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this will be our first $TAO sub-subnet 44 a neural-architecture competition to measure how position and movement patterns drive specific outcomes (here: a goal) built to supercharge our TurboVision model with action-prompting abilities, using probabilities inferred from miners’ outputs

Max

5,520 subscribers

26,989 次观看 • 1 年前 •via X (Twitter)

科学技术游戏教育

Anya Rossi• Live Now

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we live in a $TAO simulation in a few months we went from a whitepaper, a concept, a dream (kinda) to now generating 3d simulations based on our own neural network (that we’re training using Celium btw) this is what Score - Subnet 44 is about: > collecting visual data > creating visual models > generating a ‘real time’ positional value function aren’t you entertained? I love this game!

we live in a $TAO simulation in a few months we went from a whitepaper, a concept, a dream (kinda) to now generating 3d simulations based on our own neural network (that we’re training using Celium btw) this is what Score - Subnet 44 is about: > collecting visual data > creating visual models > generating a ‘real time’ positional value function aren’t you entertained? I love this game!

Max

19,378 次观看 • 1 年前

Annamalai - “The goal is to achieve 50 lakh membership and turn the movement into a political party. Members have joined with the belief that the movement would bring in a big change. Between 40 and 50 years of age, Tamil people, who have achieved a lot across the globe including in US and London do not know how to enter politics here. Our aim in the coming months is that this membership drive will keep going. As we move forward, our objective is to transform our organisation into a people's movement to talk about the various issues in the state."

Annamalai - “The goal is to achieve 50 lakh membership and turn the movement into a political party. Members have joined with the belief that the movement would bring in a big change. Between 40 and 50 years of age, Tamil people, who have achieved a lot across the globe including in US and London do not know how to enter politics here. Our aim in the coming months is that this membership drive will keep going. As we move forward, our objective is to transform our organisation into a people's movement to talk about the various issues in the state."

News Arena India

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Announcing Magic Patterns 2.0 and our $6M Series A led by Standard Capital Since our first launch in 2023, we’ve grown profitably, hit $1M ARR with no employees, and quietly built an AI design tool loved by PMs, designers, and website builders. 1,500+ product teams use us to go from idea to production every day, prompting features to life with their existing styles. Free to try with a new brand look.

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Dear friends, I’m very excited to announce our next ambitious exhibition opening in Guggenheim Bilbao on the 7th of March! We are transforming my hero — Frank Gehry’s iconic architecture with 50 channel projectors into “Living Architecture.” With respect and homage to his body of work, our Large Architecture Model, trained on 35 million images of the world's architecture, will be dreaming of the future of architecture, urban, and future cities during the 6 months of our show in real-time using light and data as a pigment. I do believe Architecture is beyond concrete, steel and glass, and I’m deeply honored to share our work in an incredible museum and grateful to the Guggenheim Bilbao team for making our dream a reality!

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THIS IS HISTORIC: "The city of Tartous has been liberated, we are here with our people from all sects, Christians, Alawites, Sunnis, Druze and Ismailis, the Syrian people are 1, to our people in Tartous, work with us to build our country, we will present a model to be proud of".

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My message to Canadian families tonight: We will be there for you. And we will unite as Canadians to protect our affected sectors. Now is the time to take back control and relaunch our economy so that we can confront President Trump's unjustified threats and tariffs from a position of strength. We will do this for our people, for our land, for our home, for Canada first for a change.

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New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

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74,468 次观看 • 1 年前

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Today, we officially opened our campaign headquarters in Asaba, the central home of our movement, from which campaign branches will be established across Delta State. This headquarters will serve as the heart of our campaign, coordinating our activities across the state while keeping our doors open to Deltans who want to engage, contribute, and be part of this journey. From Asaba, our structure will reach every part of Delta State. The goal is simple, to build a campaign that remains connected to the people, listens to the people, and carries their voices with us every step of the way. The movement is growing, the structure is taking shape, and we are only getting started. #DCI2027 #ChrisIyovwaye #DeltaCanBeBetter #BetterDelta

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Here's a quick example of what the community could be able to build with shaders + this Figma plugin...as you can see, the possibilities are endless and our outputs will not be strictly limited to just gradients. I'm all about empowering our community with math + code:

˗ˏˋ rogie ˎˊ˗

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