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Advanced PostgreSQL Indexing: Multi-Key Queries and Performance Optimization

Advanced PostgreSQL tuning gets real results: composite indexes and CTEs can cut query latency hard when slicing huge datasets. AddLATERALjoins and indexed subqueries into the mix, and you’ve got a top-N query pattern that holds up—even when hammering long ID lists... read more  

Advanced PostgreSQL Indexing: Multi-Key Queries and Performance Optimization
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I'm Building a Browser for Reverse Engineers

A researcher rolled their ownChromium forkwith a customDevTools Protocol (CDP) domain- not for fun, but to surgically probe browser internals. It reaches into Canvas, WebGL, and other trickier APIs, dodging the usual sandbox and spoofing all the bot blockers they'd rather you leave alone. It injects.. read more  

I'm Building a Browser for Reverse Engineers
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Going down the rabbit hole of Postgres 18 features by Tudor Golubenco

PostgreSQL 18 just hit stable. Big swing! Async IO infrastructureis in. That means lower overhead, tighter storage control, and less CPU getting chewed up by I/O. Adddirect IO, and the database starts flexing beyond traditional bottlenecks. OAuth 2.0? Native now. No hacks needed. UUIDv7? Built-in su.. read more  

Going down the rabbit hole of Postgres 18 features by Tudor Golubenco
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Technical Tuesday: 10 best practices for building reliable AI agents in 2025

UiPath just droppedAgent Builder in Studio- a legit development environment for AI agents that can actually handle enterprise chaos. Think production-grade: modular builds, traceable steps, and failure handling that doesn’t flake under pressure. It’s wired forschema-driven prompts,tool versioning, a.. read more  

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Write Deep Learning Code Locally and Run on GPUs Instantly

Modal cuts the drama out of deep learning ops. Devs write Python like usual, then fire off training, eval, and serving scripts to serverless GPUs - zero cluster wrangling. It handles data blobs, image builds, and orchestration. You focus on tuning with libraries like Unsloth, or serving via vLLM... read more  

Write Deep Learning Code Locally and Run on GPUs Instantly
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Serverless RL: Faster, Cheaper and More Flexible RL Training

New product, Serverless RL, available through collaboration between CoreWeave, Weights & Biases, and OpenPipe. Offers fast training, lower costs, and simple model deployment. Saves time with no infra setup, faster feedback loops, and easier entry into RL training... read more  

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The RAG Obituary: Killed by Agents, Buried by Context Windows

Agent-based setups are starting to edge out old-school RAG. As LLMs snag multi-million-token context windows and better task chops, the need for chunking, embeddings, and reranking starts to fade. Claude Code, for example, skips all that - with direct file access and smart navigation instead. Retrie.. read more  

The RAG Obituary: Killed by Agents, Buried by Context Windows
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Ansible Service Module: Start, Stop, & Manage Services

The Ansibleservicemodulehandles LinuxandWindows without choking on init system quirks. One playbook can start, stop, enable, or restart anything - no matter the OS. Idempotent, so you don’t have to babysit state. Clean and repeatable. Bonus: it’s great for wrangling fleets. Think: coordinating servi.. read more  

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How LogSeam Searches 500 Million Logs per second

LogSeam rips through500M log searches/secand pushes1.5+ TB/s throughputusing Tigris’ geo-distributed object storage. It slashes log volume by 100× with Parquet + Zstandard compression. Then it spins up compute on the fly, right where the data lives—no long-running infrastructure, no laggy reads... read more  

How LogSeam Searches 500 Million Logs per second
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Automated GitHub Self-Hosted Runner Cleanup: Lambda Functions and Auto Scaling Lifecycle Hooks

When an EC2 instance in an Auto Scaling Group shuts down, event-driven plumbing kicks in. Alifecycle hookcatches the scale-in, fires off an SNS notification, and triggers aLambda. That Lambda calls the GitHub API to yank the self-hosted runner before the instance dies. No dangling runners. No manual.. read more  

Automated GitHub Self-Hosted Runner Cleanup: Lambda Functions and Auto Scaling Lifecycle Hooks
Lustre is an open-source, parallel distributed file system built for high-performance computing environments that require extremely fast, large-scale data access. Designed to serve thousands of compute nodes concurrently, Lustre enables HPC clusters to read and write data at multi-terabyte-per-second speeds while maintaining low latency and fault tolerance.

A Lustre deployment separates metadata and file data into distinct services—Metadata Servers (MDS) handling namespace operations and Object Storage Servers (OSS) serving file contents stored across multiple Object Storage Targets (OSTs). This architecture allows clients to access data in parallel, achieving performance far beyond traditional network file systems.

Widely adopted in scientific computing, supercomputing centers, weather modeling, genomics, and large-scale AI training, Lustre remains a foundational component of modern HPC stacks. It integrates with resource managers like Slurm, supports POSIX semantics, and is designed to scale from small clusters to some of the world’s fastest supercomputers.

With strong community and enterprise support, Lustre provides a mature, battle-tested solution for workloads that demand extreme I/O performance, massive concurrency, and petabyte-scale distributed storage.