DEV Community: Walter Lee

AWS Strands Agents: A model-driven approach to building AI agents in just a few lines of code.

Walter Lee — Thu, 26 Jun 2025 17:29:16 +0000

AWS Strands Agents is an open-source Software Development Kit (SDK) designed to simplify the creation of AI agents using a model-driven approach. It allows developers to build and deploy intelligent, task-specific AI agents with minimal code by leveraging the reasoning and planning capabilities of large language models (LLMs). Strands Agents connects a model, a set of tools, and a prompt to enable agents to autonomously plan, reason, and execute tasks, ranging from simple queries to complex workflows.

Key Components:
Model: The SDK supports various LLMs, such as those from Amazon Bedrock (e.g., Claude 3.7 Sonnet, Amazon Nova Premier), Anthropic, Meta Llama, and others via LiteLLM, making it model-agnostic.
Tools: Developers can use pre-built tools (e.g., for file manipulation, API calls, or AWS service interactions) or create custom Python functions using the @tool decorator. It also supports thousands of Model Context Protocol (MCP) servers for dynamic tool integration.
Prompt: A natural language prompt defines the agent’s task and behavior, guiding the LLM to perform specific actions or respond to user queries.

How It Works:
Strands Agents operates through an agentic loop, where the LLM processes the prompt, reasons about the task, selects appropriate tools, and iterates until the task is complete. This approach eliminates the need for rigid, hardcoded workflows, allowing the model to dynamically plan and execute tasks. For example, a Strands agent can retrieve data from a knowledge base, call APIs, or even orchestrate multiple sub-agents for complex tasks.

Features:
Simplicity: Build agents with just a few lines of code by defining a prompt and tools.
Flexibility: Supports multiple LLMs and deployment options (local, AWS Lambda, Fargate, EC2).
Production-Ready: Includes observability via OpenTelemetry, security features, and reference architectures for scalable deployment.
Multi-Agent Support: Enables collaboration between agents using protocols like MCP and plans for Agent2Agent (A2A) integration.
Built-in Tools: Over 20 pre-built tools, such as semantic search with Amazon Bedrock Knowledge Bases, and support for custom tools.

Use Cases:
Automation: Automate workflows like weather data collection, storing results in AWS DynamoDB.
Development Assistance: Power tools like Amazon Q Developer for code-related tasks.
Enterprise Applications: Build agents for tasks like financial planning, research, or generating AWS architecture diagrams from natural language prompts.
Web Search: Create agents similar to Perplexity for dynamic web content retrieval.

Example:

#python
from strands import Agent
from strands_tools import calculator
agent = Agent(tools=[calculator])
response = agent("What is the square root of 1764?")

This code creates an agent that uses a calculator tool to compute the square root, leveraging an LLM to interpret and execute the task.

Deployment and Adoption:
Strands Agents is production-ready, used internally by AWS teams for tools like Amazon Q Developer, AWS Glue, and VPC Reachability Analyzer. It supports deployment in various environments, including serverless architectures with AWS Lambda or containerized setups with Fargate. The SDK is available on GitHub, with contributions from companies like Accenture, Anthropic, Meta, and PwC.

Why It Matters:
Unlike traditional frameworks requiring complex orchestration, Strands Agents simplifies AI agent development by letting the LLM handle planning and tool selection, reducing development time from months to days. Its open-source nature and support for multiple providers make it accessible for developers across domains.

For more details, check the official documentation or GitHub repository: Strands Agents SDK.

References:

AWS Certificate Manager Now Offers Exportable Public Certificates

Walter Lee — Tue, 24 Jun 2025 01:01:43 +0000

On June 17, 2025, AWS announced that AWS Certificate Manager (ACM) now supports exportable public SSL/TLS certificates, enabling secure traffic termination across AWS, hybrid, and multi-cloud workloads. This feature allows users to issue public certificates, access their private keys, and deploy them on any compute workload, from EC2 instances to on-premises servers.

