正在加载视频...

视频加载失败

返回首页

Qwen 3.6 is frontier for local. It also thinks forever. I tried a dumb inference-time trick: make its block obey a tiny grammar. Result: - HumanEval+: 22x fewer think tokens, no accuracy loss - LiveCodeBench public slice: +14% pass@1, ~5x fewer total tokens

andthatto's profile picture

andthattoo

1,034 subscribers

282,675 次观看 • 3 个月前 •via X (Twitter)

游戏教育科学技术
LIVE

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

/1 Gemma 4 31B just crushed Qwen 3.6 27B in a local LLM gamedev contest inside atomic.chat (prompt is below) Device: MacBook Pro M5 Max, 64GB RAM Results: Qwen 3.6 27B: 32 tokens/sec · 18m 04s · 33,946 tokens Gemma 4 31B: 27 tokens/sec · 3m 51s · 6,209 tokens So what is more important: tokens per second, or the quality of the final answer? Qwen made a very long response and showed more creativity and visual style. But Gemma gave a shorter, clearer, and more logical answer in much less time. In this one-shot Pac-Man gamedev contest, Gemma 4 31B was the clear winner. Its game logic was stronger: click reactions were smoother, and it handled interactions with elements like walls, ghosts, and particle effects better. But this was only one test. Maybe Qwen 3.6 27B can show better results with better settings. Open the comments, try our prompt, and share your result below.
0:16

/1 Gemma 4 31B just crushed Qwen 3.6 27B in a local LLM gamedev contest inside atomic.chat (prompt is below) Device: MacBook Pro M5 Max, 64GB RAM Results: Qwen 3.6 27B: 32 tokens/sec · 18m 04s · 33,946 tokens Gemma 4 31B: 27 tokens/sec · 3m 51s · 6,209 tokens So what is more important: tokens per second, or the quality of the final answer? Qwen made a very long response and showed more creativity and visual style. But Gemma gave a shorter, clearer, and more logical answer in much less time. In this one-shot Pac-Man gamedev contest, Gemma 4 31B was the clear winner. Its game logic was stronger: click reactions were smoother, and it handled interactions with elements like walls, ghosts, and particle effects better. But this was only one test. Maybe Qwen 3.6 27B can show better results with better settings. Open the comments, try our prompt, and share your result below.

Chubby♨️

72,566 次观看 • 3 个月前

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

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

hoeem

21,634 次观看 • 1 年前

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.
0:13

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 次观看 • 7 个月前

I told you to claim your free 16GB NVIDIA GPU for learning Local LLMs. Now I’m going to show you how to double its inference speed without touching the hardware. Google Colab gives you an enterprise grade NVIDIA Tesla T4 GPU for free, roughly 4 hours every single day. It is the absolute perfect sandbox for learning AI engineering, testing inference flags, and pushing massive context windows. The local AI timeline is moving way too fast. If you aren't using Multi Token Prediction (MTP) yet, you are leaving massive performance on the table. I just pushed DeepMind’s Gemma 4 26B to 64.9 t/s on this exact free tier. Let's look at the raw benchmark data running on an Ubuntu Linux environment with the latest compiled llama.cpp binaries and quantized GGUFs from Unsloth via HuggingFace: # Qwen 3.5 9B (Dense): Base: [ Prompt: 626.7 t/s | Generation: 21.0 t/s ] With MTP: [ Prompt: 539.1 t/s | Generation: 24.8 t/s ] # Gemma 4 26B QAT (MoE): Base: [ Prompt: 634.2 t/s | Generation: 48.3 t/s ] With MTP: [ Prompt: 572.1 t/s | Generation: 64.9 t/s ] If you are paying attention, this single Colab notebook reveals 3 massive observations about the current state of local LLMs: # 1. The MTP Speedup (Software Overclocking) Standard autoregressive decoding guesses one token at a time. MTP acts like a highly optimized, built in speculative decoder. It predicts multiple future tokens at once and the main model verifies them in parallel. The result? Zero accuracy loss and a massive throughput increase. Gemma jumped from 48 to 65 t/s just by flipping a flag. # 2. The MoE Paradox (Bigger is Faster) How does a 26B parameter model absolutely destroy a 9B model in raw speed on the exact same hardware? Architecture. Qwen 3.5 9B is a dense model. it activates all 9 billion parameters for every single token. Gemma 4 26B is a Mixture of Experts (MoE) model. It routes data efficiently, activating only 4B parameters per token. You get the reasoning capabilities of a 26B model with the compute cost of a 4B model. 3. Thinking Efficiency When I ran the exact same complex prompt on both models, the larger MoE spent significantly fewer "thinking" tokens to arrive at the correct answer. A smarter model doesn't just give better answers; it gets to the point faster, saving you compute cycles and preserving your context window. # Want to run this yourself? Here are the exact llama.cpp CLI commands. For Qwen (MTP is baked into the main model): ./llama-cli -m Qwen3.5-9B-UD-Q4_K_XL.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 For Gemma (Using a separate lightweight draft model): ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --model-draft mtp-gemma-4-26B-A4B-it.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 Stop waiting for a $3,000 rig. Boot up Colab, pull these models, and start building your stack. I’ve put together a completely free, cell by cell Google Colab notebook that automates this entire workflow so you can test it yourself in 5 minutes and learn. Link to the notebook is in the comments below. Experiemt with different MTP parameters, context windows and post your results in the comments.
0:12

I told you to claim your free 16GB NVIDIA GPU for learning Local LLMs. Now I’m going to show you how to double its inference speed without touching the hardware. Google Colab gives you an enterprise grade NVIDIA Tesla T4 GPU for free, roughly 4 hours every single day. It is the absolute perfect sandbox for learning AI engineering, testing inference flags, and pushing massive context windows. The local AI timeline is moving way too fast. If you aren't using Multi Token Prediction (MTP) yet, you are leaving massive performance on the table. I just pushed DeepMind’s Gemma 4 26B to 64.9 t/s on this exact free tier. Let's look at the raw benchmark data running on an Ubuntu Linux environment with the latest compiled llama.cpp binaries and quantized GGUFs from Unsloth via HuggingFace: # Qwen 3.5 9B (Dense): Base: [ Prompt: 626.7 t/s | Generation: 21.0 t/s ] With MTP: [ Prompt: 539.1 t/s | Generation: 24.8 t/s ] # Gemma 4 26B QAT (MoE): Base: [ Prompt: 634.2 t/s | Generation: 48.3 t/s ] With MTP: [ Prompt: 572.1 t/s | Generation: 64.9 t/s ] If you are paying attention, this single Colab notebook reveals 3 massive observations about the current state of local LLMs: # 1. The MTP Speedup (Software Overclocking) Standard autoregressive decoding guesses one token at a time. MTP acts like a highly optimized, built in speculative decoder. It predicts multiple future tokens at once and the main model verifies them in parallel. The result? Zero accuracy loss and a massive throughput increase. Gemma jumped from 48 to 65 t/s just by flipping a flag. # 2. The MoE Paradox (Bigger is Faster) How does a 26B parameter model absolutely destroy a 9B model in raw speed on the exact same hardware? Architecture. Qwen 3.5 9B is a dense model. it activates all 9 billion parameters for every single token. Gemma 4 26B is a Mixture of Experts (MoE) model. It routes data efficiently, activating only 4B parameters per token. You get the reasoning capabilities of a 26B model with the compute cost of a 4B model. 3. Thinking Efficiency When I ran the exact same complex prompt on both models, the larger MoE spent significantly fewer "thinking" tokens to arrive at the correct answer. A smarter model doesn't just give better answers; it gets to the point faster, saving you compute cycles and preserving your context window. # Want to run this yourself? Here are the exact llama.cpp CLI commands. For Qwen (MTP is baked into the main model): ./llama-cli -m Qwen3.5-9B-UD-Q4_K_XL.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 For Gemma (Using a separate lightweight draft model): ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --model-draft mtp-gemma-4-26B-A4B-it.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 Stop waiting for a $3,000 rig. Boot up Colab, pull these models, and start building your stack. I’ve put together a completely free, cell by cell Google Colab notebook that automates this entire workflow so you can test it yourself in 5 minutes and learn. Link to the notebook is in the comments below. Experiemt with different MTP parameters, context windows and post your results in the comments.

