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@anjali shared a link, 6 months ago
Customer Marketing Manager, Last9

Grafana Tempo: Setup, Configuration, and Best Practices

A practical guide to setting up Grafana Tempo, configuring key components, and understanding how to use tracing across your services.

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@laura_garcia shared a post, 6 months ago
Software Developer, RELIANOID

🍺 Cyberattack on Asahi Group: A Wake-Up Call for Japan’s Industrial Sector

Just after Japan’s new Active Cyberdefence Law (ACD Law) came into effect — a major step toward reshaping the country’s cybersecurity posture — Japan’s largest brewer, Asahi Group, has suffered a ransomware attack that disrupted production and logistics nationwide. ⚠️ This incident starkly illustrat..

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@varbear shared a link, 6 months ago
FAUN.dev()

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... read more  

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@varbear shared a link, 6 months 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.. read more  

Kafka is fast -- I'll use Postgres
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@varbear shared a link, 6 months ago
FAUN.dev()

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.. read more  

How Netflix Tudum Supports 20 Million Users With CQRS
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@varbear shared a link, 6 months ago
FAUN.dev()

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.. read more  

uv is the best thing to happen to the Python ecosystem in a decade
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@varbear shared a link, 6 months ago
FAUN.dev()

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.. read more  

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@varbear shared a link, 6 months ago
FAUN.dev()

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... read more  

The bug that taught me more about PyTorch than years of using it
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@kaptain shared a link, 6 months 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... read more  

eBPF Beginner Skill Path
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@kaptain shared a link, 6 months 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.. read more  

How to build highly available Kubernetes applications with Amazon EKS Auto Mode
GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.