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Vertex Animation Textures, now an example in PlayCanvas 🧵 Rather than animate a skeleton per character, we bake every vertex position and normal into a texture and let the vertex shader interpolate between frames. 10,000 characters. One instanced draw call.

Martin Valigursky

1,710 subscribers

46,076 次观看 • 22 天前 •via X (Twitter)

艺术科学技术

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id tech 3 shaders make water like this possible in a quake engine. the water shown here is simply additive blended textures that have either frame animation (surface, caustics) or multiple scrolling planes (waterfalls) which are lit (lightmap or vertex) and have soft, alpha blended edges. the caustics are just decals, and the water color is faked by tinting the world texture beneath the surface. the water assets account for around 4% of the game's current 70mb download size.

id tech 3 shaders make water like this possible in a quake engine. the water shown here is simply additive blended textures that have either frame animation (surface, caustics) or multiple scrolling planes (waterfalls) which are lit (lightmap or vertex) and have soft, alpha blended edges. the caustics are just decals, and the water color is faked by tinting the world texture beneath the surface. the water assets account for around 4% of the game's current 70mb download size.

KillPixel Games

24,271 次观看 • 2 个月前

The upcoming Rerun 0.17 brings a huge increase in explicit control over your visualizations 🚀 In this video for example we dynamically change the visualizer that is applied on an entity from Mesh3D to Points3D and then edit the radius of the points. Because this mesh has vertex colors, the point cloud automatically becomes colored as well. All of this can be done either from UI or from code. We’ll release 0.17 next week. Stay tuned.

The upcoming Rerun 0.17 brings a huge increase in explicit control over your visualizations 🚀 In this video for example we dynamically change the visualizer that is applied on an entity from Mesh3D to Points3D and then edit the radius of the points. Because this mesh has vertex colors, the point cloud automatically becomes colored as well. All of this can be done either from UI or from code. We’ll release 0.17 next week. Stay tuned.

Rerun

40,345 次观看 • 2 年前

Post content on social .. when it gets a lot more views then normal .. Slightly edit it and create a call To Action and turn it into an ad .. 2025 marketing PS: It would mean the world to me if you could check out VeeFriends and let me know which is your favorite character in the comments below! #marketing #socialmedia #garyvee

Post content on social .. when it gets a lot more views then normal .. Slightly edit it and create a call To Action and turn it into an ad .. 2025 marketing PS: It would mean the world to me if you could check out VeeFriends and let me know which is your favorite character in the comments below! #marketing #socialmedia #garyvee

Gary Vaynerchuk

21,571 次观看 • 1 年前

We can't quite believe it, but we've never posted an FX Animation Breakdown before! There's been such amazing FX work done on so many of our projects, we're definitely going to show you more of how it's done. This film in particular was so FX heavy, we had to highlight the crazy work that went into it. You all know we love an action sequence. But for this one, we took a pretty different approach than we usually do! The entire thing was rendered through FX animation. The animator did their rough and tie-downs but instead of going into clean-up and BG paint, the FX team rendered the characters as well as animated the background FX (which is a super rare thing to do!). The result is this abstract representation of two magnificent powers colliding in an epic battle!!! Client: Digital Extremes Character Layout: Colin Tan 2D Animation: Shane Dering 2D Animation Lighting and Render: Simon Duclos FX Animation: Christophe Madeleine, Louve Karnas Compositing: Maxime Temple #FXAnimationBreakdown

We can't quite believe it, but we've never posted an FX Animation Breakdown before! There's been such amazing FX work done on so many of our projects, we're definitely going to show you more of how it's done. This film in particular was so FX heavy, we had to highlight the crazy work that went into it. You all know we love an action sequence. But for this one, we took a pretty different approach than we usually do! The entire thing was rendered through FX animation. The animator did their rough and tie-downs but instead of going into clean-up and BG paint, the FX team rendered the characters as well as animated the background FX (which is a super rare thing to do!). The result is this abstract representation of two magnificent powers colliding in an epic battle!!! Client: Digital Extremes Character Layout: Colin Tan 2D Animation: Shane Dering 2D Animation Lighting and Render: Simon Duclos FX Animation: Christophe Madeleine, Louve Karnas Compositing: Maxime Temple #FXAnimationBreakdown

