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.
Looking for C# and .NET interview questions? This guide brings together hundreds of questions across .NET, ASP.NET Core, SQL, NoSQL, microservices, distributed systems, testing, AI, Agile, mobile, and desktop development. Whether you're preparing for a developer, senior engineer, architect, or tech lead interview, you'll find both fundamental and advanced topics in one place.
Complete Agile interview guide covering Scrum, Kanban, metrics, risk management, and enterprise scaling. For junior to senior engineers preparing for interviews
A practical guide to 17 prioritization frameworks for product managers, engineering teams, and portfolio leaders covering MoSCoW, RICE, ICE, WSJF, Cost of Delay, Kano, Opportunity Scoring, and more. Includes formulas, worked examples, limitations, and a framework selection guide.
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.
Learn four ways to manage AI conversation history in .NET: full replay, sliding window, summary buffer, and vector recall. C# code, costs, and trade-offs.
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.
Bad dev experience costs teams hours every day. Learn what DX is, why it matters nowadays, and practical fixes you can ship Monday. For devs of all levels.
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.
This part of C# / .NET interview questions and answers covering modern desktop development: WinUI 3, .NET MAUI, Avalonia UI, MVVM architecture with CommunityToolkit, performance optimization, data storage with EF Core and SQLite, secure credentials, background sync, MSIX packaging, and auto-update strategies.
This week: a comprehensive .NET testing guide covering everything from unit test fundamentals to gRPC contract testing. Netflix introduces MediaFM, their first tri-modal foundation model for deep content understanding. LinkedIn reveals how LLM embeddings and Generative Recommender models are powering the next generation of feed ranking for 1.3 billion users. And Pinterest walks through building a full MCP ecosystem — from registry and security to 66K monthly invocations, saving thousands of engineering hours.
This week: Uber shows how AI agents can automate design system documentation via MCP and how real-time batching solves hot-key payment bottlenecks at scale. Airbnb shares a transformer-based model for recommending travel destinations to exploratory users. And Pinterest details how unified context-intent embeddings power their Analytics Agent — now the most widely adopted internal agent at the company.
60+ C# / .NET testing interview questions with code examples. Covers unit testing, integration testing, gRPC, contracts, distributed systems, Playwright, and performance.