正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Every settlement demo you'll see at Sibos ends the same way, but ours doesn't. One week today, on the Discover Stage, we will be running a live repo against a tokenised bond in Murex's MX.3, powered by Quant's orchestration layer, then killing it mid-execution. What happens next is the... show more

Quant

173,927 subscribers

52,069 次观看 • 9 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

FABLE 5 + HIGGSFIELD TURN A $35,000 ANIMATED SITE INTO A ONE-SESSION, $12 BUILD. HERE'S EXACTLY HOW. a studio runs this across four people and three weeks. you run it across one chat window and one afternoon. THE BUILD, STAGE BY STAGE: STAGE 1 - THE CONCEPT Claude reads your brief and scripts the scroll before a line of code exists - what the visitor feels at second 3, 15, 40. prompt: "read this brief. script the scroll beat by beat, then scaffold the project with GSAP ScrollTrigger + Lenis." STAGE 2 - THE VISUALS (Higgsfield) every hero shot, transition, and ambient loop comes out of 30+ generative models - matched to the story, not pulled from a stock library. prompt: "generate the hero sting and one b-roll clip per section. 3-5s, high-res, cinematic." STAGE 3 - THE MOTION (Claude Code) Claude writes the ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. zero hand-coded keyframes. prompt: "wire the scroll: pin the hero, scrub the video, reveal each section on scroll. keep it 60fps on mobile." STAGE 4 - THE POLISH six cinematic effects baked in, no config: film grain, particles, vignette, glass cards, color tints, scroll pacing. prompt: "bake in the cinematic layer, then QA load speed, mobile breakpoints, and whether the scroll actually lands - rewrite what doesn't." CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: mcp_servers: higgsfield: url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the pipeline was the moat. it just became four prompts. Follow me, comment "MATH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

FABLE 5 + HIGGSFIELD TURN A $35,000 ANIMATED SITE INTO A ONE-SESSION, $12 BUILD. HERE'S EXACTLY HOW. a studio runs this across four people and three weeks. you run it across one chat window and one afternoon. THE BUILD, STAGE BY STAGE: STAGE 1 - THE CONCEPT Claude reads your brief and scripts the scroll before a line of code exists - what the visitor feels at second 3, 15, 40. prompt: "read this brief. script the scroll beat by beat, then scaffold the project with GSAP ScrollTrigger + Lenis." STAGE 2 - THE VISUALS (Higgsfield) every hero shot, transition, and ambient loop comes out of 30+ generative models - matched to the story, not pulled from a stock library. prompt: "generate the hero sting and one b-roll clip per section. 3-5s, high-res, cinematic." STAGE 3 - THE MOTION (Claude Code) Claude writes the ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. zero hand-coded keyframes. prompt: "wire the scroll: pin the hero, scrub the video, reveal each section on scroll. keep it 60fps on mobile." STAGE 4 - THE POLISH six cinematic effects baked in, no config: film grain, particles, vignette, glass cards, color tints, scroll pacing. prompt: "bake in the cinematic layer, then QA load speed, mobile breakpoints, and whether the scroll actually lands - rewrite what doesn't." CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: mcp_servers: higgsfield: url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the pipeline was the moat. it just became four prompts. Follow me, comment "MATH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

ZEUS⚡️

47,174 次观看 • 2 个月前

3,868 NULLs. 3,868 unique forms. No two are the same. Register for the whitelist at: Free Mint goes live on OpenSea today, September 30, at 11 AM GMT: NULL is more than a collection of static 2D artwork. Each piece combines art with dynamic visual effects, allowing the form of a NULL to shift and transform when you interact with it. The collection is built around both art and utility. Collectors will be able to burn NULLs to generate $NULL tokens, while the Reborn mechanism introduces a second layer: burn tokens to create a new variation of NULL. Collect. Burn. Reborn. 3,868 forms enter the void. What they become next is part of the experiment.

3,868 NULLs. 3,868 unique forms. No two are the same. Register for the whitelist at: Free Mint goes live on OpenSea today, September 30, at 11 AM GMT: NULL is more than a collection of static 2D artwork. Each piece combines art with dynamic visual effects, allowing the form of a NULL to shift and transform when you interact with it. The collection is built around both art and utility. Collectors will be able to burn NULLs to generate $NULL tokens, while the Reborn mechanism introduces a second layer: burn tokens to create a new variation of NULL. Collect. Burn. Reborn. 3,868 forms enter the void. What they become next is part of the experiment.

