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Introducing our most accurate /search yet. We trained a model to return the excerpts that best answer your query, giving agents highly relevant context from each result. It's SOTA on SimpleQA while using 10x fewer tokens than processing full pages. Now on by default for free.

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Firecrawl

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35,960 просмотров • 1 месяц назад •via X (Twitter)

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Introducing the official Firecrawl connector for Claude 🔥 Add state-of-the-art web search to your AI agents (94.7% on SimpleQA). Powered by our live indexes for fresher results across research, the open web, and more. Live in the Anthropic connector directory now!
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Introducing the official Firecrawl connector for Claude 🔥 Add state-of-the-art web search to your AI agents (94.7% on SimpleQA). Powered by our live indexes for fresher results across research, the open web, and more. Live in the Anthropic connector directory now!

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Introducing the Firecrawl plugin for Codex. Give Codex access to the most accurate search available (94.7% on SimpleQA) plus tools to scrape, crawl, and interact with any site. Find us on the OpenAI plugin marketplace today!
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Introducing the Firecrawl plugin for Codex. Give Codex access to the most accurate search available (94.7% on SimpleQA) plus tools to scrape, crawl, and interact with any site. Find us on the OpenAI plugin marketplace today!

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84,555 просмотров • 17 дней назад

Today we're releasing Monitoring by Firecrawl 📡 Just enter a URL, describe what you want to track, and /monitor notifies your AI agent via webhook the moment pages or sites change. Use up to 90% fewer LLM tokens by only ingesting what changes on a page.
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Today we're releasing Monitoring by Firecrawl 📡 Just enter a URL, describe what you want to track, and /monitor notifies your AI agent via webhook the moment pages or sites change. Use up to 90% fewer LLM tokens by only ingesting what changes on a page.

Firecrawl

101,256 просмотров • 2 месяцев назад

The Firecrawl plugin is now available in OpenCode 🔥 $ npm install -g firecrawl-cli Let agents scrape, search, and browse the web for real-time context - right from your terminal.
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The Firecrawl plugin is now available in OpenCode 🔥 $ npm install -g firecrawl-cli Let agents scrape, search, and browse the web for real-time context - right from your terminal.

Firecrawl

41,734 просмотров • 5 месяцев назад

Introducing the Firecrawl Connector for Lovable 🔥 Your Lovable apps can now scrape, search, and crawl the web out of the box powered by Firecrawl. Free for all users for a limited time! Try it out today 👇
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Introducing the Firecrawl Connector for Lovable 🔥 Your Lovable apps can now scrape, search, and crawl the web out of the box powered by Firecrawl. Free for all users for a limited time! Try it out today 👇

Firecrawl

15,864 просмотров • 8 месяцев назад

Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!
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Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!

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126,122 просмотров • 9 месяцев назад

Perplexity users can now onramp crypto powered by the MoonPay Model Context Protocol (MCP) get started in seconds on Perplexity using our new Custom Connector to give your agents access to crypto here's a look at how it works:
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Perplexity users can now onramp crypto powered by the MoonPay Model Context Protocol (MCP) get started in seconds on Perplexity using our new Custom Connector to give your agents access to crypto here's a look at how it works:

MoonPay 🟣

56,550 просмотров • 1 месяц назад

Introducing /monitor for Slack - get pinged the moment a page, site, or the web changes 📡 Our always-on AI agents scan the web around the clock and drop alerts right in your Slack channels. Just create a monitor on the dashboard, then click "Receive alerts in Slack."
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Introducing /monitor for Slack - get pinged the moment a page, site, or the web changes 📡 Our always-on AI agents scan the web around the clock and drop alerts right in your Slack channels. Just create a monitor on the dashboard, then click "Receive alerts in Slack."

