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Articles by Import Uuid
UUIDv7 doesn't have the properties you think it does
Built on Shards of Silicon I’m going to call it. UUIDv7 is weird. Most blog posts about UUIDv7 tout these benefits: monotonically increasing IDs a helpful timestamp of when the object was created; and unguessable IDs But none of these properties are strictly held. Certain RFC compliant implementations of UUIDv7 violate these properties to extreme degrees. The only useful property UUIDv7 appears to strictly provide is uniqueness and therefore collision resistance.
Introducing strands-dynamodb-storage: Durable agent storage for the Strands Agents SDK
Today, we are announcing strands-dynamodb-storage, an open source Amazon DynamoDB storage backend for the Strands Agents SDK, available now for both Python (on PyPI) and TypeScript (on npm) under the Apache 2.0 license. Artificial intelligence (AI) agents are moving from single conversations into long-running relationships with the people they serve. Everything an agent must remember across those conversations has to live somewhere durable: session state, long-term memories, and transcripts.
A VCP v1.1 Implementation Guide
On November 28, 2025, a cooling system failure at CME Group's data center froze global futures trading for 10+ hours. The incident exposed a fundamental problem: we can't cryptographically prove what happened when trading infrastructure fails. This post shows you how to implement VCP (VeritasChain Protocol) v1.1—an open standard for tamper-evident audit trails that acts as a "flight recorder" for algorithmic trading systems.
大模型应用发展与Agent前沿技术趋势（中）-CSDN博客
大模型与Agent结合的深度技术架构 分层式Agent架构设计 随着大模型规模的不断增长，传统的Agent实现方式已难以满足高性能、高可扩展性的需求。现代大模型Agent系统通常采用分层式架构设计，将复杂的决策过程分解为多个功能模块，每个模块负责特定的子任务。这种架构不仅提高了系统的可维护性，还为性能优化提供了更多可能性。 分层式Agent架构通常包含以下核心组件： 感知层（Perception Layer）：负责接收和处理原始输入数据，包括文本、图像、语音等多模态信息 理解层（Understanding Layer）：基于大模型进行语义理解、意图识别和上下文建模 规划层（Planning Layer）：制定长期目标和短期任务计划，进行路径规划和决策优化 执行层（Execution Layer）：将规划转化为具体行动，与环境进行交互 记忆层（Memory Layer）：存储和管理历史交互数据，支持长期记忆和上下文连贯性 学习层（Learning Layer）：通过强化学习、监督学习等方式持续优化Agent策略...
Adding a layer based on a query against a MS SQL Server database in pyQGIS
We want to be able to pose arbitrary queries against a MS SQL Server spatial database and display the results in QGIS. One approach is to query the database with a separate pyodbc connection, loop over all results, construct features and create a memory layer. This works, but the data is static, that is it only reflects the data as of the time of layer creation. It helps a bit, but is not really what we need. I then tried using the MSSQL data provider, and failed.
Prompt Optimization
On this page Last updated 22 days ago This tutorial will use Phoenix to compare the performance of different prompt optimization techniques. You'll start by creating an experiment in Phoenix that can house the results of each of your resulting prompts. Next you'll use a series of prompt optimization techniques to improve the performance of a jailbreak classification task. Each technique will be applied to the same base prompt, and the results will be compared using Phoenix.
8 Python Design Patterns for Resilient Distributed Systems and Microservices
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 design patterns provide essential solutions for building resilient distributed systems and microservices. As a Python architect, I've implemented these patterns across numerous projects and found them invaluable for creating maintainable, scalable systems.
Why We Need Streaming Applications with LLMs!