THE LINE

10,452 次观看 • 2 个月前

Today, let’s talk about the possibilities of We’re living in a generation where IP is exploding. Web2 and Web3 are all about IP and everything is tied to digital identity. Be it a PFP or a brand, it carries personality and value. So let’s use NFTs as an example. NFTs are built around IPs. Characters that build the community and the world of a project. (I’ve been in the NFT space for 3-4 years. So yes, I’ve seen the cycle.) Some questions : - Beyond the image, how do we bring utility to every character? - What stories do they hold? - What personalities do they have? - Can each one have a digital identity beyond just being a PFP? - And most importantly : how can it talk to millions of people without the owner replying to every message? That’s just difficult to scale. Exactly the reason why we are building To bring IPs to life. Every character deserves a story. Deserves to connect emotionally anytime, with anyone. (Using daniel matsunaga 💎👋🏻💎 space io in this example)

Clement | Imaginary Ones | Bubio.ai

10,326 次观看 • 1 年前

Introducing Replit ModelFarm, the fastest and safest way to build your next Generative AI app. Available for free on Hacker and Pro plans till October 15th. It requires zero setup, zero configuration, and zero API keys. With Replit ModelFarm, you can build a working Gen AI app in as little as 3 lines of code. Get started by installing the Replit AI library in any Python, JavaScript, or TypeScript Repl. The library implements an API for text completion, chat completion, and text embeddings. It supports streaming so your users can see model responses in real-time rather than waiting on a single output. All Hacker and Pro builders will have free access to a selection of Gen AI models offered by Google Cloud Vertex AI through Replit ModelFarm. All models are accessible from the development environment and any deployed app.

Introducing Replit ModelFarm, the fastest and safest way to build your next Generative AI app. Available for free on Hacker and Pro plans till October 15th. It requires zero setup, zero configuration, and zero API keys. With Replit ModelFarm, you can build a working Gen AI app in as little as 3 lines of code. Get started by installing the Replit AI library in any Python, JavaScript, or TypeScript Repl. The library implements an API for text completion, chat completion, and text embeddings. It supports streaming so your users can see model responses in real-time rather than waiting on a single output. All Hacker and Pro builders will have free access to a selection of Gen AI models offered by Google Cloud Vertex AI through Replit ModelFarm. All models are accessible from the development environment and any deployed app.

Replit ⠕

229,285 次观看 • 2 年前

On your open perpetual position can become an NFT and live in your wallet. You open a position like normal than mint it. The program issues a token 2022 nft, that points to your exact position. Normally your open position lives inside the protocol. With Percolator Trade your able to wrap it into an NFT that sits in your own wallet. A real, ownable object instead of a line in a dashboard. Because it’s a token, it moves like one. Send the NFT to another wallet and the position goes with it. Whoever holds the NFT owns the trade. One NFT = one position If you burn it, your back to holding the position directly. That’s the composable part, a position use to be stuck inside one account. Because it’s a token, it can now be sold OTC or used by anything that speaks tokens. Why does it matter? Because the position is bound to the NFT, it can’t be drained out from under whoever holds it so there is no middleman that holds your position. The NFT does, and the NFT is yours. One important safety detail; transfers run through a token 2022 hook that checks the positions health first, so it can’t be handed off in a broken state & the NFT layer is a separate program so it can never touch the funds the core engine holds.

On your open perpetual position can become an NFT and live in your wallet. You open a position like normal than mint it. The program issues a token 2022 nft, that points to your exact position. Normally your open position lives inside the protocol. With Percolator Trade your able to wrap it into an NFT that sits in your own wallet. A real, ownable object instead of a line in a dashboard. Because it’s a token, it moves like one. Send the NFT to another wallet and the position goes with it. Whoever holds the NFT owns the trade. One NFT = one position If you burn it, your back to holding the position directly. That’s the composable part, a position use to be stuck inside one account. Because it’s a token, it can now be sold OTC or used by anything that speaks tokens. Why does it matter? Because the position is bound to the NFT, it can’t be drained out from under whoever holds it so there is no middleman that holds your position. The NFT does, and the NFT is yours. One important safety detail; transfers run through a token 2022 hook that checks the positions health first, so it can’t be handed off in a broken state & the NFT layer is a separate program so it can never touch the funds the core engine holds.

