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Any Rei lover here? 🤨 Source: Draktar🔞Comms Open (They Eva content are something else🫡） #Evangelion #hmv #nsfw #ayanamirei

Code0999

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52,962 просмотров • 20 дней назад •via X (Twitter)

NSFW #Evangelion #hmv #nsfw #ayanamirei

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David Sacks: Anthropic Wants to Ban Open Source AI in America Listen to All-In to stay a few weeks (or months) ahead of everyone else David Sacks back in May: “I think where it's all leading to is an effort to ban open source models. There's a lot of breadcrumbs leading here. You look at a lot of the rhetoric around how models need to have guardrails, and that with open source models, the guardrails can be removed and therefore they're dangerous. You see this rhetoric already in Anthropic's blog posts. Any threat that they describe, they kind of go out of their way to take that shot at open source models. They're trying to create ideas or put predicate facts in the public record to justify an action later on. I think it's just a matter of time before they feel like they're at a position where maybe they can push for that type of ban directly.”

David Sacks: Anthropic Wants to Ban Open Source AI in America Listen to All-In to stay a few weeks (or months) ahead of everyone else David Sacks back in May: “I think where it's all leading to is an effort to ban open source models. There's a lot of breadcrumbs leading here. You look at a lot of the rhetoric around how models need to have guardrails, and that with open source models, the guardrails can be removed and therefore they're dangerous. You see this rhetoric already in Anthropic's blog posts. Any threat that they describe, they kind of go out of their way to take that shot at open source models. They're trying to create ideas or put predicate facts in the public record to justify an action later on. I think it's just a matter of time before they feel like they're at a position where maybe they can push for that type of ban directly.”

The All-In Podcast

87,353 просмотров • 1 месяц назад

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Santiago

126,434 просмотров • 11 месяцев назад

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.

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.

Santiago

39,101 просмотров • 2 лет назад

#WATCH | Delhi: At the #IndiaAIImpactSummit2026, French President Emmanuel Macron says, "India built something that no other country in the world has built. A digital identity for 1.4 billion people. A payment system that now processes 20 billion transactions every month. A health infrastructure that has issued 500 million digital health IDs. Here are the results. They call it the India Stack Open Interoperable Sovereign. That is what this summit is about. We are clearly at the beginning of a huge acceleration, and you perfectly described it during your interventions..." (Source: DD News)

#WATCH | Delhi: At the #IndiaAIImpactSummit2026, French President Emmanuel Macron says, "India built something that no other country in the world has built. A digital identity for 1.4 billion people. A payment system that now processes 20 billion transactions every month. A health infrastructure that has issued 500 million digital health IDs. Here are the results. They call it the India Stack Open Interoperable Sovereign. That is what this summit is about. We are clearly at the beginning of a huge acceleration, and you perfectly described it during your interventions..." (Source: DD News)

ANI

148,809 просмотров • 6 месяцев назад

You probably have thousands of agent traces sitting in production. And most of them are doing nothing for you. Today we’re launching the new Kitaru to change that. Kitaru ingests traces from Langfuse, Braintrust, or any OTel source, investigates what went right and wrong, groups recurring failures into cohorts, and builds evaluators around them. Then you can replay those same production cases against a different model, prompt, context, or agent setup and ask: What would have happened if we changed this? Instead of traces being something you inspect after a failure, they become part of a continuous improvement loop. Open source. Free to use. Try it out today:

You probably have thousands of agent traces sitting in production. And most of them are doing nothing for you. Today we’re launching the new Kitaru to change that. Kitaru ingests traces from Langfuse, Braintrust, or any OTel source, investigates what went right and wrong, groups recurring failures into cohorts, and builds evaluators around them. Then you can replay those same production cases against a different model, prompt, context, or agent setup and ask: What would have happened if we changed this? Instead of traces being something you inspect after a failure, they become part of a continuous improvement loop. Open source. Free to use. Try it out today:

Hamza Tahir

22,264 просмотров • 24 дней назад

These camera glasses are amazing - and they are doing something no one else is doing. Not only is the hardware is top class, but unlike the Meta Raybans, these glasses were created with an open-source OS and an App Store from the start. Instead of trying to lock you in a proprietary AI walled garden, they’re inviting developers into the conversation - to truly figure out as a community what this new class of hardware can become. This emphasis in community is what will ultimately make these glasses be more useful (and more successful) than competitors in the long run. Mentra understands the value of open source software, community and that new product classes are figured out from the bottom up, not top down. Also, developing for this is super easy and they only weigh 43 grams. That’s almost 30% lighter than the latest Meta Glasses. Absolutely insane how this startup is outclassing Meta. They’re available now at Mentra for $299. Can’t think of a better deal in this space.