Firecrawl

21,351 просмотров • 1 месяц назад

Introducing Branding Format v2 🎨 Our endpoint for extracting brand identities from the web just got a lot better. - Works with sites built on Wix, Framer, and other no-code builders - Fewer false positive logo extraction - Handles logos hidden in bg images Try it below 👇
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Introducing Branding Format v2 🎨 Our endpoint for extracting brand identities from the web just got a lot better. - Works with sites built on Wix, Framer, and other no-code builders - Fewer false positive logo extraction - Handles logos hidden in bg images Try it below 👇

Firecrawl

57,111 просмотров • 6 месяцев назад

Introducing the Branding format 🎨 You can now extract complete brand DNA from any website including color schemes, logos, frameworks and more in one API call Perfect for coding agents to clone or match existing site aesthetics. Try it out now on the playground or API today 👇
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Introducing the Branding format 🎨 You can now extract complete brand DNA from any website including color schemes, logos, frameworks and more in one API call Perfect for coding agents to clone or match existing site aesthetics. Try it out now on the playground or API today 👇

Firecrawl

111,093 просмотров • 9 месяцев назад

Tavily Agent Skills are now here ⚡ Your agents can now: search the live web pull fresh docs & pages research markets, competitors and products cite sources all without leaving the terminal. We also shipped tavily-best-practices. A skill that teaches agents how to build production-ready web-connected AI built on real-world lessons from builders like you. Works wherever you’re building agents: Claude · Cursor · Google Antigravity Install now with one line.
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Tavily Agent Skills are now here ⚡ Your agents can now: search the live web pull fresh docs & pages research markets, competitors and products cite sources all without leaving the terminal. We also shipped tavily-best-practices. A skill that teaches agents how to build production-ready web-connected AI built on real-world lessons from builders like you. Works wherever you’re building agents: Claude · Cursor · Google Antigravity Install now with one line.

Tavily

15,224 просмотров • 6 месяцев назад

Wikipedia is one of the most requested sources on Firecrawl. So we partnered with Wikimedia Enterprise 🤝 Users now get faster responses and cleaner, structured data - while Firecrawl directly contributes to the infrastructure that keeps Wikipedia running.
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Wikipedia is one of the most requested sources on Firecrawl. So we partnered with Wikimedia Enterprise 🤝 Users now get faster responses and cleaner, structured data - while Firecrawl directly contributes to the infrastructure that keeps Wikipedia running.

Firecrawl

10,585 просмотров • 5 месяцев назад

We're launching 5x faster document parsing in the Firecrawl MCP. Use /parse to turn PDFs, spreadsheets, and docs into LLM-ready data for your agents. Supports running locally or with our hosted MCP server. Try it out today with no API key required on our free tier!
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We're launching 5x faster document parsing in the Firecrawl MCP. Use /parse to turn PDFs, spreadsheets, and docs into LLM-ready data for your agents. Supports running locally or with our hosted MCP server. Try it out today with no API key required on our free tier!

Firecrawl

47,432 просмотров • 1 месяц назад

You can summarize any webpage with Firecrawl 🔥 Perfect for research, content curation, or building AI agents that need quick context! Try it out in our Playground, SDK, or your favorite no-code automation platform!
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You can summarize any webpage with Firecrawl 🔥 Perfect for research, content curation, or building AI agents that need quick context! Try it out in our Playground, SDK, or your favorite no-code automation platform!

Firecrawl

13,731 просмотров • 7 месяцев назад

Stake $OVR, Earn $UOMI – Staking Launches on December 10th The $OVR staking program is coming exclusively on Base on December 10th, giving you the opportunity to earn daily $UOMI rewards by staking your $OVR tokens. 🔹 How to get started: - Buy $OVR on Base or bridge your tokens from Ethereum to Base using a trusted bridge: - Be among the first to stake your $OVR and maximize your share of the $UOMI rewards pool. With no lock-up periods, you’ll maintain full control of your tokens while earning rewards daily. Mark your calendar and get ready to be part of the future of decentralized AI. Stay tuned for more details! 👀
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Stake $OVR, Earn $UOMI – Staking Launches on December 10th The $OVR staking program is coming exclusively on Base on December 10th, giving you the opportunity to earn daily $UOMI rewards by staking your $OVR tokens. 🔹 How to get started: - Buy $OVR on Base or bridge your tokens from Ethereum to Base using a trusted bridge: - Be among the first to stake your $OVR and maximize your share of the $UOMI rewards pool. With no lock-up periods, you’ll maintain full control of your tokens while earning rewards daily. Mark your calendar and get ready to be part of the future of decentralized AI. Stay tuned for more details! 👀