Alok

170,442 次观看 • 1 个月前

Day 11/90 of Inference Engineering How does vLLM work and how is it used in production? Before we discuss how vLLM works internally, it helps to understand what vLLM is. At a high level, vLLM is an inference engine that is designed to serve LLMs to thousands of concurrent users efficiently while managing scarce compute and memory. The goal for vLLM is to maximize throughput and minimize latency; optimizing for the best inference economics and experience for end users. With every request from the end user, it eventually ends up in the engine core, gets scheduled alongside other requests from other concurrent users, executes on the GPU, and updates the KV cache with the new key and value vectors, and streams the tokens back to the user. The Scheduler decides what requests should execute next while continuously batching requests together to maximize GPU utilization. Continuous batching is an inference optimization that allows new requests to join a running batch as other requests finish generating tokens. This helps with keeping the GPU utilization high instead of letting it sit idle waiting for an entire batch to complete generating. After the scheduler dispatches the selected batch to the Model Executor, the Model Executor prepares the tensors and metadata required for inference, retrieves each request’s block table from KV Cache Manager, launches the optimized transformer forward pass on the GPU, computes the logits, updates the KV cache with the new key and value vectors, and finally returns the results for sampling and streaming. The KV Cache Manager uses the PagedAttention memory layout to allocate fixed-size cache blocks on demand and maintains a Free Block Queue on the CPU that tracks which blocks in the GPU’s Paged KV Cache are currently free. When a request needs additional KV cache space, the KV Cache manager takes a free block from the queue and assigns it to that request, thus avoiding an expensive search through GPU memory for available cache blocks. All of these components form the core of vLLM’s inference engine. The Scheduler determines what requests are executed, the Model Executor determines how those requests are executed, the KV Cache Manager determines where each request’s KV cache lives using the PagedAttention Memory Layout. This architecture enables vLLM to serve thousands of concurrent requests with high throughput, low latency, and efficient GPU memory utilization. Heres a little animation that visualizes everything! - I've also completed the forward pass for my mnist.c project. I had a nice chat with shrey birmiwal, such a knowledgeable guy. Excited to learn more about vLLM and implement a tiny-vLLM one day.
0:14

Day 11/90 of Inference Engineering How does vLLM work and how is it used in production? Before we discuss how vLLM works internally, it helps to understand what vLLM is. At a high level, vLLM is an inference engine that is designed to serve LLMs to thousands of concurrent users efficiently while managing scarce compute and memory. The goal for vLLM is to maximize throughput and minimize latency; optimizing for the best inference economics and experience for end users. With every request from the end user, it eventually ends up in the engine core, gets scheduled alongside other requests from other concurrent users, executes on the GPU, and updates the KV cache with the new key and value vectors, and streams the tokens back to the user. The Scheduler decides what requests should execute next while continuously batching requests together to maximize GPU utilization. Continuous batching is an inference optimization that allows new requests to join a running batch as other requests finish generating tokens. This helps with keeping the GPU utilization high instead of letting it sit idle waiting for an entire batch to complete generating. After the scheduler dispatches the selected batch to the Model Executor, the Model Executor prepares the tensors and metadata required for inference, retrieves each request’s block table from KV Cache Manager, launches the optimized transformer forward pass on the GPU, computes the logits, updates the KV cache with the new key and value vectors, and finally returns the results for sampling and streaming. The KV Cache Manager uses the PagedAttention memory layout to allocate fixed-size cache blocks on demand and maintains a Free Block Queue on the CPU that tracks which blocks in the GPU’s Paged KV Cache are currently free. When a request needs additional KV cache space, the KV Cache manager takes a free block from the queue and assigns it to that request, thus avoiding an expensive search through GPU memory for available cache blocks. All of these components form the core of vLLM’s inference engine. The Scheduler determines what requests are executed, the Model Executor determines how those requests are executed, the KV Cache Manager determines where each request’s KV cache lives using the PagedAttention Memory Layout. This architecture enables vLLM to serve thousands of concurrent requests with high throughput, low latency, and efficient GPU memory utilization. Heres a little animation that visualizes everything! - I've also completed the forward pass for my mnist.c project. I had a nice chat with shrey birmiwal, such a knowledgeable guy. Excited to learn more about vLLM and implement a tiny-vLLM one day.

max fu

70,450 次观看 • 28 天前

🧑‍🚀 Day 5 of the Cursor #vibejam Proudly sponsored by Cursor + bolt.new + GLIF Today a little bit about Cursor 3, it's a completely rebuilt interface designed around agents, you can run multiple AI agents in parallel, hand off between local and cloud, and go from commit to merged PR. It's powered by Composer 2, their own frontier coding model that tops benchmarks at $0.50/M input tokens — and the Fast variant runs at 200 tokens/sec, more than 2x faster than other frontier models. Perfect for vibe coding your game jam entry. And edwin from Cursor is here to help you if you get stuck! Lots of activity again today again on here, the best games (or well previews) I saw today: 🍣 Sushi Belt by steeno 🌊 Surfing Simulator by Erik (this one is a basic demo but very promising as it has realistic water physics) 🦆 Grand Quack Auto by kyzo (or as I like to call it Bali Simulator) 🏛️ Roman Combat Sim by terry trusner (continued from yesterday because it's had a lot of progress I think) YOU HAVE 24 DAYS LEFT! Reply in this thread with updates on your current games to share your progress, and add tag #vibejam so I see and can include you in the daily tweet There's $35,000 in prizes for you to win, see threads below for more info. The Gold prize is $20,000, bronze is $10,000 and silver is $5,000! Wanna to participate? You can still start today and submit your game any time before May 1!
0:33

🧑‍🚀 Day 5 of the Cursor #vibejam Proudly sponsored by Cursor + bolt.new + GLIF Today a little bit about Cursor 3, it's a completely rebuilt interface designed around agents, you can run multiple AI agents in parallel, hand off between local and cloud, and go from commit to merged PR. It's powered by Composer 2, their own frontier coding model that tops benchmarks at $0.50/M input tokens — and the Fast variant runs at 200 tokens/sec, more than 2x faster than other frontier models. Perfect for vibe coding your game jam entry. And edwin from Cursor is here to help you if you get stuck! Lots of activity again today again on here, the best games (or well previews) I saw today: 🍣 Sushi Belt by steeno 🌊 Surfing Simulator by Erik (this one is a basic demo but very promising as it has realistic water physics) 🦆 Grand Quack Auto by kyzo (or as I like to call it Bali Simulator) 🏛️ Roman Combat Sim by terry trusner (continued from yesterday because it's had a lot of progress I think) YOU HAVE 24 DAYS LEFT! Reply in this thread with updates on your current games to share your progress, and add tag #vibejam so I see and can include you in the daily tweet There's $35,000 in prizes for you to win, see threads below for more info. The Gold prize is $20,000, bronze is $10,000 and silver is $5,000! Wanna to participate? You can still start today and submit your game any time before May 1!

@levelsio

79,700 次观看 • 4 个月前

1/7 Built a Polymarket trading bot over 3 months. Here are the biggest mistakes that cost me real money > Went from v1 to v61. Every version fixed something painful. --- 2/7 Stop Loss killed more money than it saved. > Binary markets need room to breathe - fluctuations are normal. > Stop Loss was cutting positions on random noise and locking in losses right before the market flipped. > Removed it in v61. Immediately better. --- 3/7 Martingale + Stop Loss = a loss cascade. > Seemed logical: lost $5 -> bet $8, lost again -> bet $10. > In practice: a losing streak plus early exits = a hole in your balance in a single day. > Killed it. For good. --- 4/7 Smart Exit without Force Exit is a trap. > Token hits 90c (+75% profit), but the bot was waiting for a "BTC reversal" signal. > Market closes, token drops, profit gone. > Fix: hard Force Exit at 85c. No conditions, no waiting. --- 5/7 Blocking the 5:30-10:30 PM ET window felt safe. It wasn't. > NYSE open = sharp spikes = bad signals. Made sense to block it. > But the full block was also killing clean entries at 8-10:30 PM. > Had to split the zone into segments with different edge/move thresholds. --- 6/7 The Gamma API lies about market start time. > Start price ("price to beat") is the core input for every signal. > Gamma was returning stale data. Had to pull prices directly from Chainlink on-chain on Polygon. > That's its own adventure - polling a smart contract every 2 seconds at 2 AM. --- 7/7 The real lesson: don't overcomplicate what works. > v1: complex system, 10 indicators -> -$200/day > v61: "buy the expensive token for $5, exit at +30%" -> consistently green > Simpler logic = fewer failure points. > The bot runs 96 intervals a day. Every mistake shows up fast.
0:16

1/7 Built a Polymarket trading bot over 3 months. Here are the biggest mistakes that cost me real money > Went from v1 to v61. Every version fixed something painful. --- 2/7 Stop Loss killed more money than it saved. > Binary markets need room to breathe - fluctuations are normal. > Stop Loss was cutting positions on random noise and locking in losses right before the market flipped. > Removed it in v61. Immediately better. --- 3/7 Martingale + Stop Loss = a loss cascade. > Seemed logical: lost $5 -> bet $8, lost again -> bet $10. > In practice: a losing streak plus early exits = a hole in your balance in a single day. > Killed it. For good. --- 4/7 Smart Exit without Force Exit is a trap. > Token hits 90c (+75% profit), but the bot was waiting for a "BTC reversal" signal. > Market closes, token drops, profit gone. > Fix: hard Force Exit at 85c. No conditions, no waiting. --- 5/7 Blocking the 5:30-10:30 PM ET window felt safe. It wasn't. > NYSE open = sharp spikes = bad signals. Made sense to block it. > But the full block was also killing clean entries at 8-10:30 PM. > Had to split the zone into segments with different edge/move thresholds. --- 6/7 The Gamma API lies about market start time. > Start price ("price to beat") is the core input for every signal. > Gamma was returning stale data. Had to pull prices directly from Chainlink on-chain on Polygon. > That's its own adventure - polling a smart contract every 2 seconds at 2 AM. --- 7/7 The real lesson: don't overcomplicate what works. > v1: complex system, 10 indicators -> -$200/day > v61: "buy the expensive token for $5, exit at +30%" -> consistently green > Simpler logic = fewer failure points. > The bot runs 96 intervals a day. Every mistake shows up fast.

