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🔥 zhinianboke / xianyu-auto-reply - 闲鱼自动回复管理系统是一个基于 Python + FastAPI 开发的自动化客服系统，专为闲鱼平台设计。系统通过 We

GitHub热门项目 | 闲鱼自动回复管理系统是一个基于 Python + FastAPI 开发的自动化客服系统，专为闲鱼平台设计。系统通过 WebSocket 连接闲鱼服务器，实时接收和处理消息，提供智能化的自动回复服务。同时集成闲鱼自动发货，自动评价，自动擦亮等功能，实现闲鱼虚拟商品自动化流程。 | Stars: 5,759 | 42 stars today | 语言: Python

2026-07-15 原文 →
AI 资讯

Presentation: Postgres for Production Agents: Your Relational Foundation for Enterprise AI

Gwen Shapira shares how teams are scaling AI features using PostgreSQL for mission-critical apps. She explains how to leverage Postgres's multi-modal capabilities - including JSONB parsing and high-recall HNSW vector indexing - to deliver deterministic and semantic context to LLMs. She also discusses vector quantization to speed up queries by 4x and strategies for managing agentic memory. By Gwen Shapira

2026-07-15 原文 →
AI 资讯

From A10 to M60: An Architect's Journey into Azure GPU VM Sizing for Kubernetes Inference Workloads

How an unexpected regional constraint forced us to deeply understand Azure GPU VM families, naming conventions, and workload fit. Introduction As architects, we often assume that infrastructure decisions are straightforward: "The workload is already running successfully in Region A. Let's deploy the same Kubernetes workload in Region B." That's exactly what we thought. Our workload consisted of a Visual Element Detection (VED) service hosted on Kubernetes. The application uses a PyTorch model to analyze images and detect various visual elements in an image file. The service was already running successfully on a node pool backed by Azure's NVads_A10_v5 GPU VMs. Then we hit an unexpected challenge. The target region did not offer NVads_A10_v5 instances. What looked like a simple deployment exercise became a deep dive into Azure GPU virtual machine families, GPU architectures, VM naming conventions, and workload characteristics. This article shares what I learned in the hope that it helps others who find themselves evaluating Azure GPU SKUs for AI inference workloads. I am relatively new to the world of MLOps, Model deployments, GPU Workloads etc and equally interested and excited to learn more on this front. The Workload Before discussing VM selection, let's understand the workload characteristics: Model Type : PyTorch Model Size : less than 200 MB (.pth) Image Resolution : ~2000 x 2000 Expected Throughput : 5-7 requests/sec Platform : AKS (Kubernetes) Workload Type : Inference only This is important because GPU sizing should always start from the workload and not from the VM catalog. Step 1: Understanding Azure GPU VM Families Many engineers first encounter Azure GPU machines through names like: NV12s_v3 NV6ads_A10_v5 NC4as_T4_v3 ND96isr_H100_v5 The naming can be intimidating. The first breakthrough was understanding that Azure organizes GPU VMs into three primary families: N-Series ├── NV ├── NC └── ND NV Series – Visualization and Graphics NV-series VMs are designe

2026-07-15 原文 →
AI 资讯

I Almost Hand-Wrote a FHIR Schema. Then I Found Out I Didn't Have To.

A hospital-network client wanted our system to output patient data in actual FHIR format - the standard interoperability format healthcare systems use to talk to each other - instead of whatever shape we felt like inventing. Made total sense from their side, their EHR software only accepts FHIR resources, not our custom JSON. From my side, it meant I now had to get an LLM to produce a Patient resource that was FHIR R4 compliant, field for field. I opened the FHIR R4 spec page for Patient to see what I was dealing with. Closed the tab about four minutes later. It's not one flat object - names have their own nested structure with use / family / given arrays, telecom is a list of typed contact points, addresses have their own multi-field shape, and half the fields have specific allowed value sets straight out of a separate FHIR terminology spec. This was not going to be a quick z.object({...}) . Two days into hand-writing it, and I wasn't even done I started anyway, because what else was I going to do: const PatientSchema = z . object ({ resourceType : z . literal ( " Patient " ), identifier : z . array ( z . object ({ system : z . string (), value : z . string (), }) ), name : z . array ( z . object ({ use : z . enum ([ " official " , " usual " , " nickname " , " maiden " ]), family : z . string (), given : z . array ( z . string ()), }) ), telecom : z . array ( z . object ({ system : z . enum ([ " phone " , " email " , " fax " ]), value : z . string (), use : z . enum ([ " home " , " work " , " mobile " ]). optional (), }) ), gender : z . enum ([ " male " , " female " , " other " , " unknown " ]), birthDate : z . string (), address : z . array ( z . object ({ use : z . enum ([ " home " , " work " , " temp " ]). optional (), line : z . array ( z . string ()), city : z . string (), state : z . string (), postalCode : z . string (), country : z . string (), }) ), // ...and I still hadn't gotten to maritalStatus, communication, // contact, generalPractitioner, managingOr

2026-07-15 原文 →
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