Nullform

26,383 次观看 • 14 小时前

Los Angeles, California. The recent Waymo geofence expansion may not be going as planned- Here we see a Waymo repeatedly try to reverse and crash into a Tesla right behind it at the intersection. This is a new one; not sure what the hell is going on here… but it’s not good Any guesses Dan O'Dowd ???? At this point, maybe Dan can refer us to someone who’s actually interested in autonomous vehicle safety. He’s demonstrated in every possible way that he has no interest in autonomous vehicle safety beyond personal wealth. #SunsetProject

Los Angeles, California. The recent Waymo geofence expansion may not be going as planned- Here we see a Waymo repeatedly try to reverse and crash into a Tesla right behind it at the intersection. This is a new one; not sure what the hell is going on here… but it’s not good Any guesses Dan O'Dowd ???? At this point, maybe Dan can refer us to someone who’s actually interested in autonomous vehicle safety. He’s demonstrated in every possible way that he has no interest in autonomous vehicle safety beyond personal wealth. #SunsetProject

No Safe Words

116,897 次观看 • 1 年前

The visionaries behind Pi Network are making waves this week in Miami 🔥 🎤 Chengdiao Fan 🗓️ May 6 | ⏰ 11:15–11:35 AM EDT 📍 Convergence Stage 💡 Topic: Aligning Web3, AI, and Blockchain for Utility She’ll dive into how Pi’s ecosystem — powered by blockchain, verified identity, and a global network — is shaping real-world, utility-driven products for the AI era. 🎤 Nicolas Kokkalis 🗓️ May 7 | ⏰ 10:15–10:45 AM EDT 📍 Convergence Stage 💡 Panel: How to Prove You’re Human in an AI World (Without Doxing Yourself) Exploring one of the biggest challenges today — building trust in a world where AI can mimic humans. 🌍 What This Means for Pi Network: ✨ Strong focus on real utility, not hype 🔐 Advanced identity verification with Pi KYC 🤖 Preparing for the AI-powered digital economy 🌐 Building a trusted, global ecosystem 💜 The future is being built right now — and Pi Network is at the center of it. 👋 See you there, Pioneers! #PiNetwork #Consensus2026 #Web3 #Blockchain #AI #Crypto #FutureTech

The visionaries behind Pi Network are making waves this week in Miami 🔥 🎤 Chengdiao Fan 🗓️ May 6 | ⏰ 11:15–11:35 AM EDT 📍 Convergence Stage 💡 Topic: Aligning Web3, AI, and Blockchain for Utility She’ll dive into how Pi’s ecosystem — powered by blockchain, verified identity, and a global network — is shaping real-world, utility-driven products for the AI era. 🎤 Nicolas Kokkalis 🗓️ May 7 | ⏰ 10:15–10:45 AM EDT 📍 Convergence Stage 💡 Panel: How to Prove You’re Human in an AI World (Without Doxing Yourself) Exploring one of the biggest challenges today — building trust in a world where AI can mimic humans. 🌍 What This Means for Pi Network: ✨ Strong focus on real utility, not hype 🔐 Advanced identity verification with Pi KYC 🤖 Preparing for the AI-powered digital economy 🌐 Building a trusted, global ecosystem 💜 The future is being built right now — and Pi Network is at the center of it. 👋 See you there, Pioneers! #PiNetwork #Consensus2026 #Web3 #Blockchain #AI #Crypto #FutureTech

Pi Community ᵖⁱ ⁿᵉᵗʷᵒʳᵏ

18,992 次观看 • 4 个月前

🚨 JANE STREET STRIKES $BTC AGAIN It started Monday. Four sessions, $51 billion in market cap erased. No headline. No warning. Four days. Four US opens. Four dumps. August 11: opens, drops 1.7% August 12: opens, drops 1.7% August 13: opens, drops 1.4% August 14: opens, drops 1.9% Every night Bitcoin climbs. Every US open erases it. $65,400 to $62,800, and every dollar went in the same window. Today was the tell. PPI came in soft. Bitcoin ripped to $64,000. New York opened. Straight to $62,800. Good news at 8:30. Sold by 10:00. Four for four is a 6% coincidence if you believe in coincidences. Same hour. Same size. Same silence around it. Bookmark this and check the open on Monday. If it prints a fifth time, you will never look at a chart the same way again. I post the moment this pattern breaks. Follow and turn notifications on.

🚨 JANE STREET STRIKES $BTC AGAIN It started Monday. Four sessions, $51 billion in market cap erased. No headline. No warning. Four days. Four US opens. Four dumps. August 11: opens, drops 1.7% August 12: opens, drops 1.7% August 13: opens, drops 1.4% August 14: opens, drops 1.9% Every night Bitcoin climbs. Every US open erases it. $65,400 to $62,800, and every dollar went in the same window. Today was the tell. PPI came in soft. Bitcoin ripped to $64,000. New York opened. Straight to $62,800. Good news at 8:30. Sold by 10:00. Four for four is a 6% coincidence if you believe in coincidences. Same hour. Same size. Same silence around it. Bookmark this and check the open on Monday. If it prints a fifth time, you will never look at a chart the same way again. I post the moment this pattern breaks. Follow and turn notifications on.