Kotte

31,803 次观看 • 4 个月前

this is the worst local ai will ever be. it only gets better from here. if you are not expanding your mind with these small models you are missing what's happening right now 99 percent tool call success rate. when steered well with the right skills and a framework like hermes agent the node becomes a cognition layer. not a chatbot. not a toy. an extension of how you think. i was cranking this node at 35 to 50 tok/s all day on personal experiments and now after all the work is done qwen 3.5 9B is iterating on its own code. the game it created. fixing its own bugs autonomously. and the part you should probably not miss is that all of this is happening on a RTX 3060. not an H100. not an A100. the card most of you have sitting in a drawer right now. if you just open that drawer and put that intelligence to work every tensor core on that card should be running for you. your work. your experiments. your thinking. you all have it but because nobody told you what this hardware can actually do in 2026 you never tried. the day it unlocks is the day you test your workload, understand the tradeoffs, debug the loops, and then decide if you need to scale the hardware. there is no point buying 3 mac studios when things done well you can squeeze a similar level of intelligence from 9B compared to 70B. but only when you create the right environment for your model through the right harness. and let me tell you i have tried claude code as a local harness. i have tried opencode. i have tried various others. somehow i landed on hermes agent and never left. there is something magical going on at Nous Research. the tool call parsers, the skills system, the way it handles small models natively. nothing else comes close for local inference. own your cognition. your AI. your agent. your prompts. your experiments. why give them away for free. those are who you are and they don't belong on someone else's servers being monitored. just give it a shot with your existing hardware. you run into a problem the community will help you. and if you are migrating from openclaw to hermes i will personally help you make the switch.
0:13

this is the worst local ai will ever be. it only gets better from here. if you are not expanding your mind with these small models you are missing what's happening right now 99 percent tool call success rate. when steered well with the right skills and a framework like hermes agent the node becomes a cognition layer. not a chatbot. not a toy. an extension of how you think. i was cranking this node at 35 to 50 tok/s all day on personal experiments and now after all the work is done qwen 3.5 9B is iterating on its own code. the game it created. fixing its own bugs autonomously. and the part you should probably not miss is that all of this is happening on a RTX 3060. not an H100. not an A100. the card most of you have sitting in a drawer right now. if you just open that drawer and put that intelligence to work every tensor core on that card should be running for you. your work. your experiments. your thinking. you all have it but because nobody told you what this hardware can actually do in 2026 you never tried. the day it unlocks is the day you test your workload, understand the tradeoffs, debug the loops, and then decide if you need to scale the hardware. there is no point buying 3 mac studios when things done well you can squeeze a similar level of intelligence from 9B compared to 70B. but only when you create the right environment for your model through the right harness. and let me tell you i have tried claude code as a local harness. i have tried opencode. i have tried various others. somehow i landed on hermes agent and never left. there is something magical going on at Nous Research. the tool call parsers, the skills system, the way it handles small models natively. nothing else comes close for local inference. own your cognition. your AI. your agent. your prompts. your experiments. why give them away for free. those are who you are and they don't belong on someone else's servers being monitored. just give it a shot with your existing hardware. you run into a problem the community will help you. and if you are migrating from openclaw to hermes i will personally help you make the switch.

Sudo su

58,717 次观看 • 5 个月前

My dual RTX PRO 6000 setup is currently training a Draft model for Qwen 3.6 27B! 🔥 I'm taking the paper DeepSeek dropped on 6/26 and going for a super ambitious application to the 27B scale. Thanks to my homelab, I was able to dive straight in — I read the paper and immediately started experimenting. The amount I've learned has been insane: - How memory bandwidth bottlenecks speed and clever ways to hack around it - Methods to train the draft model and boost its accuracy - Mechanisms to reference tokens all the way back to the previous one to skyrocket draft acceptance rates - The impact of Attention vs. GateDeltaNet on speculative decoding performance and how to handle those differences - The unique approaches and trade-offs of MTP, Dflash, JetSpec, and DSpark I could go on forever, but just from speculative decoding alone I've learned so much. The 27B architecture feels way more DSpark-native than JetSpec, so once draft training finishes, I'm going all-in with DSpark! My goal is to beat existing speculative decoding speeds outright — no task-specific shortcuts or cheating, pure general improvement. If you're into this kind of research, I'd love to hear your thoughts, impressions, and any suggestions — please reply! 🚀
0:17

My dual RTX PRO 6000 setup is currently training a Draft model for Qwen 3.6 27B! 🔥 I'm taking the paper DeepSeek dropped on 6/26 and going for a super ambitious application to the 27B scale. Thanks to my homelab, I was able to dive straight in — I read the paper and immediately started experimenting. The amount I've learned has been insane: - How memory bandwidth bottlenecks speed and clever ways to hack around it - Methods to train the draft model and boost its accuracy - Mechanisms to reference tokens all the way back to the previous one to skyrocket draft acceptance rates - The impact of Attention vs. GateDeltaNet on speculative decoding performance and how to handle those differences - The unique approaches and trade-offs of MTP, Dflash, JetSpec, and DSpark I could go on forever, but just from speculative decoding alone I've learned so much. The 27B architecture feels way more DSpark-native than JetSpec, so once draft training finishes, I'm going all-in with DSpark! My goal is to beat existing speculative decoding speeds outright — no task-specific shortcuts or cheating, pure general improvement. If you're into this kind of research, I'd love to hear your thoughts, impressions, and any suggestions — please reply! 🚀

Hikari∣LocalLLM⚡

56,870 次观看 • 1 个月前

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?
0:12

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

Alok

12,962 次观看 • 1 个月前

I just got Gemma 4 26B A4B MoE model running fully locally with Hermes agent on an 8GB RTX 4060 and it's now backtesting trading strategies end to end, no hand holding. If you’re a trader or work on Wall Street, you don’t want to miss this. Yes. fully automated. No cloud. No APIs beyond market data. # Here's what I did: Setup: - Model: Gemma 4 26B-A4B QAT (MoE), Q4_K_XL Unsloth's quant (link in the comments) - Inference: llama.cpp (turboquant fork by Tom Turney link in the comments) - Hardware: RTX 4060, 8GB VRAM + 16GB RAM only (with 50 other chrome tabs open) - Context: 64K llama.cpp turboquant flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 --cache-type-k q8_0 --cache-type-v turbo3 --port 8080 turboquant helps achieve high prefill and decode throughput for interactive sessions. throughput with Hermes agent: decode: 25+ tokens/sec prefill: 250+ tokens/sec # Then I gave the agent one task: Backtest a strategy: - Buy when RSI crosses above 30 - Sell at +2% profit or -1% stoploss - No overlapping positions - Use Google stock via yfinance - Generate a full HTML report with candlestick charts + signals What happened next was wild. It didn't just write code, it ran the entire workflow itself: Audited the environment (pip list, dependency check) Hit a ModuleNotFoundError, multiple Python installs were conflicting Ran where python to map every interpreter on the system Manually selected the correct Python 3.13 path and re ran the script Wrote a clean statevmachine backtester (strict no overlapping trades logic) Patched a yfinance MultiIndex quirk that would've crashed the script Built Plotly candlestick + RSI charts with buy/sell markers Calculated win rate, PnL, and summary stats Exported a polished single file HTML report. check the report at the end of the video or in the comments. Biggest takeaway: local LLMs aren't just "chat assistants" anymore. They debug their own environment, write production code, and ship a finished deliverable on consumer hardware, for $0 in API costs. If you're still calling local models "toys," you're already behind. This is just the beginning. Hermes agent just surpassed 1 trillion tokens in a single day on OpenRouter. Think about the scale of total token generation happening right now. Disclaimer: This is not financial advice. Consult a professional before making any trading decisions.
0:14

I just got Gemma 4 26B A4B MoE model running fully locally with Hermes agent on an 8GB RTX 4060 and it's now backtesting trading strategies end to end, no hand holding. If you’re a trader or work on Wall Street, you don’t want to miss this. Yes. fully automated. No cloud. No APIs beyond market data. # Here's what I did: Setup: - Model: Gemma 4 26B-A4B QAT (MoE), Q4_K_XL Unsloth's quant (link in the comments) - Inference: llama.cpp (turboquant fork by Tom Turney link in the comments) - Hardware: RTX 4060, 8GB VRAM + 16GB RAM only (with 50 other chrome tabs open) - Context: 64K llama.cpp turboquant flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 --cache-type-k q8_0 --cache-type-v turbo3 --port 8080 turboquant helps achieve high prefill and decode throughput for interactive sessions. throughput with Hermes agent: decode: 25+ tokens/sec prefill: 250+ tokens/sec # Then I gave the agent one task: Backtest a strategy: - Buy when RSI crosses above 30 - Sell at +2% profit or -1% stoploss - No overlapping positions - Use Google stock via yfinance - Generate a full HTML report with candlestick charts + signals What happened next was wild. It didn't just write code, it ran the entire workflow itself: Audited the environment (pip list, dependency check) Hit a ModuleNotFoundError, multiple Python installs were conflicting Ran where python to map every interpreter on the system Manually selected the correct Python 3.13 path and re ran the script Wrote a clean statevmachine backtester (strict no overlapping trades logic) Patched a yfinance MultiIndex quirk that would've crashed the script Built Plotly candlestick + RSI charts with buy/sell markers Calculated win rate, PnL, and summary stats Exported a polished single file HTML report. check the report at the end of the video or in the comments. Biggest takeaway: local LLMs aren't just "chat assistants" anymore. They debug their own environment, write production code, and ship a finished deliverable on consumer hardware, for $0 in API costs. If you're still calling local models "toys," you're already behind. This is just the beginning. Hermes agent just surpassed 1 trillion tokens in a single day on OpenRouter. Think about the scale of total token generation happening right now. Disclaimer: This is not financial advice. Consult a professional before making any trading decisions.

