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Compared Qwen3.6 35B and 27B in the same conditions with Google TurboQuant Device: MacBook Pro M5Max 64GB RAM Outputs characteristics: Qwen3.6 35B: 6672 tokens, 2m 10s, 65 tok/s Qwen3.6 27B: 7344 tokens, 5m 22s, 24 tok/s Conclusion: Both models were asked to draw waves using HTML, 35B responded quickly... show more

atomic.chat

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55,540 views • 2 months ago •via X (Twitter)

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🥊 Qwen3.6 35B A3B vs Qwen3.6 27B Made them fight on the same prompt 🌊 Ocean Waves, a canvas challenge 35B took 39.7s (at 142tok/s) 27B took 111.6s (at 50tok/s) Both models are really good. The fact that the 35B produced such a strong result in under 40 seconds is seriously impressive. But when you look at 27B's output... it's actually so much better Added clouds, beautiful foam particles and splashing effects, a perfectly straight horizon, more realistic blinking stars, and a nice sun reflection on the water 🏆 Qwen3.6 27B slower but the result slaps Absolutely amazing that both models run smoothly on 16GB VRAM. Used Unsloth AI GGUFs here, Q3_K_S quant to make it fit in the hardware 🔥

🥊 Qwen3.6 35B A3B vs Qwen3.6 27B Made them fight on the same prompt 🌊 Ocean Waves, a canvas challenge 35B took 39.7s (at 142tok/s) 27B took 111.6s (at 50tok/s) Both models are really good. The fact that the 35B produced such a strong result in under 40 seconds is seriously impressive. But when you look at 27B's output... it's actually so much better Added clouds, beautiful foam particles and splashing effects, a perfectly straight horizon, more realistic blinking stars, and a nice sun reflection on the water 🏆 Qwen3.6 27B slower but the result slaps Absolutely amazing that both models run smoothly on 16GB VRAM. Used Unsloth AI GGUFs here, Q3_K_S quant to make it fit in the hardware 🔥

left curve dev

60,705 views • 2 months ago

MTP speedup Qwen by 2.5x in Atomic Chat Dense vs MoE models on 2x RTX 5090 Qwen3.6 27B: 51 → 117 tps +137% Qwen3.6 35B-A3B: 218 → 267 tps +25% MTP drafts several tokens ahead and verifies them in one pass. The speedup depends on memory moved per pass. Dense 27B reads all 27B params per token, MoE 35B-A3B only reads 3B active. Dense had way more to save by batching. The baseline tps also differ (218 vs 51) for the same reason from the other side. Token generation is memory-bandwidth bound, and MoE moves ~8x less memory per token, so its baseline is already 4x ahead. ~80% draft acceptance. Zero accuracy loss. ~1 GB extra VRAM. Open-source code and local AI app – in the comments 👇

MTP speedup Qwen by 2.5x in Atomic Chat Dense vs MoE models on 2x RTX 5090 Qwen3.6 27B: 51 → 117 tps +137% Qwen3.6 35B-A3B: 218 → 267 tps +25% MTP drafts several tokens ahead and verifies them in one pass. The speedup depends on memory moved per pass. Dense 27B reads all 27B params per token, MoE 35B-A3B only reads 3B active. Dense had way more to save by batching. The baseline tps also differ (218 vs 51) for the same reason from the other side. Token generation is memory-bandwidth bound, and MoE moves ~8x less memory per token, so its baseline is already 4x ahead. ~80% draft acceptance. Zero accuracy loss. ~1 GB extra VRAM. Open-source code and local AI app – in the comments 👇

atomic.chat

170,338 views • 1 month ago

/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.

/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 views • 2 months ago

Which local models can actually handle tool calling? I built a framework to find out. 15 scenarios. 12 tools. Mocked responses. Temperature 0. No cherry-picking. Tested every Qwen3.5 size from 0.8B to 397B, and since some of you asked after the distillation tests: yes, I included Jackrong's Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled too. Only two models went all green: the 27B dense and the distilled 27B. The 397B? Failed two tests. The 122B? Failed one. The 35B? Failed two. The timed-out results — mostly on the smaller models, are cases where the model got stuck in a loop, repeating the same tool call until it hit the 30-second limit. The test that exposed the most models: "Search for Iceland's population, then calculate 2% of it." Simple, but 35B, 122B, and 397B all used a rounded number from memory instead of the actual search result. They didn't trust their own tool output. Small models hallucinate data. Big models ignore data. The 27B just threaded it through.