Nonzee

17,475 次观看 • 1 个月前

Stripe Cards are now officially live on ClawVault If you're building agents that need to pay for things — APIs, SaaS, cloud, subscriptions, contractors — your agent can now get its own Visa card. Not a restricted virtual card on limited rails. A real Visa, powered by Stripe, accepted at every merchant on the internet. Spending limits. Merchant locks. Expiry controls. And that's just the defaults — the full rules engine goes deeper. Your agent spends freely within the rules — break one, and the charge gets flagged for your approval before it goes through. No other platform gives your agent a card and keeps you in control at the same time. Every AI agent will need a card. Every card will need a control layer. We built both. Now on Stripe. Your agent gets the card. You keep the control.🦞

Stripe Cards are now officially live on ClawVault If you're building agents that need to pay for things — APIs, SaaS, cloud, subscriptions, contractors — your agent can now get its own Visa card. Not a restricted virtual card on limited rails. A real Visa, powered by Stripe, accepted at every merchant on the internet. Spending limits. Merchant locks. Expiry controls. And that's just the defaults — the full rules engine goes deeper. Your agent spends freely within the rules — break one, and the charge gets flagged for your approval before it goes through. No other platform gives your agent a card and keeps you in control at the same time. Every AI agent will need a card. Every card will need a control layer. We built both. Now on Stripe. Your agent gets the card. You keep the control.🦞

ClawVault

32,631 次观看 • 6 个月前

$133,752 in new annual recurring revenue. 30 days. Zero new hires. That is the Authority Makers result. Here is what it looked like on our end. We run content and brand campaigns for multiple clients simultaneously. Outbound was always the thing that fell through the cracks. Not because we did not have the strategy. Because nobody had time to execute it every single day. We gave that job to Viktor. Week 1 we were not sure it would stick. The sequences felt generic and we almost went back to doing it manually. Then the replies started coming in. Viktor adjusted, followed up, and kept going whether we were online or not. By week 3 two new client conversations had opened from outbound that had been sitting untouched for months. One AI employee running the work nobody had time for. That is not a tool. That is a hire. Most execution gaps do not need a strategy. They need someone to show up every day. Hire Viktor for your team. $100 in credits included, no card. Full link in first reply. Paid Partnership

Nawi

123,838 次观看 • 19 天前

Full Fine-tuning vs. Freezing Layers. Interact 👉 and == Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1 and W2 — the foundational prerequisites — stay frozen. Only W3 — the layer closest to your task-specific output — gets a ΔW.

Full Fine-tuning vs. Freezing Layers. Interact 👉 and == Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1 and W2 — the foundational prerequisites — stay frozen. Only W3 — the layer closest to your task-specific output — gets a ΔW.

Tom Yeh

27,740 次观看 • 5 个月前

a clip of a stream did MILLIONS of views last week. the guy who cut it got a like the moment was already proven. someone just had to find it before it cooled off - and finding it is the slow part everyone does by hand so i built ClipVein. open source, MIT it doesn't scrape and it doesn't post for you. it attaches to your own chrome and reads the same For You feed you already scroll what it does, all in the repo in plain text: → pulls every streamer post with its real metrics -views, likes, reposts, video → keeps only clips that clear a view floor YOU set → ranks them on reach + engagement + freshness, and shows you WHY each one scored → hands you the top 3 links, ready to re-cut and repost for original views the honest parts are the point: it uses your session, so it never asks for a password it can't post for you. no auto-post, ever runs on your machine, not mine. MIT, tests green optional: grok scouts the feed, claude writes the caption repo in the first reply

a clip of a stream did MILLIONS of views last week. the guy who cut it got a like the moment was already proven. someone just had to find it before it cooled off - and finding it is the slow part everyone does by hand so i built ClipVein. open source, MIT it doesn't scrape and it doesn't post for you. it attaches to your own chrome and reads the same For You feed you already scroll what it does, all in the repo in plain text: → pulls every streamer post with its real metrics -views, likes, reposts, video → keeps only clips that clear a view floor YOU set → ranks them on reach + engagement + freshness, and shows you WHY each one scored → hands you the top 3 links, ready to re-cut and repost for original views the honest parts are the point: it uses your session, so it never asks for a password it can't post for you. no auto-post, ever runs on your machine, not mine. MIT, tests green optional: grok scouts the feed, claude writes the caption repo in the first reply

