EF Core 10 Is INSANE — Overview of Game Changer features

Learn Vector Databases for Beginners – Embeddings, Similarity Search & ChromaDB

A vector database stores data as lists of numbers called embeddings, which lets it search by meaning instead of matching exact keywords. It's the piece behind a lot of recommendation systems, RAG apps

Modernize your .NET apps for Windows Arm64 with GitHub Copilot

The GitHub Copilot upgrade agent now supports migrating .NET applications from Windows x64 or x86 to Windows Arm64. It assesses your projects, flags blockers like architecture-tied build settings and native dependencies without Arm64 binaries, then builds a migration plan. The agent updates code, dependencies, and project configuration, and builds the migrated app for Arm64, running tests when matching hardware or CI runners are available. It focuses on portability and correctness, not Arm64 performance tuning. To start, install GitHub Copilot upgrade, pick the .NET Arm64 migration scenario, and review its plan before letting it make changes.

Introducing the MongoDB Vercel AI SDK Integration: Persistent Memory for AI Applications

This integration enables MongoDB to serve as the persistent memory layer for AI applications and agents built with Vercel’s AI SDK.

AI agent session monitoring with embedding models

AI agent session monitoring tested on 7,454 turns: one session in seven needed review, at a third of the cost of LLM evaluation. Reasoning beat the prompt.

Benchmarking Billion-Scale Vector Indexes

Benchmarking a vector index at billion-scale means tracking P99/P99.9 latency, Recall@K, QPS at a fixed recall target, index build time, RSS memory, and CPU usage, since averages hide failures. The team used three datasets for different jobs: SIFT-1B (128-d) to test raw scale, LAION-100M (768-d CLIP floats) to test real float behavior, and a self-built BookCorpus-10M (4096-d, via Llama 2) to approximate LLM-scale embeddings. Building BookCorpus-10M took 16 hours on five GPUs at about 173 vectors/sec, producing 164 GB that restores via cbbackupmgr in 2-3 minutes. Partition elimination, tagging vectors with a key like category_id, let a 10-partition layout skip 90% of the work per query, tightening P99 tail latency and raising throughput per core.

RAG vs. Vectorless RAG, clearly explained!

...explained visually (with tradeoffs in production).

Build a Jev-powered Agentic RAG

Bối cảnh là việc xây dựng một hệ thống RAG (Retrieval-Augmented Generation) có khả năng năng động. Nguyên nhân kỹ thuật là cần một framework có thể xử lý các tác vụ tự nhiên mà không bị giới hạn bởi các prompt cố định. Hệ quả là hệ thống có thể tự điều chỉnh hành vi dựa trên ngữ cảnh đầu vào. Điều đáng học là cách triển khai Jev-powered agent cho RAG giúp tăng tính linh hoạt và khả năng xử lý các tác vụ phức tạp. Bài viết cung cấp hướng dẫn chi tiết với code mẫu để triển khai hệ thống này.

Bài viết này hướng dẫn bạn cách xây dựng hệ thống Retrieval-Augmented Generation với khả năng tự chủ bằng Jev, kèm theo code ví dụ cụ thể.

HttpClient in .NET 10 - IHttpClientFactory and Resilience

Use HttpClient in .NET 10 with IHttpClientFactory, keyed and typed clients, and AddStandardResilienceHandler, with real socket and POST retry test results.