Which local models can actually handle tool calling? I built a framework to find out. 15 scenarios. 12 tools. Mocked responses. Temperature 0. No cherry-picking. Tested every Qwen3.5 size from 0.8B to 397B, and since some of you asked after the distillation tests: yes, I included Jackrong's Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled too. Only two models went all green: the 27B dense and the distilled 27B. The 397B? Failed two tests. The 122B? Failed one. The 35B? Failed two. The timed-out results — mostly on the smaller models, are cases where the model got stuck in a loop, repeating the same tool call until it hit the 30-second limit. The test that exposed the most models: "Search for Iceland's population, then calculate 2% of it." Simple, but 35B, 122B, and 397B all used a rounded number from memory instead of the actual search result. They didn't trust their own tool output. Small models hallucinate data. Big models ignore data. The 27B just threaded it through.

stevibe

428,772 views • 3 months ago

HOLY MOLY running a 35B model locally on a MacBook shouldn’t be THIS FAST 🤯 Spent my weekend in atomic.chat testing Qwen 35B vs. Qwen 27B on my local machine. I had them generate a fully animated HTML/Canvas car mini-game (demo below), ... and both models breezed through the physics and parallax scrolling without a hitch! The secret sauce here is the Atomic Chat app. Because it's perfectly optimized for Mac and uses Google's new TurboQuant under the hood, you can run heavy open-source models flawlessly while keeping top-tier output quality 👊 Other perks: → ZERO setup required → Access 1,000+ models completely free → 100% offline and private → Zero API limits ... and MUCH more! I dropped the prompt I used in the 🧵↓ Spin it up locally and let me know what you get!

HOLY MOLY running a 35B model locally on a MacBook shouldn’t be THIS FAST 🤯 Spent my weekend in atomic.chat testing Qwen 35B vs. Qwen 27B on my local machine. I had them generate a fully animated HTML/Canvas car mini-game (demo below), ... and both models breezed through the physics and parallax scrolling without a hitch! The secret sauce here is the Atomic Chat app. Because it's perfectly optimized for Mac and uses Google's new TurboQuant under the hood, you can run heavy open-source models flawlessly while keeping top-tier output quality 👊 Other perks: → ZERO setup required → Access 1,000+ models completely free → 100% offline and private → Zero API limits ... and MUCH more! I dropped the prompt I used in the 🧵↓ Spin it up locally and let me know what you get!

Charly Wargnier

100,007 views • 2 months ago

Liquid's LFM2.5-8B-A1B smashed OpenAI's gpt-oss-20b on tool calling We ran both locally on a MacBook Pro M5 Max, 64GB, and gave each the same trip-planning request that only completes if the model fires all 7 tool calls - weather for 3 cities, two currency conversions, an email and a reminder Outputs: LFM2.5-8B-A1B: 4.8 GB RAM usage, 7/7 tool-calls, 266 tok/s, 6.9s OpenAI gpt-oss-20b: 11 GB RAM usage, 3/7 tool-calls, 146 tok/s, 15.0s The 8B used less than half the RAM and still fired all 7 calls, while the 20B silently dropped more than half of its own. It also ran ~2x faster, wrapping the full agentic request in 6.9s against 15s. That's what 38T training tokens buy: a 1B-active MoE that nails the agentic tool calls a model 2.5x its active size keeps dropping

Liquid's LFM2.5-8B-A1B smashed OpenAI's gpt-oss-20b on tool calling We ran both locally on a MacBook Pro M5 Max, 64GB, and gave each the same trip-planning request that only completes if the model fires all 7 tool calls - weather for 3 cities, two currency conversions, an email and a reminder Outputs: LFM2.5-8B-A1B: 4.8 GB RAM usage, 7/7 tool-calls, 266 tok/s, 6.9s OpenAI gpt-oss-20b: 11 GB RAM usage, 3/7 tool-calls, 146 tok/s, 15.0s The 8B used less than half the RAM and still fired all 7 calls, while the 20B silently dropped more than half of its own. It also ran ~2x faster, wrapping the full agentic request in 6.9s against 15s. That's what 38T training tokens buy: a 1B-active MoE that nails the agentic tool calls a model 2.5x its active size keeps dropping

atomic.chat

90,063 views • 1 month ago

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! 🚀

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⚡

56,314 views • 18 days ago

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.

