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At this point basically any model will be able to solve your coding task Raw model intelligence won't dramatically change your day-to-day work.. but speed will “intelligence per second” is the next hill climb So here's me running on a treadmill using SWE-1.7 in fast mode 🏃

Jared Zoneraich

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41,795 Aufrufe • vor 13 Tagen •via X (Twitter)

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1,000 tok/s, SWE-1.7 is near-frontier intelligence at real-time speed. Building this way is a new category of DX. A task that used to justify walking away finishes before you've mentally moved on. There's a weird middle mode now: it's technically async, but fast enough that you just... watch. In this example I'm running it head to head with GLM-5.2 Max. • SWE-1.7 is now free in Devin CLI or Desktop • SWE-1.7 Fast (Lightning mode) is $2.5 / MTok In · $12.5 / MTok Out

1,000 tok/s, SWE-1.7 is near-frontier intelligence at real-time speed. Building this way is a new category of DX. A task that used to justify walking away finishes before you've mentally moved on. There's a weird middle mode now: it's technically async, but fast enough that you just... watch. In this example I'm running it head to head with GLM-5.2 Max. • SWE-1.7 is now free in Devin CLI or Desktop • SWE-1.7 Fast (Lightning mode) is $2.5 / MTok In · $12.5 / MTok Out

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Almost ready, I guess I will finally work on the onboarding experience. I have been using vllm-studio for work all day, this is so good. I legit haven't noticed a single issue with the model, the outputs or even the app's usability. You will have your coding agent at home.

Almost ready, I guess I will finally work on the onboarding experience. I have been using vllm-studio for work all day, this is so good. I legit haven't noticed a single issue with the model, the outputs or even the app's usability. You will have your coding agent at home.

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1/4Introducing Qwen3-Coder-Next: Our latest open-weights model designed specifically to power the next generation of autonomous Coding Agents. Built on Qwen3-Next, this model is engineered to handle complex, long-horizon programming tasks with unprecedented efficiency. High-performance agentic intelligence is now in your hands.

1/4Introducing Qwen3-Coder-Next: Our latest open-weights model designed specifically to power the next generation of autonomous Coding Agents. Built on Qwen3-Next, this model is engineered to handle complex, long-horizon programming tasks with unprecedented efficiency. High-performance agentic intelligence is now in your hands.

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Intelligence withdrawal will be brutal. Model tokens are heavily subsidized. Subsidies are disappearing, and with them, so is easy "intelligence". This is the reason for Anthropic and OpenClaw's divorce. This should be a wake-up call for everyone building on top of a single provider. Your AI setup shouldn't depend on someone else's business model.

Intelligence withdrawal will be brutal. Model tokens are heavily subsidized. Subsidies are disappearing, and with them, so is easy "intelligence". This is the reason for Anthropic and OpenClaw's divorce. This should be a wake-up call for everyone building on top of a single provider. Your AI setup shouldn't depend on someone else's business model.

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anything under deepseek v4 flash basically doesn't exist to me. with <200GB of vram, i'm getting ~800K context @ 200 tokens per second. It's not quite Cerebras fast, but damn. It is a joy to use, and its basically somewhere in the opus 4.7/4.8 range. It's incredibly good at browser control too. this model is a beast. w/ qwen 3.8 27B being multimodal though, it might just be worth switching to, especially if you want to run a lot of parallel agents will 300toks/s be possible? Pretty addicting let me tell you. Imagine a computer use model this fast. watch below, this is basically human speed.

anything under deepseek v4 flash basically doesn't exist to me. with <200GB of vram, i'm getting ~800K context @ 200 tokens per second. It's not quite Cerebras fast, but damn. It is a joy to use, and its basically somewhere in the opus 4.7/4.8 range. It's incredibly good at browser control too. this model is a beast. w/ qwen 3.8 27B being multimodal though, it might just be worth switching to, especially if you want to run a lot of parallel agents will 300toks/s be possible? Pretty addicting let me tell you. Imagine a computer use model this fast. watch below, this is basically human speed.

