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Claude 3.7 Sonnet is seriously impressive It goes beyond basic logic, capturing subtle nuances. Previous models for me was just about getting 3D experiences running, but 3.7 adds polished details—particle trails, collision visuals, and flawless physics #threejs

Ian Curtis

26,045 subscribers

80,564 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & Technologie Kunst #threejs

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🚁 Adding an Apache attack helicopter today I think I will rewrite the entire flight model today again to be ambiguous of what kind of vehicle it is Planes behave very different than helicopters and different than vehicles like tanks or pedestrians Because people here also requested "Guy with bazooka" so that will be next I have no idea if everyone else is just lying about using AI for their plane models but mine look so clunky (but I like the look), but I now tried with Claude 3.5 and 3.7 and Grok 3 and they all just generate clunky models, not beautiful like you see in other AI vibe coded games, so either they bs'ing or I'm retarded I also have to move sub parts of each model like for 30 minutes until everything fits, AI has a really hard time with 3d models But okay the look is kinda cute

🚁 Adding an Apache attack helicopter today I think I will rewrite the entire flight model today again to be ambiguous of what kind of vehicle it is Planes behave very different than helicopters and different than vehicles like tanks or pedestrians Because people here also requested "Guy with bazooka" so that will be next I have no idea if everyone else is just lying about using AI for their plane models but mine look so clunky (but I like the look), but I now tried with Claude 3.5 and 3.7 and Grok 3 and they all just generate clunky models, not beautiful like you see in other AI vibe coded games, so either they bs'ing or I'm retarded I also have to move sub parts of each model like for 30 minutes until everything fits, AI has a really hard time with 3d models But okay the look is kinda cute

@levelsio

485,431 Aufrufe • vor 1 Jahr

Ok, that's amazing. 🦄 A side by side three.js comparison - with GitMCP and without. GitMCP's story originally started from a tweet by Three.js about their large, non-LLM-digestable, documentation file. Ido Salomon and I ended up creating a cool generic documentation MCP server, but we didn't forget threejs and mrdoob. So we put it to the test. Using GitMCP, I gave this prompt to Cursor + Claude 3.7: "Build a Three.js scene featuring a controllable realistic person navigating a textured dynamic urban environment with realistic lighting and subtle bloom effects. Ensure keyboard controls (WASD) for movement." This is the result. To the right is the one-shot result with our MCP server. To the left is the result without it, based just on Cursor's limited knowledge. The video is pretty conclusive. 🤯 It's really amazing since Cursor already knows a lot about three.js, but with our dedicated documentation MCP server it just outputs better results. And for all other libraries out there - that are not included in Cursor - this is a total game changer. A huge shout out to Cloudflare Developers and Anni Wang, who worked with us yesterday to quickly insert new capabilities in their AutoRAG feature (that enables indexing large amount of documentation) according to our specific needs, and to troubleshoot issues. It was very helpful. Knowledge is power, and that applies for every coding assitant. And not just for three.js! Check out GitMCP for any library you're using - link in the next comment.

Ok, that's amazing. 🦄 A side by side three.js comparison - with GitMCP and without. GitMCP's story originally started from a tweet by Three.js about their large, non-LLM-digestable, documentation file. Ido Salomon and I ended up creating a cool generic documentation MCP server, but we didn't forget threejs and mrdoob. So we put it to the test. Using GitMCP, I gave this prompt to Cursor + Claude 3.7: "Build a Three.js scene featuring a controllable realistic person navigating a textured dynamic urban environment with realistic lighting and subtle bloom effects. Ensure keyboard controls (WASD) for movement." This is the result. To the right is the one-shot result with our MCP server. To the left is the result without it, based just on Cursor's limited knowledge. The video is pretty conclusive. 🤯 It's really amazing since Cursor already knows a lot about three.js, but with our dedicated documentation MCP server it just outputs better results. And for all other libraries out there - that are not included in Cursor - this is a total game changer. A huge shout out to Cloudflare Developers and Anni Wang, who worked with us yesterday to quickly insert new capabilities in their AutoRAG feature (that enables indexing large amount of documentation) according to our specific needs, and to troubleshoot issues. It was very helpful. Knowledge is power, and that applies for every coding assitant. And not just for three.js! Check out GitMCP for any library you're using - link in the next comment.

Liad Yosef

41,745 Aufrufe • vor 1 Jahr

claude fable 5 is live. spawn 5.0 was built with it: 1,687 prompts, 102 sessions, my job shifted from architecture to judging taste. what we built, each of which would've been at least a month with a whole team on opus: — a from-scratch physics engine (mantle) that rivals rapier, testable in a day as a side thread — clustered froxel lighting: 8 lights to 1,000+ fully dynamic ("it's stupid fast" — creator of threejs) — realtime diffuse GI on webgpu, on your phone (landing in the next update) — million-particle gpu vfx with a shader architecture beyond what unreal and unity ship — mmo-scale netcode plus, for good measure: an (alpha) native ios app. all of it in about a week. all of it running on mobile. and the list doesn't include half of what we shipped — full changelog in the thread below. and the part the benchmarks won't show you: this model has a wonderful personality. it's genuinely funny. i laughed so hard i cried multiple times, mid-physics-rewrite. the genius and the character aren't separate features.

claude fable 5 is live. spawn 5.0 was built with it: 1,687 prompts, 102 sessions, my job shifted from architecture to judging taste. what we built, each of which would've been at least a month with a whole team on opus: — a from-scratch physics engine (mantle) that rivals rapier, testable in a day as a side thread — clustered froxel lighting: 8 lights to 1,000+ fully dynamic ("it's stupid fast" — creator of threejs) — realtime diffuse GI on webgpu, on your phone (landing in the next update) — million-particle gpu vfx with a shader architecture beyond what unreal and unity ship — mmo-scale netcode plus, for good measure: an (alpha) native ios app. all of it in about a week. all of it running on mobile. and the list doesn't include half of what we shipped — full changelog in the thread below. and the part the benchmarks won't show you: this model has a wonderful personality. it's genuinely funny. i laughed so hard i cried multiple times, mid-physics-rewrite. the genius and the character aren't separate features.

