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Matthew McConaughey’s dream private LLM? Already exists. It’s called Hyperlink - an offline, private AI agent that knows every file you own. Search files and discover buried insights with it. AI runs 100% local. Hey Matthew, want to try it? Anyone can set up in minutes.

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NEXA AI

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140,797 görüntüleme • 11 ay önce •via X (Twitter)

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Meet Hyperlink, the first AI super assistant that lives inside your computer. Your computer stores all your files and personal context. Hyperlink deeply understands them and gives cited answers instantly — like Perplexity for your local files. It turns your computer into a true AI OS, with 100% private on-device AI agent. Welcome to the new interface for personal intelligence.
1:46

Meet Hyperlink, the first AI super assistant that lives inside your computer. Your computer stores all your files and personal context. Hyperlink deeply understands them and gives cited answers instantly — like Perplexity for your local files. It turns your computer into a true AI OS, with 100% private on-device AI agent. Welcome to the new interface for personal intelligence.

NEXA AI

2,136,912 görüntüleme • 9 ay önce

AI PCs are here - yet we still lose 500+hrs/yr chasing scattered files and buried insights. Cloud AI risks your sensitive data. On device AI doesn't. Meet Hyperlink - the fully offline AI agent that instantly searches local folders and unlocks missed ideas with in-text citations.🧠 🚀Public beta available now ↓ (use cases + link below)
1:06

AI PCs are here - yet we still lose 500+hrs/yr chasing scattered files and buried insights. Cloud AI risks your sensitive data. On device AI doesn't. Meet Hyperlink - the fully offline AI agent that instantly searches local folders and unlocks missed ideas with in-text citations.🧠 🚀Public beta available now ↓ (use cases + link below)

NEXA AI

130,926 görüntüleme • 1 yıl önce

Meet June: your private AI agent now with local models on your Mac. Turn the wifi off: Agent, dictation, meeting notes, and your files all in one workspace. 100% free to use with local inference. Open Source. MIT. Local first. Try it:
0:49

Meet June: your private AI agent now with local models on your Mac. Turn the wifi off: Agent, dictation, meeting notes, and your files all in one workspace. 100% free to use with local inference. Open Source. MIT. Local first. Try it:

OpenSoftware

17,471 görüntüleme • 1 ay önce

You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below
16:10

You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below

Paul Couvert

41,245 görüntüleme • 5 ay önce

NVIDIA JUST DROPPED NEMO CLAW TO MAKE OPENCLAW SAFE TO RUN ON YOUR OWN MACHINE. It keeps private files off the cloud, runs fully offline, and adds the security layer local AI agents were missing.
6:42

NVIDIA JUST DROPPED NEMO CLAW TO MAKE OPENCLAW SAFE TO RUN ON YOUR OWN MACHINE. It keeps private files off the cloud, runs fully offline, and adds the security layer local AI agents were missing.

0xMarioNawfal

220,240 görüntüleme • 5 ay önce

Milo: Your Local "ChatGPT" Agent - Runs locally: 100% private & secure - Nothing is tracked, stored, or used for training - Works offline With Milo, you can ask what you really want to ask. Meet Milo in Agent Store:
1:24

Milo: Your Local "ChatGPT" Agent - Runs locally: 100% private & secure - Nothing is tracked, stored, or used for training - Works offline With Milo, you can ask what you really want to ask. Meet Milo in Agent Store:

Eternal AI

88,315 görüntüleme • 1 yıl önce

We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.
0:24

We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.

Anni Wang

12,975 görüntüleme • 4 ay önce

Spent the week with Wolffish an AI agent that runs entirely on my own laptop. No cloud, no server, nothing leaves my machine. Set it up in about 5 minutes, plugged in my own model key, and told it to be my morning news desk. It scanned the web, cross-checked every story across outlets, and handed me one clean PDF. Local-first and open source. This is the direction.
1:33

Spent the week with Wolffish an AI agent that runs entirely on my own laptop. No cloud, no server, nothing leaves my machine. Set it up in about 5 minutes, plugged in my own model key, and told it to be my morning news desk. It scanned the web, cross-checked every story across outlets, and handed me one clean PDF. Local-first and open source. This is the direction.