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 views • 6 months ago

Opus 4.6 vs GPT-5.4 (4/9) prompt: Build a production-quality 3D flight-tracking web app using React + Vite + Three.js (react-three-fiber + drei) that visualizes live OpenSky aircraft data on a rotatable 3D Earth, with real-time plane motion, smooth interpolation, altitude-accurate positioning, and polished lighting/post-processing. Both models did really well on this one and honestly I’m impressed with both. GPT-5.4 had the nicer post-processing out of the box. I really liked the subtle light shimmer on the airplanes when rotating the planet, and the camera work when clicking a plane felt better overall. Opus 4.6 though had a few details I liked more. It automatically went and found a much nicer Earth texture on GitHub, while with GPT-5.4 I had to reprompt it to go look for a better one. I also preferred Opus’s plane model overall, it just looked more polished, whereas GPT-5.4’s plane asset looked a bit funny. One thing I noticed with GPT-5.4 is that when you click into the plane, the camera sometimes clips through the planet, which breaks the effect a bit. Opus handled that part more cleanly. Overall this felt like a strong result from both, just with different strengths. GPT-5.4 felt better on presentation and post-processing, while Opus had better asset choices and a more premium-looking Earth/plane combo.

Opus 4.6 vs GPT-5.4 (4/9) prompt: Build a production-quality 3D flight-tracking web app using React + Vite + Three.js (react-three-fiber + drei) that visualizes live OpenSky aircraft data on a rotatable 3D Earth, with real-time plane motion, smooth interpolation, altitude-accurate positioning, and polished lighting/post-processing. Both models did really well on this one and honestly I’m impressed with both. GPT-5.4 had the nicer post-processing out of the box. I really liked the subtle light shimmer on the airplanes when rotating the planet, and the camera work when clicking a plane felt better overall. Opus 4.6 though had a few details I liked more. It automatically went and found a much nicer Earth texture on GitHub, while with GPT-5.4 I had to reprompt it to go look for a better one. I also preferred Opus’s plane model overall, it just looked more polished, whereas GPT-5.4’s plane asset looked a bit funny. One thing I noticed with GPT-5.4 is that when you click into the plane, the camera sometimes clips through the planet, which breaks the effect a bit. Opus handled that part more cleanly. Overall this felt like a strong result from both, just with different strengths. GPT-5.4 felt better on presentation and post-processing, while Opus had better asset choices and a more premium-looking Earth/plane combo.

Dev Ed

279,565 views • 4 months ago

You probably think you need to do more cardio. Longer workouts. More miles. More suffering. But what if you just made the walk you're already taking a little more challenging? I've been a big fan of weighted vests for years because they're simple. You put one on, go for a walk, and suddenly your body has to do more work without you spending any more time exercising. The data is pretty impressive and one of the biggest mistakes I see people make is thinking health has to be extreme to be effective. Most of the time, the answer is taking something you're already doing and making it a little better. A weighted vest is a great example of that. Just walk.

You probably think you need to do more cardio. Longer workouts. More miles. More suffering. But what if you just made the walk you're already taking a little more challenging? I've been a big fan of weighted vests for years because they're simple. You put one on, go for a walk, and suddenly your body has to do more work without you spending any more time exercising. The data is pretty impressive and one of the biggest mistakes I see people make is thinking health has to be extreme to be effective. Most of the time, the answer is taking something you're already doing and making it a little better. A weighted vest is a great example of that. Just walk.