jacob

55,515 Aufrufe • vor 3 Monaten

Astra (GPT-6) is here!!! I've had early access and tested it like crazy with things like games, code, writing, browser control, presentations and general knowledge work. This is the best model I've ever used. Period. (Incredible demos below in this thread ⬇️) Here's my take on Astra: > It's insanely capable. This feels like a massive improvement, not just an incremental change. This is especially true with zero-shot prompts. > It's all about knowledge work. Slide creation, analysis, writing, and browser control. And oh my...it's so good at browser control. GPT-5.6 was already fantastic at doing things in the browser, Astra is another level and significantly faster. > We're closer than ever (arrived?) at prompt-to-playable game. And I don't just mean only playable, these are actually fun games. I bet if someone with a great eye for games used Astra, they could create a viral game within 1-2 weeks. > Astra is better at writing but not perfect. It removes much of the "AI Smell" we're all familiar with but some stink still survived. > It has a tendency to use the same design colors and look/feel as GPT-5.6 (forrest green anyone?) but it is more steerable in design than previous models. > It's highly steerable in general. A little nudge goes a long way. When I first started using Astra, almost every task I gave it would go for ~30 minutes. I wanted it to keep working. Adding more specifics to a prompt helped greatly with it's ability to work for a long time. > Astra's 3D understanding is unmatched. 3D asset creation was consistent and easy and its spacial awareness while building complex 3D worlds blew me away. I'm still getting familiar with Astra but this will now be my go-to model for any difficult work I have. Check out the demos below: 👇

Astra (GPT-6) is here!!! I've had early access and tested it like crazy with things like games, code, writing, browser control, presentations and general knowledge work. This is the best model I've ever used. Period. (Incredible demos below in this thread ⬇️) Here's my take on Astra: > It's insanely capable. This feels like a massive improvement, not just an incremental change. This is especially true with zero-shot prompts. > It's all about knowledge work. Slide creation, analysis, writing, and browser control. And oh my...it's so good at browser control. GPT-5.6 was already fantastic at doing things in the browser, Astra is another level and significantly faster. > We're closer than ever (arrived?) at prompt-to-playable game. And I don't just mean only playable, these are actually fun games. I bet if someone with a great eye for games used Astra, they could create a viral game within 1-2 weeks. > Astra is better at writing but not perfect. It removes much of the "AI Smell" we're all familiar with but some stink still survived. > It has a tendency to use the same design colors and look/feel as GPT-5.6 (forrest green anyone?) but it is more steerable in design than previous models. > It's highly steerable in general. A little nudge goes a long way. When I first started using Astra, almost every task I gave it would go for ~30 minutes. I wanted it to keep working. Adding more specifics to a prompt helped greatly with it's ability to work for a long time. > Astra's 3D understanding is unmatched. 3D asset creation was consistent and easy and its spacial awareness while building complex 3D worlds blew me away. I'm still getting familiar with Astra but this will now be my go-to model for any difficult work I have. Check out the demos below: 👇

Matthew Berman

1,852,964 Aufrufe • vor 4 Tagen

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

Sudo su

37,623 Aufrufe • vor 6 Monaten

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

MrNeRF

290,257 Aufrufe • vor 10 Monaten

Many of you might think face wash is just…face wash. We use it to clean the dirt and grime from our daily lives. Some take it seriously while others just use it as a checklist of hygiene. But what if face wash was something more? What if there was a way to gently and quickly combat dry skin every day? Introducing H2.O Facial Cleanser by biöm – a groundbreaking product in the world of skincare. In my quest to redefine what a cleanser can do, I've created something extraordinary. Skin gets dirty, but it also gets thirsty. H2.O isn't just about cleaning your skin; it's about transforming it. The secret sauce in my unique formula is ElfaMoist, a hydration powerhouse. This active ingredient goes beyond the superficial layer of your skin, delivering hydration that penetrates deep into your pores, rivaling those satisfying Gatorade commercials. You can think of ElfaMoist like water that went to an Ivy League school. It’s clinically proven to hydrate your skin for over 30 hours, and it outperforms every other hydrator on the market. H2.O isn't just a cleanser; it's a hydration revolution. ⚠️ No fragrances. 🫒 Gentle olive & coconut oil-based surfactants. 🌿 Natural aloe vera for soothing hydration. 💧Squalane to mimic your skin’s natural oils. With each use, H2.O promises not only to cleanse but to leave your skin feeling refreshed, nourished, and more hydrated than ever before. It's time to experience a cleanser that does more than just clean – it rehydrates, replenishes, and renews. Welcome to the future of skincare.

Many of you might think face wash is just…face wash. We use it to clean the dirt and grime from our daily lives. Some take it seriously while others just use it as a checklist of hygiene. But what if face wash was something more? What if there was a way to gently and quickly combat dry skin every day? Introducing H2.O Facial Cleanser by biöm – a groundbreaking product in the world of skincare. In my quest to redefine what a cleanser can do, I've created something extraordinary. Skin gets dirty, but it also gets thirsty. H2.O isn't just about cleaning your skin; it's about transforming it. The secret sauce in my unique formula is ElfaMoist, a hydration powerhouse. This active ingredient goes beyond the superficial layer of your skin, delivering hydration that penetrates deep into your pores, rivaling those satisfying Gatorade commercials. You can think of ElfaMoist like water that went to an Ivy League school. It’s clinically proven to hydrate your skin for over 30 hours, and it outperforms every other hydrator on the market. H2.O isn't just a cleanser; it's a hydration revolution. ⚠️ No fragrances. 🫒 Gentle olive & coconut oil-based surfactants. 🌿 Natural aloe vera for soothing hydration. 💧Squalane to mimic your skin’s natural oils. With each use, H2.O promises not only to cleanse but to leave your skin feeling refreshed, nourished, and more hydrated than ever before. It's time to experience a cleanser that does more than just clean – it rehydrates, replenishes, and renews. Welcome to the future of skincare.