Why It’s Useful

Flexibility Across Environments: Unlike traditional ACM certificates limited to AWS-integrated services like Elastic Load Balancers or CloudFront, exportable certificates can be used anywhere, simplifying TLS management for diverse infrastructures.
Streamlined Automation: ACM handles certificate issuance and renewal, reducing manual overhead. Users can export certificates via the AWS Management Console, CLI, or API, enabling seamless integration into automated workflows.
Enhanced Security: ACM employs strong encryption and key management best practices, ensuring private keys remain secure during export and deployment.

Economic Advantage

AWS’s pricing for exportable public certificates is highly competitive:

Standard Certificates: $15 per certificate issuance/renewal (valid for 395 days).
Wildcard Certificates: $149 per certificate issuance/renewal, covering unlimited subdomains.
API Calls: First 10,000 export-certificate API calls per month are free, with subsequent 10,000 calls at $0.50.

Compared to other providers like DigiCert or Sectigo, which often charge $50–$500+ annually per certificate depending on features (e.g., wildcard, extended validation), AWS’s flat pricing is cost-effective, especially for organizations managing multiple domains. Additionally, ACM’s free public certificates for AWS-integrated services remain an option for cost-conscious users, with exportable certificates adding flexibility for a modest fee.

Conclusion
AWS’s exportable public certificates combine flexibility, automation, and affordability, making ACM a compelling choice for organizations seeking to secure workloads across diverse environments without breaking the bank. For details, visit AWS Certificate Manager.

References

Unlocking AI Power with DeepSeek on Amazon Bedrock Easily

Walter Lee — Wed, 12 Mar 2025 09:10:24 +0000

Amazon Web Services (AWS) has brought DeepSeek-R1 to Amazon Bedrock, and it’s a game-changer for businesses diving into generative AI. Available as a fully managed, serverless model, DeepSeek-R1 combines cutting-edge reasoning capabilities with cost-efficiency, making it a standout option for developers and enterprises alike.

Why It’s Good:
DeepSeek-R1 shines with its exceptional accuracy and contextual understanding, honed through innovative training techniques like reinforcement learning. It’s reportedly 90-95% more affordable than comparable models, offering high performance without breaking the bank. Plus, AWS’s serverless setup means no infrastructure headaches—just plug in and go.

Benefits:
Cost-Effective: Low training and inference costs make it accessible for startups and large organizations alike.

Scalability: Seamlessly scale from experiments to production with Bedrock’s unified API and tools like Guardrails for safety.

Security: AWS Enterprise-grade encryption and access controls keep your data safe and compliant. Bedrock Guardrails can help protect and secure DeepSeek-R1 deployment in production.

Flexibility: Choose Bedrock for quick integration or SageMaker for deeper customization.

Easy to use: Simple to get started with simple API calls, e.g.

{
  "modelId": "deepseek.r1-v1:0",
  "contentType": "application/json",
  "accept": "application/json",
  "body": {
    "inferenceConfig": {
      "max_tokens": 512
    },
    "messages": [
      {
        "role": "user",
        "content": "this is where you place your input text"
      }
    ]
  }
}

Use Cases:

Code Generation: Write, debug, and document complex code with precision—perfect for software teams.

Decision Support: Tackle intricate business logic with explainable reasoning for faster, smarter choices.

Data Analysis: Extract insights from technical docs or datasets with its strong comprehension skills.

Whether you’re building AI-driven apps or solving tough problems, DeepSeek on Bedrock delivers power, affordability, and ease in one package. Check it out at aws.amazon.com/bedrock/deepseek/ and see how it can transform your workflow!

References:
DeepSeek models Users Guide

Images:

Amazon EKS Managed Node Groups Unveil New Update Strategies, "Minimal Capacity"

Walter Lee — Tue, 28 Jan 2025 18:34:02 +0000

Amazon EKS Managed Node Groups Unveil New Update Strategies

Amazon Elastic Kubernetes Service (Amazon EKS) has introduced new update strategies for its managed node groups, enhancing the way users manage their Kubernetes clusters. This update is particularly useful for several reasons:

