AbstractLess Approach

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.