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WF's Model Comparison is a game-changing tool optimized for web AND mobile use! 📱⚡ 🔍 View all 26 weather models side by side 📈 Spot trends & forecast shifts in seconds ⛈️ Track the latest model runs anytime, anywhere 👀 What if we told you this was recorded on... show more

WeatherFront

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66,399 görüntüleme • 5 gün önce •via X (Twitter)

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This got a few bookmarks, so here's a better side-by-side for you guys~! My background image is static but feels so cozy. All compositing to make my model mesh well into the surroundings and glow as if back-lit by my rooms LEDs is done in Shoost. ♡

Aluei 🤍🪽 PLUSHIE OUT NOW!! ´ˎ˗

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Animated #GaussianSplatting can now be streamed like a YouTube video. So far, you had to download large files BEFORE experiencing them anywhere, but Gracia has just released several demos that stream like a normal video. The first thing I tried was to make a side-by-side comparison, but I could hardly notice any difference. As long as you have a solid internet connection, you can play them on the web, headset and even on mobile in #augmentedreality. Interested in the latest in #3D, #XR and #AI? This is the type of news and updates I share every week on my newsletter (link on top of my profile).

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Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

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Ronin

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🎥 Lace Mobile Alpha Build This is a look at what we’re currently testing on mobile: ⚡ Quicker access to core actions 🧭 Clearer, more intentional flows 📱 An experience designed specifically for mobile use Lace Mobile is still under active development. We’re focusing on strong foundations and refining the UX step by step. A public beta is planned for later in February, offering early access for users who want to explore the mobile experience and help shape what comes next. More soon...

🎥 Lace Mobile Alpha Build This is a look at what we’re currently testing on mobile: ⚡ Quicker access to core actions 🧭 Clearer, more intentional flows 📱 An experience designed specifically for mobile use Lace Mobile is still under active development. We’re focusing on strong foundations and refining the UX step by step. A public beta is planned for later in February, offering early access for users who want to explore the mobile experience and help shape what comes next. More soon...

lace.io

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let me save you 3 hours of head scratching. if you're running local models like Qwen3.5-35B-A3B through Claude Code via llama.cpp's Anthropic endpoint, the chain will break every 3 to 5 minutes. tool call fails. flow stops. you reprompt. it recovers. 2 minutes later it stops again. the model is fine. the harness chokes on local inference latency. switch to OpenCode. same localhost endpoint. same model. same GPU. the chain doesn't break. the tradeoff: OpenCode sometimes loops. the model forgets what it already read and repeats the same tool call. but a loop you can interrupt. a broken chain kills your momentum and you start over. watch both side by side. proprietary agent vs open source agent. same 3B model. different failure modes. pick your poison.

let me save you 3 hours of head scratching. if you're running local models like Qwen3.5-35B-A3B through Claude Code via llama.cpp's Anthropic endpoint, the chain will break every 3 to 5 minutes. tool call fails. flow stops. you reprompt. it recovers. 2 minutes later it stops again. the model is fine. the harness chokes on local inference latency. switch to OpenCode. same localhost endpoint. same model. same GPU. the chain doesn't break. the tradeoff: OpenCode sometimes loops. the model forgets what it already read and repeats the same tool call. but a loop you can interrupt. a broken chain kills your momentum and you start over. watch both side by side. proprietary agent vs open source agent. same 3B model. different failure modes. pick your poison.

Sudo su

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🎉🔵ONE MONTH of WeatherFront🔴🎉 Thanks to everyone who has helped make WF a success since our launch on May 4th! What a fun journey it has been. To operational meteorologists - we hope WF has made it easier to interrogate models, construct forecasts, and quickly find the weather data you need as you serve the public and your partners/customers. To emergency managers - we hope WF has enhanced your ability to quickly respond to hazardous weather with more detailed basemaps to pinpoint affected locations and a wealth of weather data catered to your mobile device. To broadcast meteorologists - we hope WF has provided high-quality data visualization for use on social media and given you a mobile resource for radar, satellite, and model data when you’re doing live coverage on air during severe weather. To weather hobbyists - we hope WF has helped you learn more about different weather data types and equipped you with an expanded set of tools to track storms as you build a deeper passion for meteorology and stay informed about what is headed your way. To storm chasers and spotters - we hope WF has increased your situational awareness on the road or while watching from home - thanks for taking WF to all corners of the US! We’ve enjoyed seeing your content and congratulate you on a great spring season so far. To our international customers - we hope WF has provided a unique perspective to view global weather model data. We appreciate your support and look forward to bringing you additional data sources in the future. To all current and future WF users - thanks for joining us on the journey! We’re just getting started. We hope you’ll invite your friends and colleagues to give WeatherFront a try as we continue to innovate and bring even more products to your mobile devices!