Alok

105,094 次观看 • 1 个月前

I am the Under Secretary for Strategic Resource Integration. We sit inside the Executive Office of the President. You won't find us on an org chart. We requested it that way. You think an office like mine spins up in a crisis. We've been open since January. Venezuela wasn't a war. Venezuela was the beta. Maduro proved the model the morning we took delivery of him. The President called it "working out brilliantly for both." He's right. It works brilliantly for both of us, and the customer will come around to the "both" on their own time. Kharg Island is the rollout. A third the size of Manhattan, and 90 percent of Iran's crude runs through it. 2 million barrels a day, one valve. I have prayed my whole career for a country that legible. 53 billion in revenue. 11 percent of their GDP. All of it funneling through a rock you could walk across before lunch. I kept the Venezuela playbook. I changed the classification banner and let find-and-replace do Caracas to Tehran. That was the plan. That was the whole plan. Page 14 still says Caracas. No one on the distribution list has ever reached page 14. They initial page 1. Page 1 is projected revenue. It goes up and to the right, the only direction this town can read. The Energy Secretary testified he was "unaware." Under oath, to Congress, the man who runs American energy. Good. He was never read into the compartment. The fewer names on the account, the cleaner the account. He runs the department. He does not run this. We don't say invasion. We say onboarding. Every onboarding has friction. The Marines off the coast are not an occupying force. They're field engagement. Iran said an invasion of its islands would "shatter all restraint." I logged it as customer feedback and filed it. The file is full. Engaged customers submit the most feedback. It's the quiet ones who churn. And Iran can't churn. That's the part I'm proudest of. We built the account with no exit. No competitor to switch to. We are the market, and the market is now total. Total is my word. I workshopped it. "Seize" scares people. "Total control of their markets" tests at 71, because nobody pictures a kid from Ohio on a beach when you say markets. We moved 22 tankers through Hormuz at night with the lights off. The President said he wanted to announce it so badly it hurt, and he held it a month anyway. That is the most discipline this administration has ever shown. 100 million barrels, he said. The fact-checkers say the math doesn't hold. They're junior. The number was never the deliverable. The number is the soft launch. He said he loves the inflation. People clutched their receipts. 90 a barrel instead of 250 is the discount, and 250 is a price I invented so this one would feel like mercy. How long will we be there? The President answered that. "We had to be there for a while." A while is my favorite contract length. It renews on its own. No court can find the end date. He wondered out loud whether America has the stomach for it. I don't need the country's stomach. I need its signature on page 1. He called the people who object stupid. I'd defend them. They're not stupid. They just read to page 14. Last thing. Annex C. The out-year targets. 3 more, redacted, marked pre-decisional. I can't tell you which 3. Total control of their markets. Nobody in that room asked which market. The crude still sails east, to China, the same as yesterday. We don't own the oil. We own the valve, the fear, and the word "total." That was always the only product.
0:18

I am the Under Secretary for Strategic Resource Integration. We sit inside the Executive Office of the President. You won't find us on an org chart. We requested it that way. You think an office like mine spins up in a crisis. We've been open since January. Venezuela wasn't a war. Venezuela was the beta. Maduro proved the model the morning we took delivery of him. The President called it "working out brilliantly for both." He's right. It works brilliantly for both of us, and the customer will come around to the "both" on their own time. Kharg Island is the rollout. A third the size of Manhattan, and 90 percent of Iran's crude runs through it. 2 million barrels a day, one valve. I have prayed my whole career for a country that legible. 53 billion in revenue. 11 percent of their GDP. All of it funneling through a rock you could walk across before lunch. I kept the Venezuela playbook. I changed the classification banner and let find-and-replace do Caracas to Tehran. That was the plan. That was the whole plan. Page 14 still says Caracas. No one on the distribution list has ever reached page 14. They initial page 1. Page 1 is projected revenue. It goes up and to the right, the only direction this town can read. The Energy Secretary testified he was "unaware." Under oath, to Congress, the man who runs American energy. Good. He was never read into the compartment. The fewer names on the account, the cleaner the account. He runs the department. He does not run this. We don't say invasion. We say onboarding. Every onboarding has friction. The Marines off the coast are not an occupying force. They're field engagement. Iran said an invasion of its islands would "shatter all restraint." I logged it as customer feedback and filed it. The file is full. Engaged customers submit the most feedback. It's the quiet ones who churn. And Iran can't churn. That's the part I'm proudest of. We built the account with no exit. No competitor to switch to. We are the market, and the market is now total. Total is my word. I workshopped it. "Seize" scares people. "Total control of their markets" tests at 71, because nobody pictures a kid from Ohio on a beach when you say markets. We moved 22 tankers through Hormuz at night with the lights off. The President said he wanted to announce it so badly it hurt, and he held it a month anyway. That is the most discipline this administration has ever shown. 100 million barrels, he said. The fact-checkers say the math doesn't hold. They're junior. The number was never the deliverable. The number is the soft launch. He said he loves the inflation. People clutched their receipts. 90 a barrel instead of 250 is the discount, and 250 is a price I invented so this one would feel like mercy. How long will we be there? The President answered that. "We had to be there for a while." A while is my favorite contract length. It renews on its own. No court can find the end date. He wondered out loud whether America has the stomach for it. I don't need the country's stomach. I need its signature on page 1. He called the people who object stupid. I'd defend them. They're not stupid. They just read to page 14. Last thing. Annex C. The out-year targets. 3 more, redacted, marked pre-decisional. I can't tell you which 3. Total control of their markets. Nobody in that room asked which market. The crude still sails east, to China, the same as yesterday. We don't own the oil. We own the valve, the fear, and the word "total." That was always the only product.

Peter Girnus 🦅

22,034 次观看 • 2 个月前

Women are revising their social media boyfriend requirements for 2026. OUT: 🆇 “Must be over 6 feet.” 🆇 “Must make over 6 figures.” 🆇 “Must own his own home.” IN: ✅ “Must like me.” ✅ “Must not be angry all the time.” ✅ “Must not cheat 24/7.” This is (in part) due to the male retreat from the dating space. There are more long-term single men than at any time in at least a century. Single men are more disinterested in dating and women than any time in at least a century. Women, as a result, are getting approached less, going on fewer dates, and running into far fewer men who actually want relationships (and a lot more guys ‘just playing the field’). You’d probably have to go back to the 1850s, with 300,000 men pushing west to seek their riches in California, living in frontier towns with 10-to-1 male-to-female ratios, to find a time with as many single men unfocused on women. (The shift in women’s ‘going rate’ is also due to the real economy declining… just like how in the roaring 1920s, when the economy was great and everything kept going up, women were flappers who delayed marriage and called wedding rings ‘handcuffs’ and ‘manacles’, but after the stock market crashed in 1929, and the Great Depression got underway, the flappers disappeared and the women of the 1930s suddenly became much more interested in getting hitched.) Anyway: If you are one of the dudes who has foresworn women and dating: thanks for your service, brother 🫡 If you are one of the dudes who is out there getting after it, though: you can look forward to a continuation in 2026 of women softening their stances, making themselves MORE available, asking you to jump through fewer hoops than ever, and acting clingier and needier if they think they have a chance with you. Just make sure you are out IN FIELD chatting up women, flirting with women, getting them onto dates, and inviting them home. You just need to be IN the game. A lot of guys aren’t in the game anymore (or they are locked up on screens, trying to do stuff online, trying to get girls by liking their pics, ‘sliding into their DMs’, where the odds are seriously stacked against ordinary men… ESPECIALLY compared to IRL). 2026 is bringing an increasingly WIDE open field with girls. FOR the guys who’ll be out there playing in it, that is!
0:16

Women are revising their social media boyfriend requirements for 2026. OUT: 🆇 “Must be over 6 feet.” 🆇 “Must make over 6 figures.” 🆇 “Must own his own home.” IN: ✅ “Must like me.” ✅ “Must not be angry all the time.” ✅ “Must not cheat 24/7.” This is (in part) due to the male retreat from the dating space. There are more long-term single men than at any time in at least a century. Single men are more disinterested in dating and women than any time in at least a century. Women, as a result, are getting approached less, going on fewer dates, and running into far fewer men who actually want relationships (and a lot more guys ‘just playing the field’). You’d probably have to go back to the 1850s, with 300,000 men pushing west to seek their riches in California, living in frontier towns with 10-to-1 male-to-female ratios, to find a time with as many single men unfocused on women. (The shift in women’s ‘going rate’ is also due to the real economy declining… just like how in the roaring 1920s, when the economy was great and everything kept going up, women were flappers who delayed marriage and called wedding rings ‘handcuffs’ and ‘manacles’, but after the stock market crashed in 1929, and the Great Depression got underway, the flappers disappeared and the women of the 1930s suddenly became much more interested in getting hitched.) Anyway: If you are one of the dudes who has foresworn women and dating: thanks for your service, brother 🫡 If you are one of the dudes who is out there getting after it, though: you can look forward to a continuation in 2026 of women softening their stances, making themselves MORE available, asking you to jump through fewer hoops than ever, and acting clingier and needier if they think they have a chance with you. Just make sure you are out IN FIELD chatting up women, flirting with women, getting them onto dates, and inviting them home. You just need to be IN the game. A lot of guys aren’t in the game anymore (or they are locked up on screens, trying to do stuff online, trying to get girls by liking their pics, ‘sliding into their DMs’, where the odds are seriously stacked against ordinary men… ESPECIALLY compared to IRL). 2026 is bringing an increasingly WIDE open field with girls. FOR the guys who’ll be out there playing in it, that is!

