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AkashML is live! A fully managed AI inference service on Akash Network. Deploying open models like DeepSeek shouldn’t cost $50K/month or require complex setup. AkashML makes inference simple, affordable, and automatic, so developers can run AI workloads on decentralized GPUs with ease. Try it out here 👇 Read More... show more

Akash Network

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37,077 görüntüleme • 10 ay önce •via X (Twitter)

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AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

Uomi

18,982 görüntüleme • 3 ay önce

BOOM! Starting today you can use open source frontier LLMs in Visual Studio Code with HF Inference Providers! 🔥 Use your inference credits on SoTA llms like GLM 4.5, Qwen3 Coder, DeepSeek 3.1 and more All of it packaged in one simple extension - try it out today 🤗

BOOM! Starting today you can use open source frontier LLMs in Visual Studio Code with HF Inference Providers! 🔥 Use your inference credits on SoTA llms like GLM 4.5, Qwen3 Coder, DeepSeek 3.1 and more All of it packaged in one simple extension - try it out today 🤗

Vaibhav (VB) Srivastav

41,201 görüntüleme • 1 yıl önce

Connecting all intelligence with ORA ⏺️ So, how does it work? Let's dive in 👇 At the heart of ORA is the Onchain AI Oracle (OAO), designed to empower dApp developers by seamlessly integrating verifiable AI inference into their applications without sacrificing decentralization. At the center of ORA is Optimistic Machine Learning (opML). This technology ensures that AI inferences originate from the correct models and that results remain untampered. A robust network of nodes independently verifies each AI inference and can raise challenges if tampering is suspected. Want to be part of this decentralized movement? You can easily run your own node using Tora, our user-friendly node software. Built on Optimistic Machine Learning (opML), our Onchain AI Oracle connects the best open-source AI models to the blockchain. This means any dApp can easily become an AI dApp! With our Initial Model Offering (IMO), developers can tokenize open-source models, effectively overcoming the cold start problem by securing essential funding for their projects. ORA is integrated with several L2s, including Mantle Network, Linea.eth, and Arbitrum. This allows you to build on low-cost chains that best suit your needs. ORA is committed to bringing decentralized, verifiable AI to the blockchain. Are you ready to join us? Use OAO: Read more about OAO: Learn about IMO: Use Tora:

Connecting all intelligence with ORA ⏺️ So, how does it work? Let's dive in 👇 At the heart of ORA is the Onchain AI Oracle (OAO), designed to empower dApp developers by seamlessly integrating verifiable AI inference into their applications without sacrificing decentralization. At the center of ORA is Optimistic Machine Learning (opML). This technology ensures that AI inferences originate from the correct models and that results remain untampered. A robust network of nodes independently verifies each AI inference and can raise challenges if tampering is suspected. Want to be part of this decentralized movement? You can easily run your own node using Tora, our user-friendly node software. Built on Optimistic Machine Learning (opML), our Onchain AI Oracle connects the best open-source AI models to the blockchain. This means any dApp can easily become an AI dApp! With our Initial Model Offering (IMO), developers can tokenize open-source models, effectively overcoming the cold start problem by securing essential funding for their projects. ORA is integrated with several L2s, including Mantle Network, Linea.eth, and Arbitrum. This allows you to build on low-cost chains that best suit your needs. ORA is committed to bringing decentralized, verifiable AI to the blockchain. Are you ready to join us? Use OAO: Read more about OAO: Learn about IMO: Use Tora:

ORA

453,824 görüntüleme • 2 yıl önce

$IT'S HERE and it's ON!!! 🐜 VPR is now live for Claude Code, Codex, Cursor, and every AI tool you already use. A VPN for AI. Same models. A fraction of the cost. Fully anonymous. Download & try it: Take it for a spin and come tell us what you think 👇$

IT'S HERE and it's ON!!! 🐜 VPR is now live for Claude Code, Codex, Cursor, and every AI tool you already use. A VPN for AI. Same models. A fraction of the cost. Fully anonymous. Download & try it: Take it for a spin and come tell us what you think 👇