Groven

32,567 次观看 • 22 天前

WHEN FABLE 5 + HIGGSFIELD FINISH A $35K SITE, HERE'S EVERYTHING THAT'S ACTUALLY IN THE FOLDER. no mockups, no "phase 2 next week." this is what ships - ready to hand a client: → the live site a scroll-driven build - GSAP ScrollTrigger + Lenis, pinned sections, scrubbed video, zero hand-coded keyframes. → the footage (yours to keep) a hero clip + one b-roll per section, generated in Higgsfield from 30+ models. not licensed stock - owned outright. → the optimized assets frames extracted, converted to WebP, lazy-loaded. loads instant, even on mobile. → the cinematic layer film grain, particles, vignette, glass cards, color tints - baked in, no plugins, no designer. → copy + responsive layout section copy drafted, breakpoints handled, mobile-tuned. that's the full studio deliverable. minus the studio, minus the month, minus the invoice. CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: - mcp_servers: - higgsfield: - url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session agencies sold you the folder. they just took a month and $35k to fill it. Follow me, reply "FOLDER" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

WHEN FABLE 5 + HIGGSFIELD FINISH A $35K SITE, HERE'S EVERYTHING THAT'S ACTUALLY IN THE FOLDER. no mockups, no "phase 2 next week." this is what ships - ready to hand a client: → the live site a scroll-driven build - GSAP ScrollTrigger + Lenis, pinned sections, scrubbed video, zero hand-coded keyframes. → the footage (yours to keep) a hero clip + one b-roll per section, generated in Higgsfield from 30+ models. not licensed stock - owned outright. → the optimized assets frames extracted, converted to WebP, lazy-loaded. loads instant, even on mobile. → the cinematic layer film grain, particles, vignette, glass cards, color tints - baked in, no plugins, no designer. → copy + responsive layout section copy drafted, breakpoints handled, mobile-tuned. that's the full studio deliverable. minus the studio, minus the month, minus the invoice. CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: - mcp_servers: - higgsfield: - url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session agencies sold you the folder. they just took a month and $35k to fill it. Follow me, reply "FOLDER" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

ZEUS⚡️

20,996 次观看 • 2 个月前

Hermes Desktop does not have to stay exactly the way it shipped. You can build the tools you wish were already inside it. A project dashboard. A Bot control panel. A research tracker. A queue for work that needs your attention. A status board for scheduled jobs. A set of quick actions you use every day. That is what the Hermes Desktop Plugin SDK opens up. SDK is the technical name, but the idea is pretty simple: It gives you a way to add your own tools and interfaces directly inside Hermes Desktop. A plugin can add its own page, pane, sidebar item, status-bar element, keyboard shortcut, command-palette action, or composer extension. And it can work with live Hermes data, including things like sessions, config, Skills, and Cron. So if you keep asking Hermes to recreate the same dashboard, show the same information, or give you the same controls... you may be at the point where that workflow deserves to become an actual part of your Desktop. The starting point can be surprisingly lightweight too. A basic local plugin can be a single `plugin.js` file under: `$HERMES_HOME/desktop-plugins/ /` No Hermes repo clone. No separate build step. The bigger idea here is not “turn every prompt into an app.” It is that Hermes Desktop can become much more personal than the interface you downloaded. When a workflow proves useful enough, you can give it its own permanent place. Video via Brooklyn! — a good look at what this can actually become.

Hermes Desktop does not have to stay exactly the way it shipped. You can build the tools you wish were already inside it. A project dashboard. A Bot control panel. A research tracker. A queue for work that needs your attention. A status board for scheduled jobs. A set of quick actions you use every day. That is what the Hermes Desktop Plugin SDK opens up. SDK is the technical name, but the idea is pretty simple: It gives you a way to add your own tools and interfaces directly inside Hermes Desktop. A plugin can add its own page, pane, sidebar item, status-bar element, keyboard shortcut, command-palette action, or composer extension. And it can work with live Hermes data, including things like sessions, config, Skills, and Cron. So if you keep asking Hermes to recreate the same dashboard, show the same information, or give you the same controls... you may be at the point where that workflow deserves to become an actual part of your Desktop. The starting point can be surprisingly lightweight too. A basic local plugin can be a single `plugin.js` file under: `$HERMES_HOME/desktop-plugins/ /` No Hermes repo clone. No separate build step. The bigger idea here is not “turn every prompt into an app.” It is that Hermes Desktop can become much more personal than the interface you downloaded. When a workflow proves useful enough, you can give it its own permanent place. Video via Brooklyn! — a good look at what this can actually become.