Flexibility in Update Management: Previously, updates followed a single, default strategy. Now, users can choose between the "Default" **strategy, where new EC2 instances are launched before terminating old ones, and the new "Minimal Capacity" strategy. The latter approach is designed to launch fewer new instances, which is especially beneficial for node groups that have been scaled to zero or are using high-demand or limited-availability instances like those with **GPU acceleration or capacity reservations. It will help save cost, e.g. in Dev. environment or expensive GPU VMs for non mission critical workloads (e.g. a batch job).
Cost Efficiency: The Minimal Capacity strategy helps in managing costs more effectively by reducing the number of new EC2 instances launched during updates. This can lead to lower operational overhead and compute costs, particularly when dealing with expensive instance types or when scaling operations are involved.
Simplified Operations: With these strategies, Amazon EKS simplifies the operational complexity of node updates. Users no longer need to manually manage the lifecycle of their nodes as meticulously, allowing for smoother, automated upgrades that respect pod disruption budgets.
Broad Availability: These new strategies are available at no additional cost across all AWS Regions, except for AWS GovCloud (US) and China Regions, making it accessible for a wide user base without additional financial burden.

This enhancement in Amazon EKS managed node groups is a significant step towards providing users with more control, reducing costs, and ensuring high availability of applications on Kubernetes clusters. For more detailed information, you can visit the official AWS announcement page.

Learn more:

AWS EKS Upgrade phase

What is awscurl? Why is it useful?

Walter Lee — Sun, 26 Jan 2025 22:39:05 +0000

What is awscurl?

awscurl is a command-line tool that extends the functionality of curl by automatically signing HTTP requests with AWS Signature Version 4 (SigV4). This signing is necessary for making authenticated requests to AWS services, allowing users to interact with AWS APIs directly from the command line in a secure manner.

Why is it useful?

Security: It handles the complex task of request signing, which is essential for security when interacting with AWS services that require authentication.
Convenience: It simplifies the process of making authenticated requests to AWS services, much like curl does for general HTTP requests, but with the added layer of AWS authentication.
Versatility: Supports various AWS services like S3, EC2, API Gateway, etc., making it versatile for developers and system administrators who need to test or interact with AWS resources programmatically or manually.
Compatibility: It can be used in environments where direct AWS SDK usage might be cumbersome or not feasible, like in CI/CD pipelines or quick testing scenarios.

Where is the documentation?

Documentation for awscurl can be found in several places:

GitHub:

For one of the implementations, you can refer to the GitHub repository by okigan. This repo includes usage instructions, installation guides, and examples of how to use awscurl with different AWS services.
Another implementation by legal90 provides detailed command-line options and usage examples on their GitHub page.

AWS Documentation: Specific uses of awscurl for AWS services like Amazon Neptune or Amazon Managed Service for Prometheus can be found in AWS's official documentation, where they provide examples and explanations on how to connect or query these services using awscurl. See examples:

PyPI: If you're using the Python version, awscurl on PyPI has basic installation instructions and usage examples (awscurl - PyPI). See repo at https://pypi.org/project/awscurl/

These resources should provide comprehensive guidance on how to use awscurl, its features, and how to integrate it into your AWS workflow.

Sample image:

New AWS Management Console Feature: Simultaneous Sign-in for Multiple Identities

Walter Lee — Sun, 26 Jan 2025 07:31:00 +0000

The AWS Management Console now supports simultaneous sign-in for multiple AWS accounts

AWS has just rolled out a groundbreaking enhancement to its Management Console that promises to transform how cloud administrators and developers manage their AWS environments. Now, you can sign into up to five different identities at the same time within a single web browser session. Here's a deep dive into this feature, its implications, and how it could streamline your AWS operations:

What Does This Mean?

Five Identities at Once:
Previously, managing different AWS accounts or roles meant logging in and out, or using separate browsers or incognito sessions. Now, you can stay signed into up to five identities, which could be any combination of:
- Root Users: The account owner for an AWS account.
- IAM Users: Individual users within an AWS account with specific permissions.
- Federated Roles: Temporary security credentials for users authenticated through external identity providers like Active Directory, SAML, or web identity federation.
Single Browser Session:

This functionality is confined to one browser session, meaning you won't need to juggle multiple browser windows or tabs to manage different AWS contexts.

