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@kaptain shared a link, 2 weeks, 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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@kaptain shared a link, 2 weeks, 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, 2 weeks, 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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@kala shared a link, 2 weeks, 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, 2 weeks, 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, 2 weeks, 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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@kala shared a link, 2 weeks, 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, 2 weeks, 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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@devopslinks shared a link, 2 weeks, 2 days ago
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What is AWS Graviton? The custom chip powering applications for 90,000 customers

Amazon'sGravitonfamily peaks at a 192-core chip. It delivers up to25%better performance thanGraviton4and keeps energy efficiency intact. AWS says98%of its top 1,000 EC2 customers runGraviton. More than half of new EC2 capacity runs on these chips... read more  

What is AWS Graviton? The custom chip powering applications for 90,000 customers
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@devopslinks shared a link, 2 weeks, 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
AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.