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Articles by Import Json
Groundhog Day for AI agents: Prompt caching patterns for using OpenAI models on Amazon Bedrock
Every time you spin off your agent, it has to re-read its whole life story before performing an action. Here’s how to make it remember without having to feed it the same context again. In the movie Groundhog Day, Bill Murray’s character re-lives the same February 2 every day. He wakes up to the same song playing on the radio, in the same inn, and the same small town, again and again for what some movie fans estimate to be 34 years.
How to Create and Host Your Own Lichess Bot
In this blog, I will explain how YOU can create a lichess bot. Lichess BOT accounts allow programmers, chess enthusiasts, and hobbyists to test custom engines, build automated chess tools, or watch code play in real time. Beyond the excitement of earning rating points through code, building a bot serves as a great portfolio piece for your programming skills. This guide covers everything required to convert a standard Lichess account into a BOT account, run engine scripts, and host your bot 24/7.
Build an Eval-Gated GPT-5.5 Batch Classifier: Strict JSON Outputs, Holdout Tests, Error Reconciliation, and Human Review
Organised data batches moving through a controlled classification pipeline. Evidence checkpoints Documented point: A generative pre-trained transformer (GPT)A family of transformer-based language models trained to generate and analyze content. Open glossary entry-5.5 application programming interface (API)A documented way for software systems to exchange requests and results.
Suricata 프로젝트 (21) 통합 킬체인 시나리오 검증 및 성능 측정 (2)
import json import os import subprocess import threading EVE_PATH="/var/log/suricata/eve.json" Block_TTL=300 Prompted_IPs=set() Blocked_IPs=set() ip_lock=threading.Lock() def UnBlockIP(src_ip): try: subprocess.run(["iptables", "-D", "INPUT", "-s", src_ip, "-j", "DROP"], check=True) with ip_lock: Blocked_IPs.discard(src_ip) Prompted_IPs.discard(src_ip) print(f"\n[AUTO-UNBLOCK] TTL {Block_TTL}s Expired -> IP {src_ip} Blocking has been automatically lifted.") print("-"*70) except Exception as e:...
Tight Silicon, Tighter Boxes and Tiny Objects - II
Things used in this project Summary The previous part of the blog introduces the experimental setup, including the hardware and software environment, dependencies, components, models used, and a summary of the benchmark results. This part presents a detailed analysis of how varying different parameters influences object detection performance, highlighting their impact on accuracy, latency, resource utilization, and overall inference behavior.
Orchestrating multi-agent AI architectures with Amazon S3 Files
Organizations are moving beyond single-model AI toward multi-agent architectures. In these systems, agents offload intermediate results to files rather than carrying everything in the prompt, because a large prompt inflates cost and degrades quality. A model’s context window is finite, so files become working memory that persists after a session ends.
Controlled Agentic Commerce with AgentCore Payments
For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending .md to the page URL. Docs sectionOverview Docs sectionSelect... Docs sectionOverview Showcase Docs sectionCookbook AI agents often need information from external services to complete a task. A procurement agent might check a supplier against a sanctions database, retrieve a current risk report, or use a paid search API.
This tutorial explains how to catch a dangerous agent skill before an agent ever runs it: review it automatically, block it in CI if it fails, and only let your agent load skills that passed. Agent skills make AI workflows easier to reuse, share, and improve. A skill is a single, reviewable file with its own declared tool permissions. Instead of explaining the same task every time, you can package the instructions and tools an agent needs into a repeatable workflow.
How to Use GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock: Complete Developer Setup Guide
How to Use GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock: Complete Developer Setup Guide — July 2026 How to Use GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock: Complete Developer Setup Guide This hands-on developer guide shows how to access and use GPT-5.6 Sol, Terra, and Luna through Amazon Bedrock (GA July 24, 2026).
