Search Results for "command line dynamic"
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4 projects for "command line dynamic" with 2 filters applied:
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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libdropbox
Small ANSI C lib for dropbox/windows azure communication
Small ANSI C lib for dropbox and windows azure communication. Built for small platforms. Using PolarSSL for https communication. Features a small self contained https module and a modified version of the JSMN json parser. Originally based on the dropbox_uploader script. Able to do most dropbox actions. Eg. Upload file, download file, list, file info, account info, share link. Also contains a small CLI programs that interfaces with the lib. Also capable of windows azure service bus... -
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Linknx is an automation platform providing high level functionalities to EIB/KNX installation. The rules engine allows execution of actions based on complex logical conditions and timers. Lightweight design allows it to run on embedded Linux. The project has been migrated to Github in 2015: https://github.com/linknx/linknx
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pywincedb is a Python module to access native Windows CE databases and set of tools to manipulate them (conversion to/from XML, migration). Keywords: Python, WindowsCE, WinCE.
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A lyric library write in C, it provides ability to parse lyric file and access the information in a convenient way. Its main goal is to provide lyric support for embeded devices, such audio player in mp3 device and mobile phone.
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Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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