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Introducing Screenshot-kit ⚡️ Fastest way to get website screenshots & videos with one API call. No browser automation hell. Features: ✅ Screenshot any website. ✅Capture full-page screenshots (entire scrollable page) ✅ Record scrolling videos for landing pages ✅ Multi resolution support. Use for Free on replicate, Check it out... show more

Ayush Yadav

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21,252 Aufrufe • vor 6 Monaten •via X (Twitter)

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💥Another #Airdrop for our community 😉If u followed my guide on Babylon testnet 🌽U r eligible for CORN Airdrop 🪙4,800,000 Points 💯FREE 💰$6.7M funding ✅Binance backed 🪙U can claim 4.8M - 9.6M Kernel points if u followed my Babylon guide 🪙Kernel points = Corn 🪂 ⚠️Little confusing to link Bitcoin wallet, so follow steps exactly as below 👇Steps to claim your bonus points 🔗 ✅Access my sheet with invitation links ✅Click any available link ✅Join using EVM wallet (MetaMask) ✅Now logout ✅Click on Join Button ✅Select "with eligible wallet" tab ✅Now copy your sBTC taproot address (starts with tb1pj...) ✅Paste it ✅Hit "check" 💥Now u can see 4,800,000 bonus for Babylon testnet task ✅Hit "sign & join" ✅Connect Ethereum wallet ⚠️Use same metamask wallet u used to join above ✅Verify EVM wallet ✅Hit "Assign" ✅Sign txn in OKX wallet with sBTC address 💥Done, u successfully claimed points to your Ethereum address 🪙U can earn more points by staking eligible assets ✅U can withdraw them any time ✅Also link you're Galxe account to get points for Galxe task we did 🤔Is this 4.8M points big? 😉Yes, reason👇 🪙For $1000 stake I am able to farm 6000 points a day 🤯So I need to stake $1000 for 800 days to earn 4.8M points 📸Post your points screenshot in comments section, if u claimed 💙Like 🔁RT

💥Another #Airdrop for our community 😉If u followed my guide on Babylon testnet 🌽U r eligible for CORN Airdrop 🪙4,800,000 Points 💯FREE 💰$6.7M funding ✅Binance backed 🪙U can claim 4.8M - 9.6M Kernel points if u followed my Babylon guide 🪙Kernel points = Corn 🪂 ⚠️Little confusing to link Bitcoin wallet, so follow steps exactly as below 👇Steps to claim your bonus points 🔗 ✅Access my sheet with invitation links ✅Click any available link ✅Join using EVM wallet (MetaMask) ✅Now logout ✅Click on Join Button ✅Select "with eligible wallet" tab ✅Now copy your sBTC taproot address (starts with tb1pj...) ✅Paste it ✅Hit "check" 💥Now u can see 4,800,000 bonus for Babylon testnet task ✅Hit "sign & join" ✅Connect Ethereum wallet ⚠️Use same metamask wallet u used to join above ✅Verify EVM wallet ✅Hit "Assign" ✅Sign txn in OKX wallet with sBTC address 💥Done, u successfully claimed points to your Ethereum address 🪙U can earn more points by staking eligible assets ✅U can withdraw them any time ✅Also link you're Galxe account to get points for Galxe task we did 🤔Is this 4.8M points big? 😉Yes, reason👇 🪙For $1000 stake I am able to farm 6000 points a day 🤯So I need to stake $1000 for 800 days to earn 4.8M points 📸Post your points screenshot in comments section, if u claimed 💙Like 🔁RT

CryptoTelugu

173,783 Aufrufe • vor 1 Jahr

💥 Simple guide to bridge $OCT tokens to Ethereum ✅If u tried octra Testnet, all ur Testnet tokens r now migrated to mainnet 1:1 ✅U can bridge & sell them 📈Token pumped now 💵 $OCT - $0.076 👇Steps ⚠️ Few countries r blocked 🌐 So U need to use VPN 🔗 ✅Login to GitHub ✅Check on code & open codespace ✅Copy & run 2nd & 3rd commands as shown on video ✅U will see a link, open it ✅Import ur Testnet wallet seed ✅Set pin ✅Go to apps tab ✅Select bridge ✅Connect EVM wallet ✅Bridge tokens & claim $wOCT on Ethereum 💥Done 🕹️There is alternatives & easy method with 3rd party Wallet ⚠️but I don't want to risk installing that & sign txn with my evm wallet, that too for $100 🤔How much did u claim? 📸Post ur screenshot below 💙Like 🔁RT

💥 Simple guide to bridge $OCT tokens to Ethereum ✅If u tried octra Testnet, all ur Testnet tokens r now migrated to mainnet 1:1 ✅U can bridge & sell them 📈Token pumped now 💵 $OCT - $0.076 👇Steps ⚠️ Few countries r blocked 🌐 So U need to use VPN 🔗 ✅Login to GitHub ✅Check on code & open codespace ✅Copy & run 2nd & 3rd commands as shown on video ✅U will see a link, open it ✅Import ur Testnet wallet seed ✅Set pin ✅Go to apps tab ✅Select bridge ✅Connect EVM wallet ✅Bridge tokens & claim $wOCT on Ethereum 💥Done 🕹️There is alternatives & easy method with 3rd party Wallet ⚠️but I don't want to risk installing that & sign txn with my evm wallet, that too for $100 🤔How much did u claim? 📸Post ur screenshot below 💙Like 🔁RT

CryptoTelugu

32,170 Aufrufe • vor 2 Monaten

Check out mistral.rs, our #Rust-based open source inference engine allowing for fast #LLM serving for a variety of architectures including X-LoRA mixture-of-expert (MoE) models, Llama-3, Mistral/Mixtral, Gemma & many others. Built on the Hugging Face #Candle framework for #Rust w/ custom CUDA kernels in the backend (as well as support for Metal, Apple Accelerate, and Intel MKL for CPU use), you can easily create a REST API OpenAI compatible server or run via Python bindings. Key features include: ✅Prefix caching, continuous batching ✅Flash Attention V2 ✅Device offloading ✅GGUF or Hugging Face models ✅2, 3, 4, 5, 6 and 8 bit quantization ✅X-LoRA MoE non-granular scalings for fast inference ✅Grammar support ✅Continuous batching ✅LoRA support with weight merging ✅LlamaIndex 🦙 integration ...and much more. Incorporation into our GraphReasoning multi-agent modeling framework & LlamaIndex 🦙 allows you to combine in-context learning with adversarial agentic strategies, to dive deep into complex scientific analyses, such as to predict material behaviors, generate hypotheses, analyze papers and data, develop new research concepts, and much more. Check out mistral.rs: Join our Discord here: Rust Trending Rust Language

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Tasker AI

418,415 Aufrufe • vor 11 Monaten

Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦙/acc) for all the work here. Come check it out: Repo:

Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦙/acc) for all the work here. Come check it out: Repo:

Jerry Liu

256,399 Aufrufe • vor 4 Monaten