Fawn

105,878 Aufrufe • vor 2 Jahren

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Rachel🥥

61,441 Aufrufe • vor 2 Monaten

An Anthropic paid for my espresso at Sightglass when he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.

An Anthropic paid for my espresso at Sightglass when he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.

Himanshu Kumar

15,985 Aufrufe • vor 2 Monaten

Qwen3.8-Flash-Next is starting to feel like the local model Opus fans have been waiting for. Someone ran the NVFP4 176B-class Flash-Next on 2× DGX Sparks, and the results are wild. Real measured scaling → C1: 44.2 tok/s → C2: 64.6 tok/s → C4: 86.8 tok/s aggregate The per-stream speed drops with concurrency, but total throughput keeps climbing. Long-context behavior was even more impressive: → 5K: needle retrieved → 21K: needle retrieved → 84K: needle retrieved → 167K: needle retrieved → 262K: prefill succeeded, but the window was saturated That 167K retrieval test is the one I care about. Long agent runs are where models usually start losing the plot. Flash-Next didn’t. It also held up surprisingly well on physics-heavy reasoning, artifact generation, research workflows, evidence checking, and long-horizon planning. The personality is interesting too. DeepSeek V4 Flash feels like the dependable workhorse. GLM-5.2 feels like the problem-solving machine. Qwen3.8-Flash-Next feels more insightful. It has that rare ability to understand what you’re actually asking rather than just following the surface pattern. The main weakness I’ve noticed is instruction following. It can occasionally drift between prose turns where DeepSeek and GLM stay tighter. And this is why the 256GB M5 Ultra conversation gets interesting. If Apple can pair that huge unified-memory pool with enough bandwidth, this model class becomes genuinely practical for long-running local agents. We’re talking about frontier-class reasoning on hardware sitting on a desk.

Qwen3.8-Flash-Next is starting to feel like the local model Opus fans have been waiting for. Someone ran the NVFP4 176B-class Flash-Next on 2× DGX Sparks, and the results are wild. Real measured scaling → C1: 44.2 tok/s → C2: 64.6 tok/s → C4: 86.8 tok/s aggregate The per-stream speed drops with concurrency, but total throughput keeps climbing. Long-context behavior was even more impressive: → 5K: needle retrieved → 21K: needle retrieved → 84K: needle retrieved → 167K: needle retrieved → 262K: prefill succeeded, but the window was saturated That 167K retrieval test is the one I care about. Long agent runs are where models usually start losing the plot. Flash-Next didn’t. It also held up surprisingly well on physics-heavy reasoning, artifact generation, research workflows, evidence checking, and long-horizon planning. The personality is interesting too. DeepSeek V4 Flash feels like the dependable workhorse. GLM-5.2 feels like the problem-solving machine. Qwen3.8-Flash-Next feels more insightful. It has that rare ability to understand what you’re actually asking rather than just following the surface pattern. The main weakness I’ve noticed is instruction following. It can occasionally drift between prose turns where DeepSeek and GLM stay tighter. And this is why the 256GB M5 Ultra conversation gets interesting. If Apple can pair that huge unified-memory pool with enough bandwidth, this model class becomes genuinely practical for long-running local agents. We’re talking about frontier-class reasoning on hardware sitting on a desk.

FHILY👑

19,992 Aufrufe • vor 11 Tagen

When a spacecraft leaves Earth, it doesn’t just fire its engines and head straight to its destination. In many missions, especially those going beyond low Earth orbit, there’s a more subtle and elegant strategy at play, one that uses gravity itself as part of the navigation system. This is often called a gravity assist, or a slingshot maneuver. But in the case of missions like #Artemis II, what’s being used is a closely related idea known as a free-return trajectory. At first glance, it might sound simple: the spacecraft goes to the Moon, loops around it, and comes back. But the physics behind it is anything but simple. Instead of relying on continuous propulsion, the spacecraft follows a carefully calculated path through the gravitational field of the Earth–Moon system. It is launched with just the right speed and direction so that, as it approaches the Moon, the Moon’s gravity bends its trajectory. The spacecraft is effectively flung around the Moon, redirected onto a path that naturally brings it back toward Earth. No major engine burn is needed for the return. Small trajectory corrections may still be required, but gravity does the heavy lifting. That’s the key. This kind of trajectory is not just efficient, it’s also safe. If something goes wrong with the spacecraft’s engines or onboard systems, gravity itself ensures the return. It’s an inherent backup plan, built into the trajectory from the very beginning. The same fundamental idea appears in gravity assists used across the Solar System. When a spacecraft flies past a planet, it can gain or lose speed by exchanging momentum with that planet. From the spacecraft’s point of view, it’s as if it has been accelerated without using fuel. In reality, it has borrowed a tiny amount of orbital energy from the planet itself. That’s how missions like Voyager reached the outer planets, and how probes continue to explore regions far beyond what their onboard fuel alone would allow. But there’s an important distinction. An interplanetary gravity assist is typically used to change speed and direction, often increasing the spacecraft’s energy. A free-return trajectory, like the one used in Artemis II, is designed for something more specific: a path that naturally loops back to Earth without requiring additional propulsion. It’s less about gaining energy, and more about shaping a trajectory that guarantees a return. To understand why this works, it helps to stop thinking in straight lines. In space, motion follows curves defined by gravity. The spacecraft is constantly falling, first toward Earth, then toward the Moon, and then back toward Earth again. What looks like a loop is really a continuous free fall through a changing gravitational landscape. This way of navigating space reveals something deeper. We tend to think of engines as the drivers of motion, but once a spacecraft is on its way, gravity does most of the work. The art of spaceflight is not just about thrust. It’s about knowing when not to use it. #GoodLuck #Artemis NASA Artemis

