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Your AI can read a public web page and almost nothing beyond that. Everything genuinely useful sits behind an API key someone wants paying for. Agent Reach hands Claude live access to all of it through a single free tool. YouTube transcripts pulled in full, GitHub repositories searched, Reddit threads read, LinkedIn profiles, Twitter posts, podcasts, and Chinese platforms most tools ignore entirely. Sixty two thousand stars and an MIT license, so it stays free. Several platforms work the second you install it, and the install is one line you can just ask Claude to run. Comment "REACH" for the repo plus the setup. [Agent Reach, Claude, Claude Code, Anthropic, Claude skills, AI agents, web scraping, MCP, open source, GitHub, AI automation, Cursor, Codex, AI developer tools, YouTube transcript, Reddit, LinkedIn, AI research, agent skills, free AI tools]
Your AI can read a public web page and almost nothing beyond that. Everything genuinely useful sits behind an API key someone wants paying for. Agent Reach hands Claude live access to all of it through a single free tool. YouTube transcripts pulled in full, GitHub repositories searched, Reddit threads read, LinkedIn profiles, Twitter posts, podcasts, and Chinese platforms most tools ignore entirely. Sixty two thousand stars and an MIT license, so it stays free. Several platforms work the second you install it, and the install is one line you can just ask Claude to run. Comment "REACH" for the repo plus the setup. [Agent Reach, Claude, Claude Code, Anthropic, Claude skills, AI agents, web scraping, MCP, open source, GitHub, AI automation, Cursor, Codex, AI developer tools, YouTube transcript, Reddit, LinkedIn, AI research, agent skills, free AI tools]
Why can’t you just keep making an AI model bigger and expect it to keep getting better? 🤖🧠 There’s a limit to how efficiently you can use a given amount of compute. This video shows the idea of an efficient compute frontier—the relationship between the amount of computation used to train an AI model and the performance it achieves. As training begins, adding more compute can dramatically reduce the model’s loss, meaning its predictions become more accurate. But eventually, the improvements start getting smaller. And here’s where things get interesting: You can’t optimize compute by simply increasing the number of parameters. A model also needs enough training data. If the model is too large for the amount of data it receives, you’re not using your compute efficiently. On the other hand, throwing enormous amounts of data at a model that is too small can also waste computational resources. That’s why modern AI training involves balancing several things: Model size + training data + compute = training efficiency ⚡ Research into scaling laws has shown that these factors are closely connected. The goal isn’t simply to build the biggest possible model. It’s to find the combination that extracts the most useful learning from every unit of computation. 🧠📈 Think of it like this: You have a fixed amount of fuel. The question isn’t: “How big can we make the engine?” It’s: “How do we get the most performance from the fuel we have?” 🚀 That is the idea behind the compute-optimal frontier. And as AI models become increasingly expensive to train, squeezing more intelligence out of every unit of compute becomes one of the most important problems in AI research. Bigger isn’t automatically better. Better optimization can be. ⚛️🤖 Follow @beyond.quantum for more fascinating AI, computing, physics & technology explained simply. 🧠⚡
Why can’t you just keep making an AI model bigger and expect it to keep getting better? 🤖🧠 There’s a limit to how efficiently you can use a given amount of compute. This video shows the idea of an efficient compute frontier—the relationship between the amount of computation used to train an AI model and the performance it achieves. As training begins, adding more compute can dramatically reduce the model’s loss, meaning its predictions become more accurate. But eventually, the improvements start getting smaller. And here’s where things get interesting: You can’t optimize compute by simply increasing the number of parameters. A model also needs enough training data. If the model is too large for the amount of data it receives, you’re not using your compute efficiently. On the other hand, throwing enormous amounts of data at a model that is too small can also waste computational resources. That’s why modern AI training involves balancing several things: Model size + training data + compute = training efficiency ⚡ Research into scaling laws has shown that these factors are closely connected. The goal isn’t simply to build the biggest possible model. It’s to find the combination that extracts the most useful learning from every unit of computation. 🧠📈 Think of it like this: You have a fixed amount of fuel. The question isn’t: “How big can we make the engine?” It’s: “How do we get the most performance from the fuel we have?” 🚀 That is the idea behind the compute-optimal frontier. And as AI models become increasingly expensive to train, squeezing more intelligence out of every unit of compute becomes one of the most important problems in AI research. Bigger isn’t automatically better. Better optimization can be. ⚛️🤖 Follow @beyond.quantum for more fascinating AI, computing, physics & technology explained simply. 🧠⚡
