Weekly engineering digest: Canva session revocations, Meta BPF scheduling, stateless MCP, and Cloudflare Kitesurf for AI agents.

This week: a practical guide to 10 ADR anti-patterns with concrete examples showing how to write architecture decisions that actually help your team. Uber shares two infrastructure deep dives — making OpenSearch zone-failure resilient with isolation groups and forced shard allocation awareness, and GitFarm, a Git-as-a-Service platform that delivers full monorepo checkouts in under 500ms. And Dropbox traces the decade-long evolution of Riviera from file preview service to a universal content processing platform now powering AI ingestion at 2.5 billion requests per day.

Weekly tech digest featuring a massive Agile & Scrum interview guide with DORA metrics and scaling patterns, Uber's Cart Assistant agentic grocery system built on multi-prompt state graphs, Slack's Shipyard immutable EC2 platform with layered AMIs and automatic instance cycling, and Stripe's graph-based auto-remediation system cutting MongoDB pager volume by 30%.

Weekly tech digest featuring a practical guide to 17 prioritization frameworks with formulas and examples, Netflix's brutally honest Service Topology production postmortem, Uber's SPIRE-backed AI agent identity framework with full actor chain propagation, and Uber's artifact storage modernization cutting 5PB/month egress by 99% with a custom validation proxy.

 Weekly tech digest featuring a comprehensive AI interview guide for .NET engineers covering LLMs, RAG, agents, and MCP with C# examples, Zalando's in-process client-side load balancer handling 1M RPS with occupancy-based bounded load, Netflix's Vera and VOID research models for controllable AI video editing, and LinkedIn's autonomous QA Agent using vision-language models for AI-driven testing at scale.

In this article, I will explain 10 anti-patterns in Architecture Decision Records and how to avoid them. Consider this article a guide to following Best Practices and creating a good Architecture Decision Record (ADR).

This week: a comprehensive 800+ question .NET interview guide covering everything from C# fundamentals to AI and distributed systems. Airbnb goes deeper into its Sitar platform with sitar-agent — the Kubernetes sidecar that reliably delivers dynamic config at the pod level. Pinterest shares the full journey of building production Ray infrastructure on Kubernetes, from KubeRay workarounds to cost governance. And Lyft explains how their metric semantic layer creates a single source of truth for key data definitions across the organization.

Weekly tech digest featuring a step-by-step LLM training guide covering SFT, RLHF, DPO, and distillation, Netflix's real-time Service Topology map built with eBPF and distributed tracing, Airbnb's geographic prior propagation for resilient corridor forecasting, and Pinterest's unified user sequence infrastructure for cost-efficient ML pipelines.

Prepare for your next .NET interview with 800+ interview questions and answers covering C#, ASP.NET Core, SQL, NoSQL, microservices, distributed systems, testing, AI, Agile, desktop and mobile development. For developers, senior engineers, architects and tech leads.

Complete Agile interview guide covering Scrum, Kanban, metrics, risk management, and enterprise scaling. For junior to senior engineers preparing for interviews

In this article, we review AI, LLM, prompting, .NET AI integration, Semantic Kernel, Microsoft Agent Framework, MCP, RAG, resilience, testing, observability, security, and AI coding tools for .NET engineers.

This week: a practical .NET guide to managing AI conversation history with four strategies from full replay to vector recall. Uber shares DeepETT, a graph-aware transformer serving 2 million real-time traffic forecasts per second across 100 million road segments, driving $100M in annual value. And Airbnb details its shift from PaaS to a unified knowledge-graph infrastructure that powers identity resolution at scale.

Learn how modern LLMs are trained, from pretraining and instruction tuning to SFT, RLHF, DPO, and model distillation. This guide explains how raw foundation models become production-ready AI assistants, coding copilots, and enterprise agents.

This week: featuring a beginner-friendly LLM explainer, Uber's Tarot platform solving Multiple Knapsack optimization for incentive allocation at scale, Airbnb's Skipper embedded workflow engine for durable execution without external dependencies, and Lyft's end-to-end mapping system for smarter pickups in gated communities.

This week: a comprehensive .NET MAUI mobile development interview guide covering framework choices, MVVM, and cross-platform architecture. Netflix introduces the Model Lifecycle Graph a metadata service that makes every ML asset discoverable across business domains. Discord shares a detailed postmortem of their March voice outage, diving deep into Elixir process mailbox overload and Kubernetes safeguards. And Pinterest shows how injecting real-time context signals into sequential recommender models improves ad relevance and targeting.

In this article, we explain what a Large Language Model is, how LLMs work, what they excel at, where they fall short, and how they fit into real software products.

A practical guide to C# mobile development interviews in 2026. Covers .NET MAUI, Xamarin, architecture, performance, UI patterns, and real-world mobile engineering concepts with clear explanations and examples.

This week: a practical guide to Developer Experience and why slow builds, flaky tests, and tribal knowledge silently kill productivity. Slack reveals how they manage context across long-running multi-agent security investigations using structured journals and critical reviews. Airbnb goes deep on building fault-tolerant metrics storage at 50M samples/sec with shuffle sharding and automated tenant management. And Pinterest introduces MIQPS — a data-driven algorithm that learns which URL parameters matter for content identity, powering smarter deduplication across millions of domains.

This week: Pinterest traces the evolution of their feed re-ranking from DPP to Sliding Spectrum Decomposition, showing why diversity drives long-term retention. Airbnb shares a battle-tested migration from StatsD to OpenTelemetry with a dual-write approach that cut metrics CPU overhead by 10x. Uber optimized Petastorm to resolve a GPU utilization bottleneck, slashing training time from 22 hours to 3 hours. And Netflix details the architecture behind their multimodal video search, unifying character, scene, and dialogue models into a real-time creative discovery tool.

This week: a comprehensive .NET desktop development interview guide covering WinUI 3, MAUI, and Avalonia. Lyft goes beyond A/B testing, using surrogates and region splits to measure long-term marketplace effects. Airbnb shares how COVID broke their forecasting models and the Bayesian architecture they built to survive the next shock. And Meta reveals the ML behind Friend Bubbles, blending social closeness models with content signals to power social discovery on Facebook Reels.