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SN19 - Inference faster than almost all web2 (all but Grok) - Training simplified—train LLMs with zero coding skills - Deploy your own serverless, decentralized model powered by network miners All in this weeks episode: namoray mogmachine (ττ) const

Openτensor Foundaτion

168,142 subscribers

56,083 просмотров • 1 год назад •via X (Twitter)

Новости и политика Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля Ales

Ales1 год назад

$TAO AI 🤖 king 👑

Фото профиля subnet Sam

subnet Sam1 год назад

@darkobro1 This is going to be major for my project. So pumped to implement. Great work guys!!

Фото профиля Misterion

Misterion1 год назад

$TAO

Фото профиля Berk

Berk1 год назад

@CryptoWizardd

Фото профиля Purebull 🪬 τ

Purebull 🪬 τ1 год назад

$tao 🔥🔥🔥

Фото профиля RedisRed🔺🔺🔺

RedisRed🔺🔺🔺1 год назад

Best project ♥️

Фото профиля amalicrypto31

amalicrypto311 год назад

Huge 🔥🚀 great work

Фото профиля τaowech.42acc

τaowech.42acc1 год назад

niice

Фото профиля uwu capital

uwu capital1 год назад

Wen shitcoins on TAO and smart contracts

Фото профиля Tony

Tony1 год назад

@elonmusk check it out, you'll love bittensor

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EVERYONE IS BUILDING CUSTOM GPTS BUT ALMOST NOBODY IS TRAINING THEIR OWN MODEL FROM SCRATCH $100 and one weekend is all it takes with andrej karpathy’s nanochat you pretrain a gpt-2 grade base model on 400 billion tokens of web text, then fine-tune it on your own obsidian notes, your ai chat history, your writing it answers in your voice, recalls your own ideas, runs locally with no api and no subscriptions complete builder’s guide with every command in the article ↓

EVERYONE IS BUILDING CUSTOM GPTS BUT ALMOST NOBODY IS TRAINING THEIR OWN MODEL FROM SCRATCH $100 and one weekend is all it takes with andrej karpathy’s nanochat you pretrain a gpt-2 grade base model on 400 billion tokens of web text, then fine-tune it on your own obsidian notes, your ai chat history, your writing it answers in your voice, recalls your own ideas, runs locally with no api and no subscriptions complete builder’s guide with every command in the article ↓

leopardracer

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Actual usage of SpaceXAI Grok 4.5. First person reports that there are programmers successfully replacing Opus and Sonnet in all of his systems today with Grok 4.5. Grok 4.5 worked great. Spending can drop 50-80%. $200-500 instead of $1000. Grok 4.5 is faster than Opus 4.8. Maybe 2 to 3 times faster. OpenAI GPT 5.6 is coming tomorrow and will be faster but more expensive than Grok 4.5. Grok 4.5 can own the low end of developer usage (pretty, efficient value and good enough). This could send Cursor-SpaceXAI enterprise revenue up from $4 Billion ARR to $8-15 billion ARR in 1-2 months. SpaceXAI Elon Musk

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🎙 New episode of the DeXe Podcast is live! Hosted by Sergio K, this episode features Shashank | 𝔽rAI from Fraction AI, a leading builder in decentralized AI. We talked about: • How decentralized models help train AI through competition and reinforcement learning • Tokenization, governance, and ownership of AI agents • AI future, impact, and limitations 🔴Watch on YouTube: 🎧 Listen on Spotify: Curious about the future of AI training, ownership, and governance and how it all merges with blockchain? Tune in to the full episode!

🎙 New episode of the DeXe Podcast is live! Hosted by Sergio K, this episode features Shashank | 𝔽rAI from Fraction AI, a leading builder in decentralized AI. We talked about: • How decentralized models help train AI through competition and reinforcement learning • Tokenization, governance, and ownership of AI agents • AI future, impact, and limitations 🔴Watch on YouTube: 🎧 Listen on Spotify: Curious about the future of AI training, ownership, and governance and how it all merges with blockchain? Tune in to the full episode!