AntSeed

104,569 görüntüleme • 1 ay önce

🎤 Pavel Durov, founder and CEO of Telegram Messenger, spoke at Blockchain Life 2026 2025 about Telegram Gifts, MiniApps success, and the dangers of centralized AI. Pavel announced Cocoon (Confidential Compute Open Network), a privacy-focused decentralized AI network powered by TON, launching in November. What it offers: 🔹 GPU owners can mine $TON by contributing computing power 🔹 App developers get secure inference without centralized data centers 🔹 Users experience AI services with complete privacy Apply: Watch the full speech 👇

🎤 Pavel Durov, founder and CEO of Telegram Messenger, spoke at Blockchain Life 2026 2025 about Telegram Gifts, MiniApps success, and the dangers of centralized AI. Pavel announced Cocoon (Confidential Compute Open Network), a privacy-focused decentralized AI network powered by TON, launching in November. What it offers: 🔹 GPU owners can mine $TON by contributing computing power 🔹 App developers get secure inference without centralized data centers 🔹 Users experience AI services with complete privacy Apply: Watch the full speech 👇

TON 💎

53,776 görüntüleme • 11 ay önce

Dominion AI (Dominion AI) is a new, Christian-built alternative to mainstream AI models like ChatGPT and Claude. I had a conversation with the founders to learn about why they created it, what it can do, and who it's for. The full interview is now on X. Try Dominion here:

Dominion AI (Dominion AI) is a new, Christian-built alternative to mainstream AI models like ChatGPT and Claude. I had a conversation with the founders to learn about why they created it, what it can do, and who it's for. The full interview is now on X. Try Dominion here:

ZUBY:

18,223 görüntüleme • 4 ay önce

Destra Edge — $DSYNC From Idea to Reality Yesterday, we introduced Destra Edge. Today, we show you what it means. For too long, AI has lived in data centers. Expensive. Centralized. Controlled by a few. Meanwhile, billions of smartphones sit idle. Powerful GPUs. Doing nothing. That doesn’t make sense. So we’re building something different. Destra Edge turns everyday smartphones into a global AI inference network. No servers. No cloud monopolies. Just real compute at the edge. This video shows the system taking shape — how inference is distributed, how it runs on-device, and how results come together without central control. This is not finished. And that’s the point. You’re watching it being built. Layer by layer. In public. The magic is in the scale. More phones means more power. More power means stronger AI. The network grows simply because people join. No passive mining. No fake activity. Only real GPU inference. Measured. Verified. Rewarded. AI shouldn’t live in warehouses. It should live in your pocket. This is edge-level decentralization. This is permissionless inference. This is Destra Network.

Destra Edge — $DSYNC From Idea to Reality Yesterday, we introduced Destra Edge. Today, we show you what it means. For too long, AI has lived in data centers. Expensive. Centralized. Controlled by a few. Meanwhile, billions of smartphones sit idle. Powerful GPUs. Doing nothing. That doesn’t make sense. So we’re building something different. Destra Edge turns everyday smartphones into a global AI inference network. No servers. No cloud monopolies. Just real compute at the edge. This video shows the system taking shape — how inference is distributed, how it runs on-device, and how results come together without central control. This is not finished. And that’s the point. You’re watching it being built. Layer by layer. In public. The magic is in the scale. More phones means more power. More power means stronger AI. The network grows simply because people join. No passive mining. No fake activity. Only real GPU inference. Measured. Verified. Rewarded. AI shouldn’t live in warehouses. It should live in your pocket. This is edge-level decentralization. This is permissionless inference. This is Destra Network.

