Anthropic, OpenAI, Block standardize MCP for AI agents

Understanding VSOCK: Fast and Secure Communication Between VMs and the Hypervisor Vsock is a socket family in Linux designed specifically for communication between a virtual machine and its hypervisor. Unlike traditional networking approaches, Vsock enables a direct, efficient, and secure channel without relying on the host’s network stack. Originally introduced by VMware directly into the Linux kernel, Vsock is now also available in QEMU/KVM and Hyper‑V (though the latter uses closed‑source implementation). What makes Vsock particularly powerful is its flexibility. Applications running inside a VM or on the host can use the VM Sockets API to establish fast, low‑overhead communication. The interface is compatible with familiar UDP and TCP semantics, supporting both stream‑oriented (SOCK_STREAM) and datagram‑oriented (SOCK_DGRAM) communication. The protocol family is defined as AF_VSOCK. Because Vsock bypasses the host’s networking stack entirely, virtual machines can even operate without any network interface at all, communicating solely through VM sockets. This can significantly reduce the attack surface and simplify secure architectures. Each #VM and the host are assigned a 32‑bit CID (Context Identifier), and communication happens over 32‑bit port numbers. #Ports below 1024 are considered privileged, similar to traditional networking. Vsock is a great example of how #virtualization #technologies continue to evolve, offering developers and #system engineers more efficient and #secure ways to build modern infrastructure.

🚀 Boosted My Linux Server Performance — Here’s What Actually Worked Over the past few days, I focused on squeezing maximum performance out of my Linux machine. No fancy hardware upgrades — just deep system-level tuning. Here’s what made a real difference 👇 🔧 1. Removed unnecessary services Stopped background processes that were silently consuming CPU & RAM. ⚡ 2. Tuned memory (Game changer) Reduced swappiness Enabled ZRAM (huge improvement for low RAM systems) 🧠 3. Identified resource-hungry processes Used htop, iotop, and vmstat to pinpoint bottlenecks instead of guessing. 💽 4. Optimized disk I/O Switched scheduler for SSD Added noatime to reduce disk writes 🔥 5. Set CPU to performance mode Ensured the system isn’t throttling when load increases. 🌐 6. Tuned kernel network parameters Improved connection handling for server workloads. 📉 7. Controlled logging & background load Reduced unnecessary log growth and system overhead. 💡 Key Learning: Performance issues are rarely about “low specs” — they’re about inefficient resource usage. If you're running: Linux servers AI workloads Trading systems Backend APIs 👉 System tuning alone can give you a massive performance boost without spending ₹1. I’m now diving deeper into: Kernel-level optimizations AI infrastructure tuning High-performance system design If you're working on similar problems, let’s connect 🤝 #Linux #DevOps #PerformanceOptimization #SystemDesign #AIInfrastructure #Backend #Tech

"I don't think running VMs inside Podman is possible." So I did what any reasonable person would do: I booted the same VM twice inside a rootless Podman container and timed both runs. The first boot runs without KVM, which means pure software emulation where QEMU translates every CPU instruction on the fly. Your modern multi-GHz processor ends up spending its time pretending to be a slower, imaginary one, and you feel it: Alpine Linux took 22 seconds to reach the login prompt. The second boot adds two flags — `--device /dev/kvm` on the Podman side and `-enable-kvm -cpu host` on the QEMU side. Instead of translating instructions, KVM hands them directly to the CPU, and the same VM boots in 7.8 seconds. Three times faster, with no changes to the image, the ISO, or the machine. What surprised me most wasn't the speed gap. It was that none of this requires `--privileged`. Passing `--device /dev/kvm` gives the container access to exactly that one device and nothing else, so you get hardware acceleration without handing the container more trust than it needs. Next up in Part 3: the VM is fast but has no memory between runs. We fix that with persistent qcow2 disk images. Link in the comments 👇 #KVM #Podman #Virtualization #Linux #QEMU #LearnInPublic