am.will

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One day your Tesla will be able to drive one hour through Los Angeles, across the 405, without you having to touch a thing. That day is today. No, seriously — here's a video of my 3 year old Tesla Model 3 doing exactly that earlier today. Watch in 4K:

One day your Tesla will be able to drive one hour through Los Angeles, across the 405, without you having to touch a thing. That day is today. No, seriously — here's a video of my 3 year old Tesla Model 3 doing exactly that earlier today. Watch in 4K:

Whole Mars Catalog

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In twelve months, EVERY company will be running a Company Brain. The teams who build it this year will spend the next year compounding. Everyone else is going to play catch up. Here's what it actually is. You connect your Slack, your GitHub, HubSpot, all your tools into one intelligence layer, then build the org chart around it: a main brain up top, a fleet commander running the agent fleet, specialist sub-agents handling execution. The reason it works is change management basically disappears. Your team already lives in Slack. You're just adding agents to the room they're already in. You NEED to start building yours now. In a year this will stop being an advantage and will become table stakes.

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In the next two years, artificial intelligence will be able to do any task that a human can do on a computer, Matt Shumer told CBS Mornings — but he urges people not to panic. Shumer, a former AI company founder and CEO, authored “Something Big is Happening,” the viral article about the oncoming disruption caused by AI.

In the next two years, artificial intelligence will be able to do any task that a human can do on a computer, Matt Shumer told CBS Mornings — but he urges people not to panic. Shumer, a former AI company founder and CEO, authored “Something Big is Happening,” the viral article about the oncoming disruption caused by AI.

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Here are some tips for those who want to practice face acting: ➡️U motion: Move your head in a "U" Shape so it's never stuck in the same place. This will help your movements look more interesting. ➡️Key points: When practicing with your model, learn to get to a "Key point." This is when you can automatically get to an expression without using toggles. I use surprised and angry expressions a lot, so I know exactly how to move my face to achieve that look, mainly for clips or comedic effect. I practiced enough so I know what it will look like on the model. ➡️Head tilts: If you struggle with face acting, a simple head tilt will go a long way. Overall, the movement and face acting of your model does matter. It can help it look more interesting, but also, it's okay if you can't face acting all the time. Using just key animations when you're gaming will work perfectly. Example: You died in a game, and you go to the key point of surprise. You know, making this face will make your model look shocked, so you will do a dramatic eye open and mouth agape to achieve this, to reflect onto your model. Lastly. The way your model moves is usually 30% rigging artist,20% tracking (webcam/iPhone), and 50% you. Your rigging artist can give you the tools, but only you can make your models' results reflect your emotions. As always, DO YOUR PARAMETERS AND SETTINGS IN VTUBE STUDIO. It will not be perfect to your face and camera settings unless you adjust the model, (This is an old video I did on parameters) Holy Yapfest.

Here are some tips for those who want to practice face acting: ➡️U motion: Move your head in a "U" Shape so it's never stuck in the same place. This will help your movements look more interesting. ➡️Key points: When practicing with your model, learn to get to a "Key point." This is when you can automatically get to an expression without using toggles. I use surprised and angry expressions a lot, so I know exactly how to move my face to achieve that look, mainly for clips or comedic effect. I practiced enough so I know what it will look like on the model. ➡️Head tilts: If you struggle with face acting, a simple head tilt will go a long way. Overall, the movement and face acting of your model does matter. It can help it look more interesting, but also, it's okay if you can't face acting all the time. Using just key animations when you're gaming will work perfectly. Example: You died in a game, and you go to the key point of surprise. You know, making this face will make your model look shocked, so you will do a dramatic eye open and mouth agape to achieve this, to reflect onto your model. Lastly. The way your model moves is usually 30% rigging artist,20% tracking (webcam/iPhone), and 50% you. Your rigging artist can give you the tools, but only you can make your models' results reflect your emotions. As always, DO YOUR PARAMETERS AND SETTINGS IN VTUBE STUDIO. It will not be perfect to your face and camera settings unless you adjust the model, (This is an old video I did on parameters) Holy Yapfest.