How It Works

User Interface:
Upon enabling this feature, you'll see a new menu in the top right corner of the AWS Management Console where you can switch between your logged-in identities or roles.
Session Control:
Each identity maintains its own session, complete with its own set of permissions and access controls. This means you can jump from a highly privileged role to a more restricted one without the overhead of re-authentication.
Security Considerations:
AWS ensures that each session is isolated. For example, if one session expires or is logged out, it does not affect the others. However, users must be vigilant about managing these sessions, as having multiple active sessions increases the attack surface.

Benefits

Increased Productivity:
Developers and admins can now manage resources across multiple accounts or roles without constant sign-in/sign-out, reducing downtime and enhancing workflow.
Simplified Management:
For organizations with complex AWS account structures or those employing multi-account strategies, this feature simplifies oversight and management tasks.
Cost Optimization:
By allowing more dynamic switching between roles, users can more efficiently manage resources, potentially saving costs by better aligning permissions with tasks.

Security and Best Practices

Session Management:
Be mindful of open sessions. Regularly review and close sessions that are no longer needed.
Permission Boundaries:
Even with this feature, best practices for least privilege should be maintained. Do not elevate permissions unnecessarily just because switching between roles is easier.
Monitoring and Logging:
Use AWS CloudTrail to keep an eye on who is doing what across your accounts, especially now that one user could theoretically perform actions under multiple identities.

Implementation Tips

Enable Gradually:
If you're in a large organization, consider a phased rollout to understand the impact on your security and compliance posture.
Training: Ensure your team is trained on how to use this feature responsibly, focusing on security implications.
Feedback Loop:
AWS often improves features based on user feedback. Engage with AWS through forums or support to suggest enhancements or report issues.

Conclusion

This new feature in the AWS Management Console represents a significant step forward for user experience in cloud management, offering flexibility without compromising on security, provided users adhere to best practices. Whether you're a small startup or a large enterprise, this capability can redefine how you approach AWS administration, making your team's life easier while potentially enhancing operational efficiency.

Explore this feature, give AWS your feedback, and see how it reshapes your AWS management strategy.

References:

AWS Official Announcement

AWS Management Console Documentation

AWS re:Post (Community Discussion)

Technical Blog Post on AWS Console Enhancements

Sample images:

AWS's Game-Changing Innovations for Database Scaling: Aurora Serverless v2, Aurora Limitless Database, and RDS Data API

Walter Lee — Tue, 21 Jan 2025 18:41:54 +0000

Introduction:

Amazon Web Services (AWS) has been at the forefront of cloud computing innovations, particularly in the realm of databases where scalability has traditionally been a significant challenge. This post delves into three recent AWS innovations that are reshaping how we think about scaling relational databases: Aurora Serverless v2, Aurora Limitless Database, and the RDS Data API. These solutions address some of the most persistent scaling headaches by offering seamless scalability, simplified sharding, and streamlined database connectivity.

1. Aurora Serverless v2: The Ultimate in Vertical Scaling

Overview:
Aurora Serverless v2 is an evolution of the original Aurora Serverless, designed to handle unpredictable workloads with automatic scaling. It's particularly aimed at applications where the database load can spike or drop dramatically, such as web apps, mobile backends, or IoT data ingestion.

Key Features:

Automatic Scaling: Aurora Serverless v2 can scale capacity up or down in seconds without any manual intervention, from 0.5 ACUs (Aurora Capacity Units) up to thousands, based on the current load. This means applications can maintain performance without over-provisioning resources.
Improved Performance: It offers up to 4x better performance than its predecessor due to enhancements in how compute resources are managed and utilized.
Cost Efficiency: Users only pay for the database capacity they use, which can lead to significant savings for applications with variable workloads.

Impact:
The ease of scaling with Aurora Serverless v2 means developers can focus more on building features rather than managing infrastructure. This service effectively tackles the problem of over-provisioning, reducing both cost and complexity.

2. Aurora Limitless Database: Sharding Made Simple

Overview:
Scaling horizontally has always been complex due to the need for manual sharding. Aurora Limitless Database introduces an automatic sharding capability that simplifies this process dramatically.