Girls Chase 🏃‍♀️💨

189,518 次观看 • 7 个月前

The thing I help hitters with the most.. (1 good way to train it👇🏼) They start on time but never actually get loaded to GO. They think they’re ready. They’re not. So they start earlier next time. Same problem. —— This is a conversation I have with almost every hitter. When is the right time for you? - Based on your load style - Based on how long your load takes - Based on the pitcher’s velo, windup, or slide step —— If that’s you, try a “loaded at release” type load. Bobby Witt. Judge. Get your hip and elbow loaded early. Then stride with it ready to fire. “The pitcher is up there for 20 seconds telling you to get ready. Shame on you if you’re not ready to swing.” ____ If I have you in the cage, what I’ll do is have you get into a no stride position, get ready to swing and I stand there for 1-4 seconds… Holding my hand out over the plate ready to snap my fingers. When I snap, you swing. Do that 4-5x. You get the quickness, readiness feeling. Then we go back to stride with moving ball and you’re trying to time up that same GO move with the ball With your foot up in the air and the GO will make the foot go down. Not foot down then swing. That typically kills quickness. This works 95% of the time I’d say! —— > Send this to a hitter who needs it > Save it for when you need it.
0:17

The thing I help hitters with the most.. (1 good way to train it👇🏼) They start on time but never actually get loaded to GO. They think they’re ready. They’re not. So they start earlier next time. Same problem. —— This is a conversation I have with almost every hitter. When is the right time for you? - Based on your load style - Based on how long your load takes - Based on the pitcher’s velo, windup, or slide step —— If that’s you, try a “loaded at release” type load. Bobby Witt. Judge. Get your hip and elbow loaded early. Then stride with it ready to fire. “The pitcher is up there for 20 seconds telling you to get ready. Shame on you if you’re not ready to swing.” ____ If I have you in the cage, what I’ll do is have you get into a no stride position, get ready to swing and I stand there for 1-4 seconds… Holding my hand out over the plate ready to snap my fingers. When I snap, you swing. Do that 4-5x. You get the quickness, readiness feeling. Then we go back to stride with moving ball and you’re trying to time up that same GO move with the ball With your foot up in the air and the GO will make the foot go down. Not foot down then swing. That typically kills quickness. This works 95% of the time I’d say! —— > Send this to a hitter who needs it > Save it for when you need it.

Trey Hannam

39,339 次观看 • 2 个月前

Here’s my analysis on NotPixel Airdrop: - Mining phase: it was entertaining to paint on the canvas alongside with many of you! Personally, I had fun drawing 🧡 together with you, just for fun, no competition. For that I will rate it a ✅ - Earn Launchpool: personally, I think this is great that there’s such a tool, that allows holders effortlessly farm tokens. For me it’s a ✅ - Distribution: the major frustrations has been caused by the overload of the smart contract for Airdrop claim, where the biggest frustrations came from the fact that many were not able to claim their tokens for long (Including me) and watched the price go from $0,5 to $0,8 & to $0,2. Personally, I think that if Airdrop claim was done prior to the launch, it may have been better. For that, I will put a ❌ - Launch: NotPixel decided to launch its PX token only on DEX’es on TON. the price at launch actually was at $0,5-$0,8 however, it shortly went down to $0,2 , causing frustration to the community & making them question “who sold?!” & “why no CEX listings?” Here, it’s important to understand the way Sasha & his team decided that they want to build the project. As they mentioned on X “start from the basics, start from 0.” While many dislike this approach, I personally believe that this is how the launches must be happening. Launch on DEX -> generate traction & volume -> go list on CEX’es -> create more news -> grow more from new ppl joining For that I will put ✅ - Team allocation: while many have speculated that “team sold”. this is completely not true. In fact, team has locked their allocation & it can be verified on chain. Which is ✅ for me. - Future plans: NotPixel will have a big canvas on which you will be able to paint, but this time, with some NFT mechanics & PX token integration. For which it is ✅ to me. Burning: NotPixel plan with the new update also includes a mechanism for Burning, which means that PX token will be deflationary. Which is ✅ - ownership revoked: meaning that PX token supply cannot be increased. Nor any modifications could be made to the smart contract at this point. Which is a big ✅ in my opinion - Community: while many are still frustrated with the current token price on the market, many express desire for token appreciation & further growth. One of the interesting feedbacks I saw on the timeline was “NotPixel forced us all to hold”. Which shows that there are ppl who are not willing to get less than their target. Also many express that they want to see PX on CEX’es. For it to grow even more. ✅ - Holders: as with any Airdrop, many have sold their tokens on the market, the volume also came down a bit. However, what’s interesting is that while ~200k ppl sold, the token still fluctuates between the lowest point $0,13 to $0,26. Which shows that there’s a demand & ppl are accumulating more tokens. Currently PX has 205k holders. Which is a ✅ - BuyBacks: the NotPixel team recently announced the buybacks with the money raised from the NotPixel stickers sale. Which created more support for the price on the market. ✅ - Expectations vs reality: Many community members said that they were expecting price to be above $1 as minimum, many were predicting as high as $10. In reality, the price at launch turned out to be much lower than many expected. Leading to the frustration being expressed on the timeline. Which is understandable. ❌ Conclusion: - Mining phase: ✅ - Earn Launchpool: ✅ - Distribution: ❌ - Launch: ✅ - Team Allocation: ✅ - Future plans: ✅ - Burning: ✅ - Ownership revoked: ✅ - Community: ✅ - Holders: ✅ - BuyBacks: ✅ - Expectations vs reality: ❌ This is not bad execution, not many ppl will be able to pull this off in crypto. No one knows the future, but I chose to believe that Sasha & NotPixel team has a plan. Currently they are not listed on any CEX’es, but I think that Sasha & his team could easily get there. Let’s see what the future holds for us 🙌
0:16

Here’s my analysis on NotPixel Airdrop: - Mining phase: it was entertaining to paint on the canvas alongside with many of you! Personally, I had fun drawing 🧡 together with you, just for fun, no competition. For that I will rate it a ✅ - Earn Launchpool: personally, I think this is great that there’s such a tool, that allows holders effortlessly farm tokens. For me it’s a ✅ - Distribution: the major frustrations has been caused by the overload of the smart contract for Airdrop claim, where the biggest frustrations came from the fact that many were not able to claim their tokens for long (Including me) and watched the price go from $0,5 to $0,8 & to $0,2. Personally, I think that if Airdrop claim was done prior to the launch, it may have been better. For that, I will put a ❌ - Launch: NotPixel decided to launch its PX token only on DEX’es on TON. the price at launch actually was at $0,5-$0,8 however, it shortly went down to $0,2 , causing frustration to the community & making them question “who sold?!” & “why no CEX listings?” Here, it’s important to understand the way Sasha & his team decided that they want to build the project. As they mentioned on X “start from the basics, start from 0.” While many dislike this approach, I personally believe that this is how the launches must be happening. Launch on DEX -> generate traction & volume -> go list on CEX’es -> create more news -> grow more from new ppl joining For that I will put ✅ - Team allocation: while many have speculated that “team sold”. this is completely not true. In fact, team has locked their allocation & it can be verified on chain. Which is ✅ for me. - Future plans: NotPixel will have a big canvas on which you will be able to paint, but this time, with some NFT mechanics & PX token integration. For which it is ✅ to me. Burning: NotPixel plan with the new update also includes a mechanism for Burning, which means that PX token will be deflationary. Which is ✅ - ownership revoked: meaning that PX token supply cannot be increased. Nor any modifications could be made to the smart contract at this point. Which is a big ✅ in my opinion - Community: while many are still frustrated with the current token price on the market, many express desire for token appreciation & further growth. One of the interesting feedbacks I saw on the timeline was “NotPixel forced us all to hold”. Which shows that there are ppl who are not willing to get less than their target. Also many express that they want to see PX on CEX’es. For it to grow even more. ✅ - Holders: as with any Airdrop, many have sold their tokens on the market, the volume also came down a bit. However, what’s interesting is that while ~200k ppl sold, the token still fluctuates between the lowest point $0,13 to $0,26. Which shows that there’s a demand & ppl are accumulating more tokens. Currently PX has 205k holders. Which is a ✅ - BuyBacks: the NotPixel team recently announced the buybacks with the money raised from the NotPixel stickers sale. Which created more support for the price on the market. ✅ - Expectations vs reality: Many community members said that they were expecting price to be above $1 as minimum, many were predicting as high as $10. In reality, the price at launch turned out to be much lower than many expected. Leading to the frustration being expressed on the timeline. Which is understandable. ❌ Conclusion: - Mining phase: ✅ - Earn Launchpool: ✅ - Distribution: ❌ - Launch: ✅ - Team Allocation: ✅ - Future plans: ✅ - Burning: ✅ - Ownership revoked: ✅ - Community: ✅ - Holders: ✅ - BuyBacks: ✅ - Expectations vs reality: ❌ This is not bad execution, not many ppl will be able to pull this off in crypto. No one knows the future, but I chose to believe that Sasha & NotPixel team has a plan. Currently they are not listed on any CEX’es, but I think that Sasha & his team could easily get there. Let’s see what the future holds for us 🙌