Percolator

12,359 次观看 • 1 个月前

a🧵on how to do the movable crater tech, You will learn this step by step, It should only take a couple of minutes to do it so lock in, Simple Overview: 1) Draw a black texture that represents a crack (use lasso or draw then fill tool) 2) Use a png to svg converter to make it into svg (you MUST make the crack black!) 3) Make the mesh of the crater 4) Export it as fbx and Import it in Roblox Studio 5) Create a viewport put the thing inside play with some lighting,material of crater n ur done 6) Make another viewport and make it so that it duplicates the character of the player every frame and puts in the viewport so that you can see it to be above the crater viewport Now for the detailed tutorial:

a🧵on how to do the movable crater tech, You will learn this step by step, It should only take a couple of minutes to do it so lock in, Simple Overview: 1) Draw a black texture that represents a crack (use lasso or draw then fill tool) 2) Use a png to svg converter to make it into svg (you MUST make the crack black!) 3) Make the mesh of the crater 4) Export it as fbx and Import it in Roblox Studio 5) Create a viewport put the thing inside play with some lighting,material of crater n ur done 6) Make another viewport and make it so that it duplicates the character of the player every frame and puts in the viewport so that you can see it to be above the crater viewport Now for the detailed tutorial:

QwinkleTee

40,103 次观看 • 1 年前

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⚠️ MUSK JUST WIRED $685 MILLION TO A COMPANY THAT WILL NEVER SHIP A ROBOT Optimus isn't built at Fremont. It's built by suppliers nobody names. The real chain: – Sanhua: actuator assemblies, the "muscle" in every joint. That's where the $685M went – Tuopu: rotary actuators and joint modules, the "skeleton" connectors – Rare earth magnets: 3.5kg per unit, one country controls 85-90% of global supply 70% of Optimus components trace back to Chinese suppliers. When China restricted magnet exports last year, Musk confirmed on an earnings call it hit production directly. That's the actual bottleneck between "50,000 units" and "50 million." Every gold rush made more millionaires selling picks than panning for gold.

Shredder

210,148 次观看 • 17 天前

I havent seen anyone post about it but one thing that stood out to me the most from the MW4 briefing is how sick the Tactical Nuke animation is. Tactical Nuke is back but the animation is completely different. You call it in a small phone and then a buckle into a skyhook a plane grabs right before the nuke drops and you look back at the map getting nuked. You also flip the map off as you fly away Its one of the sickest things I saw. The video below is an example from MW3 of what i mean.

I havent seen anyone post about it but one thing that stood out to me the most from the MW4 briefing is how sick the Tactical Nuke animation is. Tactical Nuke is back but the animation is completely different. You call it in a small phone and then a buckle into a skyhook a plane grabs right before the nuke drops and you look back at the map getting nuked. You also flip the map off as you fly away Its one of the sickest things I saw. The video below is an example from MW3 of what i mean.

Tridzo

388,828 次观看 • 2 个月前

This is life in Dubai. This is the UAE. 🇦🇪 We are so protected here that everyone is having fun ice skating at the Dubai Ice Rink right now. Life is normal, and no one is afraid. Why would we be afraid? We’re protected by the best air defenses in the region. I moved here a year and a half ago, and I wouldn’t want to live anywhere else. Grateful every single day to call it home.

This is life in Dubai. This is the UAE. 🇦🇪 We are so protected here that everyone is having fun ice skating at the Dubai Ice Rink right now. Life is normal, and no one is afraid. Why would we be afraid? We’re protected by the best air defenses in the region. I moved here a year and a half ago, and I wouldn’t want to live anywhere else. Grateful every single day to call it home.