When a spacecraft leaves Earth, it doesn’t just fire its engines and head straight to its destination. In many missions, especially those going beyond low Earth orbit, there’s a more subtle and elegant strategy at play, one that uses gravity itself as part of the navigation system. This is often called a gravity assist, or a slingshot maneuver. But in the case of missions like #Artemis II, what’s being used is a closely related idea known as a free-return trajectory. At first glance, it might sound simple: the spacecraft goes to the Moon, loops around it, and comes back. But the physics behind it is anything but simple. Instead of relying on continuous propulsion, the spacecraft follows a carefully calculated path through the gravitational field of the Earth–Moon system. It is launched with just the right speed and direction so that, as it approaches the Moon, the Moon’s gravity bends its trajectory. The spacecraft is effectively flung around the Moon, redirected onto a path that naturally brings it back toward Earth. No major engine burn is needed for the return. Small trajectory corrections may still be required, but gravity does the heavy lifting. That’s the key. This kind of trajectory is not just efficient, it’s also safe. If something goes wrong with the spacecraft’s engines or onboard systems, gravity itself ensures the return. It’s an inherent backup plan, built into the trajectory from the very beginning. The same fundamental idea appears in gravity assists used across the Solar System. When a spacecraft flies past a planet, it can gain or lose speed by exchanging momentum with that planet. From the spacecraft’s point of view, it’s as if it has been accelerated without using fuel. In reality, it has borrowed a tiny amount of orbital energy from the planet itself. That’s how missions like Voyager reached the outer planets, and how probes continue to explore regions far beyond what their onboard fuel alone would allow. But there’s an important distinction. An interplanetary gravity assist is typically used to change speed and direction, often increasing the spacecraft’s energy. A free-return trajectory, like the one used in Artemis II, is designed for something more specific: a path that naturally loops back to Earth without requiring additional propulsion. It’s less about gaining energy, and more about shaping a trajectory that guarantees a return. To understand why this works, it helps to stop thinking in straight lines. In space, motion follows curves defined by gravity. The spacecraft is constantly falling, first toward Earth, then toward the Moon, and then back toward Earth again. What looks like a loop is really a continuous free fall through a changing gravitational landscape. This way of navigating space reveals something deeper. We tend to think of engines as the drivers of motion, but once a spacecraft is on its way, gravity does most of the work. The art of spaceflight is not just about thrust. It’s about knowing when not to use it. #GoodLuck #Artemis NASA Artemis

Erika 

235,073 Aufrufe • vor 5 Monaten

CONTEXT MATTERS!!! A lot has been said about Yusuf’s diary session, particularly his comment that his previous connections were good people, but he felt they lacked “depth.” But before you judge his words, watch the clips. Here is Yusuf having a genuine conversation with the same people he spoke about in his Diary Session. He listened, engaged and shared parts of his own life, just like everyone else in the house does. And then watch the second clip. Yusuf is literally being mimicked and mocked by Bells while sitting right there. Imagine opening up about your life and your experiences, only for those same experiences to become something someone uses to ridicule you. So when Biggie asked Yusuf about his connections in the house, what exactly was he supposed to call that? Was he supposed to pretend everything was perfect? Was he supposed to describe a connection as deep when he personally didn't feel that depth? Yusuf was answering a question about HIS experience. He never said they were bad people. In fact, he specifically acknowledged that they were good people. He simply said he didn't enjoy having certain conversations with them because he felt they didn't connect with him on a deeper level. And somehow, that has been twisted into Yusuf believing he is better than everyone else. It is even more painful because Yusuf has never pretended to come from a life of privilege. When these Housemates sit down and talk about their lives, their families, their relationships, their struggles and their experiences, Yusuf is allowed to talk about his own life too. If your life has been easier, beautiful. If you have had opportunities Yusuf didn't have, beautiful. If you didn't have to struggle for certain things, you are blessed. But please don't assume that because you didn't experience something, somebody else must be lying about experiencing it. Yusuf has spoken about where he comes from because it is part of his story. He has spoken about the struggles, the sacrifices, the auditions, the doors that didn't open and the few that finally did. He even spoke about getting an audition with Ruth Kadiri in March and how that opportunity became significant in his journey. He isn't saying he suffered the most. He is saying, “This is MY story.” And nobody should have to apologise for telling their own story simply because someone else finds it uncomfortable. Yusuf came into Biggie’s House with a dream. A dream to be seen, to showcase his talent and to change his circumstances. He isn't asking for pity. He is asking for a chance. So before you judge his words, watch the context. Before you call him arrogant, watch what happened before that Diary Session. Before you decide that his story is a lie, remember that you were not there for the life he lived before Biggie’s House. Let Yusuf be Yusuf. And Yusfuls, if you believe he deserves the opportunity to keep telling his story and fighting for the future he dreams of, KEEP VOTING. We are not here to compete over who suffered the most. We are here to make sure Yusuf's journey doesn't end here. 🎥: Big Brother Naija #YusufMuhammad #BBNaija

CONTEXT MATTERS!!! A lot has been said about Yusuf’s diary session, particularly his comment that his previous connections were good people, but he felt they lacked “depth.” But before you judge his words, watch the clips. Here is Yusuf having a genuine conversation with the same people he spoke about in his Diary Session. He listened, engaged and shared parts of his own life, just like everyone else in the house does. And then watch the second clip. Yusuf is literally being mimicked and mocked by Bells while sitting right there. Imagine opening up about your life and your experiences, only for those same experiences to become something someone uses to ridicule you. So when Biggie asked Yusuf about his connections in the house, what exactly was he supposed to call that? Was he supposed to pretend everything was perfect? Was he supposed to describe a connection as deep when he personally didn't feel that depth? Yusuf was answering a question about HIS experience. He never said they were bad people. In fact, he specifically acknowledged that they were good people. He simply said he didn't enjoy having certain conversations with them because he felt they didn't connect with him on a deeper level. And somehow, that has been twisted into Yusuf believing he is better than everyone else. It is even more painful because Yusuf has never pretended to come from a life of privilege. When these Housemates sit down and talk about their lives, their families, their relationships, their struggles and their experiences, Yusuf is allowed to talk about his own life too. If your life has been easier, beautiful. If you have had opportunities Yusuf didn't have, beautiful. If you didn't have to struggle for certain things, you are blessed. But please don't assume that because you didn't experience something, somebody else must be lying about experiencing it. Yusuf has spoken about where he comes from because it is part of his story. He has spoken about the struggles, the sacrifices, the auditions, the doors that didn't open and the few that finally did. He even spoke about getting an audition with Ruth Kadiri in March and how that opportunity became significant in his journey. He isn't saying he suffered the most. He is saying, “This is MY story.” And nobody should have to apologise for telling their own story simply because someone else finds it uncomfortable. Yusuf came into Biggie’s House with a dream. A dream to be seen, to showcase his talent and to change his circumstances. He isn't asking for pity. He is asking for a chance. So before you judge his words, watch the context. Before you call him arrogant, watch what happened before that Diary Session. Before you decide that his story is a lie, remember that you were not there for the life he lived before Biggie’s House. Let Yusuf be Yusuf. And Yusfuls, if you believe he deserves the opportunity to keep telling his story and fighting for the future he dreams of, KEEP VOTING. We are not here to compete over who suffered the most. We are here to make sure Yusuf's journey doesn't end here. 🎥: Big Brother Naija #YusufMuhammad #BBNaija

