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Lessons from building Code: How we use skills

The Claude Code team catalogs Anthropic's hundreds of internal skills into 9 categories, arguing the best skills fit one cleanly and that verification skills deliver the highest measurable gains, worth an engineer-week each... read more  

Lessons from building Code: How we use skills
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The Smallest Brain You Can Build

Devarsh Ranpara builds a single-input perceptron from scratch in Python with browser demos, using the weight, bias, and decision boundary to show why a line forced through zero cannot separate classes that sit far from it... read more  

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Using local LLMs for agentic coding

Alex Ewerlöf walks through running open-weight models likeGemma 4locally for agentic coding via LM Studio, wiring them into Copilot and Pi as custom endpoints, with the practical traps around context length, KV-cache quantization, and cold-start prompt processing... read more  

Using local LLMs for agentic coding
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Hacking Google with A.I. for $500,000

A security researcher used an AI fuzzing harness against 1,500+ Google APIs and earned $500,000 in bug bounties, surfacing access-control flaws across Google Voice, Widevine, AdExchange, and internal Cloud Console GraphQL endpoints... read more  

Hacking Google with A.I. for $500,000
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I built a Go microservices framework in 2017.

Aafaq Zahid open-sourced Keel, a Go microservices framework he extracted from eight years of production systems... read more  

I built a Go microservices framework in 2017.
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Kubernetes' Default CoreDNS Configuration is insecure

CoreDNS pods insecure option is the default in Kubernetes as it allows for the creation of arbitrary DNS A records. Combined with wildcard SSL certs, it poses a security risk, highlighted by Cilium's handling of network policies in the face of DNS manipulation. Time to shift to a more secure DNS con.. read more  

Kubernetes' Default CoreDNS Configuration is insecure
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Eliminating Kubernetes Image Signature Replication

The Kubernetes image promoter no longer replicates container image signatures across regions. The rewrite drops that replication entirely, cuts latency, and simplifies the codebase, while keeping signature verification working seamlessly for end users. Next, the project is moving to OCI 1.1 referrer.. read more  

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Benchmarking KubeVirt performance with virtbench

Portworx released "virtbench," an open-source CLI that lets platform teams run reproducible KubeVirt benchmarks and assess VM readiness, rather than rely on pod health as a proxy... read more  

Benchmarking KubeVirt performance with virtbench
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Breaking free of a single datacenter: Practical geo-distributed AI operations with the k0smos platforms

This post discusses the challenges of leveraging distributed resources for AI workloads and the role of Kubernetes in addressing these challenges. The k0smos stack is highlighted as a solution for operating geo-distributed AI infrastructure, divided into three technical layers: k0s, k0smotron, and k.. read more  

Breaking free of a single datacenter: Practical geo-distributed AI operations with the k0smos platforms
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From Dashboard to Headlamp: Understanding the Transition

The Kubernetes Dashboard project has been archived, with Headlamp now carrying the legacy forward by offering a visual interface with enhanced capabilities like multi-cluster visibility and application-centric views. Headlamp keeps familiar workflows, while expanding to support multi-cluster environ.. read more  

From Dashboard to Headlamp: Understanding the Transition
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).