Guide on choosing between on-device and Private Cloud Compute foundation models in Swift for iOS apps.
Learn how to fix common SwiftUI LazyVStack jank issues like row state loss and layout hitches before switching to List.
Explores WasmGC in Wasmtime, a WebAssembly runtime for cloud sandboxing, and its impact on garbage-collected languages.
Final part of migrating agentic code from Python to C# using Microsoft Agent Framework, implementing workflow executors.
A developer recounts how a minor Terraform change accidentally deleted their production OpenSearch cluster, analyzing four key mistakes.
Jesse Liberty discusses AI in an interview on .NET Live, sharing his extensive tech experience.
Analysis of Midjourney's pivot to medical ultrasound, sparking debate between tech boosters and medical practitioners over AI in healthcare.
A curated roundup of IT/tech links including AI, .NET, Blazor, web development, Windows, and DevOps news for June 22, 2026.
Azure Local Simplified Machine Provisioning enables edge infrastructure deployment with minimal on-site interaction using Azure for centralized control.
Compares Legacy LAPS, Windows LAPS, and LAPS for Azure Arc for local admin password management.
Explains Azure Bicep resource-derived types, member access with [*] syntax, and strongly typed parameters.
Deep dive into BYON (Bring Your Own Network) setup for Microsoft Foundry, covering networking, governance, and architecture.
Explores the n queens problem on a toroidal chessboard when n is prime, discussing solutions with queens on lines of constant slope.
![FFConf 2026 is live: Things I Learnt [blog]](https://crawljobs-blogs-media.s3.us-east-1.amazonaws.com/alldevblogs/thumbs/article-7a97c51fab59-full-fb311433.webp)
A web developer shares CSS techniques and lessons learned while designing the FFConf 2026 site, including vertical text, polygons, and contrast handling.
Explains how AI-ready metadata prevents query failures by making ownership, freshness, lineage, quality, and policy visible at execution time for LLM-driven analytics.
Explores autonomous materialization for agentic analytics, focusing on performance, governance, and lifecycle management in data platforms.
Explores the Dremio Agentic Lakehouse concept: data built for AI agents and platform management automated by agents, with architecture patterns and production considerations.
Explores using ClickHouse for low-latency analytical loops in active agent systems, emphasizing validation, safety, and architecture patterns.
Explains why AI agents need a context layer with lineage, quality, freshness, and ownership for reliable analytics.
Explores how lakehouses serve as an operating layer for agentic AI, focusing on governed data access, cost controls, and production reliability.
