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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, 3 days, 9 hours 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, 3 days, 9 hours 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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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, 3 days, 9 hours 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, 3 days, 9 hours 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, 3 days, 9 hours 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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@devopslinks shared a link, 3 days, 9 hours ago
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Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways

Quantum computers could decrypt data stored today in anticipation of future decryption, posing security risks despite the estimated decade-long timeline. Industry-wide PQC standards are being published by NIST to defend against such threats, including algorithms like ML-KEM and ML-DSA. The industry .. read more  

Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways
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pgit: I Imported the Linux Kernel into PostgreSQL

pgitingested 20 years of the Linux kernel: 1.43M commits, 24.4M file versions. The dataset lives inPostgreSQLwithpg-xpatch- 2.7GB on disk. A 2-hour import on a 24-core EPYC built a queryableSQLDB. Most delta-decompressed queries return in <10s. No preprocessing required... read more  

pgit: I Imported the Linux Kernel into PostgreSQL
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@devopslinks shared a link, 3 days, 9 hours ago
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Why We Chose the Harder Path: Hardened Images, One Year Later

Docker Hardened Images surpassed500k daily pullsand now hosts2,000+ hardened images, all built in aSLSA Build Level 3pipeline. It compiles tens of thousands ofDebianandAlpinepackages from source. It runs 1M+ builds. It ships17 signed attestationsper image. It auto-rebuilds customized images under SL.. read more  

Why We Chose the Harder Path: Hardened Images, One Year Later
Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.