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Skill Issues: How We Discovered Supply Chain Attack Vectors in an AI Agent Skills Marketplace

Orca Security researchers identified four attack primitives in an AI coding-agent skills marketplace: install-count inflation without authentication, security scans at creation and popularity thresholds, same-name overrides without user alerts, and bulk updates without per-skill review or version pi.. read more  

Skill Issues: How We Discovered Supply Chain Attack Vectors in an AI Agent Skills Marketplace
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@varbear shared a link, 3 weeks, 5 days ago
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Slop Creep: The Great Enshittification of Software

The argument is that coding agents accelerate codebase decay by removing the natural speed limit on bad architectural decisions, compressing months of compounding mistakes into days. The defense is to invest ten times more in the planning phase, with concrete code snippets for the data models and ab.. read more  

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@kaptain shared a link, 3 weeks, 5 days ago
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CNCF Project Antrea Compromised in Daring GitHub Attack

A throwaway GitHub account compromised CNCF projectAntrea's Jenkins infrastructure on May 2 by opening a malicious PR and firing/test-*slash-commands that detonated the workflow against PR-fork code with credentials in scope. The same operator ran parallel campaigns against at least seven other proj.. read more  

CNCF Project Antrea Compromised in Daring GitHub Attack
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@kaptain shared a link, 3 weeks, 5 days ago
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v1.36: Moving Volume Group Snapshots to GA

Volume group snapshots reachedGAin Kubernetesv1.36, with the API promoted togroupsnapshot.storage.k8s.io/v1. The feature lets aVolumeGroupSnapshotobject take crash-consistent snapshots across multiple PVCs selected by label, removing the need to quiesce applications that span separate data and log v.. read more  

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@kaptain shared a link, 3 weeks, 5 days ago
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How Cloud Native Infrastructure Powers AI on Kubernetes

A vendor piece from Mirantis arguing that GPU multi-tenancy on Kubernetes is widely misrepresented, with most platforms shipping namespace-based isolation while production GPU clouds require hardware-enforced separation through MIG partitioning, cluster-per-tenant architecture, and DPU-based network.. read more  

How Cloud Native Infrastructure Powers AI on Kubernetes
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@kaptain shared a link, 3 weeks, 5 days ago
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v1.36: Declarative Validation Graduates to GA

Declarative validation graduated toGAin Kubernetesv1.36, replacing handwritten Go validation with+k8s:marker tags on field definitions... read more  

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@kaptain shared a link, 3 weeks, 5 days ago
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v1.36: Server-Side Sharded List and Watch

Alpha inv1.36, server-side sharded list and watch adds ashardSelectorfield toListOptionsso the API server uses an FNV-1a hash onmetadata.uidormetadata.namespaceto send each controller replica only its slice of the resource collection. This eliminates the cost of every replica deserializing the full .. read more  

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@kala shared a link, 3 weeks, 5 days ago
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Orchestrating AI Code Review at scale

Cloudflare engineers built an AI code review platform on OpenCode. They split GitLab integration, model providers, prompts, and policy into separate plugins. A coordinator assigns up to seven domain reviewers across security, performance, code quality, documentation, release checks, and AGENTS.md co.. read more  

Orchestrating AI Code Review at scale
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@kala shared a link, 3 weeks, 5 days ago
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How We Built an AI Second Brain for 60K Knowledge Workers

Meta built an AI agent system internally called the AI Second Brain that now has over 63,000 installs and ~10,000 daily active users across engineering, PM, design, legal, finance, comms, and sales, growing from zero in roughly three months after a non-technical PM's adoption post. The architecture .. read more  

How We Built an AI Second Brain for 60K Knowledge Workers
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@kala shared a link, 3 weeks, 5 days ago
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Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph

Netflix's Saish Sali, Nipun Kumar, and Sura Elamurugu describe the Metadata Service (MDS), a graph layer built to connect siloed ML tooling (model registry, pipeline orchestrator, experimentation platform, feature store, dataset platform, identity) across personalization, studio, payments, and ads. .. read more  

INTELLECT-3 is a frontier-class 100B+ Mixture-of-Experts language model developed by Prime Intellect and trained end-to-end using their large-scale asynchronous RL framework, PRIME-RL. Built on the GLM-4.5-Air base model, INTELLECT-3 combines supervised fine-tuning with long-horizon reinforcement learning across hundreds of verifier-backed environments spanning math, code, science, logic, and agentic tasks.

The model was trained on a high-performance cluster of 512 NVIDIA H200 GPUs across 64 nodes, supported by Prime Intellect’s Sandboxes execution engine, deterministic compute orchestration, and Lustre-backed distributed storage. The result is a model that surpasses many larger systems in reasoning benchmarks while remaining fully open-source.

Prime Intellect released not only the model weights but also the full training recipe: PRIME-RL, Verifiers, the Environments Hub, datasets, and evaluation suites. INTELLECT-3 is positioned as a foundation for organizations seeking to post-train or customize their own frontier-grade models without relying on proprietary AI labs.