Frederic Branczyk

For our 4th episode, we have four conversations from KubeCon EU 2024.We talk to Jesse Suen about Argo CD & Kargo, Solomon Hykes shares the next evolution of Dagger, and Justin Cormack dives into Docker & AI. We also catch up with Frederic Branczyk & Thor Hansen on the latest Parca & Polar Signals Cloud updates since our last conversation.Each conversation has a video version too:Jesse Suen: 🎬 GitOps & ClickOps beyond KubernetesSolomon Hykes: 🎬 Pipelines as FunctionsJustin Cormack: 🎬 Works on my ComputerFrederic Branczyk & Thor Hansen: 🎬 always BPFLINKS1. Jesse Suen- What's New in Kargo v0.5.0- 🎬 Navigating Multi-stage Deployment Pipelines via the GitOps Approach 2. Solomon Hykes- Introducing Dagger Functions- A Keynote heard around the world - KubeCon EU 2024 Recap- 🎬 Local & open-source AI Developer Meetup3. Justin Cormack- AI Trends Report 2024: AI’s Growing Role in Software Development- Building a Video Analysis and Transcription Chatbot with the GenAI Stack4. Frederic Branczyk & Thor Hansen- Correlating Tracing with Profiling using eBPFLET'S MAKE IT BETTERIf you enjoyed this episode, I will appreciate your feedback on Apple Podcasts or Spotify.If there is something that would have made it better for you, let me know: makeitwork@gerhard.io

This episode looks into the observability tool Parca & Polar Signals Cloud with Frederic Branczyk and Thor Hansen. We discuss experiences and discoveries using Parca for detailed system-wide performance analysis, which transcends programming languages.We highlight a significant discovery related to kube-prometheus and the unnecessary CPU usage caused by Prometheus exporter's attempts to access BTRFS stats, leading to a beneficial configuration change for Kubernetes users globally.We also explore Parca Agent's installation on Kubernetes 1.28 running on Talos 1.5, the process of capturing memory profiles with Parca, and the efficiency of the Parca Agent in terms of memory and CPU usage.We  touch upon the continuous operation of the Parca Agent, the importance of profiling for debugging and optimization, and the potential of profile-guided optimizations in Go 1.22 for enhancing software efficiency.🎬 Screensharing videos that go with this episode:First impressions: Parca Agent on K8s 1.28 running as Talos 1.5See where your Go code allocates memoryHow to debug a memory issue with Parca?See which line of your Go code allocates the most memory🎁 Access the audio & all videos as a single conversation at makeitwork.gerhard.ioLINKSGo Profile-guided optimizationView Profiling Data within CodeAnnouncing Continuous Memory Profiling for RustEPISODE CHAPTERS