Ian Miles Cheong

47,024 次观看 • 5 个月前

OpenCode Go is now wired into Codex!! The pricing is insane. $10 gets you 10,000 DeepSeek requests every 5 hours (no weekly limits). I converted that into DeepSeek API dollars because I thought I was reading it wrong, and the same 5 hours of usage would run somewhere between $10 and $30 depending on how big your context gets. So one afternoon of use already covers the whole sub. It comes with Kimi K3 as well. Both sit in the picker next to my other models now. Next time we hit a limit in the middle of a loop, we can grab it and keep going.

OpenCode Go is now wired into Codex!! The pricing is insane. $10 gets you 10,000 DeepSeek requests every 5 hours (no weekly limits). I converted that into DeepSeek API dollars because I thought I was reading it wrong, and the same 5 hours of usage would run somewhere between $10 and $30 depending on how big your context gets. So one afternoon of use already covers the whole sub. It comes with Kimi K3 as well. Both sit in the picker next to my other models now. Next time we hit a limit in the middle of a loop, we can grab it and keep going.

Ziwen

428,082 次观看 • 13 天前

we just released a new blog "Training a coding agent using the OpenCode harness in remote HF sandboxes with TRL and OpenEnv" you can take a real coding agent (OpenCode), let it run its own tool loop against real coding problems, and train it with RL on the exact tokens it produced and every rollout runs in its own remote HF sandbox, so rollouts scale out beyond one machine the loop: - OpenCode owns its tool loop inside an OpenEnv sandbox - an in-sandbox proxy records the real token ids + logprobs, per turn - a hidden-test verifier scores the result, and that is the reward - TRL trains with AsyncGRPO, weights sync back to vLLM over NCCL blog + runnable example:

we just released a new blog "Training a coding agent using the OpenCode harness in remote HF sandboxes with TRL and OpenEnv" you can take a real coding agent (OpenCode), let it run its own tool loop against real coding problems, and train it with RL on the exact tokens it produced and every rollout runs in its own remote HF sandbox, so rollouts scale out beyond one machine the loop: - OpenCode owns its tool loop inside an OpenEnv sandbox - an in-sandbox proxy records the real token ids + logprobs, per turn - a hidden-test verifier scores the result, and that is the reward - TRL trains with AsyncGRPO, weights sync back to vLLM over NCCL blog + runnable example:

Sergio Paniego

36,159 次观看 • 13 天前

One day you and I will visit all countries together, We will collect sunrises like postcards and learn the language of streets, We will get lost on purpose and call it freedom Sharing silence on long trains and laughter in crowded markets. We will stand before oceans that have never heard our names, Walk through cities older than our fears, Sleep under skies that remind us how small and lucky we are. Borders will blur into memories Miles will turn into stories. And home will no longer be a place on a map. But the space between your hand and mine. As the world opens itself to us. One journey at a time.

One day you and I will visit all countries together, We will collect sunrises like postcards and learn the language of streets, We will get lost on purpose and call it freedom Sharing silence on long trains and laughter in crowded markets. We will stand before oceans that have never heard our names, Walk through cities older than our fears, Sleep under skies that remind us how small and lucky we are. Borders will blur into memories Miles will turn into stories. And home will no longer be a place on a map. But the space between your hand and mine. As the world opens itself to us. One journey at a time.

Poetry.

85,079 次观看 • 7 个月前

GPT Image 2 + Seedance 2 = cartoon chase scene. I wanted to see how far I could push storyboard-to-video consistency, so I created a full 15-second cat vs mouse sequence inside a completely destroyed house. The process was actually simple: - generated a 3x3 cinematic storyboard sheet in GPT Image 2 - designed every frame like a real animation pre-production sketch - added camera directions, motion cues, timing notes, and destruction progression - used Seedance 2 to animate the entire sequence into one continuous cinematic shot flow What surprised me most was how well Seedance 2.0 understood the visual continuity between frames. The flying debris, motion blur, exaggerated jumps, and camera movement felt surprisingly cohesive. . . .

GPT Image 2 + Seedance 2 = cartoon chase scene. I wanted to see how far I could push storyboard-to-video consistency, so I created a full 15-second cat vs mouse sequence inside a completely destroyed house. The process was actually simple: - generated a 3x3 cinematic storyboard sheet in GPT Image 2 - designed every frame like a real animation pre-production sketch - added camera directions, motion cues, timing notes, and destruction progression - used Seedance 2 to animate the entire sequence into one continuous cinematic shot flow What surprised me most was how well Seedance 2.0 understood the visual continuity between frames. The flying debris, motion blur, exaggerated jumps, and camera movement felt surprisingly cohesive. . . .