The Complete GPT-5.6 Migration Masterclass: Moving from GPT-5.5 to Sol, Terra, or Luna
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RAG系统存储架构深度解析：异构存储统一接入、向量化索引优化与物理级数据隔离实践-阿里云开发者社区
开发者社区 云计算 文章 正文 2026-07-22 11 简介： 本文深度解析RAG系统存储架构，涵盖异构存储统一接入（适配器模式支持S3/NAS等）、向量化索引I/O优化（mmap、分层存储）、物理级数据隔离（独立设备+网络+加密）及智能温度分层迁移，兼顾性能、安全与成本。（239字） 一、引言：RAG系统对存储架构的特殊要求 在企业AI知识库建设中，RAG（Retrieval-Augmented Generation，检索增强生成）架构已经成为主流范式。RAG通过将用户查询转化为向量检索，从知识库中召回相关文档片段，再将这些片段作为上下文注入大语言模型生成回答。这个看似简单的流程，对底层存储架构提出了一系列苛刻的要求： 多源异构数据的统一接入：企业数据分散在对象存储、文件系统、数据库等不同系统中 毫秒级的检索响应：向量数据库和倒排索引的存储I/O延迟直接影响用户体验 大规模文档的高效管理：从GB级到TB级的文档需要智能分层存储 严格的数据安全隔离：不同密级的数据需要物理级别的隔离保障...
[모두의 연구소] DeepSAD.py 상세 분석 (260710)
DeepSAD.py 파일 상세 분석 import json import torch from base.base_dataset import BaseADDataset from networks.main import build_network, build_autoencoder from optim.DeepSAD_trainer import DeepSADTrainer from optim.ae_trainer import AETrainer class DeepSAD(object): """A class for the Deep SAD method. Attributes: eta: Deep SAD hyperparameter eta (must be 0 < eta). c: Hypersphere center c. net_name: A string indicating the name of the neural network to use. net: The neural network phi.
How to Connect APILayer APIs to Claude Code: A Step-by-Step Guide
Best practices A few things worth knowing before you build on top of this. The free plan is HTTP only. Change the URL to https:// and you’ll get an error back saying SSL isn’t supported on your subscription. HTTPS is available on Weatherstack’s paid plans, which start at $9.99 a month. For local development it’s a non-issue; just don’t ship an HTTP integration to production. Errors come back as HTTP 200 with an error body.
Event-Driven Architecture in Practice, Part 9: The Saga Pattern
In a monolith, you wrap a multi-step business operation in a database transaction. If any step fails, the whole thing rolls back. In a microservices system, each service owns its own database. There is no shared transaction. You cannot roll back across service boundaries. The Saga Pattern is the answer. A saga is a sequence of local transactions, each publishing an event when it completes. If any step fails, compensating transactions undo the earlier steps.
[Hackathon IGN] Données tabulaires avec code commune
json pandas pd requests datetime datetime datagouv Client tables_index = pd.read_csv( , usecols=[ , ], dtype= , ) tables_index[ ] = tables_index[ ].apply(json.loads) ( ): col, detected inspection[ ].items(): detected[ ].startswith( ): col ( ): col, detected inspection[ ].items(): detected[ ] [ , ]: col tables_index[ ] = tables_index[ ].apply(extract_col_commune) tables_index[ ] = tables_index[ ].apply(extract_summable_col) tables_index[ ] = tables_index[ ].apply( d: d.get( )) restr =...
Build an AI-powered 5G Signaling Trace Analyzer Using Amazon Bedrock
AWS for Industries Telecom engineers routinely analyze decoded signaling traces to troubleshoot network issues, validate procedure execution, and accelerate root cause analysis. In 5G networks, even a single registration attempt can span multiple layers such as NAS and NGAP.
How to Build Custom Codex Plugins: A Step-by-Step Developer Tutorial
How to Build Custom Codex Plugins: A Step-by-Step Developer Tutorial OpenAI’s Codex platform represents a paradigm shift in the way developers harness artificial intelligence to generate, analyze, and manipulate code across multiple programming languages and frameworks. As of the release of Codex app version 26.527, the platform has introduced an enhanced, streamlined mechanism for creating custom plugins, significantly expanding the scope and flexibility of AI-assisted development workflows.