Key Features:

Automatic Sharding: Based on a shard key, the database automatically distributes data across multiple shards, ensuring balanced data distribution and load.
Scalability: By allowing indefinite horizontal scaling, it supports applications that grow beyond the capacity of a single database instance.
Ease of Use: Developers don't need to manage or even understand the intricacies of sharding; the system does it transparently.

Impact:
Aurora Limitless Database makes what was once a skilled and error-prone process into an automated, background task. This innovation is particularly useful for applications anticipating high growth or those already dealing with large datasets, providing a scalable solution without the traditional headaches of database partitioning.

3. RDS Data API: Streamlining Connection Management

Overview:
The RDS Data API is a tool for connecting to Amazon RDS instances without needing to manage database connections directly, especially useful in serverless environments.

Key Features:

Connection Management: It abstracts the complexity of database connections, reducing the need for connection pooling and handling in application code.
Security: By using AWS IAM for authentication, it enhances security by not requiring database credentials to be embedded in application code.
Compatibility: It supports multiple database engines including MySQL, PostgreSQL, and MariaDB, making it versatile for different use cases.

Impact:
This API simplifies the development process in serverless architectures by removing the overhead of connection management. It's particularly beneficial for applications where the number of connections can fluctuate or where latency in establishing a database connection could impact performance. Developers can now write more concise, secure, and efficient code.

Conclusion:

AWS has effectively addressed some of the most daunting challenges in database scaling with Aurora Serverless v2, Aurora Limitless Database, and the RDS Data API. Each innovation brings a different approach to scaling, from vertical (Aurora Serverless v2) to horizontal (Aurora Limitless Database), and simplifying interaction (RDS Data API). These tools not only make databases more scalable but also more accessible to developers, reducing the complexity and cost associated with traditional database management. As the digital landscape continues to grow, these solutions from AWS are set to redefine the standards for database scalability and management in the cloud era.

References:

podcast - https://podcasts.apple.com/us/podcast/704-704-scaling-your-relational-database-on-aws-are/id1122785133?i=1000684720184 (credits to host Simon Elisha and Josh Hart)
Amazon RDS features - https://aws.amazon.com/rds/features/
Using Aurora Serverless v2 -
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-serverless-v2.html
Amazon Aurora PostgreSQL Limitless Database - https://aws.amazon.com/blogs/aws/amazon-aurora-postgresql-limitless-database-is-now-generally-available/
Using RDS Data API - https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/data-api.html
Database AWS Well-Architected Framework - https://docs.aws.amazon.com/wellarchitected/2023-04-10/framework/perf-db.html
AWS Data for SaaS - https://github.com/aws-samples/data-for-saas-patterns

Images:

Exploring new AWS EKS auto mode. What is it ? Why it is useful ? How to quick start ?

Walter Lee — Mon, 06 Jan 2025 17:17:22 +0000

What is AWS EKS Auto Mode?

Amazon EKS Auto Mode is a feature introduced by AWS to simplify the management of Kubernetes clusters by automating key aspects of cluster infrastructure. This includes compute, storage, and networking, providing a more hands-off approach to running Kubernetes on AWS. With EKS Auto Mode, AWS takes over responsibilities like node scaling, OS patching, and optimizing compute costs, which traditionally require significant administrative effort from the user side.

Why is EKS Auto Mode Useful?

Reduced Operational Overhead: EKS Auto Mode reduces the need for deep Kubernetes expertise, as AWS handles routine cluster management tasks. This allows teams to focus more on application development rather than on infrastructure maintenance.

Cost Efficiency: By dynamically scaling resources based on demand, EKS Auto Mode helps in optimizing costs, ensuring you only pay for what you use without over-provisioning.

Enhanced Security: Automated updates and patching ensure that your cluster environment stays secure with the latest security fixes without manual intervention.

High Availability: EKS Auto Mode leverages AWS's infrastructure to provide high availability across multiple Availability Zones, ensuring resilience and uptime for your applications.

Simplified Cluster Management: It simplifies the deployment of new workloads and the management of existing ones by automating node provisioning, scaling, and health maintenance.