Beginnersblog

38,440 次观看 • 3 个月前

Homa is excited to announce that Valentine is now a playable character in Aquarium Land, one of our most popular games. Creating a successful IP is challenging, as it requires gaining mass-market attention. This involves hundreds of millions of people acknowledging a character, following its journey, and becoming part of its community. To achieve this, Homa is investing significant resources in promoting Valentine through comics, illustrations, animations, stories, and games. By integrating Valentine into Aquarium Land, we take the first step in bridging awareness between Web2 and Web3. We aim to onboard millions of users to take part in Valentine's journey, play as her, build an emotional connection with the character, and read her stories. Aquarium Land has been downloaded more than 25 million times worldwide and has over 300,000 daily active players. As a Homa Holder, you are part of our ambitious vision: making Valentine a character that will make history by becoming part of the culture. Join us on our journey to go beyond Web3 and onboard millions of players and fans across the globe.

Homa is excited to announce that Valentine is now a playable character in Aquarium Land, one of our most popular games. Creating a successful IP is challenging, as it requires gaining mass-market attention. This involves hundreds of millions of people acknowledging a character, following its journey, and becoming part of its community. To achieve this, Homa is investing significant resources in promoting Valentine through comics, illustrations, animations, stories, and games. By integrating Valentine into Aquarium Land, we take the first step in bridging awareness between Web2 and Web3. We aim to onboard millions of users to take part in Valentine's journey, play as her, build an emotional connection with the character, and read her stories. Aquarium Land has been downloaded more than 25 million times worldwide and has over 300,000 daily active players. As a Homa Holder, you are part of our ambitious vision: making Valentine a character that will make history by becoming part of the culture. Join us on our journey to go beyond Web3 and onboard millions of players and fans across the globe.

homa

19,927 次观看 • 3 年前

Do you actually understand what's happening. He took an old laptop and turned it into a box that replaces every AI subscription he pays for. Now that box brings in $2,500 a month, per client, on repeat. No new hardware. Just parts he already had, mounted on a printed plate, running six agents without breaking a sweat. The agents don't sleep. One scouts topics. One writes drafts. One rejects anything below standard. One pushes finished work out the door. One watches over the rest. Fifteen clients. Thirty pieces of content each, every month. Four hours a week from him. The system he built out of spare parts now earns more than most people's actual job. Everyone else recycled their old laptop. He recycled his into income.

Do you actually understand what's happening. He took an old laptop and turned it into a box that replaces every AI subscription he pays for. Now that box brings in $2,500 a month, per client, on repeat. No new hardware. Just parts he already had, mounted on a printed plate, running six agents without breaking a sweat. The agents don't sleep. One scouts topics. One writes drafts. One rejects anything below standard. One pushes finished work out the door. One watches over the rest. Fifteen clients. Thirty pieces of content each, every month. Four hours a week from him. The system he built out of spare parts now earns more than most people's actual job. Everyone else recycled their old laptop. He recycled his into income.

Superior

16,950 次观看 • 1 个月前

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

Zentrix⌚️

14,015 次观看 • 1 个月前

A CHINESE TRADER BUILT A SECOND BRAIN IN OBSIDIAN THAT GENERATES 3 TRADING IDEAS EVERY MORNING AT 6AM AND MADE $180,000 IN 6 MONTHS. No Bloomberg terminal. No analytics desk. No team of analysts. A Mac Mini by the wall. An iPhone in his pocket. One local Obsidian vault. Six N8N pipelines running 24/7, pulling every article he reads, every podcast he listens to, and every voice note he drops into a Telegram bot—directly into the vault. Every night, a neural network reads across 4,000 connected notes and finds the strongest connections between fresh information and old theses. Every morning at 6AM, a brief lands in his inbox: - 3 trading ideas with confidence scores - The emerging thesis of the week - Any note that contradicts an active position The system only wakes him up when a fresh note contradicts his thesis, or when an idea breaks 90% confidence. Everything else runs without him. The monthly bill: $120 in API costs. The monthly return: approximately $30,000 into the account. Traditional quant funds pay teams of 8 people to produce the same flow of insights. He pays $120 and a Mac Mini. The full system breakdown is in the article below. Bookmark this before you pay for a Bloomberg subscription.

