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| 🔗 Stories, Tutorials & Articles |
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| SLI Evolution Stages ✅ |
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| A new SLI evolution model lays out a maturity roadmap—from rebranded latency/error metrics to ones that actually track business impact. It replaces shallow signals and pulls in the stuff that matters: how service failures hit user goals, tasks, and bottom lines. |
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| Writing Load Balancer From Scratch In 250 Line of Code |
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| A developer rolled out a fully working Go load balancer with a clean Round Robin setup—and hooks for dropping in smarter strategies like Least Connection or IP Hash. Backend servers live in a custom server pool. Swapping balancing logic? Just plug into the interface. |
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| Writing an operating system kernel from scratch ✅ |
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A barebones time-sharing OS kernel, written in Zig, running on RISC-V. It leans on OpenSBI for console I/O and timer interrupts. Threads? Statically allocated, each running in user mode (U-mode). The kernel stays in supervisor mode (S-mode), where it catches system calls and context switches via timer ticks.
One neat trick: kernel and userland share a single binary. No dynamic linking. No loaders. Everything stitched together upfront. |
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| Best 20 Linux Commands for Daily Use in Production Servers |
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A fresh roundup drops 20 go-to Linux commands for production sysadmins, dialing in on modern defaults like htop > top, ss > netstat, and ip > ifconfig.
Expect the usual suspects—journalctl, rsync, crontab—all still pulling weight for logs, file sync, and scheduled jobs. |
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| %CPU Utilization Is A Lie ✅ |
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Stress tests on the Ryzen 9 5900X uncovered a big gap between reported CPU utilization and what the chip actually pushes. Around 50% on paper? Could mean close to full throttle in reality—thanks to sneaky behaviors from SMT resource sharing and Turbo frequency scaling.
Takeaway: Raw utilization metrics can’t be trusted for capacity planning anymore. They lie. Benchmark real throughput instead. |
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| Magical systems thinking |
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AI now writes over 25% of Google’s and as much as 90% of Anthropic’s code. That’s not a trend—it’s a regime change.
Still, the mess in large public systems reminds us: clever analysis isn’t enough. Complex systems don’t behave; they misbehave.
When the machines are churning out code, the smarter move is to evolve from working systems—not architect them from scratch. Bottom-up beats big plans. |
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| Scaling Prometheus: Managing 80M Metrics Smoothly |
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| Flipkart ditched its creaky StatsD + InfluxDB stack for a federated Prometheus setup—built to handle 80M+ time-series metrics without choking. The move leaned into pull-based collection, PromQL's firepower, and hierarchical federation for smarter aggregation and long-haul queries. |
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