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Measuring Developer Productivity with Amazon Q Developer and Jellyfish

Amazon Q Developer now plugs into Jellyfish. Teams get a clearer view of how AI fits into the real flow of work—prompt usage, code adoption, PR throughput. Not just surface stats. The setup pipes data from AWS S3 straight into Jellyfish’s analytics engine. It tags AI users, tracks velocity gains, an.. read more  

Measuring Developer Productivity with Amazon Q Developer and Jellyfish
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AWS, Microsoft and Google unite behind Linux Foundation DocumentDB database to cut enterprise costs and limit vendor lock-in

Document databases are crucial for AI apps in the gen AI era. Microsoft's open-source DocumentDB project, based on PostgreSQL, is moving to the Linux Foundation, offering a vendor-neutral, open-source alternative to MongoDB. DocumentDB's compatibility with MongoDB drivers and open source governance .. read more  

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Sandboxed to Compromised: New Research Exposes Credential Exfiltration Paths in AWS Code Interpreters

Researchers poked holes insandboxed Bedrock AgentCore code interpreters—and found a way to leak execution role credentials through theMicroVM Metadata Service (MMDS). No outside network? Doesn’t matter. The exploit dodges basic string filters in requests and lets non-agentic code swipe AWS creds to .. read more  

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Deploy a containerized application with Kamal and Terraform

A Docker-first workflow combinesTerraformandKamalinto a lean, Elastic Beanstalk-ish alternative—without the bloat. Terraform spins up a three-tier VPC and wires it toECR. Kamal takes it from there, booting containers on a raw EC2 box: app, proxy, monitor. One script. Done... read more  

Deploy a containerized application with Kamal and Terraform
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Which LLM writes the best analytical SQL?

Tinybird threw 19 top LLMs at a 200M-row GitHub dataset, testing how well they could turn plain English into solid SQL. Most models kept their syntax clean—but when it came to writing SQL that actually ran well and returned the right results, they lagged behind human pros. Messy schemas or tricky pr.. read more  

Which LLM writes the best analytical SQL?
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Being on the Same Page During an Incident: Not Actually Telepathy

Collaboration in incident response is crucial for effective resolution, starting with establishing a basic compact among responders. Grounding is a process that ensures alignment and common ground is maintained throughout an incident, encompassing initial common ground, public events so far, and the.. read more  

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v1.34: Introducing CPU Manager Static Policy Option for Uncore Cache Alignment

Kubernetes 1.34 bumps theCPU Manager uncore-cache alignment policyto beta. It’s aimed at nodes withsplit uncore cache architectures. The policy groups all a container’s CPUs under the same uncore cache—cutting latency and easing contention for workloads that hate waiting. System shift:Kubernetes kee.. read more  

v1.34: Introducing CPU Manager Static Policy Option for Uncore Cache Alignment
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v1.34: DRA has graduated to GA

Kubernetes 1.34 turnsDynamic Resource Allocation (DRA)loose into General Availability—enabled by default. That cements native support for high-maintenance gear like GPUs, FPGAs, and any other quirky hardware your workloads need. The release also packs a fresh mix of alpha/beta features: tighter admi.. read more  

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v1.34: Service Account Token Integration for Image Pulls Graduates to Beta

Kubernetes v1.34 bumpsServiceAccount token integration for Kubelet Credential Providersto beta. That means image pulls can now ditch long-lived secrets for workload-scoped tokens. Cleaner, safer, and more locked down per ServiceAccount... read more  

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Container Logs in Kubernetes: How to View and Collect Them

This guide shows how to wrangle container logs in Kubernetes—usingkubectl, shell tools, structured logging, and the Kubernetes Dashboard. It covers the basics and dives into how to scale up log collection and make observability less painful across clusters... read more  

Container Logs in Kubernetes: How to View and Collect Them
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).