🎉🔵ONE MONTH of WeatherFront🔴🎉 Thanks to everyone who has helped make WF a success since our launch on May 4th! What a fun journey it has been. To operational meteorologists - we hope WF has made it easier to interrogate models, construct forecasts, and quickly find the weather data you need as you serve the public and your partners/customers. To emergency managers - we hope WF has enhanced your ability to quickly respond to hazardous weather with more detailed basemaps to pinpoint affected locations and a wealth of weather data catered to your mobile device. To broadcast meteorologists - we hope WF has provided high-quality data visualization for use on social media and given you a mobile resource for radar, satellite, and model data when you’re doing live coverage on air during severe weather. To weather hobbyists - we hope WF has helped you learn more about different weather data types and equipped you with an expanded set of tools to track storms as you build a deeper passion for meteorology and stay informed about what is headed your way. To storm chasers and spotters - we hope WF has increased your situational awareness on the road or while watching from home - thanks for taking WF to all corners of the US! We’ve enjoyed seeing your content and congratulate you on a great spring season so far. To our international customers - we hope WF has provided a unique perspective to view global weather model data. We appreciate your support and look forward to bringing you additional data sources in the future. To all current and future WF users - thanks for joining us on the journey! We’re just getting started. We hope you’ll invite your friends and colleagues to give WeatherFront a try as we continue to innovate and bring even more products to your mobile devices!

WeatherFront

33,176 görüntüleme • 1 yıl önce

This is the fastest I've seen Llama 3.3 running anywhere! Llama 3.3 70B running at 652 t/s is lightning fast. And if you want Llama 3.1, here are the speeds I was able to get: • Llama 3.1 8B: 1006 t/s • Llama 3.1 70B: 709 t/s • Llama 3.1 405B: 206 t/s (You can access all of these models for free! See the link below.) This speed is incredible, but the interesting part is what's happening behind the scenes: These models aren't running on a GPU! In this video, I'm using the SambaNova cloud to access these models. They built a custom chip (SN40L) optimized for AI workflows. A single SN40L chip can hold hundreds of models (trillions of parameters) in memory! The speed alone is a huge deal, but the big advantage is for agentic workflows running multiple specialized models. A GPU can only host a single model and switch (unload and load) to a different model if necessary. An SN40L, on the other hand, can host every model at once, making it much faster. Here is the video where you can see how fast these chips are:

This is the fastest I've seen Llama 3.3 running anywhere! Llama 3.3 70B running at 652 t/s is lightning fast. And if you want Llama 3.1, here are the speeds I was able to get: • Llama 3.1 8B: 1006 t/s • Llama 3.1 70B: 709 t/s • Llama 3.1 405B: 206 t/s (You can access all of these models for free! See the link below.) This speed is incredible, but the interesting part is what's happening behind the scenes: These models aren't running on a GPU! In this video, I'm using the SambaNova cloud to access these models. They built a custom chip (SN40L) optimized for AI workflows. A single SN40L chip can hold hundreds of models (trillions of parameters) in memory! The speed alone is a huge deal, but the big advantage is for agentic workflows running multiple specialized models. A GPU can only host a single model and switch (unload and load) to a different model if necessary. An SN40L, on the other hand, can host every model at once, making it much faster. Here is the video where you can see how fast these chips are:

Santiago

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You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

Santiago

55,609 görüntüleme • 2 yıl önce

Some of our top customers are still choosing Llama 3.1 8B. For a while, we jumped to whatever hottest, latest model was taking up our twitter feed. 🙈 But as we are quickly realizing, to create a SOTA product, you need a model that fits your exact use case. Here’s what our customers tell us: > a lot of the legwork is actually around prompting > there’s an art to selecting and combining multiple models > benchmarks only show part of the picture. you have to understand the unique quirks of each model. Especially as model releases become more and more frequent, we need a clear way to evaluate new models. We have to break free of the naive trend to migrate to the ‘latest and greatest’. And you can easily achieve this using tools like Cerebras and Braintrust to swap models safely (without breaking production).

