#Automation-Framework

[certivo]

Ultimate Guide to Regulatory Compliance Frameworks & Global Product Compliance Solutions

Explore Certivo’s comprehensive suite of regulatory compliance frameworks, tailored for businesses navigating complex global regulations. From RoHS, REACH, and PFAS to Prop 65 and CSRD, our platform offers global compliance solutions through customizable workflows, end-to-end reporting, and automated enforcement. As an environmental compliance software and chemical compliance platform, Certivo ensures efficient product compliance management across international markets. Discover how our software streamlines certification tracking, risk assessments, and audit readiness — empowering your team to stay ahead of regulatory changes. Simplify compliance today with Certivo’s unified solution.

[yethiconsulting]

Best Practices for Designing a Test Automation Framework

Designing a robust test automation framework is essential for scalable, maintainable, and efficient testing. A well-structured framework helps teams standardize test processes, accelerate execution, and improve code reusability. Here are key best practices to follow:

Define a Clear Architecture

Choose a layered structure that separates test scripts, utilities, and test data. This modularity improves maintainability and enables easy updates.

Select the Right Tools and Tech Stack

Choose tools that align with your application, team skillsets, and CI/CD goals—like Selenium, TestNG, Cypress, or Playwright. Integrate with version control and build tools for automation framework continuity.

Use Data-Driven and Keyword-Driven Approaches

Implement reusable test logic that supports parameterization. This reduces redundancy and allows flexibility in running tests with various datasets.

Enable Logging, Reporting, and Exception Handling

Build in detailed logs and custom reports for quick debugging. Include robust error handling to prevent script failures from breaking the entire suite.

Ensure Scalability and Maintainability

Design the framework to scale with your application. Follow coding standards, comment code clearly, and regularly refactor for performance.

A well-designed framework is the foundation for long-term test automation success.

[jcmarchi]

Transforming LLM Performance: How AWS’s Automated Evaluation Framework Leads the Way

New Post has been published on https://thedigitalinsider.com/transforming-llm-performance-how-awss-automated-evaluation-framework-leads-the-way/

Transforming LLM Performance: How AWS’s Automated Evaluation Framework Leads the Way

Large Language Models (LLMs) are quickly transforming the domain of Artificial Intelligence (AI), driving innovations from customer service chatbots to advanced content generation tools. As these models grow in size and complexity, it becomes more challenging to ensure their outputs are always accurate, fair, and relevant.

To address this issue, AWS’s Automated Evaluation Framework offers a powerful solution. It uses automation and advanced metrics to provide scalable, efficient, and precise evaluations of LLM performance. By streamlining the evaluation process, AWS helps organizations monitor and improve their AI systems at scale, setting a new standard for reliability and trust in generative AI applications.

Why LLM Evaluation Matters

LLMs have shown their value in many industries, performing tasks such as answering questions and generating human-like text. However, the complexity of these models brings challenges like hallucinations, bias, and inconsistencies in their outputs. Hallucinations happen when the model generates responses that seem factual but are not accurate. Bias occurs when the model produces outputs that favor certain groups or ideas over others. These issues are especially concerning in fields like healthcare, finance, and legal services, where errors or biased results can have serious consequences.

It is essential to evaluate LLMs properly to identify and fix these issues, ensuring that the models provide trustworthy results. However, traditional evaluation methods, such as human assessments or basic automated metrics, have limitations. Human evaluations are thorough but are often time-consuming, expensive, and can be affected by individual biases. On the other hand, automated metrics are quicker but may not catch all the subtle errors that could affect the model’s performance.

For these reasons, a more advanced and scalable solution is necessary to address these challenges. AWS’s Automated Evaluation Framework provides the perfect solution. It automates the evaluation process, offering real-time assessments of model outputs, identifying issues like hallucinations or bias, and ensuring that models work within ethical standards.

AWS’s Automated Evaluation Framework: An Overview

AWS’s Automated Evaluation Framework is specifically designed to simplify and speed up the evaluation of LLMs. It offers a scalable, flexible, and cost-effective solution for businesses using generative AI. The framework integrates several core AWS services, including Amazon Bedrock, AWS Lambda, SageMaker, and CloudWatch, to create a modular, end-to-end evaluation pipeline. This setup supports both real-time and batch assessments, making it suitable for a wide range of use cases.

Key Components and Capabilities

Amazon Bedrock Model Evaluation

At the foundation of this framework is Amazon Bedrock, which offers pre-trained models and powerful evaluation tools. Bedrock enables businesses to assess LLM outputs based on various metrics such as accuracy, relevance, and safety without the need for custom testing systems. The framework supports both automatic evaluations and human-in-the-loop assessments, providing flexibility for different business applications.

LLM-as-a-Judge (LLMaaJ) Technology

A key feature of the AWS framework is LLM-as-a-Judge (LLMaaJ), which uses advanced LLMs to evaluate the outputs of other models. By mimicking human judgment, this technology dramatically reduces evaluation time and costs, up to 98% compared to traditional methods, while ensuring high consistency and quality. LLMaaJ evaluates models on metrics like correctness, faithfulness, user experience, instruction compliance, and safety. It integrates effectively with Amazon Bedrock, making it easy to apply to both custom and pre-trained models.

Customizable Evaluation Metrics

Another prominent feature is the framework’s ability to implement customizable evaluation metrics. Businesses can tailor the evaluation process to their specific needs, whether it is focused on safety, fairness, or domain-specific accuracy. This customization ensures that companies can meet their unique performance goals and regulatory standards.