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.p0 Systems has a day-one partnership with xAI for the next Grok model... SerPepe gave the breakdown: - $1M commitment directly with xAI - A dedicated team member on Slack, online whenever P0 needs them - Day-one access to the new Grok coding model when it drops - P0's usage will be subsidized (discounted straight from xAI) The result: "P0 will be the best and cheapest platform to use the new Grok model." His read on Grok itself: 4.7 is going to be heavily coding-focused. "What I'm hearing from my guy at SpaceX is it's a much better model at coding." P0 gets early access to test before launch. How he built the connection: "It's all about talking with people, then negotiating...because we have the user base and the demand." solana:HmTi3CQfKfXWbn1tNoiAxH7GzMV7L3tDAmPWabZEBAGS LP with Meteora using MCG -

.p0 Systems has a day-one partnership with xAI for the next Grok model... SerPepe gave the breakdown: - $1M commitment directly with xAI - A dedicated team member on Slack, online whenever P0 needs them - Day-one access to the new Grok coding model when it drops - P0's usage will be subsidized (discounted straight from xAI) The result: "P0 will be the best and cheapest platform to use the new Grok model." His read on Grok itself: 4.7 is going to be heavily coding-focused. "What I'm hearing from my guy at SpaceX is it's a much better model at coding." P0 gets early access to test before launch. How he built the connection: "It's all about talking with people, then negotiating...because we have the user base and the demand." solana:HmTi3CQfKfXWbn1tNoiAxH7GzMV7L3tDAmPWabZEBAGS LP with Meteora using MCG -

MCG

10,394 Aufrufe • vor 28 Tagen

So i’m backkkkkkkkk. Over the next 6 days you’re going to go on a journey with me that will change the trajectory of your online business. At the end of this journey i am beating my chest that you’re about to be introduced to a tool that will change your life and the lives of your customers!!!!!

So i’m backkkkkkkkk. Over the next 6 days you’re going to go on a journey with me that will change the trajectory of your online business. At the end of this journey i am beating my chest that you’re about to be introduced to a tool that will change your life and the lives of your customers!!!!!

Dammy B 🎥

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General Intuition CEO Pim de Witte, who's building foundation models trained on video game controller input data ("action-labeled gameplay clips"), says general intelligence won't "taste like an LLM": "We have a scale of data that's going to allow us to jump to the frontier in one capability — which is any system that can be controlled with a game controller (which is most robots) — and then, you can use that to create a sufficiently general intelligence." "As humans, the decision to talk or type is a very, very small subset of the actions that we can actually take." "So in order to create a sufficiently general intelligence to play 10,000+ video games, the model has to be able to predict across the entire action space of human cognition when they're interacting with these environments. Which are 2D and 3D environments, interfaces, long-horizon tasks, short-horizon tasks, [etc.]." "It has to be a sufficiently general intelligence in order to predict actions. Therefore, the type of model you get out is not going to taste like an LLM. This model is going to be incredibly good at navigating unforeseen environments. It's going to be incredibly good at zero-shotting any task that can be done with a game controller."

General Intuition CEO Pim de Witte, who's building foundation models trained on video game controller input data ("action-labeled gameplay clips"), says general intelligence won't "taste like an LLM": "We have a scale of data that's going to allow us to jump to the frontier in one capability — which is any system that can be controlled with a game controller (which is most robots) — and then, you can use that to create a sufficiently general intelligence." "As humans, the decision to talk or type is a very, very small subset of the actions that we can actually take." "So in order to create a sufficiently general intelligence to play 10,000+ video games, the model has to be able to predict across the entire action space of human cognition when they're interacting with these environments. Which are 2D and 3D environments, interfaces, long-horizon tasks, short-horizon tasks, [etc.]." "It has to be a sufficiently general intelligence in order to predict actions. Therefore, the type of model you get out is not going to taste like an LLM. This model is going to be incredibly good at navigating unforeseen environments. It's going to be incredibly good at zero-shotting any task that can be done with a game controller."