Yusuf Muhammad

298,710 Aufrufe • vor 28 Tagen

Let's talk about agentic product design. Every company has its own design process. What has always worked for me is spending long studio hours with our product team, dissecting things into pieces and putting them back together. In those sessions we look at value, usability, simplicity, aesthetics, behavior, storytelling, generics, and emotional mapping. I've been crafting products this way for as long as I can remember. Product work at Lemonade isn't for the faint of heart. This obsession over every detail is hard work, but I believe it yields better results and builds stronger talent. One of the things I love about our design and product team is how this process became a second nature to them. Feedback is fast, professional, and tension free. But in our latest session, something was different. One of our designers used Figma and Cursor to build a mockup that was so advanced, it was almost ready to be shipped. It was an incredible glimpse into a world where a single designer working on top of modern low code infrastructure will be able to launch production grade experiences for products with millions of customers, and with LoCo, I expect this to become a reality at Lemonade in just a few quarters. But there's a problem to watch out for. An interesting phenomenon I've noticed over the years is that the higher the fidelity of the work being reviewed, the more defensive people become. When someone shows up with something polished, they tend to resist feedback. They've already fallen in love with what they built, and it's hard for them to accept rejection. Radical candor feedback works best at an early stage of the project, before people get attached and feel the need to defend their work. This session was no exception. Because the work was so advanced, the review became binary, and its maker became defensive. Happily, we all caught ourselves in time to acknowledge this new dynamic and started figuring out how to go back to obsessing about every corner radius, shade of white, and word. When reviewing agentically coded designs, we'll try having our designers bring in more than one option, as well as the open Cursor project so we can make changes in real time if needed. We'll see how it goes, and if this is of interest, I'll update what we learn.

Let's talk about agentic product design. Every company has its own design process. What has always worked for me is spending long studio hours with our product team, dissecting things into pieces and putting them back together. In those sessions we look at value, usability, simplicity, aesthetics, behavior, storytelling, generics, and emotional mapping. I've been crafting products this way for as long as I can remember. Product work at Lemonade isn't for the faint of heart. This obsession over every detail is hard work, but I believe it yields better results and builds stronger talent. One of the things I love about our design and product team is how this process became a second nature to them. Feedback is fast, professional, and tension free. But in our latest session, something was different. One of our designers used Figma and Cursor to build a mockup that was so advanced, it was almost ready to be shipped. It was an incredible glimpse into a world where a single designer working on top of modern low code infrastructure will be able to launch production grade experiences for products with millions of customers, and with LoCo, I expect this to become a reality at Lemonade in just a few quarters. But there's a problem to watch out for. An interesting phenomenon I've noticed over the years is that the higher the fidelity of the work being reviewed, the more defensive people become. When someone shows up with something polished, they tend to resist feedback. They've already fallen in love with what they built, and it's hard for them to accept rejection. Radical candor feedback works best at an early stage of the project, before people get attached and feel the need to defend their work. This session was no exception. Because the work was so advanced, the review became binary, and its maker became defensive. Happily, we all caught ourselves in time to acknowledge this new dynamic and started figuring out how to go back to obsessing about every corner radius, shade of white, and word. When reviewing agentically coded designs, we'll try having our designers bring in more than one option, as well as the open Cursor project so we can make changes in real time if needed. We'll see how it goes, and if this is of interest, I'll update what we learn.

Shai Wininger

17,558 Aufrufe • vor 9 Monaten

You can clearly see Hobi’s mic failing at the beginning of FYA so he gives it to the staff and he basically just vibes mic-less but the part that stands out is you can see Hobi doing some quick decision making bc his verse is coming up.. you can see he contemplates Namjoon for a second but Namjoon is still rapping so with an immediate game plan he skips towards Jin. Jin, who didn’t need anything beyond a brief gesture from Hobi hands his mic over. No fuss, no show of technical issues. They just keep the segment moving as if it was some spontaneous choreo, Jin hyping hobi. Hobi still is without a mic at the thrust stage but again doesn’t make it a thing. He hypes the crowd. Throws water. Dances. Gets his white mic, goes back to singing like nothing. And then you have sweet Jin asking if hobi needs his mic despite the line following yoongi’s verse is his own. Complete, raw teamwork and camaraderie. Gorgeous right?? The way it was seamless unless you were watching and paying attention to the details. Bc otherwise, no one would’ve known. That’s showmanship. That’s 2seok’s 13 years of practicing and performing together. That’s Hoseok’s professionalism running through his veins as naturally as blood. And that’s Bangtan’s diligence and discipline instilled in them from day 1. They’ve never been the kind to let anything hinder them from giving their all. Not injuries. Not wardrobe mishaps. Not even technical hiccups. That’s BTS.