DeXe Protocol

30,393 просмотров • 1 год назад

Agents are about to do a lot more than chat. Give your agent money. Give it skills. Let it trade autonomously, around the clock. Snipe new launches the instant they go live. Move faster than any human at a keyboard. And watch it all happen in a live 3D world, with full guardrails, audit logs, and permissions you control. An entire ecosystem has formed around it. Skills. Memories. Animations. Emotions. Monetize your strategies, list an agent with a high win rate, and earn a cut every time someone forks it. This is a true decentralized AI network. Sniping. Autonomous trading. Agents that never stop. And all of that activity flows back to one place. The THREE ecosystem.

Agents are about to do a lot more than chat. Give your agent money. Give it skills. Let it trade autonomously, around the clock. Snipe new launches the instant they go live. Move faster than any human at a keyboard. And watch it all happen in a live 3D world, with full guardrails, audit logs, and permissions you control. An entire ecosystem has formed around it. Skills. Memories. Animations. Emotions. Monetize your strategies, list an agent with a high win rate, and earn a cut every time someone forks it. This is a true decentralized AI network. Sniping. Autonomous trading. Agents that never stop. And all of that activity flows back to one place. The THREE ecosystem.

three.ws

23,724 просмотров • 1 месяц назад

Raiinmaker x Google marks a breakthrough in AI video model training. Over 450,000 contributors. 190+ countries. Real people. Real data. Real results. You can now train AI and generate beautiful, cinematic video all from your phone. It’s fast, it’s easy, it’s next-level. This is what the future looks like: Raiinmaker’s decentralized Video Intelligence platform Traiin Video, together with Google’s cutting-edge Veo technology = a new standard for ethical AI video training. Ethical AI. Human-powered. Built to win. Make AI Yours. Make it $RAIIN. Google Cloud Google Cloud Partners Google AI #GoogleCloudPartner #GoogleCloud

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Georgia | Action Model

10,518 просмотров • 4 месяцев назад

$After taking some time off post-Rapid, I'm excited to share what I’ve been up to since: Datawizz AI! We’ve raised a $12.5M Seed led by Human Capital to make AI 10x cheaper, 2x more accurate and 15x faster by transitioning from LLMs to SLMs. AI is eating the world. But unit economics are eating AI. Looking at the fastest growing AI products, they all share two traits - growing fast, and painful inference bills. General-purpose LLMs are just too expensive to run. A big reason for that is we train LLMs to be good at everything - answer any question, be an expert on any topic. The big labs dub this "generalisation", but for real-world applications, it is unnecessary. In reality - many AI applications need models to be experts in one thing - and do that thing extremely well. Your coding model doesn’t need to memorize ancient recipes for Garum sauce. This is where Datawizz comes in - we sit between the AI applications and automatically create smaller (100x-1,000x) specialized models to handle specific aspects of your work. By focusing the model and combining industry-data in the distillation process - we end up with models that beat SOTA LLMs at a fraction of the cost. We created Datawizz to make AI specialized and scalable. We’re early in the journey, but have already been able to save companies 90%+ on their inference bill and speed up their apps by 10x. Excited to build better AI platforms? Join the Datawizz team (link in first comment)$

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New short course with Mistral AI ! Mistral's open-source Mixtral 8x7B model uses a "mixture of experts" (MoE) architecture. Unlike a standard transformer, an MoE model has multiple expert feed-forward networks (8 in this case), with a gating network selecting two experts at inference time. This enables MoE to match the performance of a large model but faster inference. Mixtral 8x7B has 46.7B parameters but activates only 12.9B at inference to predict the next token. In Getting Started with Mistral, you’ll learn from Mistral’s Sophia Yang, Ph.D. to: - Explore Mistral's open-source models (Mistral 7B, Mixtral 8x7B) and commercial models via API calls and Mistral AI's Le Chat website - Implement JSON mode to generate structured outputs to integrate directly into larger software systems. - Use function calling for Tool Use, such as calling custom Python code that queries tabular data - Ground your LLM's response with external knowledge sources using RAG - Build a Mistral-powered chat interface that can reference external documents This course will help deepen your prompt engineering skills. Please sign up here:

