondering why price did not care about their 327minute Breaker Block which they think is the key to the market. Study Taurs feed and take note of how he mostly uses: 3M, 1M, 3W, 2W, 1W, 5D, 4D, 3D, 2D, 1D, 12H, 8H, 6H, 4H, 2H, 1H, 30m, 15m + micro time frames. The only thing left is time and repetition, you must show up each day and really do this, for months. Maybe you start to see result's, you catch your first key swing and where able to trade where others froze. Congratulations. Learn from these winners and repeat the actions. Find what assets work best for you, find your style, refine and grow. --- The last thing I will include is a short list of tools or links that can be helpful. - Trading view tutorial: - Dictionary: - Market news Calendar: --- Thank you too all those who have read this, I hope this has been helpful for the beginners who want to start but are just not sure how. 🫶 Don't just bookmark this and move on, start 🙃

Ace

45,565 次观看 • 10 个月前