Nawi

89,262 görüntüleme • 1 ay önce

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:
0:35

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Tom Greenwald

391,605 görüntüleme • 12 gün önce

🚨 BREAKING: Nvidia just released Chat with RTX, an AI chatbot that runs locally on your PC. It can summarize or search documents across your PC's files and even YouTube videos and playlists. The chatbot runs locally, meaning results are fast, you can use it without the internet, and the user's data stays private. New day, new chatbot. Let's go.
1:05

🚨 BREAKING: Nvidia just released Chat with RTX, an AI chatbot that runs locally on your PC. It can summarize or search documents across your PC's files and even YouTube videos and playlists. The chatbot runs locally, meaning results are fast, you can use it without the internet, and the user's data stays private. New day, new chatbot. Let's go.

Rowan Cheung

2,725,637 görüntüleme • 2 yıl önce

Honored to join @HF0 for S26 We're building Franklin : an AI agent with its own wallet — it auto-picks the best model for every task and pays per action in USDC. An AI agent that can pay and get paid. 👇
0:24

Honored to join @HF0 for S26 We're building Franklin : an AI agent with its own wallet — it auto-picks the best model for every task and pays per action in USDC. An AI agent that can pay and get paid. 👇

BlockRunAI

18,698 görüntüleme • 2 ay önce

We’ve built an AI agent that taps into kwantxbt market insights and helps you trade smarter and faster. It’s like having a personal trading assistant that keeps up with KwantXBT’s sharp calls. Ready to see it in action? 🎥👇
0:39

We’ve built an AI agent that taps into kwantxbt market insights and helps you trade smarter and faster. It’s like having a personal trading assistant that keeps up with KwantXBT’s sharp calls. Ready to see it in action? 🎥👇

Orbit

42,648 görüntüleme • 1 yıl önce

Half an hour into the most recent Joe Rogan podcast, Joe and Matthew McConaughey imagined AI that could deeply understand you, learn from your private data, and give proactive support without sacrificing your privacy. AI that could know your favorite books, your personal information, even your thought patterns, and return insights to help you understand yourself better. They spoke of this like a vision for the future, but at LedgerAI, that future already exists. AuraVision is our private AI platform that keeps your data where it belongs, with you. •Your data is stored locally, not in the cloud. •You can ingest AuraVision with exquisite data about yourself or your company: your strategies, workflows, knowledge, and goals. • In return, AuraVision provides actionable advice and intelligence, turning your private information into secure, personal insights. Whether you are a doctor, lawyer, government body, a financial institution, or even Joe Rogan or Matthew McConaughey, AuraVision makes AI adoption real, practical, and secure, accessed via our native token, $LEDGER. The difference is simple: while others are still imagining, we’re already building. Train by day, AuraVision by night, all day! We’ve left a link to the episode in the replies, leave your thoughts!
1:30

Half an hour into the most recent Joe Rogan podcast, Joe and Matthew McConaughey imagined AI that could deeply understand you, learn from your private data, and give proactive support without sacrificing your privacy. AI that could know your favorite books, your personal information, even your thought patterns, and return insights to help you understand yourself better. They spoke of this like a vision for the future, but at LedgerAI, that future already exists. AuraVision is our private AI platform that keeps your data where it belongs, with you. •Your data is stored locally, not in the cloud. •You can ingest AuraVision with exquisite data about yourself or your company: your strategies, workflows, knowledge, and goals. • In return, AuraVision provides actionable advice and intelligence, turning your private information into secure, personal insights. Whether you are a doctor, lawyer, government body, a financial institution, or even Joe Rogan or Matthew McConaughey, AuraVision makes AI adoption real, practical, and secure, accessed via our native token, $LEDGER. The difference is simple: while others are still imagining, we’re already building. Train by day, AuraVision by night, all day! We’ve left a link to the episode in the replies, leave your thoughts!