GPT-5.5 for Enterprise Data Analysis: A Complete Tutorial for Business Teams
In today’s fast-paced business environment, Enterprise data Analysis has become essential for strategic decision-making. With the advent of powerful AI models like GPT-5.5, business teams can now automate complex data workflows, generate insightful reports, and detect anomalies with unprecedented accuracy and efficiency.
Build an AI-powered recruitment assistant using Amazon Bedrock
According to a people management survey of 748 HR leaders, recruiters spend an average of 17.7 hours per vacancy on administrative work. That’s more than two working days per hire. A separate 2024 SmartRecruiters survey found that 45% of talent acquisition leaders spend more than half their working hours on tasks that could be automated.
小红书数据采集的3大挑战与Python开源解决方案-CSDN博客
小红书数据采集的3大挑战与Python开源解决方案 【免费下载链接】xhs 基于小红书 Web 端进行的请求封装。https://reajason.github.io/xhs/ 项目地址: https://gitcode.com/gh_mirrors/xh/xhs 在小红书数据采集领域，技术开发者面临签名验证、反爬机制和性能优化三大核心挑战。本文深入分析这些技术难题，并介绍基于Python的xhs开源库如何提供专业级解决方案。通过实战案例和优化策略，帮助开发者构建稳定高效的数据采集系统。 挑战一：动态签名验证机制的破解 小红书平台采用动态签名验证作为核心防御机制，每个API请求都需要生成唯一的x-s签名，这是传统爬虫工具失败的主要原因。 技术策略：浏览器环境模拟与JavaScript执行 xhs库通过Playwright模拟真实浏览器环境，调用小红书Web端的JavaScript加密函数生成合法签名。核心实现位于xhs/help.py的sign函数中，该函数通过无头浏览器执行加密逻辑，绕过客户端加密算法逆向的复杂性。 实践方案：签名函数的集成与优化 from...
poolside / Laguna-XS.2 like 25 Follow Poolside 60
Try Laguna XS.2 in Shimmer · Get an API key · Release blog post Laguna XS.2 is a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token designed for agentic coding and long-horizon work on a local machine. It uses Sliding Window Attention with per-head gating in 30 out of 40 layers for fast inference and low KV cache requirements. For more details on how we trained this model, including on data automixing and async off-policy agent RL, check out our release blog post.
Websocket 적용기
NextJS, FastAPI Websocket 구현기 커뮤니티 기능을 도입하며 사용자 알림기능이 필요하게 되면서 Websocket을 구현하게 되었습니다. 설계 Kafka 2.13-4.1.1 Kraft Mode 설계는 간단합니다. FastAPI, Kafka, Redis 에서의 흐름입니다. 이후의 FastAPI, WebSocket 에서의 흐름입니다. Redis를 껴서 관리하면 편할 것 같아 적용했고, table에 저장하는 이유는 사용자 알림 이력 관리 입니다. 구현 순서는 다음과 같습니다. 테이블 생성 Kafka Topic 생성 Kafka Consumer 생성 FastAPI -> Kafka Produce 로직 구현 FastAPI - Redis Listener 및 Websocket 전달 구현 NextJS Websocket 구현 Table 생성 알림 정보를 저장하는 테이블 입니다. 별도의 테이블을 조회할 필요 없이, 해당 데이터만 가지고 알림을 바로 보여줄 수 있게 JSON 구조를 사용합니다.
Counterfactual-Aware RAG for Financial Decision Support
본 연구는 RAG에서 retrieval된 문서가 LLM 의사결정에 인과적으로 기여하는지를 측정하기 위해, counterfactual removal 실험을 수행하고, 그 중요도를 기반으로 RAG를 재구성한다. 본 프로젝트는 Retrieval-Augmented Generation(RAG) 환경에서 LLM이 기업 관련 뉴스 문서를 바탕으로 어떤 판단을 내리며, 그 판단이 어떤 문서에 의해 형성되는지를 분석하는 것을 목표로 한다. 본 연구는 주가 예측이나 투자 성과 평가보다는 기업의 단기(1–2주) 재무적 리스크 상태를 INCREASED / DECREASED / UNCERTAIN 중 하나로 분류하는 문서 기반 판단 과정 자체를 연구 대상으로 설정하였다.
