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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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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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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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Development gets better with Age

A longtime AWS insider, Werner Vogels, breaks down the shift from slow-and-steady software growth to the generative AI rocket ride. Capabilities soared. Guardrails? Not so much. No docs, no handrails - just launch and learn. AWS didn’t chase the hype. It pulled a classic AWS move: doubled down on B2.. read more  

Development gets better with Age
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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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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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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 AWS S3 serves 1 petabyte per second on top of slow HDDs

AWS S3 doesn’t need fancy hardware. It wrings performance out ofcheap HDDs,log-structured merge trees, anderasure coding. The trick? Shard everything. Hit it in parallel. Randomized placementdodges hotspots.Hedged requestsrace the slowest links. And when things get lopsided, S3 rebalances - constant.. read more  

How AWS S3 serves 1 petabyte per second on top of slow HDDs
Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.