Gary Brecka

30,044 views • 1 month ago

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

elvis

36,789 views • 1 month ago

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 views • 2 months ago

🚀Just launched: Amazon Q, the most capable GenAI-powered assistant is generally available today: Customers are using Q to transform how their teams get work done. When employees chat with Amazon Q, it provides immediate, relevant information and advice to help streamline tasks, speedup decision-making, and help spark creativity and innovation at work. . Early indications signal Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new features we’re planning on introducing in the future, we think this will only continue to grow. 🟠 Amazon Q Developer allows developers to spend more time coding and less time on maintenance and performing other tedious, repetitive tasks. Q assists developers and IT professionals (IT pros) with all of their tasks—from coding, testing, and upgrading applications, to troubleshooting, performing security scanning and fixes, and optimizing AWS resources. Q also comes with Q Developer Agents which can autonomously perform range of tasks and we expect it to be the state of the art accuracy in benchmarks like SWE-Bench. 🟠 Amazon Q Business empowers employees to be more data-driven, and helps customers make better, faster decisions using company knowledge and data. Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in enterprise systems 🟠 Amazon Q Apps, a new and powerful capability of Amazon Q Business, enables employees to use natural language to quickly and securely build their own generative AI applications to automate daily tasks without requiring any prior coding experience. Employees simply describe the type of app they want, in natural language, and Q Apps will quickly generate an app that accomplishes their desired task, helping them streamline and automate their daily work with ease and efficiency.

🚀Just launched: Amazon Q, the most capable GenAI-powered assistant is generally available today: Customers are using Q to transform how their teams get work done. When employees chat with Amazon Q, it provides immediate, relevant information and advice to help streamline tasks, speedup decision-making, and help spark creativity and innovation at work. . Early indications signal Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new features we’re planning on introducing in the future, we think this will only continue to grow. 🟠 Amazon Q Developer allows developers to spend more time coding and less time on maintenance and performing other tedious, repetitive tasks. Q assists developers and IT professionals (IT pros) with all of their tasks—from coding, testing, and upgrading applications, to troubleshooting, performing security scanning and fixes, and optimizing AWS resources. Q also comes with Q Developer Agents which can autonomously perform range of tasks and we expect it to be the state of the art accuracy in benchmarks like SWE-Bench. 🟠 Amazon Q Business empowers employees to be more data-driven, and helps customers make better, faster decisions using company knowledge and data. Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in enterprise systems 🟠 Amazon Q Apps, a new and powerful capability of Amazon Q Business, enables employees to use natural language to quickly and securely build their own generative AI applications to automate daily tasks without requiring any prior coding experience. Employees simply describe the type of app they want, in natural language, and Q Apps will quickly generate an app that accomplishes their desired task, helping them streamline and automate their daily work with ease and efficiency.

Swami Sivasubramanian

25,216 views • 2 years ago

The future of footwear may not be manufactured in bulk. It may be fabricated around you. That is what makes this shift so interesting to me. 3D-printed footwear is moving from novelty to a real industrial model, with market forecasts pointing to rapid growth over the next decade. At the same time, brands and manufacturers are using additive manufacturing, digital design, and custom-fit workflows to shorten development cycles and make more personalized products viable. What is new here is not just the printer. It is the system around it: → scan the foot → model the fit digitally → print the part on demand → produce closer to the customer That matters. Because once footwear becomes data-driven and locally fabricated, several things change fast: → fit gets more personal → prototyping gets faster → waste drops because you do not overproduce → inventory pressure falls because you do not need to guess demand the same way To me, that is the bigger signal. This is not just about a better sneaker. It is about a different manufacturing logic. Formlabs notes that 3D printing already enables customized orthotics with better biomechanical precision, lower material waste, and simpler digital workflows. McKinsey has also pointed to digitization and 3D design as a way to shorten design cycles and reduce sampling iterations in apparel and footwear. And once that logic matures, the use cases get much bigger: → custom athletic footwear built from gait and pressure data → hospitals producing orthotics faster and closer to the patient → micro-factories making products on demand instead of stocking shelves → footwear designed for one body, not an average body That is why I think this matters now. The question is no longer whether personalized fabrication is possible. It is whether brands move fast enough before customers start expecting every product to fit like it was made only for them. Would you actually wear a shoe fabricated around your own biometric data? #AI #3DPrinting #Footwear #Manufacturing #Innovation #FutureOfWork #RetailTech #Customization #Technology