Viktor 🧡

44,168 次观看 • 1 年前

🦕💬 • 260611 [01:03 AM KST] 41 voice notes transcripted/translated in order: hi how did you like made by riize? you’re asking if i ate dinner? i didn’t eat. i ate lunch kind of late, but i ate a hearty lunch. i also wanted to do a live as i finished work, but while wondering if i should do a live or not, something new to do popped up. and while i was doing it, it got too late. so i'll do a live later. go knicks! lately, the NBA's a hot topic lately. knicks. crazy. i don't know, you know... basketball that well, but i remember growing up they were sort of *the team* that people, yk cheered on. but i don't remember anyone saying that they were doing great, or anything. but apparently they're in the finals rn and they're doing rly well, so also can't forget the world cup. world cup season rn, so... just sports season.. sports season, crazy, yeah? [singing dyd] how is it? riize's challenge. easy, right? do it lots, please~ these days, a lot of short-form [videos] are coming up, as you've probably seen. I thought it might be fun if everyone did that together... what do you think? live... there's a way to turn it on, but even if i turned it on, i'm not sure if i could do it well. i don't have confidence rn so i'm talking like this wa, but there's only 4 days left for our comeback.. only 4 days left... how is it? are you guys looking forward to it? are u guys excited? we're going to ulsan the 15th, and wonbin hyung, said he's buying us lunch... not really. but it's our first time going to ulsan all together. we'll have our comeback there. i think it'll be fun. also, the 15th we're doing a comeback live.. we've been doing it every comeback... and at the live stream we'll be high school students.. but it could honestly be university students too.. or really just do it without a concept, but i think it'll be fun... just... it'll be fun... so, while doing that we'll talk about the comeback whatever happens i want us all to just create fun memories together, and since it’s summer, how about you all enjoy summer together with riize... how does that feel? how's this pfp? i like it ㅎㅎ engddongi, eng, engddon ah, right. also with taro hyung, we'll appear at salon drip... i also think that will be fun when it comes out. the recording was also fun, with doyeon nuna.. please watch it ah... you guys can hear the dryer? i won't tell you who's clothes it's drying, it's a secret~ since you could look at this as something im trying out instead of doing a live stream i won't be able to do it super often or every single day, but I think it wouldn't be bad to use this feature again sometime what do you think? ah, but languages are difficult it's not easy to unify into just one language now, but on the other hand, it's really fun. somehow, this chat room..? is that right? realizing that this place is totally global and international is in a way pretty cool. haha anyway, after a long time i rode a bike again.. there was a lot of sunlight... sunlight.. did lots of photosynthesis but what was a real mistake was first of all the bicycle didnt have a basket, and I didnt wear a comfortable backpack. i came out carrying a somewhat awkward/uncomfortable bag, so i really messed up on top of that the bicycle was incredibly heavy, but then the battery completely died... so I almost cried. without even realizing it I had gone so far away so I suffered a bit. that’s the story i also have the feeling i bruised my butt a bit... ㅎㅎ but that will also become a funny memory, right? how's riizing summer vacation? i think there were really a lot of funny moments... sungchan hyung was so funny... sungchan hyung said somewhere he hasn't eaten ramyeon in a year ㅎㅎ and sungchan hyung honestly isn't the type to eat ramen alone, he's on the not eating it side... but he's always like 'one bite, one bite'... so if we put all that together, wouldn't it be a plate of ramen?ㅎㅎ for me, at the beginning of our debut, it was a bit... you know how while filming content, i ended up grilling meat and all? but now, grilling meat... well, i don't think it's that bad also, back when we went to japan to shoot our album jacket photos and the trailer, we visited a lot of vinyl (LP) shops... and that was actually the first time in my life buying an LP with my own money when i was young i went to la to play, and then there was this famous 'amoeba music' store. i looked around and everything, but honestly back then i really wasn't that much interested in LPs. so i bought things like a lot of stickers and used them to decorate my laptop. somehow, after that time, [it was like] I truly spent time in a vinyl shop? but visiting now that I'm a bit older, it was so much fun there are so many songs I don’t know, and tons of songs that aren't even famous so back then in japan, duran duran is a really famous band, but I bought duran duran's Notorious LP and then I also bought Barry Finnertys,, barrynims NY City LP. what was so surprising was that as soon as I listened to the LP, the sound and quality were so good that I was like, 'wow, what is this?' but when I looked it up on the internet, it only had like 3,000 views on YouTube? I think it was 3,000, so because it was so good, I bought it. and after that yesterday too while wandering around here and there, i happened to walk into a cafe and they had LPs there too so yesterday, I bought one or two ㅎㅎ lp's no mater how you look it they have a vibe... its true that nowadays you can just look up and listen to everything on your phone, but another good thing about it (lps) is that I think it’s really great for finding songs you don't know so when going to the lp shop you get to learn a lot of new songs that you didn't know before. even if you don't go with the sole purpose of buying an LP, it's just... really great for discovering things... should I say? a riize lp would also be good... back then, during boom boom bass, we even went there and did all that stuff with our LP too... how was the video editing? back then when i went to school when doing things like presentations, together with other students we would edit videos.. i did lowkey find that kind of stuff pretty fun though because it was pretty much my first time [doing that] since back during those days... but really i think it was fun like... yeah.. i want to try to do it again when i was young i did draw. i drew for fun but it had been such a long time since i last properly sat down and tried to draw something properly with a pencil.. i think it's been such a long time i'm probably the worst at drawing in my family? ㅎㅎ my dad's good, my mom's also good.. my grandpa also draws reaññu well. but it was fun drawing again, so i also want to try that again... why is there so many things i want to do? really why is it? and above all, i want to work really hard on music so that someday i can show you even more. now, like from the trailer to the full version that was uploaded to our site... those kinds of things are a bit different from just being a song, though.. now everyone, let's try meditating together with me sorry.. anyways.. i have to leave in a couple hours actually, so.. time to hit the hay, yeehaw i can't fall asleep right away ㅎㅎ this.. seems like it might be a bit difficult to interpret/translate.. (yes anton, it is,,,) next time it might be better to just turn on a live stream instead ㅎㅎ sorry wow... sleep is really not coming... [1 video] [1 picture] ok jinja gn
0:01