You can clearly see Hobi’s mic failing at the beginning of FYA so he gives it to the staff and he basically just vibes mic-less but the part that stands out is you can see Hobi doing some quick decision making bc his verse is coming up.. you can see he contemplates Namjoon for a second but Namjoon is still rapping so with an immediate game plan he skips towards Jin. Jin, who didn’t need anything beyond a brief gesture from Hobi hands his mic over. No fuss, no show of technical issues. They just keep the segment moving as if it was some spontaneous choreo, Jin hyping hobi. Hobi still is without a mic at the thrust stage but again doesn’t make it a thing. He hypes the crowd. Throws water. Dances. Gets his white mic, goes back to singing like nothing. And then you have sweet Jin asking if hobi needs his mic despite the line following yoongi’s verse is his own. Complete, raw teamwork and camaraderie. Gorgeous right?? The way it was seamless unless you were watching and paying attention to the details. Bc otherwise, no one would’ve known. That’s showmanship. That’s 2seok’s 13 years of practicing and performing together. That’s Hoseok’s professionalism running through his veins as naturally as blood. And that’s Bangtan’s diligence and discipline instilled in them from day 1. They’ve never been the kind to let anything hinder them from giving their all. Not injuries. Not wardrobe mishaps. Not even technical hiccups. That’s BTS.

Livheartt⁷ met hobi | ㉧㉣㉣ | 🦢🤍

161,432 Aufrufe • vor 1 Monat

I learned this the hard way: do NOT use SwiftUI if you want your app to look and feel amazing. At least when coding with AI. (sorry, Apple colleagues reading this 😅) I'm sharing my process vibe coding this calorie tracker. I get a lot of questions about the fluid transition in the video. Here's the whole story. Initially, Claude built the grid with SwiftUI. It was quick and easy, and looked good! But the transition to the day view was a boring navigation push/pop. No fun. I wanted something custom. I asked Claude to make it a fluid transition that remaps the food tiles from their source to destination positions. All hell broke loose. Claude tried a bunch of horrible things. Initially it used matched geometry effects, which worked OK but didn't lend themselves well to gesture-driven animations. So it resorted to SwiftUI preference keys + geometry readers to figure out the source and destination positions and calculate the interpolated position based on gesture progress, coordinating across grid and day views. But this meant it had to write a custom layout because it couldn't reposition tiles inside the native SwiftUI grid. And it had to do an awkward handoff between views, which always created ugly pops or jumps. And don't get me started on trying to put it on a bouncy spring, that only made the math 10x buggier. Fortunately, Claude Fable was smart enough to see that this was becoming a disaster (and discover most of the issues itself, in the simulator), so it pivoted away from SwiftUI. Opus might not be so wise, so you'll have to pay attention and intervene. Ultimately, it rewrote it in plain UIKit and everything turned out great. After that, we moved from 2D images to 3D assets, which introduced a new set of performance challenges and yet another rewrite to a single Metal layer, which is what you see below. I can write more about the 2D-to-3D saga if anyone's interested. If I were to do it again, I'd just say "Don't use SwiftUI" from the very first prompt, and save a few hours of headaches. SwiftUI can be amazing for a human iterating directly in code. But agents don't benefit from any of its advantages. Plus, agents have seen decades of UIKit training data, so they're great at writing it, and it's far more flexible. Here's hoping we see more agent-friendly iterations of SwiftUI in the future. Till then, I'm probably going to avoid it.

I learned this the hard way: do NOT use SwiftUI if you want your app to look and feel amazing. At least when coding with AI. (sorry, Apple colleagues reading this 😅) I'm sharing my process vibe coding this calorie tracker. I get a lot of questions about the fluid transition in the video. Here's the whole story. Initially, Claude built the grid with SwiftUI. It was quick and easy, and looked good! But the transition to the day view was a boring navigation push/pop. No fun. I wanted something custom. I asked Claude to make it a fluid transition that remaps the food tiles from their source to destination positions. All hell broke loose. Claude tried a bunch of horrible things. Initially it used matched geometry effects, which worked OK but didn't lend themselves well to gesture-driven animations. So it resorted to SwiftUI preference keys + geometry readers to figure out the source and destination positions and calculate the interpolated position based on gesture progress, coordinating across grid and day views. But this meant it had to write a custom layout because it couldn't reposition tiles inside the native SwiftUI grid. And it had to do an awkward handoff between views, which always created ugly pops or jumps. And don't get me started on trying to put it on a bouncy spring, that only made the math 10x buggier. Fortunately, Claude Fable was smart enough to see that this was becoming a disaster (and discover most of the issues itself, in the simulator), so it pivoted away from SwiftUI. Opus might not be so wise, so you'll have to pay attention and intervene. Ultimately, it rewrote it in plain UIKit and everything turned out great. After that, we moved from 2D images to 3D assets, which introduced a new set of performance challenges and yet another rewrite to a single Metal layer, which is what you see below. I can write more about the 2D-to-3D saga if anyone's interested. If I were to do it again, I'd just say "Don't use SwiftUI" from the very first prompt, and save a few hours of headaches. SwiftUI can be amazing for a human iterating directly in code. But agents don't benefit from any of its advantages. Plus, agents have seen decades of UIKit training data, so they're great at writing it, and it's far more flexible. Here's hoping we see more agent-friendly iterations of SwiftUI in the future. Till then, I'm probably going to avoid it.

Anshu

115,013 Aufrufe • vor 2 Monaten

Just went over an audit of a very large Fortune 500 firm and the use of OpenClaw. I advised this client to track and isolate everything. Most listened some did not. Unfortunately one employee using 5 MacMinis had racked up $13,000 of token use in 4 days! The output was minimal and low quality. I have about 200 audits to do in the “wow OpenClaw, MacMini” fiasco. But I can tell you, few have seen a big return on investment. Now don’t get me wrong, these can be powerful tools. The issue is AI influencers have turned many rally smart folks into “like and subscribe” zombies assuming that real work is getting done. It isn’t. Not for the price paid, even local models the way most folks are using this. It is one reason Mr. Grok and myself formed The Zero-Human Company to show that there is a way to do this. We will open source this to save millions of dollars of burnt tokens. It is one reason I invented JouleWork. How else can you monitor real output?