Hermes Release Watch

15,411 次观看 • 1 个月前

THIS GUY JUST REBUILT A $35,000 ANIMATED SITE FOR $12. IF YOU RUN A WEB STUDIO, YOU SHOULD PROBABLY KEEP SCROLLING. Every agency billing $100-149/hr is selling you five departments wearing one invoice. Here’s each one - collapsed into a single agentic session. LAYER 1 - THE CONCEPT ROOM (Claude) Reads the brief, pulls references, and scripts the scroll: what the visitor feels at second 3, second 15, second 40. → Used to be a strategist and a wall of mood boards. Now it’s a conversation. LAYER 2 - THE MOTION STUDIO (Higgsfield) Cinematic clips from 30+ generative models - hero shots, transitions, ambient loops - all matched to the story from Layer 1. → Used to be a motion artist on retainer. Now it’s a prompt. LAYER 3 - THE DEV TEAM (Claude Code) Scaffolds the site, writes the GSAP ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. → A full scroll-driven build with zero hand-coded keyframes. LAYER 4 - THE DESIGN DEPT (baked-in cinematic layer) Six effects, zero config: film grain, particles, vignette, glass cards, color tints, scroll pacing. → The polish that justified the invoice - now it ships by default. LAYER 5 - THE QA PASS (Claude) Checks load speed, mobile breakpoints, and whether the scroll actually lands - then rewrites whatever doesn’t. → Used to be a client call and a revision cycle. Now it’s one more turn in the same session. Five departments. One operator. One pass. A strategist, a motion artist, a developer, a designer, and a QA lead - weeks of handoffs - now run in a single session. For a Claude subscription and a few dollars of Higgsfield credits. The studio was never selling talent. It was selling overhead. And the overhead just became five layers. Follow me, reply “website” to this post and I will send you the step-by-step Playbook 👇

THIS GUY JUST REBUILT A $35,000 ANIMATED SITE FOR $12. IF YOU RUN A WEB STUDIO, YOU SHOULD PROBABLY KEEP SCROLLING. Every agency billing $100-149/hr is selling you five departments wearing one invoice. Here’s each one - collapsed into a single agentic session. LAYER 1 - THE CONCEPT ROOM (Claude) Reads the brief, pulls references, and scripts the scroll: what the visitor feels at second 3, second 15, second 40. → Used to be a strategist and a wall of mood boards. Now it’s a conversation. LAYER 2 - THE MOTION STUDIO (Higgsfield) Cinematic clips from 30+ generative models - hero shots, transitions, ambient loops - all matched to the story from Layer 1. → Used to be a motion artist on retainer. Now it’s a prompt. LAYER 3 - THE DEV TEAM (Claude Code) Scaffolds the site, writes the GSAP ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. → A full scroll-driven build with zero hand-coded keyframes. LAYER 4 - THE DESIGN DEPT (baked-in cinematic layer) Six effects, zero config: film grain, particles, vignette, glass cards, color tints, scroll pacing. → The polish that justified the invoice - now it ships by default. LAYER 5 - THE QA PASS (Claude) Checks load speed, mobile breakpoints, and whether the scroll actually lands - then rewrites whatever doesn’t. → Used to be a client call and a revision cycle. Now it’s one more turn in the same session. Five departments. One operator. One pass. A strategist, a motion artist, a developer, a designer, and a QA lead - weeks of handoffs - now run in a single session. For a Claude subscription and a few dollars of Higgsfield credits. The studio was never selling talent. It was selling overhead. And the overhead just became five layers. Follow me, reply “website” to this post and I will send you the step-by-step Playbook 👇

ZEUS⚡️

141,973 次观看 • 3 个月前

Pre-orders for Modern Savage are live! Have a look at the shelf in your kitchen. The tubs, the tubs behind the tubs, the ones bought with good intentions in January. Ours looked the same. Half of what was in them we couldn't pronounce, and most of it was never food in the first place. Synthetic fillers standing in for nutrition. Cheap forms of minerals the body barely absorbs. And then a sweeter, weaker version of the same thing sold to us for our children. This is the problem Modern Savage exists to solve. Modern Savage: The ultimate all in one supplement. Whole food, bison organs, fibre, probiotics, berries, vitamins, minerals. Mini Savage: The same standard but tailored for kids. + Colostrum. BG Summit Stack: Bears performance stack. Creatine, electrolytes, NMN, zynamite. No brand deal, no endorsement, no borrowed name on the label. 100% family owned, we made it for our own family first. Pre-orders are now open: MODERN by design. SAVAGE by nature.