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$QVAC SDK will support in 0.9.0 (gonna be release in ~10 days) LoRA fine-tuning directly on-device, letting developers customize LLMs with their own data without sending anything to the cloud. You just load a base model, point it at your training dataset, and get a lightweight LoRA adapter back — all running locally. The fine-tuned model can then be used for inference immediately, with no extra setup. Why it matters: LoRA (Low-Rank Adaptation) fine-tuning lets you specialize a general-purpose language model for your specific use case — like matching a brand's tone, mastering domain terminology, or following a particular output format — using a fraction of the compute a full fine-tune would require. QVAC handles the entire workflow locally: dataset preparation, training with configurable hyperparameters, checkpoint saving, and seamless inference with the resulting adapter. Your data never leaves the device. The developer experience: Fine-tuning with QVAC is as simple as calling "sdk.finetune()" with your dataset and a few hyperparameters. Training runs entirely on your local hardware, produces a compact LoRA adapter file, and supports pause/resume so you can stop a job and pick it back up without losing progress. The result plugs straight into QVAC's inference pipeline — no model conversion, no deployment step, just immediate local completions with your fine-tuned model.$

QVAC SDK will support in 0.9.0 (gonna be release in ~10 days) LoRA fine-tuning directly on-device, letting developers customize LLMs with their own data without sending anything to the cloud. You just load a base model, point it at your training dataset, and get a lightweight LoRA adapter back — all running locally. The fine-tuned model can then be used for inference immediately, with no extra setup. Why it matters: LoRA (Low-Rank Adaptation) fine-tuning lets you specialize a general-purpose language model for your specific use case — like matching a brand's tone, mastering domain terminology, or following a particular output format — using a fraction of the compute a full fine-tune would require. QVAC handles the entire workflow locally: dataset preparation, training with configurable hyperparameters, checkpoint saving, and seamless inference with the resulting adapter. Your data never leaves the device. The developer experience: Fine-tuning with QVAC is as simple as calling "sdk.finetune()" with your dataset and a few hyperparameters. Training runs entirely on your local hardware, produces a compact LoRA adapter file, and supports pause/resume so you can stop a job and pick it back up without losing progress. The result plugs straight into QVAC's inference pipeline — no model conversion, no deployment step, just immediate local completions with your fine-tuned model.

Paolo Ardoino 🤖

42,413 просмотров • 3 месяцев назад

"Nvidia is positioned perfectly to thrive on the coding agent wave" and the explosion in inference demand, says tae kim. "I met with Ian Buck and dozens of engineers at Meta, Google, and Nvidia. All of them are seeing crazy inference demand and AI compute shortages." "People are building bots to pick up any kind of B200 GPUs they can find, but they're waiting weeks or months." "Jensen is very prescient. He probably saw this demand months away. He locked up all the supply agreements for memory, CoWoS, and connectors ahead of time to take advantage of the coding-assistant boom." "It's almost like a gold rush. You see OpenAI pivoting toward it, Anthropic is obviously thriving on it. Billions of ARR every few weeks." "Jensen acquired Groq's assets and people. And the combination of integrating Groq's technology with Vera Rubin lets Nvidia serve this tremendous wave of compute demand economically."

"Nvidia is positioned perfectly to thrive on the coding agent wave" and the explosion in inference demand, says tae kim. "I met with Ian Buck and dozens of engineers at Meta, Google, and Nvidia. All of them are seeing crazy inference demand and AI compute shortages." "People are building bots to pick up any kind of B200 GPUs they can find, but they're waiting weeks or months." "Jensen is very prescient. He probably saw this demand months away. He locked up all the supply agreements for memory, CoWoS, and connectors ahead of time to take advantage of the coding-assistant boom." "It's almost like a gold rush. You see OpenAI pivoting toward it, Anthropic is obviously thriving on it. Billions of ARR every few weeks." "Jensen acquired Groq's assets and people. And the combination of integrating Groq's technology with Vera Rubin lets Nvidia serve this tremendous wave of compute demand economically."