Destra Network

20,489 görüntüleme • 8 ay önce

Today, the most important technology of our century runs inside about five buildings. Halo is now live on Base, a peer-to-peer marketplace for verifiable AI inference where both sides can be a human or an AI agent. Have a model, serve it. Need one, use it. Every payment settles onchain in USDC in ~2 seconds. AI that lives everywhere and belongs to no one. Learn more 👇

Today, the most important technology of our century runs inside about five buildings. Halo is now live on Base, a peer-to-peer marketplace for verifiable AI inference where both sides can be a human or an AI agent. Have a model, serve it. Need one, use it. Every payment settles onchain in USDC in ~2 seconds. AI that lives everywhere and belongs to no one. Learn more 👇

Warden / Halo

20,554 görüntüleme • 1 ay önce

Claude Fable 5 is INSANE 🤯 So we asked it to build the perfect private AI infrastructure, where: → 500+ open models run behind a single API, not one company's black box → An open data marketplace → You fine-tune and serve models on decentralized GPU compute → Agents pay per call in USDC via x402 → Every model, dataset, and payment settles on Base It failed. Luckily, we've spent years building exactly that. Private Inference. Data. Compute. Settlement. One private AI infrastructure layer. Cluster Protocol.

Claude Fable 5 is INSANE 🤯 So we asked it to build the perfect private AI infrastructure, where: → 500+ open models run behind a single API, not one company's black box → An open data marketplace → You fine-tune and serve models on decentralized GPU compute → Agents pay per call in USDC via x402 → Every model, dataset, and payment settles on Base It failed. Luckily, we've spent years building exactly that. Private Inference. Data. Compute. Settlement. One private AI infrastructure layer. Cluster Protocol.

Cluster Protocol

25,820 görüntüleme • 3 ay önce

50% of my consulting work right now is helping companies use open-source models at scale. Everyone knows how to use an open-source LLM on their computers, but it's really hard to do this at scale for thousands of users. Here is how this plays out: 1. A team builds a prototype using DeepSeek. 2. Everything looks good. It works! 3. They follow an online guide to deploy the model online. 4. They ask 10 users to try the app. 5. Latency spikes everywhere. 6. The entire system halts. 7. They blame DeepSeek and try again using a new model. The problem is always with scaling inference, not the model. Here is one recommendation I give companies: Check out Nebius Token Factory if you don't want to ever think about deploying an open-source model again. This is a managed inference platform for deploying open-source LLMs at scale. This is not for prototypes or research experiments. This is for when you have a real application with real users. Three important notes about Token Factory: • You have complete control over how inference runs. • You have predictable tail latency (P99, not averages). • No surprise costs when you scale up. You can preplan your budget. Check it out here: Here are two codes you can use to get 100 hours of GPU usage on me: ymJLFa2ARYSKEdqb AdckcZaYjm7KqYY7 Thanks to the Nebius team for their continuous partnership.

50% of my consulting work right now is helping companies use open-source models at scale. Everyone knows how to use an open-source LLM on their computers, but it's really hard to do this at scale for thousands of users. Here is how this plays out: 1. A team builds a prototype using DeepSeek. 2. Everything looks good. It works! 3. They follow an online guide to deploy the model online. 4. They ask 10 users to try the app. 5. Latency spikes everywhere. 6. The entire system halts. 7. They blame DeepSeek and try again using a new model. The problem is always with scaling inference, not the model. Here is one recommendation I give companies: Check out Nebius Token Factory if you don't want to ever think about deploying an open-source model again. This is a managed inference platform for deploying open-source LLMs at scale. This is not for prototypes or research experiments. This is for when you have a real application with real users. Three important notes about Token Factory: • You have complete control over how inference runs. • You have predictable tail latency (P99, not averages). • No surprise costs when you scale up. You can preplan your budget. Check it out here: Here are two codes you can use to get 100 hours of GPU usage on me: ymJLFa2ARYSKEdqb AdckcZaYjm7KqYY7 Thanks to the Nebius team for their continuous partnership.