These camera glasses are amazing - and they are doing something no one else is doing. Not only is the hardware is top class, but unlike the Meta Raybans, these glasses were created with an open-source OS and an App Store from the start. Instead of trying to lock you in a proprietary AI walled garden, they’re inviting developers into the conversation - to truly figure out as a community what this new class of hardware can become. This emphasis in community is what will ultimately make these glasses be more useful (and more successful) than competitors in the long run. Mentra understands the value of open source software, community and that new product classes are figured out from the bottom up, not top down. Also, developing for this is super easy and they only weigh 43 grams. That’s almost 30% lighter than the latest Meta Glasses. Absolutely insane how this startup is outclassing Meta. They’re available now at Mentra for $299. Can’t think of a better deal in this space.

Lucas Rizzotto

11,283 просмотров • 7 месяцев назад

The infantilization of the Arabs is a prime example of white supremacy! Interviewer: "Do you have other option?" ד״ר עינת וילף Dr. Einat Wilf : "Absolutely! The Palestinians (I know it sounds radical and revolutionary) are perfectly capable people of taking care of themselves! They are perfectly capable of deciding, that they are not going to spend their considerable talents on fighting the Jewish state, and actually building something for themselves, rather than destroying for others." Interviewer: "So they are capable, but they are not doing it." Einat: "Because the world continues to indulge them, as if they can't do it, and therefore continues to funnel money as if they are a poor helpless people, who cannot do anything. If anything, October 7th should put an end to any notion of the poor Palestinians. Can we agree that October 7th required massive investment in infrastructure? Imagine that they would have used that money to pay teacher salaries, care for their own... But no, the world released them from that responsibility and told them: "You go focus on b*tchering Jews, we'll fund everything else that you need". Video from milz513 on TikTok, originally shared by on Instagram. *********************** 𝗗𝗶𝘀𝗰𝗹𝗮𝗶𝗺𝗲𝗿: This content is intended solely for educational purposes. I unequivocally denounce any form of violence, hate, harassment, or bullying. This page does not endorse or promote dangerous acts, organizations, or any form of violence. In accordance with section 107 of the Copyright Act of 1976, this content is made available for “fair use” purposes, including criticism, comment, news reporting, teaching, scholarship, education, and research.

The infantilization of the Arabs is a prime example of white supremacy! Interviewer: "Do you have other option?" ד״ר עינת וילף Dr. Einat Wilf : "Absolutely! The Palestinians (I know it sounds radical and revolutionary) are perfectly capable people of taking care of themselves! They are perfectly capable of deciding, that they are not going to spend their considerable talents on fighting the Jewish state, and actually building something for themselves, rather than destroying for others." Interviewer: "So they are capable, but they are not doing it." Einat: "Because the world continues to indulge them, as if they can't do it, and therefore continues to funnel money as if they are a poor helpless people, who cannot do anything. If anything, October 7th should put an end to any notion of the poor Palestinians. Can we agree that October 7th required massive investment in infrastructure? Imagine that they would have used that money to pay teacher salaries, care for their own... But no, the world released them from that responsibility and told them: "You go focus on b*tchering Jews, we'll fund everything else that you need". Video from milz513 on TikTok, originally shared by on Instagram. *********************** 𝗗𝗶𝘀𝗰𝗹𝗮𝗶𝗺𝗲𝗿: This content is intended solely for educational purposes. I unequivocally denounce any form of violence, hate, harassment, or bullying. This page does not endorse or promote dangerous acts, organizations, or any form of violence. In accordance with section 107 of the Copyright Act of 1976, this content is made available for “fair use” purposes, including criticism, comment, news reporting, teaching, scholarship, education, and research.

lelemSLP

108,378 просмотров • 2 лет назад

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

Akshay 🚀

83,187 просмотров • 10 месяцев назад

Neil on open source v. frontier models: "The labs pay an immense premium to be 3-6 months ahead of everything else. I think that's probably still worth it. But I don't know that the premium is going to last that long. If you look at enterprise deployments, they don't move at three to six month speed. A lot of enterprises are probably still on Opus 4.6 or Opus 4.7. I don't think there's any way to call a winner in this race, and certainly I don't even think this is a race that can be decided ever. It's a continual process. I don't think open source ever goes away. If there's a vacuum because one leader steps out, a new leader will step in. It just gets easier every day to train a frontier class model."

