TGINSIGHT SIMILAR POSTS
Source channel @githubtrending · Post #15173 · Sep 27
#cplusplus#c_plus_plus#cpp#datachannel#libdatachannel#libnice#p2p#peer_to_peer#peerconnection#rfc_8831#rfc_8834#rtcdatachannel#rtcpeerconnection#sctp#webrtc#webrtc_datachannel#webrtc_video#websocket libdatachannel is a lightweight, easy-to-use C/C++ library that lets you add real-time peer-to-peer data, media, and WebSocket communication to your apps across many platforms like Linux, Windows, macOS, Android, and iOS. It simplifies WebRTC by providing a smaller, simpler alternative to Google's library, with compatibility for browsers like Firefox and Chrome. You can use it to connect native apps directly to web browsers with minimal dependencies, supporting secure connections via GnuTLS, Mbed TLS, or OpenSSL. It also supports compiling to WebAssembly for browser use, making it flexible for cross-platform real-time communication development[1][4]. This helps you build fast, efficient apps for video, audio, or data sharing without heavy libraries. https://github.com/paullouisageneau/libdatachannel
Search: #runtime
@golang · Post #58 · 04/22/2018, 08:22 PM
Why are goroutines not lightweight threads? Kartik Khare shows us his meaning about goroutines, lightweight threads and their difference in GoLang. There are no code examples inside but good thoughts about parallelism, threads and useful links at the end of the article :) #development#runtime#language https://codeburst.io/why-goroutines-are-not-lightweight-threads-7c460c1f155f
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@golang · Post #64 · 06/21/2018, 04:17 PM
Hi there! Which ways do you use to avoid memory leaks for REST API? In the following article by Iman Tumorang describes an excellent example of memory leaks, his solution, and results. Must have to read for everyone 😉 #development#runtime#architecture https://hackernoon.com/avoiding-memory-leak-in-golang-api-1843ef45fca8
@githubtrending · Post #15382 · 01/01/2026, 12:30 PM
#jupyter_notebook#agent#agentic_ai#agents#authentication#bedrock#core#gateway#identity_management#memory_management#production_code#runtime Amazon Bedrock AgentCore lets you build, deploy, and run AI agents securely at scale with any framework like CrewAI or LangGraph and any model, without managing complex infrastructure. It offers serverless runtime for long tasks up to 8 hours, gateway to connect tools like Slack or APIs easily, memory for personalized experiences, identity management, built-in code interpreter and browser tools, plus observability. This saves time by skipping heavy setup, speeds prototypes to production, cuts costs with pay-per-use, and boosts security—helping you create powerful agents faster for real business needs. https://github.com/awslabs/amazon-bedrock-agentcore-samples