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Turn traffic into pipeline and prospects into customersDocket is an AI-powered sales enablement platform designed to unify go-to-market (GTM) data through its proprietary Sales Knowledge Lake™ and activate it with intelligent AI agents. The platform helps marketing teams increase pipeline generation by 15% by engaging website visitors in human-like conversations and qualifying leads. For sales teams, Docket improves seller efficiency by 33% by providing instant product knowledge, retrieving collateral, and creating personalized documents. Built for GTM teams, Docket integrates with over 100 tools across the revenue tech stack and offers enterprise-grade security with SOC 2 Type II, GDPR, and ISO 27001 compliance. Customers report improved win rates, shorter sales cycles, and dramatically reduced response times. Docket’s scalable, accurate, and fast AI agents deliver reliable answers with confidence scores, empowering teams to close deals faster.
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Failed Payment Recovery for Subscription BusinessesFlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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hlchunk.nvim
This is the lua implementation of nvim-hlchunk
Similar to indent-blankline, this plugin can highlight the indent line, and highlight the code chunk according to the current cursor position. Using profile.nvim for performance analysis, all experiments were conducted on macOS with alacrity, Neovim window height was 66 lines, and the code file was typescript.js, starting from the first line to the five hundredth line. The average rendering time per render was 0.7ms. -
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civetweb
Embedded C/C++ Web Server
...This project is based on the Mongoose web server, but maintains the MIT license and is free from GPL licenses. Installation Guide https://github.com/civetweb/civetweb/blob/master/docs/Installing.md Contributions are welcome and encouraged. The official source code is at https://github.com/civetweb/civetweb Problems may be reported at https://github.com/civetweb/civetweb/issues Use the "Civetweb" Google groups for questions and discussion https://groups.google.com/d/forum/civetweb
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