Neil on open source v. frontier models: "The labs pay an immense premium to be 3-6 months ahead of everything else. I think that's probably still worth it. But I don't know that the premium is going to last that long. If you look at enterprise deployments, they don't move at three to six month speed. A lot of enterprises are probably still on Opus 4.6 or Opus 4.7. I don't think there's any way to call a winner in this race, and certainly I don't even think this is a race that can be decided ever. It's a continual process. I don't think open source ever goes away. If there's a vacuum because one leader steps out, a new leader will step in. It just gets easier every day to train a frontier class model."

Patrick OShaughnessy

32,180 просмотров • 17 дней назад

US Secretary of Commerce Howard Lutnick says our immigration system is broken. Anyone coming to America should not be eligible to receive any handouts, zero government benefits He explains immigration only works if you end the Welfare State “You can’t really open the border and say, ‘I’m gonna give my money to anybody who wants to come in — That’s getting it wrong.” “So when people say, ‘Open immigration is how we built America,’ they are right.” “And open immigration would be something interesting if we gave people nothing.” “Because if we gave them nothing, if they weren’t smart enough, capable, entrepreneurial enough, driven enough, they would starve to death. And so they wouldn’t stay here. They would self-deport and leave.” “But if you’re gonna give them welfare, and you’re gonna give them food stamps, and you’re gonna give them housing, and you give them this, that, then everybody’s gonna come and just leech off of us and we’re gonna pay, and pay, and pay.”

US Secretary of Commerce Howard Lutnick says our immigration system is broken. Anyone coming to America should not be eligible to receive any handouts, zero government benefits He explains immigration only works if you end the Welfare State “You can’t really open the border and say, ‘I’m gonna give my money to anybody who wants to come in — That’s getting it wrong.” “So when people say, ‘Open immigration is how we built America,’ they are right.” “And open immigration would be something interesting if we gave people nothing.” “Because if we gave them nothing, if they weren’t smart enough, capable, entrepreneurial enough, driven enough, they would starve to death. And so they wouldn’t stay here. They would self-deport and leave.” “But if you’re gonna give them welfare, and you’re gonna give them food stamps, and you’re gonna give them housing, and you give them this, that, then everybody’s gonna come and just leech off of us and we’re gonna pay, and pay, and pay.”

Wall Street Apes

6,378,975 просмотров • 8 месяцев назад

Google just gave away the real secret to showing up in AI answers. And honestly, it contradicts a lot of the viral vidoes you’ve been watching. Because Google basically said: stop obsessing over tricks like llms.txt, PerfectSchema, and all these technical hacks people are trying to sell you. That’s not what gets you cited. Here’s what does. AI doesn’t need to cite generic content. If your article says the same thing as everyone else, the AI can answer the question without you. But if your content has something original. A real test, a number you measured, a customer insight, a process you actually use, now the AI has a reason to reference you. That’s the difference between commodity content and non-commodity content. But here’s where most people get stuck. They don’t know what to create first. That’s why tools like AnswerThePublic are useful. You can see the real questions people are already asking around your topic, then use those questions as the starting point. But don’t just answer them generically. Add something only you know. Your data. Your experience. Your customer conversations. Your examples. Because in the AI era, the goal isn’t just to write content. It’s to become the source AI wants to cite.

Google just gave away the real secret to showing up in AI answers. And honestly, it contradicts a lot of the viral vidoes you’ve been watching. Because Google basically said: stop obsessing over tricks like llms.txt, PerfectSchema, and all these technical hacks people are trying to sell you. That’s not what gets you cited. Here’s what does. AI doesn’t need to cite generic content. If your article says the same thing as everyone else, the AI can answer the question without you. But if your content has something original. A real test, a number you measured, a customer insight, a process you actually use, now the AI has a reason to reference you. That’s the difference between commodity content and non-commodity content. But here’s where most people get stuck. They don’t know what to create first. That’s why tools like AnswerThePublic are useful. You can see the real questions people are already asking around your topic, then use those questions as the starting point. But don’t just answer them generically. Add something only you know. Your data. Your experience. Your customer conversations. Your examples. Because in the AI era, the goal isn’t just to write content. It’s to become the source AI wants to cite.