The future of footwear may not be manufactured in bulk. It may be fabricated around you. That is what makes this shift so interesting to me. 3D-printed footwear is moving from novelty to a real industrial model, with market forecasts pointing to rapid growth over the next decade. At the same time, brands and manufacturers are using additive manufacturing, digital design, and custom-fit workflows to shorten development cycles and make more personalized products viable. What is new here is not just the printer. It is the system around it: → scan the foot → model the fit digitally → print the part on demand → produce closer to the customer That matters. Because once footwear becomes data-driven and locally fabricated, several things change fast: → fit gets more personal → prototyping gets faster → waste drops because you do not overproduce → inventory pressure falls because you do not need to guess demand the same way To me, that is the bigger signal. This is not just about a better sneaker. It is about a different manufacturing logic. Formlabs notes that 3D printing already enables customized orthotics with better biomechanical precision, lower material waste, and simpler digital workflows. McKinsey has also pointed to digitization and 3D design as a way to shorten design cycles and reduce sampling iterations in apparel and footwear. And once that logic matures, the use cases get much bigger: → custom athletic footwear built from gait and pressure data → hospitals producing orthotics faster and closer to the patient → micro-factories making products on demand instead of stocking shelves → footwear designed for one body, not an average body That is why I think this matters now. The question is no longer whether personalized fabrication is possible. It is whether brands move fast enough before customers start expecting every product to fit like it was made only for them. Would you actually wear a shoe fabricated around your own biometric data? #AI #3DPrinting #Footwear #Manufacturing #Innovation #FutureOfWork #RetailTech #Customization #Technology

Pascal Bornet

47,443 views • 2 months ago

Europe is quietly becoming what the United States once promised the world. More and more people are looking at their best years ahead and choosing a place where everyday life is designed to work. Where the future feels stable enough to plan for. Where safety is not a luxury product. Where you can build a good life without gambling your health, your family, or your dignity on one bad month. In much of Europe, the “dream” is not about becoming a billionaire. It is about becoming unafraid. It is the freedom of walking home at night without scanning every shadow. The comfort of knowing that if you get sick, you do not need to calculate whether you can afford to be treated. The relief of having a society that still believes children should carry backpacks, not trauma, and definitely not weapons. The calm of streets built for human beings, not just cars. The ability to take a holiday without feeling like you are committing career suicide. The basic decency of labor protections that assume you are a person first and a resource second. And then there is the part people underestimate until they live it: the texture of life. The cities are older and more beautiful than you expect. The distances are smaller. Weekends are real. Food is real. Public spaces are not just decorative, they are functional. Parks are full. Cafes are full. Trains take you somewhere, often across borders, without turning travel into a stress test. You can live in one country, work with another, and visit a third like it is normal because, in many places, it is. The European dream is also a quiet confidence in the social contract. That if you contribute, the system does not abandon you. That you can raise a family without feeling like you are one accident away from ruin. That “getting ahead” does not require burning out. That a good society is one where normal people can live normal lives and still feel proud of them. This is why more and more Americans are not just visiting Europe, but staying. Some come for studies and never leave. Some arrive for a job and realise the lifestyle is the real promotion. Some originally planned a one year experiment and then cannot imagine going back to a place where stress is treated as a personality trait and insecurity is marketed as freedom. Europe is not perfect. It has bureaucracy. It has politics. It has problems that deserve criticism. But in many European countries, life is still built around a simple idea: society should reduce fear, not monetise it. That is the new dream. And people can feel it the moment they arrive. If you could choose one thing to trade for a better life, what would it be: more income, or more security? And what do you think your country would have to change for people to stop leaving, and start staying? Stay connected, Follow Gandalv Gandalv