🦕💬 • 260611 [01:03 AM KST] 41 voice notes transcripted/translated in order: hi how did you like made by riize? you’re asking if i ate dinner? i didn’t eat. i ate lunch kind of late, but i ate a hearty lunch. i also wanted to do a live as i finished work, but while wondering if i should do a live or not, something new to do popped up. and while i was doing it, it got too late. so i'll do a live later. go knicks! lately, the NBA's a hot topic lately. knicks. crazy. i don't know, you know... basketball that well, but i remember growing up they were sort of *the team* that people, yk cheered on. but i don't remember anyone saying that they were doing great, or anything. but apparently they're in the finals rn and they're doing rly well, so also can't forget the world cup. world cup season rn, so... just sports season.. sports season, crazy, yeah? [singing dyd] how is it? riize's challenge. easy, right? do it lots, please~ these days, a lot of short-form [videos] are coming up, as you've probably seen. I thought it might be fun if everyone did that together... what do you think? live... there's a way to turn it on, but even if i turned it on, i'm not sure if i could do it well. i don't have confidence rn so i'm talking like this wa, but there's only 4 days left for our comeback.. only 4 days left... how is it? are you guys looking forward to it? are u guys excited? we're going to ulsan the 15th, and wonbin hyung, said he's buying us lunch... not really. but it's our first time going to ulsan all together. we'll have our comeback there. i think it'll be fun. also, the 15th we're doing a comeback live.. we've been doing it every comeback... and at the live stream we'll be high school students.. but it could honestly be university students too.. or really just do it without a concept, but i think it'll be fun... just... it'll be fun... so, while doing that we'll talk about the comeback whatever happens i want us all to just create fun memories together, and since it’s summer, how about you all enjoy summer together with riize... how does that feel? how's this pfp? i like it ㅎㅎ engddongi, eng, engddon ah, right. also with taro hyung, we'll appear at salon drip... i also think that will be fun when it comes out. the recording was also fun, with doyeon nuna.. please watch it ah... you guys can hear the dryer? i won't tell you who's clothes it's drying, it's a secret~ since you could look at this as something im trying out instead of doing a live stream i won't be able to do it super often or every single day, but I think it wouldn't be bad to use this feature again sometime what do you think? ah, but languages are difficult it's not easy to unify into just one language now, but on the other hand, it's really fun. somehow, this chat room..? is that right? realizing that this place is totally global and international is in a way pretty cool. haha anyway, after a long time i rode a bike again.. there was a lot of sunlight... sunlight.. did lots of photosynthesis but what was a real mistake was first of all the bicycle didnt have a basket, and I didnt wear a comfortable backpack. i came out carrying a somewhat awkward/uncomfortable bag, so i really messed up on top of that the bicycle was incredibly heavy, but then the battery completely died... so I almost cried. without even realizing it I had gone so far away so I suffered a bit. that’s the story i also have the feeling i bruised my butt a bit... ㅎㅎ but that will also become a funny memory, right? how's riizing summer vacation? i think there were really a lot of funny moments... sungchan hyung was so funny... sungchan hyung said somewhere he hasn't eaten ramyeon in a year ㅎㅎ and sungchan hyung honestly isn't the type to eat ramen alone, he's on the not eating it side... but he's always like 'one bite, one bite'... so if we put all that together, wouldn't it be a plate of ramen?ㅎㅎ for me, at the beginning of our debut, it was a bit... you know how while filming content, i ended up grilling meat and all? but now, grilling meat... well, i don't think it's that bad also, back when we went to japan to shoot our album jacket photos and the trailer, we visited a lot of vinyl (LP) shops... and that was actually the first time in my life buying an LP with my own money when i was young i went to la to play, and then there was this famous 'amoeba music' store. i looked around and everything, but honestly back then i really wasn't that much interested in LPs. so i bought things like a lot of stickers and used them to decorate my laptop. somehow, after that time, [it was like] I truly spent time in a vinyl shop? but visiting now that I'm a bit older, it was so much fun there are so many songs I don’t know, and tons of songs that aren't even famous so back then in japan, duran duran is a really famous band, but I bought duran duran's Notorious LP and then I also bought Barry Finnertys,, barrynims NY City LP. what was so surprising was that as soon as I listened to the LP, the sound and quality were so good that I was like, 'wow, what is this?' but when I looked it up on the internet, it only had like 3,000 views on YouTube? I think it was 3,000, so because it was so good, I bought it. and after that yesterday too while wandering around here and there, i happened to walk into a cafe and they had LPs there too so yesterday, I bought one or two ㅎㅎ lp's no mater how you look it they have a vibe... its true that nowadays you can just look up and listen to everything on your phone, but another good thing about it (lps) is that I think it’s really great for finding songs you don't know so when going to the lp shop you get to learn a lot of new songs that you didn't know before. even if you don't go with the sole purpose of buying an LP, it's just... really great for discovering things... should I say? a riize lp would also be good... back then, during boom boom bass, we even went there and did all that stuff with our LP too... how was the video editing? back then when i went to school when doing things like presentations, together with other students we would edit videos.. i did lowkey find that kind of stuff pretty fun though because it was pretty much my first time [doing that] since back during those days... but really i think it was fun like... yeah.. i want to try to do it again when i was young i did draw. i drew for fun but it had been such a long time since i last properly sat down and tried to draw something properly with a pencil.. i think it's been such a long time i'm probably the worst at drawing in my family? ㅎㅎ my dad's good, my mom's also good.. my grandpa also draws reaññu well. but it was fun drawing again, so i also want to try that again... why is there so many things i want to do? really why is it? and above all, i want to work really hard on music so that someday i can show you even more. now, like from the trailer to the full version that was uploaded to our site... those kinds of things are a bit different from just being a song, though.. now everyone, let's try meditating together with me sorry.. anyways.. i have to leave in a couple hours actually, so.. time to hit the hay, yeehaw i can't fall asleep right away ㅎㅎ this.. seems like it might be a bit difficult to interpret/translate.. (yes anton, it is,,,) next time it might be better to just turn on a live stream instead ㅎㅎ sorry wow... sleep is really not coming... [1 video] [1 picture] ok jinja gn

🧼

37,315 次观看 • 2 个月前

Update on the STONES Collection. 1. Staking is approaching! We have been working on two staking approaches: one entirely on-chain with FluidTokens on Bitcoin, and the other with the talented specifically developed for the BLOCK and STONES collections! Very soon, you’ll be able to stake your stones to earn even more LORD.OF.THE.STONES. The staking launch is scheduled for the beginning of next week. 2. Airdrop of a Stone for All BLOCK Holders: We are working around the clock for this to happen as earlier as possible but it may take a couple more days to set up this global airdrop so that it can be executed all at once. 3. Updated Visuals on Magic Eden: The visuals for the STONES on Magic Eden are currently being updated to load quickly. Soon, it will also be possible to view the stone’s native inscription in 3D directly on Magic Eden’s viewer. 4. I will be hosting / co-hosting one Space per week on Twitter and one on the collection’s Discord to stay in touch with the community and provide live updates on the project’s progress. 5. Rune Token “Lord.Of.The.Stones” Airdrop: The date for the Rune Token airdrop will also be revealed at the beginning of next week. The more stones you have at the time of the airdrop, the more tokens you will receive. Please visit ➜ for details and further information. STONES ARE FOREVER.
0:18

Update on the STONES Collection. 1. Staking is approaching! We have been working on two staking approaches: one entirely on-chain with FluidTokens on Bitcoin, and the other with the talented specifically developed for the BLOCK and STONES collections! Very soon, you’ll be able to stake your stones to earn even more LORD.OF.THE.STONES. The staking launch is scheduled for the beginning of next week. 2. Airdrop of a Stone for All BLOCK Holders: We are working around the clock for this to happen as earlier as possible but it may take a couple more days to set up this global airdrop so that it can be executed all at once. 3. Updated Visuals on Magic Eden: The visuals for the STONES on Magic Eden are currently being updated to load quickly. Soon, it will also be possible to view the stone’s native inscription in 3D directly on Magic Eden’s viewer. 4. I will be hosting / co-hosting one Space per week on Twitter and one on the collection’s Discord to stay in touch with the community and provide live updates on the project’s progress. 5. Rune Token “Lord.Of.The.Stones” Airdrop: The date for the Rune Token airdrop will also be revealed at the beginning of next week. The more stones you have at the time of the airdrop, the more tokens you will receive. Please visit ➜ for details and further information. STONES ARE FOREVER.

CL Art Studio

17,993 次观看 • 1 年前

If you thought the Gemma 4 31B (dense) model was fast, sit down. I just benched the updated Gemma 4 26B A4B MoE on a single RTX 4090 (24 GB VRAM) 9,200 t/s prefill. 160 t/s decode. 250,000 context window. All on a single consumer RTX 4090. The numbers are completely unhinged. The 31B is a dense behemoth. But the 26B is a Mixture of Experts (MoE), specifically an Active 4 Billion (A4B). It holds 26B parameters of knowledge but only activates 4B per token. Because its inference memory footprint is so light, I didn’t even need KV cache quantization to hit a quarter million context. Compiled the latest llama.cpp from source on Ubuntu 22 (CUDA 13). Fed it a 28k token prompt, and manually cranked the batch sizes (-b 2048 -ub 2048) to absolutely redline the Tensor Cores. Here is the benchmarking breakdown: # 1. The Baseline (No MTP) Even without speculative decoding, the A4B architecture flies. llama.cpp flags: ./build/bin/llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 250000 -ngl 99 -fa on -b 2048 -ub 2048 --port 8080 -v Context Ceiling: 250,000 tokens (21.5 GB VRAM) Prefill: 9,200 t/s (Absurd) Decode: 124 t/s # 2. The MTP Overdrive Injected the new MTP draft model to enable Speculative Decoding. llama.cpp flags: ./build/bin/llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --spec-type draft-mtp --spec-draft-model mtp-gemma-4-26B-A4B-it.gguf --spec-draft-n-max 4 --spec-draft-p-min 0.7 -c 250000 -ngl 99 -fa on -b 2048 -ub 2048 --port 8080 -v Context Ceiling: 250,000 tokens (22.96 GB VRAM) Prefill: 7,054 t/s (MTP draft overhead slightly caps prefill) Decode: 156 t/s # The Agentic Architecture Insight Why does this matter? Because you can now build a killer local agentic loop on a consumer desktop. Use the 31B dense model (from the previous post) as your heavy, deliberate Orchestrator / Verifier / Planner. Pass the actual execution tasks to this 26B MoE. At 160 t/s, this MoE can chew through code generation, tool calling, and massive RAG document retrieval over a 250k context window almost instantly, drastically speeding up your agentic loop. If you own a single RTX 3090 or 4090 and haven't tried this specific stack yet, you need to pull these latest updates and run it. Local inference just leveled up. Hugging Face links to the Unsloth 26B QAT quants and MTP drafters are in the replies. performance graphs also available in the replies.
0:17