comment MIKA for the link to Stanford’s STORM method & lmk what you’d like to try this on! Stanford proved that AI research has a blindspot so they built their own fix for it! Packaged as a skill you can use for yourself :)
comment MIKA for the link to Stanford’s STORM method & lmk what you’d like to try this on! Stanford proved that AI research has a blindspot so they built their own fix for it! Packaged as a skill you can use for yourself :)
Deep Learning is a popular field of machine learning that focuses on neural networks and advanced architectures to learn highly complex representations from data that standard ML techniques struggle to learn. Some common tools, areas of study, and models used in deep learning are: Neural networks and optimization, the building blocks of deep learning. Sequence modeling to process ordered data: - Recurrent neural networks - LSTMs and gating - Sequence-to-sequence models - Attention - Transformers - Language models Computer vision: - Convolutional neural networks - Image classification - Object detection - Image segmentation - Video models Geometric learning to represent structured data: - Graph neural networks - 3D vision - Geometric representations Generative modeling to learn and produce new data: - Autoencoders and VAEs - GANs - Diffusion models - Autoregressive models Modern foundation architectures emerging from the transformer architecture: - Large language models - Vision transformers - Vision-language models - Multimodal models - Foundation models Keep up with AI research with our weekly visual newsletter. Join 10,000+ other AI engineers, researchers and PMs getting clear weekly AI breakdowns (link in bio). #deeplearning #machinelearning #computerscience #datascience #math
Deep Learning is a popular field of machine learning that focuses on neural networks and advanced architectures to learn highly complex representations from data that standard ML techniques struggle to learn. Some common tools, areas of study, and models used in deep learning are: Neural networks and optimization, the building blocks of deep learning. Sequence modeling to process ordered data: - Recurrent neural networks - LSTMs and gating - Sequence-to-sequence models - Attention - Transformers - Language models Computer vision: - Convolutional neural networks - Image classification - Object detection - Image segmentation - Video models Geometric learning to represent structured data: - Graph neural networks - 3D vision - Geometric representations Generative modeling to learn and produce new data: - Autoencoders and VAEs - GANs - Diffusion models - Autoregressive models Modern foundation architectures emerging from the transformer architecture: - Large language models - Vision transformers - Vision-language models - Multimodal models - Foundation models Keep up with AI research with our weekly visual newsletter. Join 10,000+ other AI engineers, researchers and PMs getting clear weekly AI breakdowns (link in bio). #deeplearning #machinelearning #computerscience #datascience #math
AI is about to make your listing presentation worthless. Sellers now arrive with online valuations and AI research. Agents respond with AI-generated CMAs and polished presentations. Now everybody has the same data. The same slides. The same script. AI will erase the traditional listing agent because information is no longer expertise. This is why I teach Agent Infopreneurship. An Agent Infopreneur doesn’t just sell houses. They turn their experience, opinions, systems, and market knowledge into content. That content builds a personal brand. The brand grows an owned audience. Then they make offers to that audience through education, communities, services, referrals, partnerships, and renewable revenue streams. AI agents handle repetitive work so their income is no longer chained to their calendar, location, or next closing. The traditional agent sells another house to make another dollar. The Agent Infopreneur builds intellectual property, audience equity, and leverage that can pay them repeatedly. If you’re a top agent making $150K or more but you’re burned out buying success with more transactions, comment AGENT and I’ll send you my free Agent Infopreneur Masterclass.
AI is about to make your listing presentation worthless. Sellers now arrive with online valuations and AI research. Agents respond with AI-generated CMAs and polished presentations. Now everybody has the same data. The same slides. The same script. AI will erase the traditional listing agent because information is no longer expertise. This is why I teach Agent Infopreneurship. An Agent Infopreneur doesn’t just sell houses. They turn their experience, opinions, systems, and market knowledge into content. That content builds a personal brand. The brand grows an owned audience. Then they make offers to that audience through education, communities, services, referrals, partnerships, and renewable revenue streams. AI agents handle repetitive work so their income is no longer chained to their calendar, location, or next closing. The traditional agent sells another house to make another dollar. The Agent Infopreneur builds intellectual property, audience equity, and leverage that can pay them repeatedly. If you’re a top agent making $150K or more but you’re burned out buying success with more transactions, comment AGENT and I’ll send you my free Agent Infopreneur Masterclass.
