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Tired of generic, hallucinating LLMs? We use AI for almost everything these days, whether it’s helping with math homework or looking up remedies when we’re sick. It's become part of our daily routines. Now imagine if Einstein helped you solve that homework, or Eminem wrote the lyrics for your...

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OpenLedger

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172,568 просмотров • 1 год назад •via X (Twitter)

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Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.
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Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

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39,101 просмотров • 2 лет назад

Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move
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Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move

OpenLedger

61,662 просмотров • 1 год назад

Today we introduced Gemini Enterprise, built with our most advanced Gemini models. It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context. Have a look at how it helps you build an agent for almost anything:
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Today we introduced Gemini Enterprise, built with our most advanced Gemini models. It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context. Have a look at how it helps you build an agent for almost anything:

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AI creators now have the economic infrastructure to build, deploy, and earn; it's 100% open-source and community-driven. OpenLedger offers unmatched transparency, flexibility, and the tools you need to build the future of AI on your terms. OpenLedger is a decentralized, open-source blockchain designed for sourcing, training, and monetizing AI models. Learn more about in this video 👇
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AI creators now have the economic infrastructure to build, deploy, and earn; it's 100% open-source and community-driven. OpenLedger offers unmatched transparency, flexibility, and the tools you need to build the future of AI on your terms. OpenLedger is a decentralized, open-source blockchain designed for sourcing, training, and monetizing AI models. Learn more about in this video 👇

MosesMee

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Right now, almost everything you do online feeds centralized AI models. Reddit posts helped train large language models. Instagram photos power image generation. Now your daily online activity is being used to automate work itself. Action Model is pushing back with a community-owned AI model, where users stay in control and own what they help train. - Train passively or guide it step-by-step - Private data stays masked and in your control - Review, edit, or delete training anytime This isn’t a vision, it’s the standard the future of AI should be built on.

Action Model

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Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by South Park Commons to share how companies are owning their intelligence with Applied Compute.

Applied Compute

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Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.

Manos Koukoumidis

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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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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.

Santiago

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Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:
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Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:

Daniel San

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We just launched InstaAgents ... our agentic AI builder for real business use. Most AI projects fail because teams lack the tools, the talent, and a way to evaluate agents. Testing reliability is hard, especially for multi-agent setups and long workflows. We built something different. With InstaAgents you can: ◾ Build and deploy agents that automate real workflows ◾ Describe your task, workflow, and goals and our AI generator builds full multi-agent flows ◾ Use 50+ templates or create your own ◾ Connect your data and APIs and deploy anywhere Designed for real use cases in healthcare, deeptech, manufacturing, logistics, retail, legal, finance, operations, and more. We’re improving evals, adding more industry cases, and you have access to thousands of vetted AI experts from DataInsta who can help you deploy and scale. Try a free demo.
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We just launched InstaAgents ... our agentic AI builder for real business use. Most AI projects fail because teams lack the tools, the talent, and a way to evaluate agents. Testing reliability is hard, especially for multi-agent setups and long workflows. We built something different. With InstaAgents you can: ◾ Build and deploy agents that automate real workflows ◾ Describe your task, workflow, and goals and our AI generator builds full multi-agent flows ◾ Use 50+ templates or create your own ◾ Connect your data and APIs and deploy anywhere Designed for real use cases in healthcare, deeptech, manufacturing, logistics, retail, legal, finance, operations, and more. We’re improving evals, adding more industry cases, and you have access to thousands of vetted AI experts from DataInsta who can help you deploy and scale. Try a free demo.

Abu

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Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:
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Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,332 просмотров • 10 месяцев назад

Today we’re introducing Superhuman Docs, the evolution of Coda (Superhuman Docs) into the best place for teams and AI to work together. I co-founded Coda to reimagine the modern document and enable anyone to make a doc as powerful as an app. Superhuman Docs builds on that mission, and is reimagined for how teams work with AI now. With Superhuman Docs, AI is no longer a solo experience. Docs AI works with you the way a teammate would, with full context of everything your team has built: your data, your briefs, your roadmaps, your connected tools. You can ask it to summarize a sprint, draft a decision record from your meeting notes, or pull a competitor analysis into a new page, and that work stays right where your teammates can pick it up. I’m incredibly proud of the team for bringing Docs to life! There’s so much more we’re launching, including a way to create custom interfaces on top of your data just by describing it. We’re also introducing Superhuman Databases for teams to manage up to 1 million rows, launching Docs MCP to open it up to the AI tools you already use, and debuting a brand new Mac app. We’ve been building toward this moment for a long time, and Docs starts rolling out today. If you’re an existing Coda user, everything you rely on in your docs will remain the same, and if you’re new, now is a great time to jump in.
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Today we’re introducing Superhuman Docs, the evolution of Coda (Superhuman Docs) into the best place for teams and AI to work together. I co-founded Coda to reimagine the modern document and enable anyone to make a doc as powerful as an app. Superhuman Docs builds on that mission, and is reimagined for how teams work with AI now. With Superhuman Docs, AI is no longer a solo experience. Docs AI works with you the way a teammate would, with full context of everything your team has built: your data, your briefs, your roadmaps, your connected tools. You can ask it to summarize a sprint, draft a decision record from your meeting notes, or pull a competitor analysis into a new page, and that work stays right where your teammates can pick it up. I’m incredibly proud of the team for bringing Docs to life! There’s so much more we’re launching, including a way to create custom interfaces on top of your data just by describing it. We’re also introducing Superhuman Databases for teams to manage up to 1 million rows, launching Docs MCP to open it up to the AI tools you already use, and debuting a brand new Mac app. We’ve been building toward this moment for a long time, and Docs starts rolling out today. If you’re an existing Coda user, everything you rely on in your docs will remain the same, and if you’re new, now is a great time to jump in.

Shishir

23,206 просмотров • 1 месяц назад