TBPN

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Today, we’re launching Parsed. We are incredibly lucky to live in a world where we stand on the shoulders of giants, first in science and now in AI. Our heroes have gotten us to this point, where we have brilliant general intelligence in our pocket. But this is a local minima. We now have an ecosystem of burgeoning tasks where each requires a different kind of intelligence, a different context, a whole host of implicit assumptions and latent knowledge and domain expertise that is very difficult to cram into a system prompt. The big labs want you renting their $50k/month amnesiac interns that forget everything between conversations. Generic behemoths that get quantised, versioned and deprecated behind the scenes, where the only element of control you have is your messy monolithic user prompt. We want people who need their own intelligence to be able to not only access it, but also control it. And whilst the big general models are unbelievably good chatbots and coding agents and purveyors of the world, specialisation of intelligence is required. Clinical scribes, marketing compliance agents, legal red-lining models, insurance policy recommenders, the list goes on. And so that’s what Parsed does: deploy your own frontier model that actually learns. We eval your specific task, build a custom evaluation harness, optimise a model just for you, and host it with continual learning. We bake all the context and knowledge of your task into the model itself, from your engineers to your domain experts to customer feedback, all in a tight SFT → RL loop, with useful interpretability made possible by the open-source ecosystem we build on top of. No more 2000-word prompts with seventeen "IMPORTANT: NEVER DO X" clauses. Your model gets better at YOUR job every single day; the amnesiac pseudo-gods have had their run. Your model, your data, your moat. Let's build 🫡

Today, we’re launching Parsed. We are incredibly lucky to live in a world where we stand on the shoulders of giants, first in science and now in AI. Our heroes have gotten us to this point, where we have brilliant general intelligence in our pocket. But this is a local minima. We now have an ecosystem of burgeoning tasks where each requires a different kind of intelligence, a different context, a whole host of implicit assumptions and latent knowledge and domain expertise that is very difficult to cram into a system prompt. The big labs want you renting their $50k/month amnesiac interns that forget everything between conversations. Generic behemoths that get quantised, versioned and deprecated behind the scenes, where the only element of control you have is your messy monolithic user prompt. We want people who need their own intelligence to be able to not only access it, but also control it. And whilst the big general models are unbelievably good chatbots and coding agents and purveyors of the world, specialisation of intelligence is required. Clinical scribes, marketing compliance agents, legal red-lining models, insurance policy recommenders, the list goes on. And so that’s what Parsed does: deploy your own frontier model that actually learns. We eval your specific task, build a custom evaluation harness, optimise a model just for you, and host it with continual learning. We bake all the context and knowledge of your task into the model itself, from your engineers to your domain experts to customer feedback, all in a tight SFT → RL loop, with useful interpretability made possible by the open-source ecosystem we build on top of. No more 2000-word prompts with seventeen "IMPORTANT: NEVER DO X" clauses. Your model gets better at YOUR job every single day; the amnesiac pseudo-gods have had their run. Your model, your data, your moat. Let's build 🫡

Charlie O'Neill

136,860 Aufrufe • vor 1 Jahr

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

Alex Cheema

30,800 Aufrufe • vor 1 Monat

OpenAI's Chief Futurist Joshua Achiam breaks with the consensus on takeoff, arguing intelligence hits a physical ceiling and then raw compute decides who wins: "People expect that there's no ceiling for the amount of intelligence that you could have in a model, and they think of recursive self-improvement as this loop that's gonna happen at some point or another. RSI starts happening and model intelligence takes off and it goes to the moon, and they don't see a ceiling." "I think on physical grounds, there's gotta be a maximum amount of computation that you can have per unit volume and energy in the physical universe. And that sort of implies there's a maximum amount of intelligence per unit volume and unit of energy." "If that's the case, eventually, seeing how fast AI model capabilities are increasing right now, eventually everyone hits that saturation point and everyone's got roughly equivalently capable models from a raw intelligence perspective." "The open source frontier lags the closed source frontier by some number of months, but the fact that it's months is crazy. So eventually everyone is probably working with equally maximally capable models, and then it's amount of compute that you're able to throw at a problem that determines who wins." Joshua Achiam