How to Quick Start with EKS Auto Mode

Here's a straightforward guide to get started with EKS Auto Mode:

Prerequisites:

Ensure you have an AWS account and appropriate permissions to create EKS clusters.
Install and configure the AWS CLI, kubectl, and eksctl on your machine.

Create an EKS Auto Mode Cluster Using the AWS Management Console:

Log into the AWS Management Console.
Navigate to the Amazon EKS service.
Click on "Add cluster" and choose "Create cluster".
Select "Auto Mode" under cluster configuration options.
Follow the prompts to specify your cluster name, Kubernetes version, and network configuration. AWS will handle setting up the VPC or you can choose an existing one.
Review your settings and create the cluster. AWS will manage the setup of necessary roles and networking.

Using the AWS CLI or eksctl:

For AWS CLI:

aws eks create-cluster --name my-auto-mode-cluster --enable-auto-mode

For eksctl:

eksctl create cluster --name=my-auto-mode-cluster --enable-auto-mode

Post-Creation Steps:

After the cluster is created, update your kubectl configuration to interact with your new EKS cluster:

aws eks update-kubeconfig --name my-auto-mode-cluster

You can now deploy your applications or try deploying a sample application to see how EKS Auto Mode scales resources automatically.

Monitor and Manage:

Use the EKS console, AWS CLI, or kubectl to monitor your cluster's health and performance. EKS Auto Mode will automatically scale nodes based on workload demands.

By leveraging EKS Auto Mode, you can benefit from AWS's managed services expertise, reducing the complexities typically associated with Kubernetes cluster management. This allows organizations to focus more on innovation and less on infrastructure management.

Learn more:
1/ Main doc
2/ User guide
3/ pricing

Sample config details:

Exploring new AWS Aurora DSQL. What is it ? Why it is important ? How to quickstart ?

Walter Lee — Mon, 16 Dec 2024 20:05:08 +0000

What is New with AWS DSQL?

Amazon Web Services (AWS) has introduced Amazon Aurora DSQL, a new serverless, distributed SQL database. Here are the key aspects of this service:

Serverless Architecture: Aurora DSQL is designed to be fully serverless, meaning there's no need to provision, patch, or manage database instances. All updates and security patching occur with no downtime and zero impact on performance.

Distributed SQL: It provides a distributed SQL database with active-active architecture across multiple regions, supporting virtually unlimited scalability.

PostgreSQL Compatibility: DSQL is PostgreSQL-compatible, allowing developers to use familiar PostgreSQL tools, drivers, and SQL features.

Performance & Scalability:
Offers 4x faster reads and writes compared to other popular distributed SQL databases.
Automatically scales to meet workload demand without the need for sharding or instance upgrades.

High Availability:
Ensures 99.99% availability in a single region and 99.999% in multi-region configurations.
Uses an active-active setup for automated failure recovery and no single point of failure.

Concurrency Control: Employs optimistic concurrency control (OCC) instead of traditional locking approaches, which is particularly useful in distributed environments for reducing contention.

Security Features: Integrates with AWS Identity and Access Management (IAM) for authentication and authorization, and all data is encrypted at rest and in transit.

Why is AWS DSQL Important?

Simplification of Architectures: DSQL simplifies scaling and application architecture, especially when paired with other serverless AWS services like Lambda, potentially allowing for fully serverless setups that can handle high throughput and remain resilient to availability zone (AZ) failures without managing infrastructure.

Innovative Transaction Handling: It introduces new methods for handling transactions, conflict detection, and durability, which are crucial for ensuring data integrity and consistency in distributed environments.

Performance Leap: The performance claims, particularly the 4x speed improvement over competitors like Google Spanner, are significant for applications requiring low-latency and high-throughput transactions across regions.

Ease of Use for PostgreSQL Users: Developers familiar with PostgreSQL can leverage this database without significant changes to their existing codebase or development practices, yet gain benefits of distributed systems.

Multi-Region Consistency: With strong consistency across regions, it's crucial for applications where data must be consistent across multiple geographic locations, addressing a common challenge in distributed databases.