Europe is quietly becoming what the United States once promised the world. More and more people are looking at their best years ahead and choosing a place where everyday life is designed to work. Where the future feels stable enough to plan for. Where safety is not a luxury product. Where you can build a good life without gambling your health, your family, or your dignity on one bad month. In much of Europe, the “dream” is not about becoming a billionaire. It is about becoming unafraid. It is the freedom of walking home at night without scanning every shadow. The comfort of knowing that if you get sick, you do not need to calculate whether you can afford to be treated. The relief of having a society that still believes children should carry backpacks, not trauma, and definitely not weapons. The calm of streets built for human beings, not just cars. The ability to take a holiday without feeling like you are committing career suicide. The basic decency of labor protections that assume you are a person first and a resource second. And then there is the part people underestimate until they live it: the texture of life. The cities are older and more beautiful than you expect. The distances are smaller. Weekends are real. Food is real. Public spaces are not just decorative, they are functional. Parks are full. Cafes are full. Trains take you somewhere, often across borders, without turning travel into a stress test. You can live in one country, work with another, and visit a third like it is normal because, in many places, it is. The European dream is also a quiet confidence in the social contract. That if you contribute, the system does not abandon you. That you can raise a family without feeling like you are one accident away from ruin. That “getting ahead” does not require burning out. That a good society is one where normal people can live normal lives and still feel proud of them. This is why more and more Americans are not just visiting Europe, but staying. Some come for studies and never leave. Some arrive for a job and realise the lifestyle is the real promotion. Some originally planned a one year experiment and then cannot imagine going back to a place where stress is treated as a personality trait and insecurity is marketed as freedom. Europe is not perfect. It has bureaucracy. It has politics. It has problems that deserve criticism. But in many European countries, life is still built around a simple idea: society should reduce fear, not monetise it. That is the new dream. And people can feel it the moment they arrive. If you could choose one thing to trade for a better life, what would it be: more income, or more security? And what do you think your country would have to change for people to stop leaving, and start staying? Stay connected, Follow Gandalv Gandalv

Gandalv

988,824 views • 4 months ago

I know a lot of people hate on AI. I get it. Be creative. But for those of use who can't draw or animate, this is more of a quick and cheap fix. and there's nothing wrong with that. I wish others were more open about sharing what they've learned using AI and not hoard their knowledge like it's worth gold. The only way we can learn and improve is if others share what they've learned. I understand people are protective of what prompts they use. Ok fine, don't share them. But you can't even give a tip? Like "add this phrase to the prompt for better [fill in the blank]" or "use [blank] for a more realistic atmosphere]. Just pisses me off. I'm happy to share all I've learned with anyone who askes. Same happened in the 3d printing community with crushable printed houses. No one wanted to share anything. So I gave up on that. #sizetwitter #giantess #AIart #giantessai #Sora2 #soraArt

I know a lot of people hate on AI. I get it. Be creative. But for those of use who can't draw or animate, this is more of a quick and cheap fix. and there's nothing wrong with that. I wish others were more open about sharing what they've learned using AI and not hoard their knowledge like it's worth gold. The only way we can learn and improve is if others share what they've learned. I understand people are protective of what prompts they use. Ok fine, don't share them. But you can't even give a tip? Like "add this phrase to the prompt for better [fill in the blank]" or "use [blank] for a more realistic atmosphere]. Just pisses me off. I'm happy to share all I've learned with anyone who askes. Same happened in the 3d printing community with crushable printed houses. No one wanted to share anything. So I gave up on that. #sizetwitter #giantess #AIart #giantessai #Sora2 #soraArt

Carly {Amazingly Unavoidable}

14,459 views • 9 months ago

🎬 $PALM Presents: the long-awaited Creator Studio. A token holdings based usage system to use the latest generative AI for animations and graphics. Forget about downloadable tools or paying high subscription fees for no usage for mediocre results. Creator Studio allows you to access the tools our developers use for high-quality animated video making with the latest Generative AI tools. Creator Studio is part of the Parrot Framework that assigns tasks to AI agents when possible. You can navigate in a seamless Web UI, making the quality, three-dimensional, non-trippy AI videos and sequences you've ever wanted. We support up to 100 images per user stored in the cloud for you to make video sequences of up to 5 minutes with. Most importantly, we don't charge you a fee - your usage depends on your amount of $PALM tokens! The usage is reset monthly, so if you suddenly run out and need more points to complete your creation, you know what to do - buy some $PALM. This and more is being deployed live at where you can see a preview.