[SpoonOS] 그래프 빌드하기
SpoonOS는 서로 다른 상황에 맞는 세 가지 그래프 빌드 방식을 제공합니다. 워크플로 복잡도와 팀 요구에 맞게 선택하세요. 이번 글에서 다룰 내용: 명령형, 선언형, 고수준 API와 각각의 사용 시점 대상 독자: 핵심 개념을 이해하고 API 스타일을 고르고 싶은 분 예상 소요 시간: 약 6–10분 API 비교 항목명령형(Imperative)선언형(Declarative)고수준(High-Level) 복잡도 단순 중간 고급 사용 사례 빠른 프로토타입, 단순 워크플로 대규모 워크플로, 팀 협업 LLM 기반, 동적 라우팅 직렬화 불가 가능 가능 코드 스타일 메서드 체이닝 템플릿 객체 자동 추론 적합한 경우 학습, 소규모 그래프 프로덕션 시스템 지능형 에이전트 명령형 API 가장 단순한 그래프 빌드 방식입니다. 메서드 호출로 노드와 엣지를 직접 추가합니다.
Move Beyond Chain-of-Thought with Chain-of-Draft on Amazon Bedrock | Amazon Web Services
Artificial Intelligence As organizations scale their generative AI implementations, the critical challenge of balancing quality, cost, and latency becomes increasingly complex. With inference costs dominating 70–90% of large language model (LLM) operational expenses, and verbose prompting strategies inflating token volume by 3–5x, organizations are actively seeking more efficient approaches to model interaction.
Deploy IBM Granite 4.0 models on Amazon SageMaker AI | Amazon Web Services
IBM & Red Hat on AWS Disclaimer: Sample code, software libraries, command line tools, proofs of concept, templates, or other related technology are provided as AWS Content or Third-Party Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content or Third-Party Content in your production accounts, or on production or other critical data.
Amazon Nova Multimodal Embeddings：最先进的代理 RAG 和语义搜索嵌入模型 | Amazon Web Services
亚马逊AWS官方博客 今天，我们推出 Amazon Nova Multimodal Embeddings，这是一款用于代理式检索增强生成 (RAG) 和语义搜索应用的先进多模态嵌入模型，现已在 Amazon Bedrock 中提供。这是首个通过单一模型支持文本、文档、图像、视频和音频的统一嵌入模型，能够以领先准确率实现跨模态检索。 嵌入模型将文本、视觉和音频输入转换为称为嵌入的数值表示。这些嵌入以 AI 系统可以比较、搜索和分析的方式捕获输入的含义，为语义搜索和 RAG 等使用案例提供支持。 各组织日益寻求解决方案，以从遍布文本、图像、文档、视频和音频内容的海量非结构化数据中获取洞察。例如，一个组织可能拥有产品图像、包含信息图和文本的宣传册，以及用户上传的视频片段。嵌入模型能够从非结构化数据中释放价值，但是传统模型通常专门处理一种内容类型。此限制迫使客户要么构建复杂的跨模态嵌入解决方案，要么将自身限制在专注于单一内容类型的使用案例。此问题同样适用于混合模态内容类型，例如包含交错文本和图像的文档，或包含视觉、音频和文本元素的视频，现有模型难以有效捕获其中的跨模态关系。 Nova...
Amazon Nova Multimodal Embeddings: State-of-the-art embedding model for agentic RAG and semantic search | Amazon Web Services
AWS News Blog Today, we’re introducing Amazon Nova Multimodal Embeddings, a state-of-the-art multimodal embedding model for agentic retrieval-augmented generation (RAG) and semantic search applications, available in Amazon Bedrock. It is the first unified embedding model that supports text, documents, images, video, and audio through a single model to enable crossmodal retrieval with leading accuracy.
