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Bryan Cantrill: How Kubernetes Broke the AWS Cloud Monopoly

Bryan Cantrill says Kubernetes didn’t just organize containers, it cracked open the cloud market. By letting teams provision infrastructure without locking into provider APIs, it broke AWS’s first-mover grip. That shift putcloud neutralityon the table, and suddenly multi-cloud wasn’t just a buzzword.. read more  

Bryan Cantrill: How Kubernetes Broke the AWS Cloud Monopoly
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@kala shared a link, 1 month, 2 weeks ago
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8 plots that explain the state of open models

Starting 2026, Chinese companies are dominating the open AI model scene, with Qwen leading in adoption metrics. Despite the rise of new entrants like Z.ai, MiniMax, Kimi Moonshot, and others, Qwen's position seems secure. DeepSeek's large models are showing potential to compete with Qwen, but the Ch.. read more  

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Build an AI-powered website assistant with Amazon Bedrock

AWS spun up a serverless RAG-based support assistant usingAmazon BedrockandBedrock Knowledge Bases. It pulls in docs via a web crawler and S3, then stuffs embeddings intoAmazon OpenSearch Serverless. Access is role-aware, locked down withCognito. Everything spins up clean withAWS CDK... read more  

Build an AI-powered website assistant with Amazon Bedrock
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@kala shared a link, 1 month, 2 weeks ago
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Where good ideas come from (for coding agents)

A new way to build agents treats prompting ascontext navigation, steering the LLM through ideas like a pilot, not tossing it prompts and hoping for magic. It maps neatly onto Steven Johnson’s seven patterns of innovation. For coding agents to actually pull their weight, users need to bring more than.. read more  

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Agentic AI, MCP, and spec-driven development: Top blog posts of 2025

AI speeds up dev - but it’s a double-edged keyboard. It sneaks in subtle bugs and brittle logic that break under pressure. To keep things sane, teams are fighting back withguardrail patterns,AI-aware linters, andtest suites hardened for hallucinated code... read more  

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Towards Generalizable and Efficient Large-Scale Generative Recommenders

Authors discuss their approach to scaling generative recommendation models from O(1M) to O(1B) parameters for Netflix tasks, improving training stability, computational efficiency, and evaluation methodology. They address challenges in alignment, cold-start adaptation, and deployment, proposing syst.. read more  

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Weaponizing the AWS CLI for Persistence

Researchers pulled off a slick persistence trick usingAWS CLI aliases. They chained dynamic alias renaming with command execution to swipe credentials, without breaking expected CLI behavior. No red flags. Perfect fit forautomated environmentslike CI/CD pipelines. Backdoors, no AWS CLI tampering req.. read more  

Weaponizing the AWS CLI for Persistence
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Cloud Workload Threats - Runtime Attacks in 2026

Cloud-native breaches keep slipping through the cracks, not because no one’s watching, but because they’re watching the wrong things. Static checks and posture tools can’t catch what happens in motion. That’s where most attacks live now: at runtime. Think app-layer exploits, poisoned dependencies, s.. read more  

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21 Lessons From 14 Years at Google

A seasoned Google engineer drops 21 sharp principles for scaling engineering beyond just writing code. Think:clarity beats cleverness,users over egos,alignment over being “right.”The core message? Build systems humans can work with - especially under stress. Favorites: kill pointless work, treat pro.. read more  

21 Lessons From 14 Years at Google
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Terraform governing with OPA

When managing infrastructure with Terraform, enforcing tagging standards, instance type restrictions, preventing public exposure, enforcing regions, and other best practices are essential with Open Policy Agent (OPA). OPA evaluates Terraform plans before apply to ensure compliance with organization'.. read more  

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).