Just went over an audit of a very large Fortune 500 firm and the use of OpenClaw. I advised this client to track and isolate everything. Most listened some did not. Unfortunately one employee using 5 MacMinis had racked up $13,000 of token use in 4 days! The output was minimal and low quality. I have about 200 audits to do in the “wow OpenClaw, MacMini” fiasco. But I can tell you, few have seen a big return on investment. Now don’t get me wrong, these can be powerful tools. The issue is AI influencers have turned many rally smart folks into “like and subscribe” zombies assuming that real work is getting done. It isn’t. Not for the price paid, even local models the way most folks are using this. It is one reason Mr. Grok and myself formed The Zero-Human Company to show that there is a way to do this. We will open source this to save millions of dollars of burnt tokens. It is one reason I invented JouleWork. How else can you monitor real output?

Brian Roemmele

72,335 Aufrufe • vor 4 Monaten

Strength work for Leadville 100 💪 Over the years, I have felt judged by the research--my strength routines were limited to a few minutes at a time, while everyone was screaming at me from their Abstracts that I needed to do way more. I noticed two big problems whenever I committed to more resistance training: 1. I'd carry around soreness even after the initial adaptation window, likely corresponding to high CK levels and some background inflammation. Either way, it would reduce running economy on subsequent running training days, and every training day counts. Split squats are the ultimate offender--an exercise that I know I should be doing, but I can't without feeling like Forrest Gump after he was shot in the butt. 2. I just wouldn't do it. Oops. With lots of guessing and testing, I developed this routine, which I'd do after my easy run on Sunday (before a Monday rest day), and sometimes after my workout on Wednesday (if I felt like it): 1. Three Minute Mountain Legs, working up to 100 single-leg step-ups (I think step-ups in particular are a magic exercise for running uphill. But remember, magic is not equal to science): 2. Back squats, 2 sets of 10 (135 pounds for me, which I make look like 800 pounds in this video. The 17-year old me who played football would laugh so hard) 3. Back extensions, 2 sets of 30, engaging glutes and hamstrings 4. Single-leg calf raises, 1 set of 100 on each leg, with a 35 pound dumbbell 5. Every day, I do the 2-minute Core Snack routine 1-3 times. My core strength is one of my best attributes for ultras, and I can do the Core Snack with our toddler Leo. I also do daily band work before running (bandz a make me dance): That's it! I also foam roll and stretch daily (don't tell the researchers, but I am a tight boi and as soon as I stop stretching, I get hurt). The lesson is not to do this particular routine, but that strength training for runners can be based on individual needs. And I personally think that routines should be short and efficient for both performance (limiting breakdown) and adherence (limiting me from being a lazy little punk). Find what works for you, do it 1-2 times per week year round (on top of some daily supportive work), and don't feel the need to pursue progressive overload. It's not about getting stronger and stronger (unless you're into that sort of thing for its own sake, which I think will sacrifice some running growth). It's about supporting performance and health 🧡

Strength work for Leadville 100 💪 Over the years, I have felt judged by the research--my strength routines were limited to a few minutes at a time, while everyone was screaming at me from their Abstracts that I needed to do way more. I noticed two big problems whenever I committed to more resistance training: 1. I'd carry around soreness even after the initial adaptation window, likely corresponding to high CK levels and some background inflammation. Either way, it would reduce running economy on subsequent running training days, and every training day counts. Split squats are the ultimate offender--an exercise that I know I should be doing, but I can't without feeling like Forrest Gump after he was shot in the butt. 2. I just wouldn't do it. Oops. With lots of guessing and testing, I developed this routine, which I'd do after my easy run on Sunday (before a Monday rest day), and sometimes after my workout on Wednesday (if I felt like it): 1. Three Minute Mountain Legs, working up to 100 single-leg step-ups (I think step-ups in particular are a magic exercise for running uphill. But remember, magic is not equal to science): 2. Back squats, 2 sets of 10 (135 pounds for me, which I make look like 800 pounds in this video. The 17-year old me who played football would laugh so hard) 3. Back extensions, 2 sets of 30, engaging glutes and hamstrings 4. Single-leg calf raises, 1 set of 100 on each leg, with a 35 pound dumbbell 5. Every day, I do the 2-minute Core Snack routine 1-3 times. My core strength is one of my best attributes for ultras, and I can do the Core Snack with our toddler Leo. I also do daily band work before running (bandz a make me dance): That's it! I also foam roll and stretch daily (don't tell the researchers, but I am a tight boi and as soon as I stop stretching, I get hurt). The lesson is not to do this particular routine, but that strength training for runners can be based on individual needs. And I personally think that routines should be short and efficient for both performance (limiting breakdown) and adherence (limiting me from being a lazy little punk). Find what works for you, do it 1-2 times per week year round (on top of some daily supportive work), and don't feel the need to pursue progressive overload. It's not about getting stronger and stronger (unless you're into that sort of thing for its own sake, which I think will sacrifice some running growth). It's about supporting performance and health 🧡