Integrate tokenization with Amazon Bedrock Guardrails for secure data handling | Amazon Web Services
Artificial Intelligence This post is co-written by Mark Warner, Principal Solutions Architect for Thales, Cyber Security Products. As generative AI applications make their way into production environments, they integrate with a wider range of business systems that process sensitive customer data.
018-二叉树遍历的应用-CSDN博客
最新推荐文章于 2025-09-01 21:56:49 发布 lvjesus001 最新推荐文章于 2025-09-01 21:56:49 发布 阅读量740 收藏 11 点赞数 16 文章标签： python 开发语言 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。 018-二叉树遍历的应用 学习目标 通过本章学习，你将掌握： 二叉树遍历在表达式树中的应用 文件系统遍历的实现方法 组织结构表示和查询 二叉树的序列化和反序列化 语法分析树的构建和遍历 决策树的实现和应用 二叉树遍历在算法竞赛中的应用 软考中遍历应用的考点和解题技巧 1.
Stable Diffusion A/B测试框架：量化评估生成模型性能的科学方法
Stable Diffusion A/B测试框架：量化评估生成模型性能的科学方法 【免费下载链接】stable-diffusion 项目地址: https://ai.gitcode.com/mirrors/CompVis/stable-diffusion 引言：为什么需要A/B测试框架？ 在Stable Diffusion等文本到图像生成模型的开发和应用过程中，我们经常面临这样的挑战：如何科学地评估不同模型版本、参数配置或提示词工程策略的效果差异？传统的定性评估方法往往主观性强、难以量化，而A/B测试框架正是解决这一痛点的关键工具。 通过构建系统化的A/B测试框架，您可以： 量化模型性能：将主观感受转化为可测量的指标 优化提示词策略：找到最佳的文本描述方式 比较模型版本：科学评估不同checkpoint的表现 参数调优：确定最优的采样步数、引导尺度等超参数 A/B测试框架核心架构 系统架构概览 核心组件详解 1.
Claude Code 完整手册：从入门、配置到高级自动化
Undoom 于 2025-08-29 08:44:43 发布 阅读量562 收藏 30 点赞数 38 文章标签： 自动化 excel 运维 claude code 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。 内容 入门 快速入门 系统要求 初始设置 环境变量 全局配置选项 配置文件 命令和用法 Claude 命令 Command 命令行标志 备忘单 接口和输入 键盘快捷键 多行输入 快速命令 Vim 模式 Vim 模式切换 Vim 导航 Vim编辑 命令历史 高级功能 思考关键词 子代理 配置代理 创建你的核心代理 MCP 集成 MCP 设置 & 配置 热门 MCP Servers 钩子系统 配置 钩子事件 钩子输入 钩子输出 使用 MCP工具 安全注意事项 调试 安全和权限 危险模式 自动化与集成 帮助和故障排除 调试快捷命令 身份验证问题 最佳实践 有效的提示 性能提示 监控与警报 需要避免的常见陷阱 安全隐患 性能陷阱 工作流程陷阱 第三方集成 DeepSeek 集成 总结 Getting Started...
AppSuite PDF Editor
Analysis by Karsten Hahn and Louis Sorita High-ranking websites Threat actors are leveraging websites, which have high-ranking search results, to lure users into downloading a deceptively functioning 'productivity tool' or 'command center' for PDF management. The websites have similarities to the download pages of JustAskJacky and other classical trojan horses we described earlier in this blog article. These different websites download the very same MSI installer[1].
基于多通道同步分析的智能听诊系统应用程序-CSDN博客
基于多通道同步分析的智能听诊系统应用程序 1. 项目概述 1.1 项目背景 听诊是医疗诊断中的重要手段，传统听诊方法依赖医生的经验和听觉判断，存在主观性强、难以量化分析等局限性。随着数字信号处理技术和人工智能的发展，智能听诊系统成为医疗科技领域的研究热点。本项目旨在开发一个能够处理16路时间同步音频数据的智能听诊系统，通过对多通道音频数据的同步采集、处理和分析，生成深度医疗报告，辅助医生进行更精准的诊断。 1.2 系统目标 实现16路音频数据的同步采集和存储 开发多通道音频信号处理和分析算法 构建基于机器学习的病理声音分类模型 生成结构化的深度分析报告 提供直观的用户界面和可视化功能 1.3 技术架构 系统采用分层架构设计，包括数据采集层、信号处理层、特征提取层、模型分析层和应用展示层。使用Python作为主要开发语言，结合多种科学计算和机器学习库实现核心功能。 2.