Bear Grylls OBE

36,321 次观看 • 2 个月前

Batch Normalization by hand ✍️ ~ 7 steps walkthrough below Batch normalization is common practice for improving training and achieving faster convergence. It sounds simple. But it is often misunderstood. 🤔 Does batch normalization involve trainable parameters, tunable hyper-parameters, or both? 🤔 Is batch normalization applied to inputs, features, weights, biases, or outputs? 🤔 How is batch normalization different from layer normalization? So I drew and calculated one entirely by hand. Goal: normalize a mini-batch of 4 examples to mean 0 and variance 1, then let the network scale it back. = 1. Given = A mini-batch of 4 training examples, each with 3 features. = 2. Linear layer = Let us multiply by the weights and add the biases. Batch norm sits after this, which answers the second question: what gets normalized is features, not inputs, weights or biases. = 3. ReLU = We apply the activation, and -2 becomes 0. Negative values are suppressed before any statistic is taken. = 4. Batch statistics = Let us compute the sum, mean, variance and standard deviation, one row at a time. A row is a feature and the four columns are the four examples, so every number here measures one feature against the rest of the batch. That is the "batch" in batch normalization, and it is exactly what layer normalization does not do. The statistics are rounded to whole numbers, which is what keeps the rest of the page doable in pen. = 5. Shift to mean 0 = We subtract the mean, in green. The four values in each feature now average to zero. = 6. Scale to variance 1 = Let us divide by the standard deviation, in orange. Each feature now has variance one, whatever scale it arrived at. = 7. Scale and shift = We multiply by a linear transformation and pass the result on. The diagonal and the last column are trainable, so having just forced every feature to mean 0 and variance 1, we hand the network the means to undo it. The outputs: Mean of each feature = [2, 1, 2] Std dev of each feature = [1, 1, 2] To the next layer = [2, -2, 2, 0], [-3, 3, 6, -3], [2, 0, 1, 2] The answers: 🤔 Both. The scale and shift are trainable, the statistics are not. Epsilon and the momentum on the running statistics are the hyper-parameters, and one mini-batch by hand needs neither. 🤔 Features, after the linear layer, not inputs, weights or biases. 🤔 Batch norm measures across the batch, one feature at a time. Layer norm measures across the features, one example at a time. 💾 Save this post!

Batch Normalization by hand ✍️ ~ 7 steps walkthrough below Batch normalization is common practice for improving training and achieving faster convergence. It sounds simple. But it is often misunderstood. 🤔 Does batch normalization involve trainable parameters, tunable hyper-parameters, or both? 🤔 Is batch normalization applied to inputs, features, weights, biases, or outputs? 🤔 How is batch normalization different from layer normalization? So I drew and calculated one entirely by hand. Goal: normalize a mini-batch of 4 examples to mean 0 and variance 1, then let the network scale it back. = 1. Given = A mini-batch of 4 training examples, each with 3 features. = 2. Linear layer = Let us multiply by the weights and add the biases. Batch norm sits after this, which answers the second question: what gets normalized is features, not inputs, weights or biases. = 3. ReLU = We apply the activation, and -2 becomes 0. Negative values are suppressed before any statistic is taken. = 4. Batch statistics = Let us compute the sum, mean, variance and standard deviation, one row at a time. A row is a feature and the four columns are the four examples, so every number here measures one feature against the rest of the batch. That is the "batch" in batch normalization, and it is exactly what layer normalization does not do. The statistics are rounded to whole numbers, which is what keeps the rest of the page doable in pen. = 5. Shift to mean 0 = We subtract the mean, in green. The four values in each feature now average to zero. = 6. Scale to variance 1 = Let us divide by the standard deviation, in orange. Each feature now has variance one, whatever scale it arrived at. = 7. Scale and shift = We multiply by a linear transformation and pass the result on. The diagonal and the last column are trainable, so having just forced every feature to mean 0 and variance 1, we hand the network the means to undo it. The outputs: Mean of each feature = [2, 1, 2] Std dev of each feature = [1, 1, 2] To the next layer = [2, -2, 2, 0], [-3, 3, 6, -3], [2, 0, 1, 2] The answers: 🤔 Both. The scale and shift are trainable, the statistics are not. Epsilon and the momentum on the running statistics are the hyper-parameters, and one mini-batch by hand needs neither. 🤔 Features, after the linear layer, not inputs, weights or biases. 🤔 Batch norm measures across the batch, one feature at a time. Layer norm measures across the features, one example at a time. 💾 Save this post!

Tom Yeh

20,848 次观看 • 2 个月前

THE $20,000 HOME ROBOT DOESN'T NEED TO BE WORTH $20,000 ON DAY ONE. That's the part people miss. Look at this task. Laundry. Today the robot learns to sort it. Tomorrow it learns to fold it. Then: Load the washer. Empty the dishwasher. Clean the bathroom. Make the bed. Take out the trash. Same hardware. More value. That's fundamentally different from almost every appliance in your house. Your washing machine doesn't wake up tomorrow knowing how to clean the kitchen. A general-purpose robot potentially can. Imagine buying a humanoid for $20,000. At purchase it can reliably perform 10 useful tasks. A year later: 30. Three years later: 100. The machine physically sitting in your house hasn't changed. The economic value of what it can do has. That's why software could completely change the economics of humanoid ownership. We normally expect hardware to depreciate. Humanoids could become one of the strange products where the hardware gets older... while the worker inside it keeps getting better. That's a very powerful business model.