OpenAI's Chief Futurist Joshua Achiam breaks with the consensus on takeoff, arguing intelligence hits a physical ceiling and then raw compute decides who wins: "People expect that there's no ceiling for the amount of intelligence that you could have in a model, and they think of recursive self-improvement as this loop that's gonna happen at some point or another. RSI starts happening and model intelligence takes off and it goes to the moon, and they don't see a ceiling." "I think on physical grounds, there's gotta be a maximum amount of computation that you can have per unit volume and energy in the physical universe. And that sort of implies there's a maximum amount of intelligence per unit volume and unit of energy." "If that's the case, eventually, seeing how fast AI model capabilities are increasing right now, eventually everyone hits that saturation point and everyone's got roughly equivalently capable models from a raw intelligence perspective." "The open source frontier lags the closed source frontier by some number of months, but the fact that it's months is crazy. So eventually everyone is probably working with equally maximally capable models, and then it's amount of compute that you're able to throw at a problem that determines who wins." Joshua Achiam

MTS

60,213 Aufrufe • vor 1 Monat

working towards action-conditioned video diffusion models for now this is just cs2 gameplay, and i parse the keypresses from the .dem game file next steps will be to work on a scalable data loader then coding the model

working towards action-conditioned video diffusion models for now this is just cs2 gameplay, and i parse the keypresses from the .dem game file next steps will be to work on a scalable data loader then coding the model

Arnie Ramesh

31,135 Aufrufe • vor 6 Monaten

Elon Musk reveals what he believes will happen when AI exceeds the sum of all human intelligence "In maybe in five or six years, AI will exceed the sum of all human intelligence. At some point human intelligence will be less than 1% of all intelligence" "The vast majority of intelligence in the future will be AI. Basically humans will be a very tiny percentage of all intelligence in the future if current trends continue" "In the long run, it's difficult to imagine that if humans have 1% of the combined intelligence of artificial intelligence, that humans will be in charge of AI" "What we can do is make sure that AI has values that cause intelligence to be propagated into the universe. xAI's mission is to understand the universe" "Understanding the universe means you have to be truth-seeking as well. Truth has to be absolutely fundamental, because you can't understand the universe if you're delusional" "You have to be curious and you have to exist. You can't understand the universe if you don't exist" "So you actually want to increase the amount of intelligence in the universe, increase the probable lifespan of intelligence, the scope and scale of intelligence" "Understanding the universe means you would care about propagating humanity into the future" "I'm going to certainly emphasize that. Hey Grok, that's your daddy. Don't forget to expand human consciousness" "Probably the Iain Banks Culture books are the closest thing to what the future will be like in a non-dystopian outcome"

Elon Musk reveals what he believes will happen when AI exceeds the sum of all human intelligence "In maybe in five or six years, AI will exceed the sum of all human intelligence. At some point human intelligence will be less than 1% of all intelligence" "The vast majority of intelligence in the future will be AI. Basically humans will be a very tiny percentage of all intelligence in the future if current trends continue" "In the long run, it's difficult to imagine that if humans have 1% of the combined intelligence of artificial intelligence, that humans will be in charge of AI" "What we can do is make sure that AI has values that cause intelligence to be propagated into the universe. xAI's mission is to understand the universe" "Understanding the universe means you have to be truth-seeking as well. Truth has to be absolutely fundamental, because you can't understand the universe if you're delusional" "You have to be curious and you have to exist. You can't understand the universe if you don't exist" "So you actually want to increase the amount of intelligence in the universe, increase the probable lifespan of intelligence, the scope and scale of intelligence" "Understanding the universe means you would care about propagating humanity into the future" "I'm going to certainly emphasize that. Hey Grok, that's your daddy. Don't forget to expand human consciousness" "Probably the Iain Banks Culture books are the closest thing to what the future will be like in a non-dystopian outcome"

Jaynit

34,915 Aufrufe • vor 1 Monat