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@kaptain shared a link, 1 week, 2 days ago
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Building a fault-tolerant metrics storage system at Airbnb

Airbnb built a metrics system that ingests50M samples/s, stores2.5PBof logical time series, and hosts1.3B active series. They use tenant-per-service grouping andshuffle sharding. They enforce per-tenant guardrails and a consolidatedcontrol plane. They shard queries and compaction. They run zone-awar.. read more  

Building a fault-tolerant metrics storage system at Airbnb
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@kaptain shared a link, 1 week, 2 days ago
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v1.36: User Namespaces in are finally GA

Kubernetesv1.36promotesUser Namespacesto GA on Linux. It brings rootless workload isolation. Kubelet leans on kernelID-mapped mounts. It sidesteps expensivechownby remappingUID/GIDat mount time and confines privileged processes. No more mass-chown screams... read more  

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@kaptain shared a link, 1 week, 2 days ago
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Why MicroVMs: The Architecture Behind Sandboxes

Docker Sandboxes puts each agent session in a dedicatedmicroVM. Each microVM runs a privateDocker daemoninside the VM boundary. That blocks access to the host. A new cross‑platformVMMruns on macOS, Windows, and Linux hypervisors. It slashes cold starts and runs fullDockerbuild, run, and compose work.. read more  

Why MicroVMs: The Architecture Behind Sandboxes
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@kaptain shared a link, 1 week, 2 days ago
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The AI-driven shift in vulnerability discovery: What maintainers and bug finders need to know

AI modelslet non-experts craft real and fake vulnerabilities at scale. They spit out low-quality noise and the occasional high-value report. Reports floodOSS maintainers. Triage, patching, release cadences, and downstreamupgrade/compliancepipelines buckle under the load. Guidance recommends publishi.. read more  

The AI-driven shift in vulnerability discovery: What maintainers and bug finders need to know
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@kala shared a link, 1 week, 2 days ago
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Introducing Coregit

Coregit reimplements Git's object model inTypeScriptand runs onCloudflare Workersas a serverless edge Git API. Its commit endpoint accepts up to 1,000 file changes per request and replaces 105+ GitHub calls with one. Yes - one. It acknowledges writes inDurable Objects(~2ms), then flushes objects toR.. read more  

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@kala shared a link, 1 week, 2 days ago
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How LLMs Work — A Visual Deep Dive

A complete walkthrough of how large language models like ChatGPT are built, from raw internet text to a conversational assistant... read more  

How LLMs Work — A Visual Deep Dive
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@kala shared a link, 1 week, 2 days ago
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The PR you would have opened yourself

ASkillports models fromtransformerstomlx-lm. It bootstraps an env, discovers variants, downloads checkpoints, writes MLX implementations, and runs layered tests. It produces disclosed PRs with per-layer diffs, dtype checks, generation examples, numerical comparisons, and a reproducible, non-agentict.. read more  

The PR you would have opened yourself
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@kala shared a link, 1 week, 2 days ago
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A GitHub agentic workflow

The developer automated parsing of unstructured release notes withGitHub agentic workflows. The pipeline compilesMarkdowntoYAML, then runs an agent. The setup requires afine-grained Copilot token. It enforces a hardenedsandboxpolicy and forbids Marketplace actions. CI runs a compile-then-compare che.. read more  

A GitHub agentic workflow
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@kala shared a link, 1 week, 2 days ago
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Introducing Ternary Bonsai: Top Intelligence at 1.58 Bits

PrismML unveilsTernary Bonsai: a family of1.58-bitLMs in1.7B,4B, and8Bsizes. Models use ternary weights {-1,0,+1} with group-wise quantization. Weights are ternary (-1,0,+1). Each group of128weights shares anFP16scale. That cuts memory by ~9x versus 16-bit and boosts benchmark scores. The8Bhits 75.5.. read more  

Introducing Ternary Bonsai: Top Intelligence at 1.58 Bits
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@devopslinks shared a link, 1 week, 2 days ago
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Betterleaks: The Gitleaks Successor Built for Faster Secrets Scanning

BetterleakssupplantsGitleaksas a drop-in CLI. Scans run faster. It's written inPure Go- no CGO - and performs parallel git scans. It replaces entropy heuristics with token-efficient detection viaBPE. It addsCELrule validation. Its roadmap includes LLM assist and auto-revocation... read more  

Betterleaks: The Gitleaks Successor Built for Faster Secrets Scanning
Lustre is an open-source, parallel distributed file system built for high-performance computing environments that require extremely fast, large-scale data access. Designed to serve thousands of compute nodes concurrently, Lustre enables HPC clusters to read and write data at multi-terabyte-per-second speeds while maintaining low latency and fault tolerance.

A Lustre deployment separates metadata and file data into distinct services—Metadata Servers (MDS) handling namespace operations and Object Storage Servers (OSS) serving file contents stored across multiple Object Storage Targets (OSTs). This architecture allows clients to access data in parallel, achieving performance far beyond traditional network file systems.

Widely adopted in scientific computing, supercomputing centers, weather modeling, genomics, and large-scale AI training, Lustre remains a foundational component of modern HPC stacks. It integrates with resource managers like Slurm, supports POSIX semantics, and is designed to scale from small clusters to some of the world’s fastest supercomputers.

With strong community and enterprise support, Lustre provides a mature, battle-tested solution for workloads that demand extreme I/O performance, massive concurrency, and petabyte-scale distributed storage.