THE $20,000 HOME ROBOT DOESN'T NEED TO BE WORTH $20,000 ON DAY ONE. That's the part people miss. Look at this task. Laundry. Today the robot learns to sort it. Tomorrow it learns to fold it. Then: Load the washer. Empty the dishwasher. Clean the bathroom. Make the bed. Take out the trash. Same hardware. More value. That's fundamentally different from almost every appliance in your house. Your washing machine doesn't wake up tomorrow knowing how to clean the kitchen. A general-purpose robot potentially can. Imagine buying a humanoid for $20,000. At purchase it can reliably perform 10 useful tasks. A year later: 30. Three years later: 100. The machine physically sitting in your house hasn't changed. The economic value of what it can do has. That's why software could completely change the economics of humanoid ownership. We normally expect hardware to depreciate. Humanoids could become one of the strange products where the hardware gets older... while the worker inside it keeps getting better. That's a very powerful business model.

Mag1strY0da

41,785 次观看 • 6 天前

A 20-YEAR-OLD MIT STUDENT HACKED HIS OWN BRAIN TO FOCUS. THE FIX IS STARING AT A WALL FOR 10 MINUTES it starts the way your day probably starts: him on the couch, phone in hand, brain refusing to turn on then he does something that looks insane: he sits and stares at a blank wall. no phone. no music. for ten straight minutes here's the part most people miss: your brain isn't lazy. it's overloaded. every notification, every tab, every scroll is another input fighting for space - and boredom is the only thing that clears the queue staring at a wall feels unbearable for about 3 minutes. then something flips. the noise drains out. and for the first time all day your mind goes quiet enough to actually think then he goes from frozen on the couch to locked in at his laptop, moving through work he'd been avoiding for hours no app. no productivity system with 40 steps just 10 minutes of doing absolutely nothing - the one thing nobody can sit still long enough to try save this for the next time you can't start. staring at a wall might be the most productive thing you do all week

A 20-YEAR-OLD MIT STUDENT HACKED HIS OWN BRAIN TO FOCUS. THE FIX IS STARING AT A WALL FOR 10 MINUTES it starts the way your day probably starts: him on the couch, phone in hand, brain refusing to turn on then he does something that looks insane: he sits and stares at a blank wall. no phone. no music. for ten straight minutes here's the part most people miss: your brain isn't lazy. it's overloaded. every notification, every tab, every scroll is another input fighting for space - and boredom is the only thing that clears the queue staring at a wall feels unbearable for about 3 minutes. then something flips. the noise drains out. and for the first time all day your mind goes quiet enough to actually think then he goes from frozen on the couch to locked in at his laptop, moving through work he'd been avoiding for hours no app. no productivity system with 40 steps just 10 minutes of doing absolutely nothing - the one thing nobody can sit still long enough to try save this for the next time you can't start. staring at a wall might be the most productive thing you do all week

Paone

180,913 次观看 • 2 个月前

this is more useful than my entire degree Elon Musk's rocket company signed a $60,000,000,000 deal for Cursor in June, and eight days ago the two of them put a worker on sale for $200 a month: it gets its own computer in the cloud, signs into your accounts, clicks through your real apps, and hands back finished work instead of a draft for you to paste i ran one against my receipts folder on sunday and got back 14 filed, 2 it held because they needed a card number, and a saved method i never wrote myself Grok Bot is the one you train by doing your own job in front of it, and the whole handover fits in four messages tonight: 1. write out one job you did today the way you would brief a new hire: what has to be finished, which sites and files to work from, what to hand back, and where it stops and asks you 2. let it run once on something safe to get wrong, then correct the result until it is worth your name 3. say "save what we just did as a skill", and add the one rule about what always needs your approval 4. say "run that skill every weekday at 8 and post the result here. if the source is missing, tell me instead of using yesterday's numbers" xAI wrote that order into its own manual: one real job, then the saved method, then the clock. a schedule sitting on top of a method nobody checked replaces two hours of your clicking with two hours of your mistake turns out you never get to pick the brain, and that is the part i would argue about: the manual says there is no model picker for members or admins, no plan to add one, and the bill follows whichever model answered bookmark this, then open the piece below: which jobs deserve a worker of their own, and which ones quietly burn the seat ↓