If you thought the Gemma 4 31B (dense) model was fast, sit down. I just benched the updated Gemma 4 26B A4B MoE on a single RTX 4090 (24 GB VRAM) 9,200 t/s prefill. 160 t/s decode. 250,000 context window. All on a single consumer RTX 4090. The numbers are completely unhinged. The 31B is a dense behemoth. But the 26B is a Mixture of Experts (MoE), specifically an Active 4 Billion (A4B). It holds 26B parameters of knowledge but only activates 4B per token. Because its inference memory footprint is so light, I didn’t even need KV cache quantization to hit a quarter million context. Compiled the latest llama.cpp from source on Ubuntu 22 (CUDA 13). Fed it a 28k token prompt, and manually cranked the batch sizes (-b 2048 -ub 2048) to absolutely redline the Tensor Cores. Here is the benchmarking breakdown: # 1. The Baseline (No MTP) Even without speculative decoding, the A4B architecture flies. llama.cpp flags: ./build/bin/llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 250000 -ngl 99 -fa on -b 2048 -ub 2048 --port 8080 -v Context Ceiling: 250,000 tokens (21.5 GB VRAM) Prefill: 9,200 t/s (Absurd) Decode: 124 t/s # 2. The MTP Overdrive Injected the new MTP draft model to enable Speculative Decoding. llama.cpp flags: ./build/bin/llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --spec-type draft-mtp --spec-draft-model mtp-gemma-4-26B-A4B-it.gguf --spec-draft-n-max 4 --spec-draft-p-min 0.7 -c 250000 -ngl 99 -fa on -b 2048 -ub 2048 --port 8080 -v Context Ceiling: 250,000 tokens (22.96 GB VRAM) Prefill: 7,054 t/s (MTP draft overhead slightly caps prefill) Decode: 156 t/s # The Agentic Architecture Insight Why does this matter? Because you can now build a killer local agentic loop on a consumer desktop. Use the 31B dense model (from the previous post) as your heavy, deliberate Orchestrator / Verifier / Planner. Pass the actual execution tasks to this 26B MoE. At 160 t/s, this MoE can chew through code generation, tool calling, and massive RAG document retrieval over a 250k context window almost instantly, drastically speeding up your agentic loop. If you own a single RTX 3090 or 4090 and haven't tried this specific stack yet, you need to pull these latest updates and run it. Local inference just leveled up. Hugging Face links to the Unsloth 26B QAT quants and MTP drafters are in the replies. performance graphs also available in the replies.

Alok

40,993 次观看 • 18 天前

somebody explain this because i refuse to accept it someone ran 48 scored trials and one agent beat a whole fleet of them on all 6 task families, at 0.93 cents a run against 1.9, while openai's best fleet shape was paying $0.008 for every single point of accuracy it bought i read it expecting a hit piece and found the opposite: the fleets that partitioned the dependency graph properly lifted pass rate 14% and cut wall-clock 2.10x on the same tasks, and one of them beat claude code with agent teams the thing that decides it has a name, Graph Engineering, and it is a property of the diagram rather than the model: - partition on the real dependency graph pulled from static analysis, never by folder or by file, because the gains land hardest on the most dependency-dense projects - isolate the structural hub files first, since those are the nodes every partition would otherwise have to share - measure the critical path and treat it as the floor, because a chain that genuinely feeds itself cannot be replaced by more workers and wrapping it in a scheduler does not shorten it - match the topology to the coupling instead of defaulting to parallel: on coupled work a static parallel shape drops below a single agent, so the mismatch is worse than no orchestration - remember each worker serialises its own subtasks, which adds edges inside every agent that were never in your plan - budget the fan-out before you fire it, because three agents already burn roughly three times the tokens and the multiplier compounds across sessions - check worker count against your rate limit, since fifteen workers at ten requests a second walk straight through a hundred-per-second ceiling and cascade - put a script gate in front of the planner: it costs 0.15 seconds and zero tokens, and it lets the expensive model skip 43 to 63% of the steps for at most 1.4 points of accuracy the catch is the coordination tax, and it scales with how clever the shape looks: 58% extra reasoning turns for independent workers, 263% decentralised, 285% centralised, and 515% for the hybrid setup everyone reaches for first the same paper found that hybrid then collapses hardest on tool-heavy work at a 0.452 success rate, while the plainer decentralised shape beat centralised outright despite carrying more overhead, because parallel efficiency is what survives bookmark this, the whole build sits in the article ↓
0:15

somebody explain this because i refuse to accept it someone ran 48 scored trials and one agent beat a whole fleet of them on all 6 task families, at 0.93 cents a run against 1.9, while openai's best fleet shape was paying $0.008 for every single point of accuracy it bought i read it expecting a hit piece and found the opposite: the fleets that partitioned the dependency graph properly lifted pass rate 14% and cut wall-clock 2.10x on the same tasks, and one of them beat claude code with agent teams the thing that decides it has a name, Graph Engineering, and it is a property of the diagram rather than the model: - partition on the real dependency graph pulled from static analysis, never by folder or by file, because the gains land hardest on the most dependency-dense projects - isolate the structural hub files first, since those are the nodes every partition would otherwise have to share - measure the critical path and treat it as the floor, because a chain that genuinely feeds itself cannot be replaced by more workers and wrapping it in a scheduler does not shorten it - match the topology to the coupling instead of defaulting to parallel: on coupled work a static parallel shape drops below a single agent, so the mismatch is worse than no orchestration - remember each worker serialises its own subtasks, which adds edges inside every agent that were never in your plan - budget the fan-out before you fire it, because three agents already burn roughly three times the tokens and the multiplier compounds across sessions - check worker count against your rate limit, since fifteen workers at ten requests a second walk straight through a hundred-per-second ceiling and cascade - put a script gate in front of the planner: it costs 0.15 seconds and zero tokens, and it lets the expensive model skip 43 to 63% of the steps for at most 1.4 points of accuracy the catch is the coordination tax, and it scales with how clever the shape looks: 58% extra reasoning turns for independent workers, 263% decentralised, 285% centralised, and 515% for the hybrid setup everyone reaches for first the same paper found that hybrid then collapses hardest on tool-heavy work at a 0.452 success rate, while the plainer decentralised shape beat centralised outright despite carrying more overhead, because parallel efficiency is what survives bookmark this, the whole build sits in the article ↓

Argona

32,433 次观看 • 15 天前

$FEET🦶 by Feet Pix 🦶📸 weekly update: Airdrop for NFT holders coming soon 🦶 New CEX listing coming this week 🛒 Halving for stakers coming in 7 days 🥩 FEET is the perfect mix of $PEPE, $ETH, and $BTC 🧠 Highlights 🔦 Last Friday, the FP of #feetpixwtf doubled up from 0.05 to 0.12 📈, the best performance of the week for an NFT collection 🏆 The team is keeping its promise of using the creator fees to buy back and burn the NFTs, and we are approaching our 1:1 airdrop! 🎁 It will be unlocked once the supply goes down to 8,300; we are 87 NFTs away! It's impressive—it was 10,000 on February 14th! 🤯 The community is expanding, with Chinese influencers and worldwide celebrities tweeting about $FEET 🦶🌍 The Twitter account is about to reach 20K followers! 🫂 I think there will be many chances for feet to become mainstream during the summer (everyone takes pics of themselves on a beautiful beach) 👙⛱️ The price is stable with over 1M market cap, so we have great chances to go parabolic once the volume kicks in! 🎢 The team has no team tokens, and the ownership is renounced, so there will be no selling pressure from the devs.🦄 It's just a matter of time! The rise, as I've always said, is #inevitable!! 🚀 Feet fam, let’s make some noise! 💥 Like, RT this long tweet, and for those who FOLLOW me and read the tweet until here, I will do a giveaway of 50M $FEET to a lucky feet fan ❤️🦶🍀 #FEET #CRYPTO
0:11

$FEET🦶 by Feet Pix 🦶📸 weekly update: Airdrop for NFT holders coming soon 🦶 New CEX listing coming this week 🛒 Halving for stakers coming in 7 days 🥩 FEET is the perfect mix of $PEPE, $ETH, and $BTC 🧠 Highlights 🔦 Last Friday, the FP of #feetpixwtf doubled up from 0.05 to 0.12 📈, the best performance of the week for an NFT collection 🏆 The team is keeping its promise of using the creator fees to buy back and burn the NFTs, and we are approaching our 1:1 airdrop! 🎁 It will be unlocked once the supply goes down to 8,300; we are 87 NFTs away! It's impressive—it was 10,000 on February 14th! 🤯 The community is expanding, with Chinese influencers and worldwide celebrities tweeting about $FEET 🦶🌍 The Twitter account is about to reach 20K followers! 🫂 I think there will be many chances for feet to become mainstream during the summer (everyone takes pics of themselves on a beautiful beach) 👙⛱️ The price is stable with over 1M market cap, so we have great chances to go parabolic once the volume kicks in! 🎢 The team has no team tokens, and the ownership is renounced, so there will be no selling pressure from the devs.🦄 It's just a matter of time! The rise, as I've always said, is #inevitable!! 🚀 Feet fam, let’s make some noise! 💥 Like, RT this long tweet, and for those who FOLLOW me and read the tweet until here, I will do a giveaway of 50M $FEET to a lucky feet fan ❤️🦶🍀 #FEET #CRYPTO

Belen Franchese

166,097 次观看 • 3 年前