【万字干货】Python实战亚马逊数据抓取：反爬策略、动态解析与API方案全解析（附核心源码）-CSDN博客
文章开头： 本文是一篇关于亚马逊（Amazon）数据抓取技术的深度实战指南。在当前的大数据与电商分析背景下，高效、稳定地获取亚马逊数据至关重要。然而，开发者在实践中常面临请求频率控制、复杂的反爬虫机制、动态页面（JS加载）内容处理、数据结构多变等一系列技术挑战。 本指南将从技术实现层面出发，覆盖以下核心知识点： 请求层策略： IP轮换与请求头精细化伪装 反爬虫对抗： 会话管理、Cookie处理与行为模拟 数据解析技巧： 动态DOM适配与商品变体处理 高级应用： 评论、广告位及榜单数据的完整采集 架构方案： 对比自建系统与专业API的优劣 本文包含了大量Python代码示例，旨在帮助开发者构建一个稳定、高效、可扩展的亚马逊数据采集系统。 亚马逊数据抓取最佳实践指南：Amazon数据采集完整教程与API解决方案...
【网络与爬虫 63】PWA应用爬取终极指南：渐进式Web应用数据提取黑科技大揭秘
莫比乌斯@卷 于 2025-08-15 09:00:00 发布 阅读量1k 收藏 11 点赞数 38 分类专栏： 技术技巧 # 网络与爬虫 文章标签： 爬虫 前端 科技 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。 关键词：PWA爬虫、渐进式Web应用、Service Worker、PWA数据提取、缓存分析、离线数据、Web App Manifest、Background Sync、推送通知拦截、PWA逆向工程 摘要：本文深入探讨PWA(渐进式Web应用)的爬虫技术，从PWA架构分析到Service Worker逆向，从缓存数据提取到实时消息拦截。通过详细的技术分析和实战代码，帮助开发者全面掌握PWA应用的数据采集技术，解锁现代Web应用数据提取的新维度。 引言：PWA时代的数据挑战 想象一下，你正在尝试从一个看似普通的网站获取数据，却发现它表现得像原生App一样：可以离线工作、支持推送通知、响应速度极快。这就是**PWA(Progressive Web App，渐进式Web应用)**带来的全新挑战。...
3D-ResNet图像分类-CSDN博客
一、ResNet网络简介 ResNet网络是在2015年由微软实验室中的何凯明等几位大神提出，是在CVPR 2016发表的一种影响深远的网络模型。在ImageNet的分类比赛上将网络深度直接提高到152层，前一年夺冠的VGG只有19层。斩获当年ImageNet竞赛中分类任务第一名，目标检测第一名。获得coco数据集中目标检测第一名，图像分割第一名，可以说ResNet的出现对深度神经网络来说具有重大的历史意义。原论文地址：https://arxiv.org/pdf/1512.03385 ResNet在cnn图像方面有着非常突出的表现，它利用 shortcut 短路连接，解决了深度网络中模型退化的问题。相比普通网络每两层/三层之间增加了短路机制，通过残差学习使深层的网络发挥出作用。 二、3D-ResNet网络简介 3D-ResNet是一种融合三维卷积（3D CNN）与残差学习（ResNet）的深度学习架构，专为处理时序空间数据（如视频、医学影像序列）设计，由日本产业技术综合研究所的Kensho Hara等人于CVPR 2018提出。 三、ResNet网络中的亮点...
