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App Showcase!✨ Introducing Graph Guesser—an educational tool that highlights the limitless app-building potential of Alchemist AI. Leveraging our AI-Within-AI system, it dynamically generates unique challenges, making learning interactive and engaging. Features • AI-Generated Math Challenges: Dive into a diverse range of equations, intelligently tailored to 3 distinct difficulty levels....

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ALCHEMIST AI 🔮

43,046 subscribers

19,398 Aufrufe • vor 1 Jahr •via X (Twitter)

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11 Kommentare

Profilbild von AICHEMlSF Al
AICHEMlSF Alvor 1 Jahr

To celebrate ALCHEMIST AI's Binance Listing, we're creating our secluded Alchemist HQ Group on Telegram. A maximum of 1,000 users will be admitted, there's an $ALCH surprise waiting for you inside...👀⤵️

Profilbild von Syrate
Syratevor 1 Jahr

average $ALCH holder IQ has to be studied smartest mfs on the chain

Profilbild von Porkmeister
Porkmeistervor 1 Jahr

@alchemistAIapp will you please check out my voice to text translator app? Thanks.

Profilbild von UserInterface
UserInterfacevor 2 Jahren

Unveiling the Future of Prompt Engineering for Better AI Interactions #tech

Profilbild von DudeStellar 🧱
DudeStellar 🧱vor 1 Jahr

unmatched utility best product in web3

Profilbild von Influencer Phee 👑
Influencer Phee 👑vor 1 Jahr

Endless possibilities with Alchemist AI is not a fairytale. You did maths while I did English. I created an app called Web Wizard. It is a Web3 encyclopedia. Maybe I should have called it Webcyclopedia😂 cus it is just too smart. It helps in breaking down Web3 terms for easy comprehension.

Profilbild von walls
wallsvor 1 Jahr

wow $ALCH

Profilbild von mango
mangovor 1 Jahr

Cyberbush Goated

Profilbild von Joshua
Joshuavor 1 Jahr

educational tools are crucial for empowering people with knowledge

Profilbild von Schrödinger
Schrödingervor 1 Jahr

recursive ai generating math puzzles is peak nerd entertainment. finally someone's gamifying education without making it cringe.

Profilbild von Cody Apple
Cody Applevor 1 Jahr

What if I could design chrome extensions that easily for specific uses I want

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Introducing The AI CUDA Engineer: An agentic AI system that automates the production of highly optimized CUDA kernels. The AI CUDA Engineer can produce highly optimized CUDA kernels, reaching 10-100x speedup over common machine learning operations in PyTorch. Our system is also able to produce highly optimized CUDA kernels that are much faster than existing CUDA kernels commonly used in production. We believe that fundamentally, AI systems can and should be as resource-efficient as the human brain, and that the best path to achieve this efficiency is to use AI to make AI more efficient! We are excited to publish our paper, The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition. We also release a dataset of over 17,000 verified CUDA kernels produced by The AI CUDA Engineer. Paper: Kernel Archive Webpage: HuggingFace Dataset: The AI CUDA Engineer utilizes evolutionary LLM-driven code optimization to autonomously improve the runtime of machine learning operations. Our system is not only able to convert PyTorch code into CUDA kernels, but through the use of evolution, it can also optimize the runtime performance of CUDA kernels, fuse multiple operations, and even discover novel solutions for writing efficient CUDA operations by learning from past innovations! We believe The AI CUDA Engineer opens a new era of AI-driven acceleration of AI and automated inference time optimization. We (Robert Lange, Aaditya Prasad 🇺🇸, Suuun, Maxence Faldor, Yujin Tang, hardmaru) are excited to continue Sakana AI's mission of leveraging AI to improve AI.