Cost Efficiency: Being serverless, it allows for a pay-as-you-go model, potentially reducing costs for applications with variable workloads by only charging for what is used.

Aurora DSQL represents AWS's continued investment in database technologies, aiming to provide solutions that meet the evolving needs of modern applications for scalability, performance, and ease of management.

How to quickstart?

Follow the steps at Getting started with Aurora DSQL

When you use the commands at step 6 to include and copy sample database, you will see errors because the github repo has a different path name.

so need to fix below:
instead :
"\include samples/department-insert-multirow.sql
\copy example.invoice(created, purchaser, amount) from samples/invoice.csv csv
"

use below with correct path name, so the command will WORK : 
\include aurora-dsql-samples/quickstart_data/department-insert-multirow.sql
\copy example.invoice(created, purchaser, amount) from aurora-dsql-samples/quickstart_data/invoice.csv csv

see repo at https://github.com/aws-samples/aurora-dsql-samples/tree/main/quickstart_data
after I git clone , then 
[cloudshell-user@ip-10-130-76-171 quickstart_data]$ pwd
/home/cloudshell-user/aurora-dsql-samples/quickstart_data
[cloudshell-user@ip-10-130-76-171 quickstart_data]$ ls
department-insert-multirow.sql  invoice.csv

References/sources:

Step by step guide with screen captures to quickstart:

Stand With Ukraine using Super Fast and Accurate Azure Cognitive LUIS AI language translator

Walter Lee — Mon, 07 Mar 2022 01:23:27 +0000

Overview of My Submission:

This demo is to use Azure Cognitive LUIS AI translator to translate Ukrainian language tweets by Ukraine President @ZelenskyyUa into 15 different and most widely used major languages so many others in the world can read and understand the current crisis at Ukraine. Azure Cognitive Services Translator is very accurate, fast, developer friendly and powerful. Only a few lines of codes can translate Ukrainian language ACCURATELY into 15 most widely used major languages in ~0.5 seconds.

My Ukrainian friends told me it is VERY NATIVELY ACCURATE and much better than Google Translate.

Submission Category:

# AI Aces

Link to Code on GitHub:

main - https://github.com/leewalter/Azure-hackathon-LUIS-AI-StandWithUkraine

src - https://github.com/leewalter/Azure-hackathon-LUIS-AI-StandWithUkraine/blob/main/translator.js

outputs:
1/ Natively accurate translations:
https://github.com/leewalter/Azure-hackathon-LUIS-AI-StandWithUkraine/blob/main/Azure%20AI%20translate%20into%2015%20diff%20lang%203.png

2/ Super Fast: 15 translations in only 0.5s!
https://github.com/leewalter/Azure-hackathon-LUIS-AI-StandWithUkraine/blob/main/Azure%20AI%20translate%20into%2015%20diff%20lang%204.png

Additional Resources / Info

1/ see my original post at LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:6906044994268860416/
"I read many #tweets from President @ZelenskyyUa lately but could not understand Uk words, so thinking to write an app to translate them to diff languages so more people can read it easily !"

2/ If this submission wins any prize money from Azure hackathlon, all proceeds will be donated to the President @ZelenskyyUa and his government !

𝑮𝒐𝒐𝒈𝒍𝒆 𝒍𝒐𝒈4𝒋𝒔𝒄𝒂𝒏𝒏𝒆𝒓 𝒕𝒐 𝒔𝒄𝒂𝒏 𝒂𝒏𝒅 𝒓𝒆𝒎𝒐𝒗𝒆 𝑱𝒏𝒅𝒊𝑳𝒐𝒐𝒌𝒖𝒑 !

Walter Lee — Wed, 29 Dec 2021 16:03:02 +0000

New OSS tool to scan and remove the vulnerable class from detected JARs in-place easily. Read more: https://github.com/google/log4jscanner

Free Oracle OCI learning and certification exam till 2/2022

Walter Lee — Thu, 23 Dec 2021 06:52:53 +0000

see offers at https://education.oracle.com/learn/oracle-cloud-infrastructure/pPillar_640/