Over the Reality 🌐

155,324 просмотров • 1 год назад

Laguna S 2.1 performs at GLM-5.2 level on building popular games with 6x fewer params! We gave three local models the same task: build three popular arcade games that play themselves. Each game is one self-contained HTML file with a bot that plays it. Prompts: – Geometry Dash – Doodle Jump – Air Hockey Outputs: Laguna S 2.1: 10.3K tokens GLM-5.2: 26.4K tokens Hy3: 10.4K tokens Laguna held its quality against a 753B model. We think Laguna's Geometry Dash looked the best of the three, the cube clears every spike and block. GLM won Air Hockey. Its table looked the most detailed of all. Hy3 was the only model that added shooting to its Doodle Jump. But Laguna is the only model in our benchmark that runs on a MacBook with 128GB!
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Laguna S 2.1 performs at GLM-5.2 level on building popular games with 6x fewer params! We gave three local models the same task: build three popular arcade games that play themselves. Each game is one self-contained HTML file with a bot that plays it. Prompts: – Geometry Dash – Doodle Jump – Air Hockey Outputs: Laguna S 2.1: 10.3K tokens GLM-5.2: 26.4K tokens Hy3: 10.4K tokens Laguna held its quality against a 753B model. We think Laguna's Geometry Dash looked the best of the three, the cube clears every spike and block. GLM won Air Hockey. Its table looked the most detailed of all. Hy3 was the only model that added shooting to its Doodle Jump. But Laguna is the only model in our benchmark that runs on a MacBook with 128GB!

atomic.chat

66,917 просмотров • 1 месяц назад

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3
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Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 просмотров • 7 месяцев назад

Introducing Auto Clips: a new feature that automatically creates Clips based on the best parts of your stream using a variety of signals from the broadcast. You can also pick these moments while streaming by saying “Clip That.” Auto Clips is still in development, and we’ll have more to share on it later this year. Sign up for Alpha access here:
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Introducing Auto Clips: a new feature that automatically creates Clips based on the best parts of your stream using a variety of signals from the broadcast. You can also pick these moments while streaming by saying “Clip That.” Auto Clips is still in development, and we’ll have more to share on it later this year. Sign up for Alpha access here:

Twitch

81,681 просмотров • 10 месяцев назад

Exciting news! We are thrilled to announce the introduction of a highly anticipated feature on Solchat: the ability to share images! From now on, you can share images with your friends through private messages (wallet to wallet). Rest assured that all images are securely saved on IPFS using Visit our Dapp at to experience this new feature firsthand.
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Exciting news! We are thrilled to announce the introduction of a highly anticipated feature on Solchat: the ability to share images! From now on, you can share images with your friends through private messages (wallet to wallet). Rest assured that all images are securely saved on IPFS using Visit our Dapp at to experience this new feature firsthand.

SolChat

51,524 просмотров • 2 лет назад

Today we're introducing Slite as the first self-maintaining Knowledge Base Your wiki docs go outdated everyday, and your agents depend on them for your most ambitious workflows. Slite monitors your docs' accuracy from all other work tools (slack, jira, linear, codebase, etc) while you sleep and updates them. It even directly talks to your AI agents to give them accurate context without burning their context window or tool calls. You've been trying to build this 'company brain' for months now with hacky MVPs This is the final version of what you need, already built to be collaborative, headless, and secure. Check it out and HMU if you need this for your team, I'll be running personal onboardings for the next few days!
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Today we're introducing Slite as the first self-maintaining Knowledge Base Your wiki docs go outdated everyday, and your agents depend on them for your most ambitious workflows. Slite monitors your docs' accuracy from all other work tools (slack, jira, linear, codebase, etc) while you sleep and updates them. It even directly talks to your AI agents to give them accurate context without burning their context window or tool calls. You've been trying to build this 'company brain' for months now with hacky MVPs This is the final version of what you need, already built to be collaborative, headless, and secure. Check it out and HMU if you need this for your team, I'll be running personal onboardings for the next few days!

Christophe Pasquier 🇺🇦

17,796 просмотров • 2 месяцев назад