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Nobody is talking about this. There's an open-source AI platform with 17.3K stars that connects to 40+ of your internal tools and runs completely on your own servers. It's called Onyx. Most companies using ChatGPT Enterprise are feeding sensitive internal data to OpenAI's cloud every single day. Onyx flips... that. You self-host it. You control the LLM. Your documents, Slack messages, Notion pages, and Jira tickets stay on your infrastructure. The connectors are the killer feature. Onyx pulls live knowledge from Slack, Notion, Google Drive, Confluence, GitHub, Jira, and 35+ more then indexes everything with hybrid search and a knowledge graph that stays accurate at tens of millions of documents. Document permissioning mirrors your existing access controls automatically. If someone doesn't have access to a Confluence page, the AI won't surface it to them in answers. No extra config. No permission rebuilds. Everything else: → Custom agents with unique instructions and MCP tool access → Deep Research for complex multi-step questions → Code interpreter, image generation, web search → SSO, RBAC, full enterprise security stack → Docker, Kubernetes, Terraform full deployment flexibility MIT License on Community Edition. 17.3K stars. 2.3K forks. 192 contributors.show more

Hasan Toor

437,525 subscribers

11,633 просмотров • 5 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 13

Фото профиля Hasan Toor

Hasan Toor5 месяцев назад

Link:

Фото профиля Kanika

Kanika5 месяцев назад

Connectors + permissions done right is huge.

Фото профиля QyupyTzy

QyupyTzy5 месяцев назад

R u accept donation through on ur girhub?

Фото профиля Resona Hub

Resona Hub5 месяцев назад

Looks like a customized platform, powerful and safe

Фото профиля AI's Nest

AI's Nest5 месяцев назад

It seems strong!

Фото профиля Rasel Hossen

Rasel Hossen5 месяцев назад

Amazing

Фото профиля Home

Home5 месяцев назад

The connector layer is where most of the value sits. Pulling structured context from tools like Slack, Notion, and Jira turns AI from a generic assistant into something actually useful inside a company.

Фото профиля Arsalan

Arsalan5 месяцев назад

Onyx looks seriously strong

Фото профиля sabir hussain

sabir hussain5 месяцев назад

Self hosted AI with connectors is powerful

Фото профиля hira

hira5 месяцев назад

this is exactly what enterprises need

Фото профиля nick

nick5 месяцев назад

running ai tools on your own infra isnt new, companies just got lazy because hosted apis are easier. the data sovereignty conversation shouldve happened years ago

Фото профиля Nav Toor

Nav Toor5 месяцев назад

finally an AI tool that won't show people stuff they're not supposed to see

Фото профиля Zynemorg

Zynemorg5 месяцев назад

"Nobody is talking about this." = I don't write my own content. Lazy AI Slop.

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Avi Chawla

11,982 просмотров • 6 месяцев назад

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Akshay 🚀

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Pratham

20,933 просмотров • 7 месяцев назад

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Santiago

89,664 просмотров • 1 год назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

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Andrew Ng

142,256 просмотров • 1 год назад

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

Factory

328,209 просмотров • 1 год назад

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

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Vikas gupta

18,053 просмотров • 19 дней назад

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

Madni Aghadi

397,670 просмотров • 2 месяцев назад

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,702 просмотров • 2 лет назад

This is how you can deploy the open-source version of grok bot on your own servers or that Mac mini sitting on your desk. "npx openmausbot@latest" → Connect your AI provider → Choose a model → Scan a QR code to pair your phone Close the laptop. Your bots keep working. Check in, send tasks, and answer approvals from your phone. We have crossed 2.3k GitHub stars, and this community keeps finding use cases I never imagined. This feature now takes it to the next level. I am sharing everything in the discord channel including the test-flight link for the app More than happy to walk each one of you through the process. I dont care if you have technical knowledge or not. Its for everyone.

This is how you can deploy the open-source version of grok bot on your own servers or that Mac mini sitting on your desk. "npx openmausbot@latest" → Connect your AI provider → Choose a model → Scan a QR code to pair your phone Close the laptop. Your bots keep working. Check in, send tasks, and answer approvals from your phone. We have crossed 2.3k GitHub stars, and this community keeps finding use cases I never imagined. This feature now takes it to the next level. I am sharing everything in the discord channel including the test-flight link for the app More than happy to walk each one of you through the process. I dont care if you have technical knowledge or not. Its for everyone.