Neil Patel

16,728 просмотров • 1 месяц назад

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

443,639 просмотров • 1 месяц назад

A family called a priest after their daughter kept hearing strange noises and seeing objects move on their own in her room. A man named Jesus from Guatemala said his little girl had been crying for weeks, terrified of something no one else could see. When the priest arrived to bless the room, he said he felt a dark presence comingeS from the bed. He took a knife, cut open the bed nfer's rnom- wihat they found inside bift to prove he wasn't doing any tricks. What they found left everyone in shock: a black bag with a small wax figure inside. People believe such things are often linked to witchcraft or curses.

A family called a priest after their daughter kept hearing strange noises and seeing objects move on their own in her room. A man named Jesus from Guatemala said his little girl had been crying for weeks, terrified of something no one else could see. When the priest arrived to bless the room, he said he felt a dark presence comingeS from the bed. He took a knife, cut open the bed nfer's rnom- wihat they found inside bift to prove he wasn't doing any tricks. What they found left everyone in shock: a black bag with a small wax figure inside. People believe such things are often linked to witchcraft or curses.

Lisa

2,781,255 просмотров • 7 месяцев назад

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.

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

164,162 просмотров • 2 лет назад

Searching 32 million records in 30 milliseconds is insane! I don't know how large your database is, but there's no reason your search results are slower than this. typesense | is a crazy fast open-source search engine. They are like ElasticSearch but way easier to set up, configure, and use. They are sponsoring this post. You can do all of this with Typesense: • Fuzzy search: Typo-tolerant fuzzy search-as-you-type • Vector search: Return records that are similar to each other • Semantic search: Return records that are conceptually related • Geo Search: Return records that are close to a given lat/long They have client libraries in Python, JavaScript, PHP, Ruby, Go, Rust, Swift, .Net, Java, and pretty much any other programming language out there. You can start searching in less than a minute: 1. Run their Docker image (or one of their native binaries) 2. Import your data 3. Start searching! GitHub repository: I recorded a quick video to show you a demo. I promise you haven't seen something this fast! This is the best upgrade you'll make to your application this year. Guaranteed.

Searching 32 million records in 30 milliseconds is insane! I don't know how large your database is, but there's no reason your search results are slower than this. typesense | is a crazy fast open-source search engine. They are like ElasticSearch but way easier to set up, configure, and use. They are sponsoring this post. You can do all of this with Typesense: • Fuzzy search: Typo-tolerant fuzzy search-as-you-type • Vector search: Return records that are similar to each other • Semantic search: Return records that are conceptually related • Geo Search: Return records that are close to a given lat/long They have client libraries in Python, JavaScript, PHP, Ruby, Go, Rust, Swift, .Net, Java, and pretty much any other programming language out there. You can start searching in less than a minute: 1. Run their Docker image (or one of their native binaries) 2. Import your data 3. Start searching! GitHub repository: I recorded a quick video to show you a demo. I promise you haven't seen something this fast! This is the best upgrade you'll make to your application this year. Guaranteed.

Santiago

172,755 просмотров • 1 год назад

#WATCH | Chennai, Tamil Nadu | NSA Ajit Doval says, "We have to develop our indigenous technology. Mention of Sindoor was made here. We are really proud of how much of indigenous content was there...We decided to have 9 terrorist targets in the criss-cross of Pakistan, it was not in the border areas. We missed none. We hit nowhere else except that. It was precise to the point where we knew who was where. Entire operation took 23 minutes...You tell me one photograph that shows any Indian damage done...They wrote things, New York Times...but the images showed 13 air bases of Pakistan before and after 10th May..."

#WATCH | Chennai, Tamil Nadu | NSA Ajit Doval says, "We have to develop our indigenous technology. Mention of Sindoor was made here. We are really proud of how much of indigenous content was there...We decided to have 9 terrorist targets in the criss-cross of Pakistan, it was not in the border areas. We missed none. We hit nowhere else except that. It was precise to the point where we knew who was where. Entire operation took 23 minutes...You tell me one photograph that shows any Indian damage done...They wrote things, New York Times...but the images showed 13 air bases of Pakistan before and after 10th May..."

ANI

20,296 просмотров • 1 год назад

Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.

Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.

Dustin

687,842 просмотров • 6 месяцев назад

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

Santiago

324,621 просмотров • 2 лет назад