Some of our top customers are still choosing Llama 3.1 8B. For a while, we jumped to whatever hottest, latest model was taking up our twitter feed. 🙈 But as we are quickly realizing, to create a SOTA product, you need a model that fits your exact use case. Here’s what our customers tell us: > a lot of the legwork is actually around prompting > there’s an art to selecting and combining multiple models > benchmarks only show part of the picture. you have to understand the unique quirks of each model. Especially as model releases become more and more frequent, we need a clear way to evaluate new models. We have to break free of the naive trend to migrate to the ‘latest and greatest’. And you can easily achieve this using tools like Cerebras and Braintrust to swap models safely (without breaking production).

Cerebras

346,446 görüntüleme • 7 ay önce

Exa CEO Will Bryk explains why retrieval can help solve the tokenpocalypse: "We should not be using gigantic models for every task." "You should use a family of models of different sizes. The big model decides what to do, and it dishes out commands to the small models, and those small models can be way more accurate and reliable if they're using retrieval." "Retrieval helps small models act like big models... We do save our customers a huge amount of tokens because they can use smaller models and use retrieval." "We could save 20x on cost for customers compared to other providers by being very efficient in what information from the web the agent actually sees." Will Bryk with Sarah Wang

Exa CEO Will Bryk explains why retrieval can help solve the tokenpocalypse: "We should not be using gigantic models for every task." "You should use a family of models of different sizes. The big model decides what to do, and it dishes out commands to the small models, and those small models can be way more accurate and reliable if they're using retrieval." "Retrieval helps small models act like big models... We do save our customers a huge amount of tokens because they can use smaller models and use retrieval." "We could save 20x on cost for customers compared to other providers by being very efficient in what information from the web the agent actually sees." Will Bryk with Sarah Wang

a16z

25,144 görüntüleme • 1 ay önce

Introducing: Whale Watcher 🐳 You can now track what memecoin whales are buying and selling in real-time on AssetDash! Why did we release this feature? To help you become a more competitive trader and to simplify discovery. We realized through analysis and backtesting that what memecoin whales are buying and selling is a significant source of alpha. And we want to make sure you have access to this information in real-time. How does it work? Whale Watcher uses a number of different technologies to create "smart lists" of top whales in the biggest memecoins - and then we broadcast their buy and sell activity to you in our mobile app. How can I use Whale Watcher? It is easy. Just download our mobile app and go to the Discovery tab. This is a free tool for the community! If you are already an AssetDash member - update your mobile app to the latest version.

Introducing: Whale Watcher 🐳 You can now track what memecoin whales are buying and selling in real-time on AssetDash! Why did we release this feature? To help you become a more competitive trader and to simplify discovery. We realized through analysis and backtesting that what memecoin whales are buying and selling is a significant source of alpha. And we want to make sure you have access to this information in real-time. How does it work? Whale Watcher uses a number of different technologies to create "smart lists" of top whales in the biggest memecoins - and then we broadcast their buy and sell activity to you in our mobile app. How can I use Whale Watcher? It is easy. Just download our mobile app and go to the Discovery tab. This is a free tool for the community! If you are already an AssetDash member - update your mobile app to the latest version.