Your server shows a load average of 2.0. Is it fine? Is it dying? The answer: it depends on something you probably forgot to check. why the Load Average is Misunderstood Load average seems simple: a number representing how busy your system is. But here's the trap,the same number means completely different things depending on your hardware. The Core Problem Load average measures the average number of processes waiting to run on your CPU. The critical detail? It's not normalized to core count. 1-core machine with load 2.0: Disaster. You have 2 processes competing for 1 CPU slot. Your system is 100% overloaded, struggling under pressure. 32-core machine with load 2.0: Idling. You have 2 processes for 32 available slots. Your system is barely awake at 6% utilization. The Math Behind It Load-to-Core Ratio is what matters: Load / Cores = Real utilization 2.0 / 1 = 200% (overloaded ❌) 2.0 / 32 = 6% (healthy ✅) What You Should Do Know your core count: nproc on Linux, or check system info Do the math: Divide load average by core count Watch the ratio: < 1.0 = comfortable 1.0-2.0 = acceptable depending on workload 2.0 = investigate and optimize The Takeaway Load average isn't a lie—it's just incomplete without context. Always normalize by core count. A load of 2.0 isn't inherently good or bad; it depends entirely on your machine's architecture.

After building a two-node VM setup using ConnectX-4 cards for RDMA testing I wanted to go further and explore how congestion control works in RoCEv2 networks. Since I didn't have a hardware switch to act as the Congestion Point I wrote up a guide on how to fake it using a Linux bridge with TC RED for ECN marking. Check out the full setup and benchmarks here: https://lnkd.in/dJW6uP8X

Most Linux servers run with default sysctl values. And most of the time… nobody questions it. But in real environments (especially hosting / high-load systems), this leads to: Connection drops under load High TCP retransmissions File descriptor exhaustion Unstable performance patterns So I started building something small: eak-systune A lightweight tool that: Observes real system behavior (not assumptions) Builds a rolling baseline from live metrics Detects anomalies in CPU, TCP, FD usage, run queue Compares system state against optimized sysctl profiles Generates actionable recommendations Not based on static “best practices”. Based on how your system actually behaves under load. Still early, but already seeing interesting signals: TCP pressure patterns FD usage spikes before incidents Misaligned kernel parameters This is not another tuning script. This is about understanding before optimizing. More soon.

📺 Watch Nimrod Hauser on-demand in this 40-minute hands-on talk in the AI Engineer Europe online track: Nimrod shares 5 practical tips, in increasing complexity, for squeezing more value from third-party agentic tools. He uses Playwright MCP as the running example and demonstrates running the agent, browser steering, and instant trade-offs you need to handle on setup. If you use agentic tools or browser-use MCP servers, this is a practical guide to building AI-native testing and quality workflows. Watch: https://lnkd.in/ezvZd_e4

Stop guessing whether a performance lag is an application issue or a network bottleneck. Traditional monitoring often stops at the door, telling you requests are slow without explaining why. By leveraging eBPF to capture TCP and DNS telemetry directly from the Linux kernel, teams can finally see handshake delays and retransmissions in the same view as their APM data. It’s about moving from "we think it’s the network" to "here is the evidence." 🚀 Check out how kernel-level visibility is changing the troubleshooting game. #eBPF #Observability

Stop guessing whether a performance lag is an application issue or a network bottleneck. Traditional monitoring often stops at the door, telling you requests are slow without explaining why. By leveraging eBPF to capture TCP and DNS telemetry directly from the Linux kernel, teams can finally see handshake delays and retransmissions in the same view as their APM data. It’s about moving from "we think it’s the network" to "here is the evidence." 🚀 Check out how kernel-level visibility is changing the troubleshooting game. #eBPF #Observability

Stop guessing whether a performance lag is an application issue or a network bottleneck. Traditional monitoring often stops at the door, telling you requests are slow without explaining why. By leveraging eBPF to capture TCP and DNS telemetry directly from the Linux kernel, teams can finally see handshake delays and retransmissions in the same view as their APM data. It’s about moving from "we think it’s the network" to "here is the evidence." 🚀 Check out how kernel-level visibility is changing the troubleshooting game. #eBPF #Observability