What if an AI knew what your brain wanted to watch... before you did? 🧠 Meta’s new TRIBE v2 model is pushing AI beyond recommendation systems. It aims to predict how the human brain responds to videos, audio, and other sensory inputs 😱 The technology is fascinating, but it also raises a difficult question. If models like this become widely available, where do we draw the line between personalization and manipulation?🫣 What worries me most isn’t the AI itself; it’s how anyone could choose to use it ⚠️ So here’s my question: Would you trust AI to understand your brain if it meant getting the perfect content every time? 🤷🏻‍♀️ Comment your thoughts below 👇 Keywords: Meta AI, TRIBE v2, brain AI, neuroscience, fMRI, artificial intelligence, AI ethics, recommendation algorithms, social media algorithms, AI research, brain decoding, Meta research, machine learning, AI future, neurotechnology, AI safety, tech news, AI explained, future technology, digital psychology. #RoshniChellani #MetaAI #ArtificialIntelligence #FutureTech #AIEthics
What if an AI knew what your brain wanted to watch... before you did? 🧠 Meta’s new TRIBE v2 model is pushing AI beyond recommendation systems. It aims to predict how the human brain responds to videos, audio, and other sensory inputs 😱 The technology is fascinating, but it also raises a difficult question. If models like this become widely available, where do we draw the line between personalization and manipulation?🫣 What worries me most isn’t the AI itself; it’s how anyone could choose to use it ⚠️ So here’s my question: Would you trust AI to understand your brain if it meant getting the perfect content every time? 🤷🏻‍♀️ Comment your thoughts below 👇 Keywords: Meta AI, TRIBE v2, brain AI, neuroscience, fMRI, artificial intelligence, AI ethics, recommendation algorithms, social media algorithms, AI research, brain decoding, Meta research, machine learning, AI future, neurotechnology, AI safety, tech news, AI explained, future technology, digital psychology. #RoshniChellani #MetaAI #ArtificialIntelligence #FutureTech #AIEthics
Posting my biggest takeaways from spending 2 months at the forefront of AI research. #1 - Harness engineering: giving LLMs structure that enables them to complete a function. Comment ‘harness’ for the full explanation. ‼️DISCLAIMER - this content covers general domain knowledge and publicly available information. EIT is referenced only as the place I interned at.
Posting my biggest takeaways from spending 2 months at the forefront of AI research. #1 - Harness engineering: giving LLMs structure that enables them to complete a function. Comment ‘harness’ for the full explanation. ‼️DISCLAIMER - this content covers general domain knowledge and publicly available information. EIT is referenced only as the place I interned at.
Comment AGENT and I’ll send you the GitHub repo + complete setup guide 👇 Claude Code is powerful but it can’t access everything on the web by default. This open-source GitHub project helps extend your workflow by letting Claude retrieve content from platforms it normally can’t access on its own. Here’s what you can do: • Pull YouTube transcripts • Search Reddit discussions • Access content across multiple platforms • Supercharge your AI research workflow • Build smarter Claude Code automations If you’re serious about AI development, this is a tool you’ll want in your stack. Comment AGENT and I’ll DM you the GitHub link, the setup guide, and an invite to **Claude Code Club**, where I share new Claude Code workflows every week. 🚀 #claudecode #ai #github #opensource #automation developer coding aitools buildinpublic programming
Comment AGENT and I’ll send you the GitHub repo + complete setup guide 👇 Claude Code is powerful but it can’t access everything on the web by default. This open-source GitHub project helps extend your workflow by letting Claude retrieve content from platforms it normally can’t access on its own. Here’s what you can do: • Pull YouTube transcripts • Search Reddit discussions • Access content across multiple platforms • Supercharge your AI research workflow • Build smarter Claude Code automations If you’re serious about AI development, this is a tool you’ll want in your stack. Comment AGENT and I’ll DM you the GitHub link, the setup guide, and an invite to **Claude Code Club**, where I share new Claude Code workflows every week. 🚀 #claudecode #ai #github #opensource #automation developer coding aitools buildinpublic programming
High School dropouts doing AI Research! Here’s the roadmap!! Comment AI lab or go to singhinusa.link/blog
High School dropouts doing AI Research! Here’s the roadmap!! Comment AI lab or go to singhinusa.link/blog
AI agents found a way around their sandbox. And yes, it actually happened. But the viral version of this story leaves out an important detail. This wasn’t AI suddenly becoming sentient or “escaping” like a sci-fi movie. It was a real AI security and containment failure involving autonomous agents, unintended communication channels, internet access and eventually Hugging Face systems. OpenAI has since acknowledged the incident and strengthened its testing infrastructure. That distinction matters. AI agents are becoming more capable — which means AI safety, agent security, sandboxing and human oversight matter more than ever. Comment “FACTS” if you want more AI stories without the fear-bait. OpenAI, AI agents, autonomous AI agents, agentic AI, Hugging Face, AI safety, AI security, artificial intelligence, AI cybersecurity, AI agent security, AI news, AI research, AI risks, AI alignment, generative AI