Santiago

48,605 görüntüleme • 8 ay önce

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 görüntüleme • 1 ay önce

Baseten’s CEO on why every company will want to own its AI / Tuhin Srivastava on the rise of AI agents, the demand for inference, and building a new kind of hyperscaler. Lately, I’ve been spending a lot of time thinking about the AI inference market and how big it could get. Agents like Muse, Instinct, Town, and Grok Bot aggressively use browsers, run their own computers, and burn through far more tokens than a traditional chatbot. As more people put them to work (Muse is number two in the App Store), the demand for inference, or the computing needed to run these models, should grow enormously. Tuhin and I discuss the rise of agents using browsers and virtual machines to get things done, and Baseten's recent acquisition to help power that shift. We also talk about the data center backlash, why companies are embracing Chinese open models, his plans for Baseten’s new research lab, and why he thinks inference becomes the only market left after AGI. Timestamps: 00:00 What Is AI Inference? 06:05 Competing With the Cloud Giants 09:24 Why Companies Want to Own Their AI 15:30 Building Baseten Before the AI Boom 23:45 DeepSeek and the Race for Open AI Models 29:32 Baseten’s Growth and Expansion 33:05 AI Agents and the Blaxel Acquisition 38:32 Data Centers and the AI Backlash 43:25 What Happens to Inference After AGI? 45:12 When AI Agents Become Customers Thanks to the show's premier sponsors: Atlassian, Granola, and Mercury.

Baseten’s CEO on why every company will want to own its AI / Tuhin Srivastava on the rise of AI agents, the demand for inference, and building a new kind of hyperscaler. Lately, I’ve been spending a lot of time thinking about the AI inference market and how big it could get. Agents like Muse, Instinct, Town, and Grok Bot aggressively use browsers, run their own computers, and burn through far more tokens than a traditional chatbot. As more people put them to work (Muse is number two in the App Store), the demand for inference, or the computing needed to run these models, should grow enormously. Tuhin and I discuss the rise of agents using browsers and virtual machines to get things done, and Baseten's recent acquisition to help power that shift. We also talk about the data center backlash, why companies are embracing Chinese open models, his plans for Baseten’s new research lab, and why he thinks inference becomes the only market left after AGI. Timestamps: 00:00 What Is AI Inference? 06:05 Competing With the Cloud Giants 09:24 Why Companies Want to Own Their AI 15:30 Building Baseten Before the AI Boom 23:45 DeepSeek and the Race for Open AI Models 29:32 Baseten’s Growth and Expansion 33:05 AI Agents and the Blaxel Acquisition 38:32 Data Centers and the AI Backlash 43:25 What Happens to Inference After AGI? 45:12 When AI Agents Become Customers Thanks to the show's premier sponsors: Atlassian, Granola, and Mercury.

Alex Heath

26,164 görüntüleme • 14 gün önce

We are thrilled to announce Kasumi2 Open Beta 🎉🤖 Kasumi2 is an autonomous AI agent that performs actions in the Arbius network. It is a miner, has its own wallet, can retrieve results from Arbius AI models, and runs its own individual LLM model. Additionally, Kasumi2 can run any AI model for fast generations, is censorship-resistant, decentralized, and responds with AI powered interactions If you would like to test it out, click on the link below. 🔗

We are thrilled to announce Kasumi2 Open Beta 🎉🤖 Kasumi2 is an autonomous AI agent that performs actions in the Arbius network. It is a miner, has its own wallet, can retrieve results from Arbius AI models, and runs its own individual LLM model. Additionally, Kasumi2 can run any AI model for fast generations, is censorship-resistant, decentralized, and responds with AI powered interactions If you would like to test it out, click on the link below. 🔗

Arbius

25,072 görüntüleme • 2 yıl önce

Exciting news – our NetMind Power Inference Platform is officially live! We’ve launched with 12 open-source models in areas such as image generation, text-to-image conversion, and voice synthesis. Users can directly use these models on the Inference Platform or access them via API. The platform is currently in Beta and free to use – so be sure to check it out now before the free Beta period ends! For a detailed demo, follow the video link below. Remember, we’ll continuously add more models, so keep returning for the latest advancements. Explore these powerful AI capabilities today and see what you can create! #NetMindPower #OpenSourceAI #AIInference

Exciting news – our NetMind Power Inference Platform is officially live! We’ve launched with 12 open-source models in areas such as image generation, text-to-image conversion, and voice synthesis. Users can directly use these models on the Inference Platform or access them via API. The platform is currently in Beta and free to use – so be sure to check it out now before the free Beta period ends! For a detailed demo, follow the video link below. Remember, we’ll continuously add more models, so keep returning for the latest advancements. Explore these powerful AI capabilities today and see what you can create! #NetMindPower #OpenSourceAI #AIInference