A CHINESE TRADER BUILT A SECOND BRAIN IN OBSIDIAN THAT GENERATES 3 TRADING IDEAS EVERY MORNING AT 6AM AND MADE $180,000 IN 6 MONTHS. No Bloomberg terminal. No analytics desk. No team of analysts. A Mac Mini by the wall. An iPhone in his pocket. One local Obsidian vault. Six N8N pipelines running 24/7, pulling every article he reads, every podcast he listens to, and every voice note he drops into a Telegram bot—directly into the vault. Every night, a neural network reads across 4,000 connected notes and finds the strongest connections between fresh information and old theses. Every morning at 6AM, a brief lands in his inbox: - 3 trading ideas with confidence scores - The emerging thesis of the week - Any note that contradicts an active position The system only wakes him up when a fresh note contradicts his thesis, or when an idea breaks 90% confidence. Everything else runs without him. The monthly bill: $120 in API costs. The monthly return: approximately $30,000 into the account. Traditional quant funds pay teams of 8 people to produce the same flow of insights. He pays $120 and a Mac Mini. The full system breakdown is in the article below. Bookmark this before you pay for a Bloomberg subscription.

CyrilXBT

46,087 次观看 • 1 个月前

A CHINESE TRADER BUILT A SECOND BRAIN IN OBSIDIAN THAT GENERATES 3 TRADING IDEAS EVERY MORNING AT 6AM AND MADE $180,000 IN 6 MONTHS. No Bloomberg terminal. No analytics desk. No team of analysts. A Mac Mini by the wall. An iPhone in his pocket. One local Obsidian vault. Six N8N pipelines running 24/7, pulling every article he reads, every podcast he listens to, and every voice note he drops into a Telegram bot—directly into the vault. Every night, a neural network reads across 4,000 connected notes and finds the strongest connections between fresh information and old theses. Every morning at 6AM, a brief lands in his inbox: - 3 trading ideas with confidence scores - The emerging thesis of the week - Any note that contradicts an active position The system only wakes him up when a fresh note contradicts his thesis, or when an idea breaks 90% confidence. Everything else runs without him. The monthly bill: $120 in API costs. The monthly return: approximately $30,000 into the account. Traditional quant funds pay teams of 8 people to produce the same flow of insights. He pays $120 and a Mac Mini. The full system breakdown is in the article below. Bookmark this before you pay for a Bloomberg subscription. Follow CyrilXBT for every solo operator setup that changes what one person can build.

A CHINESE TRADER BUILT A SECOND BRAIN IN OBSIDIAN THAT GENERATES 3 TRADING IDEAS EVERY MORNING AT 6AM AND MADE $180,000 IN 6 MONTHS. No Bloomberg terminal. No analytics desk. No team of analysts. A Mac Mini by the wall. An iPhone in his pocket. One local Obsidian vault. Six N8N pipelines running 24/7, pulling every article he reads, every podcast he listens to, and every voice note he drops into a Telegram bot—directly into the vault. Every night, a neural network reads across 4,000 connected notes and finds the strongest connections between fresh information and old theses. Every morning at 6AM, a brief lands in his inbox: - 3 trading ideas with confidence scores - The emerging thesis of the week - Any note that contradicts an active position The system only wakes him up when a fresh note contradicts his thesis, or when an idea breaks 90% confidence. Everything else runs without him. The monthly bill: $120 in API costs. The monthly return: approximately $30,000 into the account. Traditional quant funds pay teams of 8 people to produce the same flow of insights. He pays $120 and a Mac Mini. The full system breakdown is in the article below. Bookmark this before you pay for a Bloomberg subscription. Follow CyrilXBT for every solo operator setup that changes what one person can build.

CyrilXBT

129,488 次观看 • 1 个月前