There is something different about LLMs compared to other machine learning models! They take a long time to return the full answer. It can take many seconds. So what do you do then? The solution to that very peculiar problem is to stream the response as it is being generated. The AiEdge Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Scale A Stateful Streamlit Chatbot with AWS ECS and EFS
You built a great Streamlit application. Everything is working well locally when your boss asked: Your PoC GenAI app is great. Let's make it available to the entire company! You deployed it into a virtual machine (maybe using an address like http://12.34.56.78:8501) and your colleagues rushed in with enthusiasm, the server got overloaded and restarted repeatedly. Some of their precious usage data was lost forever. They lost their work.
Protected ML APIs with Flama JWT Authentication
You've probably heard about the recent release of Flama 1.7 already, which brought some exciting new features to help you with the development and productionalisation of your ML APIs. This post is precisely devoted to one of the main highlights of that release: Support for JWT Authentication.
在亚马逊云科技上通过LangChain ReAct Agent开发金融多模态数据AI分析中台
项目简介： 小李哥将继续每天介绍一个基于亚马逊云科技AWS云计算平台的全球前沿AI技术解决方案，帮助大家快速了解国际上最热门的云计算平台亚马逊云科技AWS AI最佳实践，并应用到自己的日常工作里。 本次介绍的是如何在亚马逊云科技机器学习托管服务Amazon SageMaker上搭建一个多模态LangChain Agent，通过ReAct逻辑让Agent通过Amazon Bedrock AI模型托管服务上的大模型对用户问题进行推理决定如何执行操作，通过调用一系列亚马逊云科技AI服务，如Amazon Textract、Amazon Transcribe、Amazon Comprehend对多模态数据进行分析处理，最后利用Streamlit框架开发与用户交互的UI界面。本架构设计全部采用了云原生Serverless架构，提供可扩展和安全的AI解决方案。本方案的解决方案架构图如下： 方案所需基础知识 Amazon SageMaker 是亚马逊云科技提供的一站式机器学习服务，帮助开发者和数据科学家轻松构建、训练和部署机器学习模型。SageMaker...
Error handling in distributed systems: A guide to resilience patterns
Let’s face it: distributed systems are tough. Just when you think you’ve got everything working perfectly, bam, a network interruption or a mysteriously slow service reminds you that while failures are not desirable features of your application, they are unavoidable characteristics of the distributed computing landscape you build on. The good news is that with the right patterns and mindset, you can build systems that don’t just survive these failures but thrive despite them.
UUID无处不在，你就是唯一（2023.4.16）_jing_zhong的博客-CSDN博客
全球数字化浪潮滚滚而来，各行各业无时无刻不在与数字打交道，万事万物或被牵扯和囊括其中。数字（Digit or number）作为一种简单快速的识别代码，深受开发者和广大用户的喜爱，在以下诸如此类的众多场景中被广泛使用： （1）各个国家居民身份的唯一标识（ID Card） （2）全世界生产的所有电脑，每台电脑都有着唯一的序列号（硬件MAC地址） （3）各大手机厂商为用户分配的识别号，如（苹果手机AppID、华为手机HUAWEI_id） （4）全球通信运营商为用户分配的移动号码（8614879421684） （5）互联网为政府机构、大学校园教育、企业单位、个人用户都分配了唯一的IP地址，从广域网（WAN）到局域网（LAN），从IPv4到IPv6，公网环境下寄托在域名服务器DNS中的域名地址 （6）银行等业务部门为客户办理的业务单号、账单流水、卡号等 (7) 世界上所有英文期刊发表的论文的唯一标识，如（DOI）。 假如存在这样一个问题→从全球数十亿人口中寻找满足特定条件的某一个人，那么我们该如何去做这件事情呢？...
Create and deploy a reliable data ingestion service with FastAPI, SQLModel and Dramatiq
In recent years, Python has gained lot of popularity in the data science community, thanks to the vast ecosystem of libraries covering both the scientific aspect, like pandas or scikit-learn, but also the operational aspect, with highly popular web frameworks like Flask or Django. With one unique language, data scientists are able to embed their experiments and results directly in production-ready applications. In 2018, a new challenger blew up the landscape: FastAPI.
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