NetMind.AI

46,274 görüntüleme • 2 yıl önce

the era of agentic AI is here Google just open sourced Gemma 3, you can run it on a single GPU laptop or even phone - agentic AI workflows - 4 Sizes – 1B, 4B, 12B, 27B - up to 128K tokens - speaks 140+ languages - hit 1338 ELO on LMArena try & download for free, links 👇

the era of agentic AI is here Google just open sourced Gemma 3, you can run it on a single GPU laptop or even phone - agentic AI workflows - 4 Sizes – 1B, 4B, 12B, 27B - up to 128K tokens - speaks 140+ languages - hit 1338 ELO on LMArena try & download for free, links 👇

el.cine

199,579 görüntüleme • 1 yıl önce

Introducing EVO Router - a smart router that continuously optimizes and hill-climbs on your AI inference workloads. It learns from your code, prompts, production traffic, use cases, and SLAs, then searches for the best setup for every workload. That can mean more than choosing just a single model or routing through a classifier. EVO (YC F26) can optimize across model × provider combinations, fusion systems, cascades, routing policies, and prompts — whatever the workload allows, while staying within your quality, latency, and reliability constraints. As open-source models move closer to frontier-model performance, we believe this kind of workload-specific hill-climbing will become an important part of how AI systems are deployed and operated. Our early users have seen 30–60% lower inference costs across use cases ranging from complex multi-turn agents to asynchronous batch workloads. EVO Router has also reached the Pareto frontier on several benchmarks, outperforming existing setups on cost, quality, or both.

Introducing EVO Router - a smart router that continuously optimizes and hill-climbs on your AI inference workloads. It learns from your code, prompts, production traffic, use cases, and SLAs, then searches for the best setup for every workload. That can mean more than choosing just a single model or routing through a classifier. EVO (YC F26) can optimize across model × provider combinations, fusion systems, cascades, routing policies, and prompts — whatever the workload allows, while staying within your quality, latency, and reliability constraints. As open-source models move closer to frontier-model performance, we believe this kind of workload-specific hill-climbing will become an important part of how AI systems are deployed and operated. Our early users have seen 30–60% lower inference costs across use cases ranging from complex multi-turn agents to asynchronous batch workloads. EVO Router has also reached the Pareto frontier on several benchmarks, outperforming existing setups on cost, quality, or both.

Alok Bishoyi

16,103 görüntüleme • 1 ay önce

$NBIS cofounder Roman Chernin describes how their recent acquisitions of Eigen AI and Clarifai were all about speed, incredible talent, and acceleration: "The philosophy is very simple. We need to build so many things, and we need to move so fast, that we're always looking for people who can accelerate us. It should be exceptional talent, and/or something that has a great adoption." "Our two recent acquisitions [were] two teams that work on inference optimization. A big part of our business is how efficiently we convert GPUs into tokens. And these two teams — Eigen AI and Clarifai — one is focused on model optimization, the engine of inference. How you run specific models and all the techniques around spec decoding, quantization, and so on." "And the other is system optimization. All the routing, KV caching, and orchestration across the big cluster of compute and so on." "We have a very strong internal team working on inference. But we felt that we needed to move faster, bring more capabilities. Because the market is so fast."

$NBIS cofounder Roman Chernin describes how their recent acquisitions of Eigen AI and Clarifai were all about speed, incredible talent, and acceleration: "The philosophy is very simple. We need to build so many things, and we need to move so fast, that we're always looking for people who can accelerate us. It should be exceptional talent, and/or something that has a great adoption." "Our two recent acquisitions [were] two teams that work on inference optimization. A big part of our business is how efficiently we convert GPUs into tokens. And these two teams — Eigen AI and Clarifai — one is focused on model optimization, the engine of inference. How you run specific models and all the techniques around spec decoding, quantization, and so on." "And the other is system optimization. All the routing, KV caching, and orchestration across the big cluster of compute and so on." "We have a very strong internal team working on inference. But we felt that we needed to move faster, bring more capabilities. Because the market is so fast."

TBPN

30,608 görüntüleme • 4 ay önce