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@varbear shared a link, 11 hours ago
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Free software scares normal people

A developer rolled outMagicbrake- a no-fuss GUI forHandbrakeaimed at folks who don’t speak command line. One button. Drag, drop, convert. Done. It strips Handbrake down to the bones for anyone who just wants their video in a different format without decoding flags and presets...

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@varbear shared a link, 11 hours ago
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The bug that taught me more about PyTorch than years of using it

A sneaky bug inPyTorch’s MPS backendlet non-contiguous tensors silently ignore in-place ops likeaddcmul_. That’s optimizer-breaking stuff. The culprit? ThePlaceholder abstraction- meant to handle temp buffers under the hood - forgot to actually write results back to the original tensor...

The bug that taught me more about PyTorch than years of using it
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@varbear shared a link, 11 hours ago
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Aggressive bots ruined my weekend

Bear Blog went dark after getting swarmed by scrapers. The reverse proxy choked first - too many requests, not enough heads-up. Downstream defenses didn’t catch it in time. So: fire, meet upgrades. What changed: Proxies scaled 5×. Upstream got strict with rate limits. Failover now has a pulse. Resta..

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@varbear shared a link, 11 hours ago
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Kafka is fast -- I'll use Postgres

Postgres is pulling Kafka moves—without the Kafka. On a humble 3-node cluster, it held 5MB/s ingest and 25MB/s egress like a champ. Low latency. Rock-solid durability. Crank things up, andsingle-node Postgresflexed hard: 240 MiB/s in, 1.16 GiB/s out for pub/sub. Thousands of messages per second in q..

Kafka is fast -- I'll use Postgres
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@varbear shared a link, 11 hours ago
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uv is the best thing to happen to the Python ecosystem in a decade

uvis a new Rust-powered CLI from Astral that tosses Python versioning, virtualenvs, and dependency syncing into one blisteringly fast tool. It handles yourpyproject.tomllike a grown-up—auto-generates it, updates it, keeps your environments identical across machines. Need to run a tool once without t..

uv is the best thing to happen to the Python ecosystem in a decade
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@varbear shared a link, 11 hours ago
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How Netflix Tudum Supports 20 Million Users With CQRS

Netflix gutted Tudum’s old read path—Kafka, Cassandra, layers of cache—and swapped inRAW Hollow, a compressed, distributed, in-memory object store baked right into each microservice. Result? Homepage renders dropped from 1.4s to 0.4s. Editors get near-instant previews. No more read caches. No extern..

How Netflix Tudum Supports 20 Million Users With CQRS
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@kaptain shared a link, 12 hours ago
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eBPF Beginner Skill Path

This hands-on path drops devs straight into writing, loading, and poking at basiceBPFprograms withlibbpf,maps, and those all-important kernel safety checks. It starts simple - with a beginner-friendly challenge - then dives deeper into theverifierand tools for runtime introspection...

eBPF Beginner Skill Path
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@kaptain shared a link, 12 hours ago
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How to build highly available Kubernetes applications with Amazon EKS Auto Mode

Amazon EKS Auto Mode now runs the cluster for you—handling control plane updates, add-on management, and node rotation. It sticks to Kubernetes best practices so your apps stay up through node drains, pod failures, AZ outages, and rolling upgrades. It also respectsPod Disruption Budgets,Readiness Ga..

How to build highly available Kubernetes applications with Amazon EKS Auto Mode
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@kaptain shared a link, 12 hours ago
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Building a Kubernetes Platform — Think Big, Think in Planes

Thinking in planes, as introduced by the Platform Engineering reference model, helps teams describe their platform in a simple, shared language, turning a collection of tools into a platform. It forces you to think horizontally, connecting teams and technologies instead of adding more layers, creati..

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@kaptain shared a link, 12 hours ago
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AWS to Bare Metal Two Years Later: Answering Your Toughest Questions About Leaving AWS

OneUptime ditched the cloud bill and rolled their own dual-site setup. Thinkbare metal, orchestrated withMicroK8s, booted byTinkerbell, patched together withCeph,Flux, andTerraform. Result?99.993% uptimeand$1.2M/year saved—76% cheaper than even well-optimized AWS. They run it all with just~14 engine..

Grafana Tempo is a distributed tracing backend built for massive scale and low operational overhead. Unlike traditional tracing systems that depend on complex databases, Tempo uses object storage—such as S3, GCS, or Azure Blob Storage—to store trace data, making it highly cost-effective and resilient. Tempo is part of the Grafana observability stack and integrates natively with Grafana, Prometheus, and Loki, enabling unified visualization and correlation across metrics, logs, and traces.

Technically, Tempo supports ingestion from major tracing protocols including Jaeger, Zipkin, OpenCensus, and OpenTelemetry, ensuring easy interoperability. It features TraceQL, a domain-specific query language for traces inspired by PromQL and LogQL, allowing developers to perform targeted searches and complex trace-based analytics. The newer TraceQL Metrics capability even lets users derive metrics directly from trace data, bridging the gap between tracing and performance analysis.

Tempo’s Traces Drilldown UI further enhances usability by providing intuitive, queryless analysis of latency, errors, and performance bottlenecks. Combined with the tempo-cli and tempo-vulture tools, it delivers a full suite for trace collection, verification, and debugging.

Built in Go and following OpenTelemetry standards, Grafana Tempo is ideal for organizations seeking scalable, vendor-neutral distributed tracing to power observability at cloud scale.