Milind S

33,990 просмотров • 17 дней назад

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,835 просмотров • 2 лет назад

WorkOS: The Enterprise Stack for the AI Era AI companies are just B2B SaaS with a new engine. They monetize like SaaS, sell like SaaS, and scale into the enterprise like SaaS. The difference is velocity. These new products grow so fast that the old playbook of “PLG for years, enterprise later” simply does not work anymore. PMF is no longer enough. Winning your market requires crossing the Enterprise Chasm almost immediately. Enterprise auth, provisioning, RBAC, compliance, billing, IT integrations. If you wait, someone else takes your market while you are still wiring SCIM. This is why WorkOS exists. We give developers the infrastructure they need to scale up-market on day one. Even if you have never touched WorkOS, you have already used it through products like ChatGPT, Perplexity, Cursor and many others. Today we operate 81M+ enterprise user accounts, 67M API calls per day, and 38K+ connected enterprise environments. Modern AI tools already run through WorkOS. At ERC, we unveiled six major launches expanding that foundation for the AI era: • AuthKit for ChatGPT Apps: secure OAuth + MCP so developers can connect real enterprise data to 700M+ weekly ChatGPT users. • AuthKit for Platforms: embed full enterprise identity into frameworks like Supabase and Convex for zero-friction onboarding. • Stripe Usage Sync: actual per-seat billing with no glue code. Data stays consistent and invoices are always correct. • WorkOS Pipes: the fastest and most secure way to ship integrations (Salesforce, Slack, Intercom and more). • Agent-Ready API Keys: scoped, revocable keys designed for both developers and autonomous agents. • WorkOS Studio: vibe coding for the enterprise. A collaborative AI app builder with SSO, SCIM, RBAC, audit logs, workflows and third-party connectors built-in. Internal software at the speed of a prompt. AI has already changed how software gets created. The next shift is changing what software becomes. We are still “filming the play” like early cinema: using new technology to recreate old patterns. The opportunity ahead is to invent entirely new applications that only AI-native development makes possible. WorkOS is building the enterprise infrastructure that lets teams design the next era of software itself. Enterprise-ready AI is where the real acceleration happens. Let’s build that future together.

WorkOS: The Enterprise Stack for the AI Era AI companies are just B2B SaaS with a new engine. They monetize like SaaS, sell like SaaS, and scale into the enterprise like SaaS. The difference is velocity. These new products grow so fast that the old playbook of “PLG for years, enterprise later” simply does not work anymore. PMF is no longer enough. Winning your market requires crossing the Enterprise Chasm almost immediately. Enterprise auth, provisioning, RBAC, compliance, billing, IT integrations. If you wait, someone else takes your market while you are still wiring SCIM. This is why WorkOS exists. We give developers the infrastructure they need to scale up-market on day one. Even if you have never touched WorkOS, you have already used it through products like ChatGPT, Perplexity, Cursor and many others. Today we operate 81M+ enterprise user accounts, 67M API calls per day, and 38K+ connected enterprise environments. Modern AI tools already run through WorkOS. At ERC, we unveiled six major launches expanding that foundation for the AI era: • AuthKit for ChatGPT Apps: secure OAuth + MCP so developers can connect real enterprise data to 700M+ weekly ChatGPT users. • AuthKit for Platforms: embed full enterprise identity into frameworks like Supabase and Convex for zero-friction onboarding. • Stripe Usage Sync: actual per-seat billing with no glue code. Data stays consistent and invoices are always correct. • WorkOS Pipes: the fastest and most secure way to ship integrations (Salesforce, Slack, Intercom and more). • Agent-Ready API Keys: scoped, revocable keys designed for both developers and autonomous agents. • WorkOS Studio: vibe coding for the enterprise. A collaborative AI app builder with SSO, SCIM, RBAC, audit logs, workflows and third-party connectors built-in. Internal software at the speed of a prompt. AI has already changed how software gets created. The next shift is changing what software becomes. We are still “filming the play” like early cinema: using new technology to recreate old patterns. The opportunity ahead is to invent entirely new applications that only AI-native development makes possible. WorkOS is building the enterprise infrastructure that lets teams design the next era of software itself. Enterprise-ready AI is where the real acceleration happens. Let’s build that future together.