🎬 $PALM Presents: the long-awaited Creator Studio. A token holdings based usage system to use the latest generative AI for animations and graphics. Forget about downloadable tools or paying high subscription fees for no usage for mediocre results. Creator Studio allows you to access the tools our developers use for high-quality animated video making with the latest Generative AI tools. Creator Studio is part of the Parrot Framework that assigns tasks to AI agents when possible. You can navigate in a seamless Web UI, making the quality, three-dimensional, non-trippy AI videos and sequences you've ever wanted. We support up to 100 images per user stored in the cloud for you to make video sequences of up to 5 minutes with. Most importantly, we don't charge you a fee - your usage depends on your amount of $PALM tokens! The usage is reset monthly, so if you suddenly run out and need more points to complete your creation, you know what to do - buy some $PALM. This and more is being deployed live at where you can see a preview.

PaLM AI - $PALM

14,142 views • 1 year ago

🌟 "SHOULD I BUY AN IPHONE FOR TRACKING?" 🌟 tl;dr at bottom I've been using a facecam and Nvidia tracking for a long time and upgrading to a used iphone 13 combined with vbridger, the difference is HUGE. Here is my take on it! Why is Facecam > iPhone? ✅️ More affordable, esp for those using android phones ✅️ More convenient. If you launch Vtubestudio, there's a setting where your webcam automatically turns on, which is great. ✅️ Can track pretty well in the dark IF you already have a good webcam for night tracking. ❌️❌️ Stiff tracking at times ❌️ Not good at tracking specific mouth movement Why iPhone > Facecam? ✅️✅️ You can make the most of your model, since movement along the X and Y axes are a lot more accurate and wider. Also tracks eyes and overall face better. ✅️ More EXPRESSIONS. If your rigging allows for it, things like cheek puff, and tongue are able to be tracked. As far as I know, I cannot do this on facecam. ❌️❌️ WAY more expensive or requires that you have an iPhone already. Needs more set-up (need phone stand right in front of you, need to hook your phone up to a charger at all times, phone could possibly overheat as well if it's old, so you might need a cooler). TL;DR For me, if you have an extra 200 to spare for WAY better tracking, I would 1000% recommend buying a used iPhone on Amazon. iPhone X is the BARE minumum, I would recommend 12/13 so that your phone does not overheat. I do not need to use a cooler for my used iPhone 13. Being able to use my rigging to its fullest makes the model feel so so so different (in a good way). Feel free to reply with any questions, I will try to answer them!

🌟 "SHOULD I BUY AN IPHONE FOR TRACKING?" 🌟 tl;dr at bottom I've been using a facecam and Nvidia tracking for a long time and upgrading to a used iphone 13 combined with vbridger, the difference is HUGE. Here is my take on it! Why is Facecam > iPhone? ✅️ More affordable, esp for those using android phones ✅️ More convenient. If you launch Vtubestudio, there's a setting where your webcam automatically turns on, which is great. ✅️ Can track pretty well in the dark IF you already have a good webcam for night tracking. ❌️❌️ Stiff tracking at times ❌️ Not good at tracking specific mouth movement Why iPhone > Facecam? ✅️✅️ You can make the most of your model, since movement along the X and Y axes are a lot more accurate and wider. Also tracks eyes and overall face better. ✅️ More EXPRESSIONS. If your rigging allows for it, things like cheek puff, and tongue are able to be tracked. As far as I know, I cannot do this on facecam. ❌️❌️ WAY more expensive or requires that you have an iPhone already. Needs more set-up (need phone stand right in front of you, need to hook your phone up to a charger at all times, phone could possibly overheat as well if it's old, so you might need a cooler). TL;DR For me, if you have an extra 200 to spare for WAY better tracking, I would 1000% recommend buying a used iPhone on Amazon. iPhone X is the BARE minumum, I would recommend 12/13 so that your phone does not overheat. I do not need to use a cooler for my used iPhone 13. Being able to use my rigging to its fullest makes the model feel so so so different (in a good way). Feel free to reply with any questions, I will try to answer them!

Minori 🎀🍰💢 || bakaneko vampire :3

309,398 views • 1 year ago

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

@levelsio

119,210 views • 1 year ago