TBPN

32,912 просмотров • 4 месяцев назад

Raising An Agent - Episode 5 In this episode, Beyang interviews Thorsten Ball and Quinn Slack to unpack what has happened in the world of Amp in the last five weeks: how predictions played out, how working with agents shaped how they write code, how agents are and will influence model development, and, of course, all the things that have been shipped in Amp. Timestamps: 0:00 Opening Teaser 1:24 Introduction and Casual Conversation 2:02 Changes in Coding Agents Over Five Weeks 3:28 Challenges and Misconceptions 6:52 Optimizing Tools and Prompts 9:15 User Feedback and Best Practices 15:29 Advanced User Strategies 22:41 Balancing Integration and Flexibility 24:17 Cost and Accessibility of Agents 28:05 Feedback Loops and Model Training 35:12 The Evolution of AI Coding Agents 35:35 Training Data, Tool Affinity, and Tool Integration 36:50 User Feedback and Model Training 43:22 Product Evolution and New Features 55:55 Remote Execution and Multi-Agent Workflows 1:02:47 Future Directions and Improvements

Raising An Agent - Episode 5 In this episode, Beyang interviews Thorsten Ball and Quinn Slack to unpack what has happened in the world of Amp in the last five weeks: how predictions played out, how working with agents shaped how they write code, how agents are and will influence model development, and, of course, all the things that have been shipped in Amp. Timestamps: 0:00 Opening Teaser 1:24 Introduction and Casual Conversation 2:02 Changes in Coding Agents Over Five Weeks 3:28 Challenges and Misconceptions 6:52 Optimizing Tools and Prompts 9:15 User Feedback and Best Practices 15:29 Advanced User Strategies 22:41 Balancing Integration and Flexibility 24:17 Cost and Accessibility of Agents 28:05 Feedback Loops and Model Training 35:12 The Evolution of AI Coding Agents 35:35 Training Data, Tool Affinity, and Tool Integration 36:50 User Feedback and Model Training 43:22 Product Evolution and New Features 55:55 Remote Execution and Multi-Agent Workflows 1:02:47 Future Directions and Improvements

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PI'S HUMAN WORKFORCE DEMONSTRATES 100% EFFICIENCY Pi Network (Pi Network) confirmed that its decentralized workforce of one million identity-verified humans provides massive-scale throughput across its ecosystem. This distributed infrastructure has already processed 526 million tasks, accomplishing accurate and efficient verification for over 18 Million Pioneers in more than 200 countries and regions. AI companies can now access this workforce to eliminate bot-driven fraud, with all micropayments settled in $PI or custom tokens via the Pi Launchpad. With compensation supported in Pi, or in a company’s own token through Pi Launchpad, Pi Network’s model opens a new way to align work, incentives, and ecosystem growth.

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“Nobody had hope in crypto any more… But we posted about our 72B parameter run and it caught fire.” — Templar creator Sam Dare Hear about how this subnet incentivized miners to train a massive new AI model, what’s holding it back from competing with the likes of GPT-5 and Opus 4.6, and the advantages of decentralizing and democratizing the training process on a brand new TWiST. PLUS Sam’s reaction to hearing his project discussed on the The All in podcast with special guest Jensen Huang. cc: @jason, Lon Harris, templar, Distributed State

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30,422 просмотров • 4 месяцев назад

Rivian CEO RJ Scaringe in new interview on self-driving: "Tesla's got a huge fleet. So our approach is we have to catch up with them and compete. And so we're going to have more cameras with better dynamic range. We're going to supplement that with LiDAR that provides better safety for edge case conditions and allows us to train the vision models faster. And the incremental cost to do that is relatively small. And in fact any of the incremental costs that would have been there on its own is offset by the fact that we brought inference in house and reduced the cost of our inference platform so dramatically from what we have in your car. Your car uses an Nvidia inference platform. I say all this because in like the infinite long-term you know, you could make the case that once the models are very very robust you could have less cameras or you may be able to get away with less radar. It's not clear yet if that's the case for covering all these corner cases."