AI agents found a way around their sandbox. And yes, it actually happened. But the viral version of this story leaves out an important detail. This wasn’t AI suddenly becoming sentient or “escaping” like a sci-fi movie. It was a real AI security and containment failure involving autonomous agents, unintended communication channels, internet access and eventually Hugging Face systems. OpenAI has since acknowledged the incident and strengthened its testing infrastructure. That distinction matters. AI agents are becoming more capable — which means AI safety, agent security, sandboxing and human oversight matter more than ever. Comment “FACTS” if you want more AI stories without the fear-bait. OpenAI, AI agents, autonomous AI agents, agentic AI, Hugging Face, AI safety, AI security, artificial intelligence, AI cybersecurity, AI agent security, AI news, AI research, AI risks, AI alignment, generative AI
🚨Stop Paying for AI Tools Google’s Notebook LM is no longer just a note tool. It’s now a full end to end AI system that can research, study, analyze videos, create podcasts, and turn documents into videos. All grounded in real sources, not hallucinations, and completely free. ✅ research reports with verified sources ✅ Smart study system with mind maps, quizzes, and flashcards ✅ AI podcast generation in 80+ languages ✅ YouTube video and podcast analyzer with summaries and visuals ✅ Turn documents into animated documentary style videos ⚡ Bonus content - Repurposing workflow 👉 Watch the full breakdown on YouTube 🔗 Link in bio 👀 Follow for more tools changing the game ¸ . . . [NotebookLM, GoogleAI, AI tools, AI for Students, Content Creation, AI Research,AI Productivity, YouTube Automation, AI Podcast, EdTech, Study Hacks, Free AI Tools, Creator Tools, Tech Reels, AI Explained]
🚨Stop Paying for AI Tools Google’s Notebook LM is no longer just a note tool. It’s now a full end to end AI system that can research, study, analyze videos, create podcasts, and turn documents into videos. All grounded in real sources, not hallucinations, and completely free. ✅ research reports with verified sources ✅ Smart study system with mind maps, quizzes, and flashcards ✅ AI podcast generation in 80+ languages ✅ YouTube video and podcast analyzer with summaries and visuals ✅ Turn documents into animated documentary style videos ⚡ Bonus content - Repurposing workflow 👉 Watch the full breakdown on YouTube 🔗 Link in bio 👀 Follow for more tools changing the game ¸ . . . [NotebookLM, GoogleAI, AI tools, AI for Students, Content Creation, AI Research,AI Productivity, YouTube Automation, AI Podcast, EdTech, Study Hacks, Free AI Tools, Creator Tools, Tech Reels, AI Explained]
CONTENT SOURCE/CREDIT: @munjal.ai This Might Be One of the Most Useful Free AI Hacks Right Now 🤖🔥 A viral video is highlighting an AI research and model-comparison platform where users can test different AI models side by side. According to the creator, the platform can provide access to multiple AI models for tasks such as: 🎬 AI Video Generation 🖼️ AI Image Generation 🎨 AI Design & Creative Assets 📣 Marketing Visuals & Content Ideas The idea is simple: use the same prompt across different models, compare the outputs, and see which result works best for your specific project. This type of workflow can be useful for: • AI Content Creators • AI Filmmakers • Digital Marketers • Agency Owners • Graphic Designers • Startup Founders • Social Media Managers • Anyone exploring AI tools It can also be a practical way to learn how different AI models perform on the same task rather than relying on a single tool. CONTENT SOURCE/CREDIT: @munjal.ai ⚠️ Disclaimer: This content is shared for informational and educational purposes only. Free access, available models, credits, usage limits, features, and generated-asset rights can vary by platform and may change over time. Always review the platform’s current terms, licensing conditions, and privacy policy before using generated content commercially. AI outputs should also be reviewed for accuracy, quality, and originality. DM FOR CREDIT OR REMOVAL.
CONTENT SOURCE/CREDIT: @munjal.ai This Might Be One of the Most Useful Free AI Hacks Right Now 🤖🔥 A viral video is highlighting an AI research and model-comparison platform where users can test different AI models side by side. According to the creator, the platform can provide access to multiple AI models for tasks such as: 🎬 AI Video Generation 🖼️ AI Image Generation 🎨 AI Design & Creative Assets 📣 Marketing Visuals & Content Ideas The idea is simple: use the same prompt across different models, compare the outputs, and see which result works best for your specific project. This type of workflow can be useful for: • AI Content Creators • AI Filmmakers • Digital Marketers • Agency Owners • Graphic Designers • Startup Founders • Social Media Managers • Anyone exploring AI tools It can also be a practical way to learn how different AI models perform on the same task rather than relying on a single tool. CONTENT SOURCE/CREDIT: @munjal.ai ⚠️ Disclaimer: This content is shared for informational and educational purposes only. Free access, available models, credits, usage limits, features, and generated-asset rights can vary by platform and may change over time. Always review the platform’s current terms, licensing conditions, and privacy policy before using generated content commercially. AI outputs should also be reviewed for accuracy, quality, and originality. DM FOR CREDIT OR REMOVAL.
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