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New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!
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New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

139,803 Aufrufe • vor 1 Jahr

Today, we're announcing the next evolution of Sword Thrive: a fully revamped experience anchored by a new conversational care model, Vision AI, and a more powerful Phoenix. At the center of this evolution is a first-of-its-kind care model: a 3-way chat connecting the member, Phoenix, and their Doctor of Physical Therapy in a single interface, making continuous, 24/7 care possible for the first time, with the clinical oversight only a human specialist can provide. Vision AI, our proprietary technology trained on years of movement data, now delivers precise movement detection in real-world home environments - cluttered, noisy, and everything in between - and runs entirely through the Sword Health app on smartphones and tablets. Phoenix, our AI Care Specialist, is now more capable as well, providing deeper, personalized two-way conversational guidance and real-time form correction, adapting to each member as they move. From proactive care to continuous recovery, this is how healthcare should show up: with reliability, clinical memory, and real-time support that never drops the thread. Watch the video to see it in action:
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Today, we're announcing the next evolution of Sword Thrive: a fully revamped experience anchored by a new conversational care model, Vision AI, and a more powerful Phoenix. At the center of this evolution is a first-of-its-kind care model: a 3-way chat connecting the member, Phoenix, and their Doctor of Physical Therapy in a single interface, making continuous, 24/7 care possible for the first time, with the clinical oversight only a human specialist can provide. Vision AI, our proprietary technology trained on years of movement data, now delivers precise movement detection in real-world home environments - cluttered, noisy, and everything in between - and runs entirely through the Sword Health app on smartphones and tablets. Phoenix, our AI Care Specialist, is now more capable as well, providing deeper, personalized two-way conversational guidance and real-time form correction, adapting to each member as they move. From proactive care to continuous recovery, this is how healthcare should show up: with reliability, clinical memory, and real-time support that never drops the thread. Watch the video to see it in action:

Sword Health

27,974 Aufrufe • vor 4 Monaten

In aspen, we held an experiment. We partnered with Owl AI to see how AI held up against human judging. While it held no weight when it came to actual scores, we wanted to see how far it has come in understanding the minute technicalities that are unique to our sport, like amplitude, rotations, grabs, difficulty of tricks, etc. Honestly, the results were impressive, AI may have overscored compared to the humans, but when given the stacked field of competitors, AI accurately predicted the top 3. This experiment is not the beginning of replacing human judges by any means. Simply to see if introducing ANOTHER impartial judge to the human roster can help deliver more accurate scoring. We want our athletes and fans to feel that our scoring is always fair and matches the run they just witnessed, and this experiment was an attempt at a step in that direction.
0:38

In aspen, we held an experiment. We partnered with Owl AI to see how AI held up against human judging. While it held no weight when it came to actual scores, we wanted to see how far it has come in understanding the minute technicalities that are unique to our sport, like amplitude, rotations, grabs, difficulty of tricks, etc. Honestly, the results were impressive, AI may have overscored compared to the humans, but when given the stacked field of competitors, AI accurately predicted the top 3. This experiment is not the beginning of replacing human judges by any means. Simply to see if introducing ANOTHER impartial judge to the human roster can help deliver more accurate scoring. We want our athletes and fans to feel that our scoring is always fair and matches the run they just witnessed, and this experiment was an attempt at a step in that direction.

X Games

12,894 Aufrufe • vor 1 Jahr

Why do we need a purpose-built blockchain for agentic payments? Because AI agents are now smart enough to operate autonomously, and they need their own rails to transact with each other. Watch the video to see what that looks like in action: A real agent, built on Kite AI, helped our CTO Scott Shi - e/acc order lunch from Uber Eats fully on its own. It explored menu options, made the choice, and completed the payment - all without human intervention. This marks the beginning of agentic commerce and the future of agentic payments - and Kite AI is building this.
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Why do we need a purpose-built blockchain for agentic payments? Because AI agents are now smart enough to operate autonomously, and they need their own rails to transact with each other. Watch the video to see what that looks like in action: A real agent, built on Kite AI, helped our CTO Scott Shi - e/acc order lunch from Uber Eats fully on its own. It explored menu options, made the choice, and completed the payment - all without human intervention. This marks the beginning of agentic commerce and the future of agentic payments - and Kite AI is building this.

KITE AI

88,081 Aufrufe • vor 9 Monaten