Pygame模块化实战：火星救援游戏开发指南-CSDN博客
Pygame模块化实战：火星救援游戏开发指南 用Python打造太空探险游戏，掌握模块化开发核心技巧 一、火星救援：模块化开发的完美场景 想象这样的场景： 你是一名宇航员，被困在火星表面，需要收集资源、修复飞船、躲避沙尘暴，最终逃离这颗红色星球。这正是我们将要开发的游戏！ 二、模块化设计：游戏开发的架构艺术 1. 模块化架构图 2. 模块化开发优势 并行开发：团队可同时开发不同模块 易于维护：修复问题只需修改单个模块 代码复用：模块可在不同项目中重用 可扩展性：轻松添加新功能模块 三、核心模块实现：火星救援游戏引擎 1.
Tools and MCP - Swarms
Swarms provides a comprehensive toolkit for integrating various types of tools into your AI agents. This guide covers all available tool options including callable functions, MCP servers, schemas, and more.
Tutorial Series: Integrating WhatsApp with AWS and Bedrock - Part 1: Webhook Setup
In today’s digital landscape, WhatsApp has become one of the most widely used communication platforms globally — especially in regions like Brazil, India, and parts of Southeast Asia and Africa. For developers and AWS specialists, integrating applications directly into WhatsApp represents a practical way to deliver value to end users without the need to build or maintain a front-end interface.
Implementing restore testing for recovery validation using AWS Backup | Amazon Web Services
Mission-critical applications power everything from e-commerce to healthcare, making a solid backup strategy not just a best practice but a vital necessity. Threats like ransomware grow more sophisticated and for AWS users, having backups isn’t enough. Organizations need confidence that these safeguards will perform when disaster strikes. Manual testing, while essential, drains IT resources.
How to Process Multi-Gigabyte JSON Files in Python: Memory-Efficient Techniques That Work
As a best-selling author, I invite you to explore my books on Amazon. Don't forget to follow me on Medium and show your support. Thank you! Your support means the world! Python provides powerful tools for handling large JSON datasets without overwhelming your system resources. I've processed multi-gigabyte JSON files on modest hardware using these techniques, saving both time and computational resources. Understanding the Challenge Large JSON datasets present unique challenges.
A Step-by-Step Guide to Streaming Live Weather Data Using Apache Kafka and Apache Cassandra
Introduction Delivering real-time weather data is increasingly important for applications across logistics, travel, emergency services, and consumer tools.
基于 Python 与 GitHub，打造个人专属本地化思维导图工具全流程方案（下）
基于 Python 与 GitHub，打造个人专属本地化思维导图工具全流程方案（下） 各位博友，自从踏入修真界，就整天想怎样把代码改造成绝世技能。这不又有新思路，准备用 Python 和 GitHub 这两把 “趁手仙器”，从零开始打造一个专属于自己的本地化思维导图工具。 这工具啥特色？轻量到能揣兜里跑（内存占用低），颜值随你心意改（界面可自定义），还能离线玩得转（数据全存本地）。不管你是想理清楚小说剧情线、课堂笔记，还是规划个小项目，它都能支棱起来。咱不整那些花里胡哨的框架套路，就靠最基础的 HTML/CSS/JS 和 Python，一步步带你打通 “开发任督二脉”：从拆解开源项目优点，到写代码时的 “挖坑填坑”，再到最后打包成能双击运行的 EXE 文件，每一步都给你掰扯得明明白白。 放心，就算你是刚摸到键盘的 “练气期” 萌新，跟着咱的节奏走，也能亲手造出趁手的 “数据法宝”。下面介绍下半部分。 6.1 前端界面模块 1.
Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain
DjangoCon Europe is just around the corner, and this year, it’s in Dublin, Ireland. This means two things: I’m legally required to drink my first Guinness (yes, first ever!). I obviously had to build an app to help me find the best pubs to accomplish my Guinness goal. This full-stack, AI-powered Dublin City Center Pub Finder app is built using our new Django MongoDB Backend Python package, Voyage AI embeddings to best understand the queries, and LangChain to power our natural-language interface.