AssetDash

1,661,128 görüntüleme • 1 yıl önce

I'm running Llama 4 Maverick at 620 t/s! I'm living in the future! Honestly, a large language model running this fast is something straight out of a sci-fi movie. Speeds like this will enable a whole new world of applications that aren't possible today. For reference, GPT-4o, which is probably the most popular OpenAI model, runs between 60 and 110 t/s. The secret here: I'm not running AI at Meta's Llama 4 Maverick on a GPU. I'm using the SambaNova Cloud (my sponsor) and their custom SN40L chips. They are optimized from the ground up for running AI workflows. Right now, SambaNova Cloud runs DeepSeek, Qwen, Whisper, and the entire family of Llama models on these chips. You can check the speed of each of these models using SambaNova Cloud's Playground (see the attached video). It's completely free, and that's how I'm measuring their speeds. For example, I also tried DeepSeek R1 (the latest version from May) and, oh boy! DeepSeek R1 is a huge 671B parameter model. It's probably the best open reasoning model in the world, and it runs at 140 tokens per second! !!! Inference time on an SN40L is night and day from what you'll get from a GPU. Here is why this is big: If you are running an agentic workflow that uses multiple models simultaneously on a GPU, it will need to swap models in and out of memory (because not every model fits). A single SNL40 chip can simultaneously hold over 100 models (trillions of parameters) in memory. If you are using open models, try the SambaCloud API to see what lightning speed looks like. Here is how: 1. Create a free account at: 2. Check the QuickStart guide: If you try the playground, check the speed you're getting with Llama 4 and DeepSeek, and post the results below. I've seen much higher numbers than I posted here, so I'm curious to see whether geography affects the speed.

I'm running Llama 4 Maverick at 620 t/s! I'm living in the future! Honestly, a large language model running this fast is something straight out of a sci-fi movie. Speeds like this will enable a whole new world of applications that aren't possible today. For reference, GPT-4o, which is probably the most popular OpenAI model, runs between 60 and 110 t/s. The secret here: I'm not running AI at Meta's Llama 4 Maverick on a GPU. I'm using the SambaNova Cloud (my sponsor) and their custom SN40L chips. They are optimized from the ground up for running AI workflows. Right now, SambaNova Cloud runs DeepSeek, Qwen, Whisper, and the entire family of Llama models on these chips. You can check the speed of each of these models using SambaNova Cloud's Playground (see the attached video). It's completely free, and that's how I'm measuring their speeds. For example, I also tried DeepSeek R1 (the latest version from May) and, oh boy! DeepSeek R1 is a huge 671B parameter model. It's probably the best open reasoning model in the world, and it runs at 140 tokens per second! !!! Inference time on an SN40L is night and day from what you'll get from a GPU. Here is why this is big: If you are running an agentic workflow that uses multiple models simultaneously on a GPU, it will need to swap models in and out of memory (because not every model fits). A single SNL40 chip can simultaneously hold over 100 models (trillions of parameters) in memory. If you are using open models, try the SambaCloud API to see what lightning speed looks like. Here is how: 1. Create a free account at: 2. Check the QuickStart guide: If you try the playground, check the speed you're getting with Llama 4 and DeepSeek, and post the results below. I've seen much higher numbers than I posted here, so I'm curious to see whether geography affects the speed.

Santiago

34,148 görüntüleme • 1 yıl önce

👀 "You don't say that all 4-3-3s play in the same way, right? We play the different way..." 🔀 "We need time to improve the characteristics of the team... in the last game, we should have an Amad to put on the other side against 10 men" Ruben Amorim is asked whether he can draw any inspiration from Oliver Glasner's similar 3-4-3 system and discusses if wingback is an area of concern for his #MUFC side 🗣️

👀 "You don't say that all 4-3-3s play in the same way, right? We play the different way..." 🔀 "We need time to improve the characteristics of the team... in the last game, we should have an Amad to put on the other side against 10 men" Ruben Amorim is asked whether he can draw any inspiration from Oliver Glasner's similar 3-4-3 system and discusses if wingback is an area of concern for his #MUFC side 🗣️

Stretford Paddock

313,174 görüntüleme • 7 ay önce

I know we are all excited about Sora 2, but don't sleep on the other models. Runway Aleph is a powerful tool as you can essentially control AI generated characters using videos recorded with your phone. And it's now on Freepik 🔥 To use Runway Aleph on Freepik, head to the new clip editor. Then pick the Aleph model from the menu (or leave it on Auto). Add a prompt and hit generate. All covered in the short tutorial below.

I know we are all excited about Sora 2, but don't sleep on the other models. Runway Aleph is a powerful tool as you can essentially control AI generated characters using videos recorded with your phone. And it's now on Freepik 🔥 To use Runway Aleph on Freepik, head to the new clip editor. Then pick the Aleph model from the menu (or leave it on Auto). Add a prompt and hit generate. All covered in the short tutorial below.

Martin LeBlanc

12,050 görüntüleme • 9 ay önce