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37 Things I Learned About Information Retrieval in Two Years at a Vector Database Company

A Weaviate engineer pulls back the curtain on two years of hard-earned lessons in vector search—breaking downBM25,embedding models,ANN algorithms, andRAG pipelines. The real story? Retrieval workflows keep moving—from keyword-heavy (sparse) toward embedding-driven (dense). Across IR use cases, the .. read more  

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ECScape: Understanding IAM Privilege Boundaries in Amazon ECS

A new ECS security mess—ECScape—lets low-privileged tasks on EC2 act like the ECS agent. That’s bad. Real bad. Why? Because it opens the door to stealing IAM credentials from other ECS tasks sharing the same host. Here’s the trick: The attacker hits the instance metadata service (IMDS) and fakes a .. read more  

ECScape: Understanding IAM Privilege Boundaries in Amazon ECS
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How to prepare for the Bitnami Changes coming soon

The Bitnami team has delayed the deletion of the Bitnami public catalog until September 29th. They will conduct a series of brownouts to prepare users for the upcoming changes, with the affected applications list being published on the day of each brownout. Users are advised to switch to Bitnami Sec.. read more  

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Observability in Go: What Real Engineers Are Saying in 2025

Go observability still feels like pulling teeth. Manual instrumentation? Tedious. Span coverage? Spotty. Telemetry volume? Totally out of hand. Even with OpenTelemetry gaining traction, Go lags behind Java and Python when it comes to auto-instrumentation and clean context propagation. Devs are hunt.. read more  

Observability in Go: What Real Engineers Are Saying in 2025
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Availability Models: Because “Highly Available” Isn’t Saying Much

Antithesis and Jepsen want to kill hand-wavy "high availability" talk. Instead, they push for clearavailability models—majority,total,sticky, etc.—that spell out when an operationactuallyworks during failures. It's about precision, not platitudes. Why it matters:This reframes availability from a va.. read more  

Availability Models: Because “Highly Available” Isn’t Saying Much
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Google Develops KFuzzTest For Fuzzing Internal Linux Kernel Functions

Google droppedKFuzzTest, a lean fuzzing tool built to hit Linux kernel internals—way past just syscalls. It brings a clean API, docs, and sample targets to get fuzzing fast. Why it matters:KFuzzTest marks a shift. Kernel fuzzing’s no longer just about hammering syscalls—it’s going deeper into the g.. read more  

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v1.34: User preferences (kuberc) are available for testing in kubectl 1.34

Kubernetes v1.34 pusheskubectlinto the future with a betauser preferencessystem. Drop a.kubercfile in place, and you can bake in default flags, toggle features likeinteractive deleteorServer-Side Apply, and wire up custom aliases—including pre- and post-args... read more  

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The architecture of AI is different from all of the computing that came before it

AI is breaking open source out of its old habits. Compute-heavy models now demand GPU-first stacks, leaner infrastructure, and fresh rules for how we build and scale. Jonathan Bryce points out: scalability and reliability still matter—but AI’s deployment needs throw the old architecture playbook ou.. read more  

The architecture of AI is different from all of the computing that came before it
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From Novice to Pro: Mastering Lightweight Linux for Your Kubernetes Project

Alpine, Flatcar, Fedora CoreOS, Talos, and Ubuntu Core are carving out strong niches as Kubernetes-first base OSes. Each leans into immutability, container-native design, and just enough system overhead to get out of the way. That lean profile isn’t just a flex—it means lower resource drag and a de.. read more  

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CNCF Incubates OpenYurt for Kubernetes at the Edge

OpenYurt just leveled up—now officially an incubating project under the CNCF. It pushes Kubernetes out past the data center, into the messy edges of the network, without breaking upstream compatibility. No forks, no duct tape. The maintainer roster’s growing too. Folks fromVMware,Microsoft, andInte.. read more  

CNCF Incubates OpenYurt for Kubernetes at the Edge
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).