TGINSIGHT SIMILAR POSTS
Source channel @githubtrending · Post #15006 · Jul 29
#go#cli#event_driven#event_driven_architecture#queues#serverless#serverless_functions#workflow_engine#workflows Inngest lets you write reliable, long-running background functions called durable workflows that automatically handle retries, scheduling, and state management without needing to manage infrastructure like queues or servers. You write functions in your preferred language using their SDKs, run and test them locally with the Inngest Dev Server, then deploy them on your own infrastructure or Inngest’s platform. It supports complex workflows with steps that retry on failure, concurrency control, and event triggers. This saves you time and effort by simplifying event-driven app development, improving reliability, and scaling automatically without extra setup. It also offers tools for monitoring and managing workflows easily. https://github.com/inngest/inngest
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