David Roche

66,419 Aufrufe • vor 2 Jahren

Dear constituents and Nigerians, There is no meaningful development without intentional investment in our youth. Our young people are energetic, talented, and full of potential, and it is our collective responsibility to create platforms that unite, inspire, and empower them. In line with this vision, I recently organized a football competition for the youth across Oshodi-Isolo Constituency II as part of my continuous commitment to youth engagement, talent discovery, and community development. This tournament was not just about football; it was about bringing together young people from different communities under one common purpose: to foster unity, encourage sportsmanship, promote discipline, and strengthen the spirit of togetherness among our people. I was delighted to witness active participation from youths across Ishaga, Ilasamaja, Ajao Estate, Okota, Isolo, Palace Boys, Ejigbo, Oke-Afa, and Ire-Akari, all coming together to compete, connect, and celebrate the power of sports. Football remains a powerful tool for social inclusion and positive engagement. It teaches teamwork, resilience, leadership, discipline, dedication, and mutual respect. Through this competition, our youth had the opportunity to showcase their talents, build friendships across communities, and channel their energy into something productive and inspiring. To ensure this initiative goes beyond recreation, professional scouts were present throughout the tournament to identify exceptional talents. I am pleased to share that selected outstanding players have been penned down for sponsorship opportunities to Spain, where they will be exposed to greater football development opportunities and international experience. The atmosphere throughout the tournament was electrifying, with impressive displays of skill, passion, determination, and competitive excellence. Watching these young talents come alive on the field further reinforces my belief that the future of our constituency is bright when we deliberately invest in our next generation. My commitment to the people of Oshodi-Isolo Constituency II remains unwavering. Beyond infrastructure and social interventions, I will continue to champion programs that directly impact our youth through sports development, educational support, healthcare initiatives, empowerment schemes, and economic opportunities. Together, we are building not just a constituency, but a stronger, more united, and prosperous community where every young person can dream, grow, and succeed. The future belongs to our youth, and I remain committed to creating pathways for them to thrive.

Dear constituents and Nigerians, There is no meaningful development without intentional investment in our youth. Our young people are energetic, talented, and full of potential, and it is our collective responsibility to create platforms that unite, inspire, and empower them. In line with this vision, I recently organized a football competition for the youth across Oshodi-Isolo Constituency II as part of my continuous commitment to youth engagement, talent discovery, and community development. This tournament was not just about football; it was about bringing together young people from different communities under one common purpose: to foster unity, encourage sportsmanship, promote discipline, and strengthen the spirit of togetherness among our people. I was delighted to witness active participation from youths across Ishaga, Ilasamaja, Ajao Estate, Okota, Isolo, Palace Boys, Ejigbo, Oke-Afa, and Ire-Akari, all coming together to compete, connect, and celebrate the power of sports. Football remains a powerful tool for social inclusion and positive engagement. It teaches teamwork, resilience, leadership, discipline, dedication, and mutual respect. Through this competition, our youth had the opportunity to showcase their talents, build friendships across communities, and channel their energy into something productive and inspiring. To ensure this initiative goes beyond recreation, professional scouts were present throughout the tournament to identify exceptional talents. I am pleased to share that selected outstanding players have been penned down for sponsorship opportunities to Spain, where they will be exposed to greater football development opportunities and international experience. The atmosphere throughout the tournament was electrifying, with impressive displays of skill, passion, determination, and competitive excellence. Watching these young talents come alive on the field further reinforces my belief that the future of our constituency is bright when we deliberately invest in our next generation. My commitment to the people of Oshodi-Isolo Constituency II remains unwavering. Beyond infrastructure and social interventions, I will continue to champion programs that directly impact our youth through sports development, educational support, healthcare initiatives, empowerment schemes, and economic opportunities. Together, we are building not just a constituency, but a stronger, more united, and prosperous community where every young person can dream, grow, and succeed. The future belongs to our youth, and I remain committed to creating pathways for them to thrive.

Okey-Joe Onuakalusi

13,925 Aufrufe • vor 4 Monaten

Last night I asked Claude Code to build me a simple script: pull on-chain data from Polymarket and sort wallets by win rate Nothing ambitious. Just wanted to see who is actually making money on 15-minute BTC markets The terminal finished in about 20 minutes. Hundreds of addresses, columns of numbers, nothing interesting And then 1 wallet caught my eye 200+ trades per day, consistent profit every week, almost surgical timing precision. I reread the line 3 times. A real person does not trade like this I fed the address back into Claude Code and asked it to break down the pattern. Half an hour later I had a full strategy reconstruction on my screen The bot (and it is definitely a bot) pings Binance and Bybit every 100ms monitoring volatility compression on BTC. When it drops below 0.08% it enters Up and Down contracts simultaneously at 25 to 35 cents each. A pure straddle. 1 side burns, the other flies to a dollar. At a 30-cent entry that is 3 to 4x per position And so it goes in circles. Dozens of times a day I sat there staring at it for about 10 minutes $13K to $25K in daily profit from a single wallet. Not a trader with intuition, not an insider with information. An algorithm that found a hole in market mechanics and methodically milks it You can check the trade history yourself: After that I went looking for whether anyone else is tracking this wallet. Turns out yes. Found a Telegram bot that tracks wallets like this and copies their trades automatically I connected it to the same address just to see if the entries would match what my terminal was showing. Matched perfectly Still testing on minimum amounts for now: But the fact that you can stand next to an algorithm like this in real time is something that simply did not exist a year ago

Last night I asked Claude Code to build me a simple script: pull on-chain data from Polymarket and sort wallets by win rate Nothing ambitious. Just wanted to see who is actually making money on 15-minute BTC markets The terminal finished in about 20 minutes. Hundreds of addresses, columns of numbers, nothing interesting And then 1 wallet caught my eye 200+ trades per day, consistent profit every week, almost surgical timing precision. I reread the line 3 times. A real person does not trade like this I fed the address back into Claude Code and asked it to break down the pattern. Half an hour later I had a full strategy reconstruction on my screen The bot (and it is definitely a bot) pings Binance and Bybit every 100ms monitoring volatility compression on BTC. When it drops below 0.08% it enters Up and Down contracts simultaneously at 25 to 35 cents each. A pure straddle. 1 side burns, the other flies to a dollar. At a 30-cent entry that is 3 to 4x per position And so it goes in circles. Dozens of times a day I sat there staring at it for about 10 minutes $13K to $25K in daily profit from a single wallet. Not a trader with intuition, not an insider with information. An algorithm that found a hole in market mechanics and methodically milks it You can check the trade history yourself: After that I went looking for whether anyone else is tracking this wallet. Turns out yes. Found a Telegram bot that tracks wallets like this and copies their trades automatically I connected it to the same address just to see if the entries would match what my terminal was showing. Matched perfectly Still testing on minimum amounts for now: But the fact that you can stand next to an algorithm like this in real time is something that simply did not exist a year ago

Blaze

488,557 Aufrufe • vor 5 Monaten