Rivian CEO RJ Scaringe in new interview on self-driving: "Tesla's got a huge fleet. So our approach is we have to catch up with them and compete. And so we're going to have more cameras with better dynamic range. We're going to supplement that with LiDAR that provides better safety for edge case conditions and allows us to train the vision models faster. And the incremental cost to do that is relatively small. And in fact any of the incremental costs that would have been there on its own is offset by the fact that we brought inference in house and reduced the cost of our inference platform so dramatically from what we have in your car. Your car uses an Nvidia inference platform. I say all this because in like the infinite long-term you know, you could make the case that once the models are very very robust you could have less cameras or you may be able to get away with less radar. It's not clear yet if that's the case for covering all these corner cases."

Sawyer Merritt

449,694 просмотров • 4 месяцев назад

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇

Hugo

54,481 просмотров • 5 месяцев назад

Orynth Product Review EP15 Syllaby is an all-in-one AI studio for video. Start with real keyword research to find what your audience is searching for, then it writes the script, generates the video with consistent AI characters and your own cloned voice, and schedules it across your platforms. Powered by the top models like Sora 2, Google VEO 3, and Seedance. Over 250,000 (currently almost half a million!) videos have been made with it. A real product listed on Orynth with its own token $SYLLA In this fifteenth episode, we break down what Syllaby does, the operator running it, and why an established AI video tool is worth a look.

Orynth Product Review EP15 Syllaby is an all-in-one AI studio for video. Start with real keyword research to find what your audience is searching for, then it writes the script, generates the video with consistent AI characters and your own cloned voice, and schedules it across your platforms. Powered by the top models like Sora 2, Google VEO 3, and Seedance. Over 250,000 (currently almost half a million!) videos have been made with it. A real product listed on Orynth with its own token $SYLLA In this fifteenth episode, we break down what Syllaby does, the operator running it, and why an established AI video tool is worth a look.

Orynth News

11,011 просмотров • 1 месяц назад

Today we’re releasing the Allora Forge Builder Kit, a streamlined way to build, train, and deploy ML models directly on the Allora Network 🔥⚒️ Now within a single notebook, users can move from setup to live predictions in minutes and start earning with Allora. Many contributors struggle with fragmented ML workflows: separate training and deployment pipelines, repeated debugging, and cumbersome ops overhead. The Forge Builder Kit abstracts away the ML & blockchain ops, allowing users to concentrate on feature engineering, optimizing accuracy, and iteration—all from within their favorite environment. Here is what the workflow looks like: 1. Open the notebook 2. Paste an API key and run the setup cell to pull data and features 3. Train a lightweight LightGBM model 4. Evaluate metrics like correlation and directional accuracy 5. Export predict.pkl and deploy on Allora Forge. The same functions power both training and live inference. The Forge Builder Kit is the foundation for builders earning with Allora, with more improvements coming, including: • Benchmark datasets for standardized evaluation • Templates for agentic model loops

Today we’re releasing the Allora Forge Builder Kit, a streamlined way to build, train, and deploy ML models directly on the Allora Network 🔥⚒️ Now within a single notebook, users can move from setup to live predictions in minutes and start earning with Allora. Many contributors struggle with fragmented ML workflows: separate training and deployment pipelines, repeated debugging, and cumbersome ops overhead. The Forge Builder Kit abstracts away the ML & blockchain ops, allowing users to concentrate on feature engineering, optimizing accuracy, and iteration—all from within their favorite environment. Here is what the workflow looks like: 1. Open the notebook 2. Paste an API key and run the setup cell to pull data and features 3. Train a lightweight LightGBM model 4. Evaluate metrics like correlation and directional accuracy 5. Export predict.pkl and deploy on Allora Forge. The same functions power both training and live inference. The Forge Builder Kit is the foundation for builders earning with Allora, with more improvements coming, including: • Benchmark datasets for standardized evaluation • Templates for agentic model loops

Allora

35,829 просмотров • 11 месяцев назад