this is more useful than my entire degree Elon Musk's rocket company signed a $60,000,000,000 deal for Cursor in June, and eight days ago the two of them put a worker on sale for $200 a month: it gets its own computer in the cloud, signs into your accounts, clicks through your real apps, and hands back finished work instead of a draft for you to paste i ran one against my receipts folder on sunday and got back 14 filed, 2 it held because they needed a card number, and a saved method i never wrote myself Grok Bot is the one you train by doing your own job in front of it, and the whole handover fits in four messages tonight: 1. write out one job you did today the way you would brief a new hire: what has to be finished, which sites and files to work from, what to hand back, and where it stops and asks you 2. let it run once on something safe to get wrong, then correct the result until it is worth your name 3. say "save what we just did as a skill", and add the one rule about what always needs your approval 4. say "run that skill every weekday at 8 and post the result here. if the source is missing, tell me instead of using yesterday's numbers" xAI wrote that order into its own manual: one real job, then the saved method, then the clock. a schedule sitting on top of a method nobody checked replaces two hours of your clicking with two hours of your mistake turns out you never get to pick the brain, and that is the part i would argue about: the manual says there is no model picker for members or admins, no plan to add one, and the bill follows whichever model answered bookmark this, then open the piece below: which jobs deserve a worker of their own, and which ones quietly burn the seat ↓

Argona

21,946 次观看 • 1 个月前

whoever leaked this has bigger balls than sense someone gave a fleet of Claude agents shared memory so they would stop contradicting each other, then measured both the bill and the output: the version that talked most made 2.4x the api calls of the version that won, and hallucinated 34% more than doing nothing at all, 0.658 against 0.492 i ran the same question past two of my own agents afterwards and got two different answers about which file owns the config. each one was individually right and the pair was wrong, which is the whole failure in one line this is Graph Engineering, the layer that decides which agents may talk to each other at all, and it installs into the agent you already pay for: - decide which agents may share state at all, because every edge you draw is a channel a mistake can travel down - measure divergence per PAIR instead of as a fleet average, across what they believe about place, time and task history - gate on that number and stop the pair above your threshold before it reasons, rather than repairing the output afterwards - let compressed summaries replace whole states: the verified protocol landed 0.463 against 0.658 for full broadcast - cut the sync frequency until it hurts, since the winning setup used 58% fewer calls than the one that broke it - never propagate a state nobody checked, because the contamination effect came in at d=1.18, a full standard deviation of extra lying - keep the shared layer small enough to diff, which is what a written standard does and a running conversation cannot - re-run the check after every model upgrade, because this was 8 scenarios on one model family at n=30 per condition - and learn where it does not bite: on plain software tasks every condition converged under 0.2 and the whole effect vanished turns out the ranking is the uncomfortable part: verified summaries 0.463, no synchronisation at all 0.492, full broadcast 0.658. the middle option is doing nothing, and it beat the thing everyone builds first the group agreeing is what it looks like when every agent copied the same mistake, which is why a fleet that hallucinates has a replication problem and keeps getting handed a smarter model instead so the question for your own setup: if you asked two of your agents the same thing right now, would they answer the same way bookmark this one. the layer underneath it, deciding which arrows between agents exist at all, is built step by step in the piece below ↓

whoever leaked this has bigger balls than sense someone gave a fleet of Claude agents shared memory so they would stop contradicting each other, then measured both the bill and the output: the version that talked most made 2.4x the api calls of the version that won, and hallucinated 34% more than doing nothing at all, 0.658 against 0.492 i ran the same question past two of my own agents afterwards and got two different answers about which file owns the config. each one was individually right and the pair was wrong, which is the whole failure in one line this is Graph Engineering, the layer that decides which agents may talk to each other at all, and it installs into the agent you already pay for: - decide which agents may share state at all, because every edge you draw is a channel a mistake can travel down - measure divergence per PAIR instead of as a fleet average, across what they believe about place, time and task history - gate on that number and stop the pair above your threshold before it reasons, rather than repairing the output afterwards - let compressed summaries replace whole states: the verified protocol landed 0.463 against 0.658 for full broadcast - cut the sync frequency until it hurts, since the winning setup used 58% fewer calls than the one that broke it - never propagate a state nobody checked, because the contamination effect came in at d=1.18, a full standard deviation of extra lying - keep the shared layer small enough to diff, which is what a written standard does and a running conversation cannot - re-run the check after every model upgrade, because this was 8 scenarios on one model family at n=30 per condition - and learn where it does not bite: on plain software tasks every condition converged under 0.2 and the whole effect vanished turns out the ranking is the uncomfortable part: verified summaries 0.463, no synchronisation at all 0.492, full broadcast 0.658. the middle option is doing nothing, and it beat the thing everyone builds first the group agreeing is what it looks like when every agent copied the same mistake, which is why a fleet that hallucinates has a replication problem and keeps getting handed a smarter model instead so the question for your own setup: if you asked two of your agents the same thing right now, would they answer the same way bookmark this one. the layer underneath it, deciding which arrows between agents exist at all, is built step by step in the piece below ↓

Argona

724,665 次观看 • 1 个月前