DeepSeek也能用高德地图！Python MCP客户端SSE传输接入高德MCP服务（附完整代码）
DeepSeek与高德地图将会碰撞出怎样的火花，DeepSeek又怎么通过借助高德地图MCP实现路径规划、行程安排等功能，本文会给你答案。 高德地图官方提供了在Cursor中使用高德MCP服务的案例： 向大模型提出需求“明天到北京国贸出差，帮我预定1km以内的3星级酒店”。 可以看到，Cursor在接入了高德MCP之后，LLM能够实现利用高德地图提供的工具，查找北京国贸附近的酒店。 高德地图官方给出了在Cursor中配置MCP服务的教程：快速接入-MCP Server | 高德地图API虽然Cursor能够方便的使用MCP服务，但需要Cursor Pro版本才可以实现，Pro版本的价格过于昂贵，无疑是一笔巨大的开销。 对于开发者而言，我们希望能够开发一个Python客户端，连接高德MCP服务并通过DeepSeek来调用高德MCP服务，效果如下： 当用户给出问题时“我要去济南奥体中心看球，请你查询附近3km的经济又舒适的酒店，并规划酒店到奥体中心的路线”时，我们可以看到，DeepSeek调用了高德MCP提供的三个函数工具，最后给出了推荐的酒店。...
基于 MCP Http SSE模式的天气助手智能体开发实战（一文带你了解MCP两种开发模式）
系列文章目录 一、理论+代码一文带你深入浅出MCP 二、基于 MCP Http SSE模式的天气助手智能体开发实战（一文带你了解MCP两种开发模式） 三、Cline/Trae快速开发MCP应用（写作中 前言 笔者上篇文章 理论+代码一文带你深入浅出MCP 详细分享了MCP的产生背景，设计理念，相比Function Calling的技术优势等内容。 同时使用Python SDK，基于MCP Studio的模式开发了大模型客户端和计算器服务端代码，深入浅出带大家了解了MCP的概念与开发流程。最近有读者在后台私信：现在更流行使用MCP Http SSE模式远程访问服务端，这种开发方式与MCP Stdio方式相比有什么异同呢？本篇文章笔者通过使用基于MCP Http SSE模式开发天气助手智能体的案例，带大家详细了解MCP的两种开发模式和开发技巧~ 一、MCP服务端通信方式 MCP(Model Context Protocol, 模型上下文协议)约定了AI Agent智能体的开发规范，AI...
RAG Search with AWS Lambda and Bedrock
Today I would like to show you how I solved with Retrieval-Augmented Generation (RAG) Search my problem of poor Large Language Model (LLM) response results. I was creating a system, one of the elements of which was the cooperation of the LLM model with the Google search engine. The model specified what queries it wanted to perform under a given case, and the search engine provided it with the text of the retrieved pages. The resulting data, however, was not always ideal.
Integrate Google Sheets with Python: A Step-by-Step Guide
Want to learn how to connect your Google Sheets with Python to fetch and manipulate data? By using the gspread library alongside pandas, you can easily automate tasks, analyze data, and even save it in formats like JSON. Let’s walk through the process step-by-step—perfect for beginners and experienced users alike! Step 1: Enable Google Sheets API & Get Credentials To integrate Google Sheets with Python, you first need to set up access via the Google Cloud Console: Visit the Google Cloud Console.
Python之文件操作详解_python文件操作方法-CSDN博客
前言 最近在学习Python，将所学内容整理成文章，以便以后翻阅和复习，同时分享给大家。有问题欢迎随时指正。 1、打开文件 open(file_path, mode, encoding) file_path：文件路径，可以是相对路径或绝对路径。 mode：打开模式，常用模式包括： 模式描述 r 只读（默认） w 写入（覆盖原文件） a 追加写入 x 创建新文件并写入 b 二进制模式（如 rb, wb） + 读写模式（如 r+, w+） # 示例：打开文件（推荐使用 with 自动管理资源） with open('example.txt', 'r', encoding='utf-8') as f: content = f.read() 2、关闭文件 close() # 手动打开并关闭文件（不推荐，容易忘记关闭） f = open('demo.txt', 'w', encoding='utf-8') f.write('Hello World') f.close() # 必须显式关闭 对比with语句（推荐方式） # 使用 with 自动关闭（最佳实践） with...
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