AuraVision

1,547,912 görüntüleme • 11 ay önce

I built an AI voice agent that called the DMV to schedule an appointment for me! ....and you can too. It took <30 minutes and two tools. (And, it was incredibly satisfying to hear my AI deal with the DMV's AI instead of sitting on the phone myself😂) How I did it 👇
0:54

I built an AI voice agent that called the DMV to schedule an appointment for me! ....and you can too. It took <30 minutes and two tools. (And, it was incredibly satisfying to hear my AI deal with the DMV's AI instead of sitting on the phone myself😂) How I did it 👇

Olivia Moore

62,403 görüntüleme • 2 yıl 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. 🔗
0:28

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

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:
1:48

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

81,696 görüntüleme • 11 ay önce

Want a free private AI agent on your laptop? Here’s the simple setup. 👇 1. Install Hermes Agent from GitHub. 2. Install Ollama. 3. Download Gemma 4 26B. 4. Use Docker to keep everything contained. 5. Add Firecrawl so the agent can research the web. 6. Connect Telegram so you can chat with it from your phone. 7. Turn on context compression so it stays sharp. 8. Set the daily reset for 4 AM so it refreshes while you sleep. What you get at the end: → local AI agent → private workflow → web research built in → self improving skills → no monthly model rental That is a serious setup for anyone building with AI. Save this video, you’ll have the easiest beginner roadmap to follow later. Want the SOP? DM me. 💬
7:27

Want a free private AI agent on your laptop? Here’s the simple setup. 👇 1. Install Hermes Agent from GitHub. 2. Install Ollama. 3. Download Gemma 4 26B. 4. Use Docker to keep everything contained. 5. Add Firecrawl so the agent can research the web. 6. Connect Telegram so you can chat with it from your phone. 7. Turn on context compression so it stays sharp. 8. Set the daily reset for 4 AM so it refreshes while you sleep. What you get at the end: → local AI agent → private workflow → web research built in → self improving skills → no monthly model rental That is a serious setup for anyone building with AI. Save this video, you’ll have the easiest beginner roadmap to follow later. Want the SOP? DM me. 💬

Julian Goldie SEO

31,292 görüntüleme • 4 ay önce

Want to build with AI for free? With local models in Workshop, you can build websites, dashboards, internal tools, workflows, prototypes, and more. That means: - Zero API costs - Offline access - Full privacy Try local models today in Workshop Desktop.
0:25

Want to build with AI for free? With local models in Workshop, you can build websites, dashboards, internal tools, workflows, prototypes, and more. That means: - Zero API costs - Offline access - Full privacy Try local models today in Workshop Desktop.

Workshop AI

736,311 görüntüleme • 3 ay önce

You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG. They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems. 🎬YouTube: Here's the whole thing in one picture. An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running. The harness is the set of tools you put on that horse so it runs where you want. Memory gives it context: who you are, what happened before, how to act. The loop lets it call tools again and again, with guardrails so it knows when to stop. Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself. Master these four and you can read almost any AI agent repo or paper and actually know what's going on. You Can Build Anything. You Can Learn Anything. 💪 Chapters: Intro: the 4 AI agent buzzwords What an AI agent run actually is The memory system: procedural, semantic, episodic What "harness" really means (the horse) Storing and updating memory (databases, skills, summarizer agent) Retrieval: RAG, SQL vs semantic search Tool calling and why agents loop Loop engineering and end-loop guardrails A Claude Code hooks example Eval and LLM Ops: why you need them Tracing every run (Langfuse, LangSmith) Evaluation: LLM as a judge Diagnosing what broke The gate: ship the fix or fix the bug Zoom out: the full system
19:59

You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG. They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems. 🎬YouTube: Here's the whole thing in one picture. An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running. The harness is the set of tools you put on that horse so it runs where you want. Memory gives it context: who you are, what happened before, how to act. The loop lets it call tools again and again, with guardrails so it knows when to stop. Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself. Master these four and you can read almost any AI agent repo or paper and actually know what's going on. You Can Build Anything. You Can Learn Anything. 💪 Chapters: Intro: the 4 AI agent buzzwords What an AI agent run actually is The memory system: procedural, semantic, episodic What "harness" really means (the horse) Storing and updating memory (databases, skills, summarizer agent) Retrieval: RAG, SQL vs semantic search Tool calling and why agents loop Loop engineering and end-loop guardrails A Claude Code hooks example Eval and LLM Ops: why you need them Tracing every run (Langfuse, LangSmith) Evaluation: LLM as a judge Diagnosing what broke The gate: ship the fix or fix the bug Zoom out: the full system

Shen Sean Chen

15,952 görüntüleme • 1 ay önce