Michael Grinich

37,894 просмотров • 11 месяцев назад

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

Aaron Levie

44,624 просмотров • 5 месяцев назад

"Claude Code is the first AI tool that can actually recreate how a data scientist thinks." Here's my new episode with Sumeet Marwaha (Head of Data at Brex) on exactly how to build an AI analyst with Claude Code. We covered: ✅ 3 queries to build a data analyst MCP ✅ How to give the analyst both data + context ✅ Exclusive Brex data on which AI tools are crushing it in startups and enterprise Some quotes from Sumeet: "I've set up dashboards many times in my career. They end up getting ignored. Claude will always read it. Claude will always ask the right questions." "You have to set up Claude Code to not just look at the data. It should be able to search Slack, find the incident, and understand why your metrics look wrong." "This tool is absolutely crushing it based on our data. It's both the startup AND enterprise coding tool of choice." 📌 Watch now:

"Claude Code is the first AI tool that can actually recreate how a data scientist thinks." Here's my new episode with Sumeet Marwaha (Head of Data at Brex) on exactly how to build an AI analyst with Claude Code. We covered: ✅ 3 queries to build a data analyst MCP ✅ How to give the analyst both data + context ✅ Exclusive Brex data on which AI tools are crushing it in startups and enterprise Some quotes from Sumeet: "I've set up dashboards many times in my career. They end up getting ignored. Claude will always read it. Claude will always ask the right questions." "You have to set up Claude Code to not just look at the data. It should be able to search Slack, find the incident, and understand why your metrics look wrong." "This tool is absolutely crushing it based on our data. It's both the startup AND enterprise coding tool of choice." 📌 Watch now:

Peter Yang

39,600 просмотров • 8 месяцев назад

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 просмотров • 4 месяцев назад

Today we are announcing Keenable AI, an AI-native index of the best human knowledge we have, starting with the open web. AI seems to know just about everything until you ask it about something you know deeply. The answer isn’t wrong, but it's just very average. We started Keenable to fix this problem: every model and every agent should be able to query, reason over, and continuously learn from the living web. Backed by a $26M Seed from Accel and Conviction. Built by the team that took on Google at Yandex Search, with researchers and engineers from Amazon AGI, X, and Perplexity. The Web Search API and Web Query Language for AI are live now. Together they make the web queryable at AI scale. Both are free until end of September. Seven days of benchmarks, stories, and launches ahead. Let your agents search.

Today we are announcing Keenable AI, an AI-native index of the best human knowledge we have, starting with the open web. AI seems to know just about everything until you ask it about something you know deeply. The answer isn’t wrong, but it's just very average. We started Keenable to fix this problem: every model and every agent should be able to query, reason over, and continuously learn from the living web. Backed by a $26M Seed from Accel and Conviction. Built by the team that took on Google at Yandex Search, with researchers and engineers from Amazon AGI, X, and Perplexity. The Web Search API and Web Query Language for AI are live now. Together they make the web queryable at AI scale. Both are free until end of September. Seven days of benchmarks, stories, and launches ahead. Let your agents search.

Andrey Styskin

551,599 просмотров • 1 месяц назад

Over the next 8 weeks, I’ll be training my creative team on EVERYTHING they need to know about using AI to make ads. Jimmy slagle and I have decided to open the doors and sell access to these live trainings for those looking to make more winning ads with AI. We’ll be covering: - Prompting techniques to generate headlines and scripts that convert - Building workflows in tools like Gumloop and Poppy that know your brand inside-out - How to use AI research tools like DeepResearch and Reddit Answers to surface relevant insights instantly - Breakdowns of real prompts that led to high-performing ads - Step-by-step training on how to create static and motion ads using tools like Flux, Kling, and ChatGPT Image Gen - How to create AI knowledge bases that your whole team can use - Becoming your team’s go-to AI expert (even if you're not technical) - What tools are coming next (like Veo 3 and Sora-style video generation) and how to prepare for the future …basically all the skills we think you’ll need to become an AI-first Creative Strategist. Enterprise brands pay us $1,000/hr to teach this stuff. But we’re giving you access to 8x sessions for just $1,499. And you’ll also get our SOPs, Prompt Libraries and exclusive AI trainings. The first live session is on June 12th, then they’re every Thursday for 8 weeks. to see the full curriculum and book your spot.

Alex Cooper

90,270 просмотров • 1 год назад

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 просмотров • 1 год назад