Architecture and Workflow

The architecture of AWS’s evaluation framework is modular and scalable, allowing organizations to integrate it easily into their existing AI/ML workflows. This modularity ensures that each component of the system can be adjusted independently as requirements evolve, providing flexibility for businesses at any scale.

Data Ingestion and Preparation

The evaluation process begins with data ingestion, where datasets are gathered, cleaned, and prepared for evaluation. AWS tools such as Amazon S3 are used for secure storage, and AWS Glue can be employed for preprocessing the data. The datasets are then converted into compatible formats (e.g., JSONL) for efficient processing during the evaluation phase.

Compute Resources

The framework uses AWS’s scalable compute services, including Lambda (for short, event-driven tasks), SageMaker (for large and complex computations), and ECS (for containerized workloads). These services ensure that evaluations can be processed efficiently, whether the task is small or large. The system also uses parallel processing where possible, speeding up the evaluation process and making it suitable for enterprise-level model assessments.

Evaluation Engine

The evaluation engine is a key component of the framework. It automatically tests models against predefined or custom metrics, processes the evaluation data, and generates detailed reports. This engine is highly configurable, allowing businesses to add new evaluation metrics or frameworks as needed.

Real-Time Monitoring and Reporting

The integration with CloudWatch ensures that evaluations are continuously monitored in real-time. Performance dashboards, along with automated alerts, provide businesses with the ability to track model performance and take immediate action if necessary. Detailed reports, including aggregate metrics and individual response insights, are generated to support expert analysis and inform actionable improvements.

How AWS’s Framework Enhances LLM Performance

AWS’s Automated Evaluation Framework offers several features that significantly improve the performance and reliability of LLMs. These capabilities help businesses ensure their models deliver accurate, consistent, and safe outputs while also optimizing resources and reducing costs.

Automated Intelligent Evaluation

One of the significant benefits of AWS’s framework is its ability to automate the evaluation process. Traditional LLM testing methods are time-consuming and prone to human error. AWS automates this process, saving both time and money. By evaluating models in real-time, the framework immediately identifies any issues in the model’s outputs, allowing developers to act quickly. Additionally, the ability to run evaluations across multiple models at once helps businesses assess performance without straining resources.

Comprehensive Metric Categories

The AWS framework evaluates models using a variety of metrics, ensuring a thorough assessment of performance. These metrics cover more than just basic accuracy and include:

Accuracy: Verifies that the model’s outputs match expected results.

Coherence: Assesses how logically consistent the generated text is.

Instruction Compliance: Checks how well the model follows given instructions.

Safety: Measures whether the model’s outputs are free from harmful content, like misinformation or hate speech.

In addition to these, AWS incorporates responsible AI metrics to address critical issues such as hallucination detection, which identifies incorrect or fabricated information, and harmfulness, which flags potentially offensive or harmful outputs. These additional metrics are essential for ensuring models meet ethical standards and are safe for use, especially in sensitive applications.

Continuous Monitoring and Optimization

Another essential feature of AWS’s framework is its support for continuous monitoring. This enables businesses to keep their models updated as new data or tasks arise. The system allows for regular evaluations, providing real-time feedback on the model’s performance. This continuous loop of feedback helps businesses address issues quickly and ensures their LLMs maintain high performance over time.

Real-World Impact: How AWS’s Framework Transforms LLM Performance

AWS’s Automated Evaluation Framework is not just a theoretical tool; it has been successfully implemented in real-world scenarios, showcasing its ability to scale, enhance model performance, and ensure ethical standards in AI deployments.

Scalability, Efficiency, and Adaptability

One of the major strengths of AWS’s framework is its ability to efficiently scale as the size and complexity of LLMs grow. The framework employs AWS serverless services, such as AWS Step Functions, Lambda, and Amazon Bedrock, to automate and scale evaluation workflows dynamically. This reduces manual intervention and ensures that resources are used efficiently, making it practical to assess LLMs at a production scale. Whether businesses are testing a single model or managing multiple models in production, the framework is adaptable, meeting both small-scale and enterprise-level requirements.

By automating the evaluation process and utilizing modular components, AWS’s framework ensures seamless integration into existing AI/ML pipelines with minimal disruption. This flexibility helps businesses scale their AI initiatives and continuously optimize their models while maintaining high standards of performance, quality, and efficiency.

Quality and Trust

A core advantage of AWS’s framework is its focus on maintaining quality and trust in AI deployments. By integrating responsible AI metrics such as accuracy, fairness, and safety, the system ensures that models meet high ethical standards. Automated evaluation, combined with human-in-the-loop validation, helps businesses monitor their LLMs for reliability, relevance, and safety. This comprehensive approach to evaluation ensures that LLMs can be trusted to deliver accurate and ethical outputs, building confidence among users and stakeholders.

Successful Real-World Applications

Amazon Q Business

AWS’s evaluation framework has been applied to Amazon Q Business, a managed Retrieval Augmented Generation (RAG) solution. The framework supports both lightweight and comprehensive evaluation workflows, combining automated metrics with human validation to optimize the model’s accuracy and relevance continuously. This approach enhances business decision-making by providing more reliable insights, contributing to operational efficiency within enterprise environments.

Bedrock Knowledge Bases

In Bedrock Knowledge Bases, AWS integrated its evaluation framework to assess and improve the performance of knowledge-driven LLM applications. The framework enables efficient handling of complex queries, ensuring that generated insights are relevant and accurate. This leads to higher-quality outputs and ensures the application of LLMs in knowledge management systems can consistently deliver valuable and reliable results.

The Bottom Line

AWS’s Automated Evaluation Framework is a valuable tool for enhancing the performance, reliability, and ethical standards of LLMs. By automating the evaluation process, it helps businesses reduce time and costs while ensuring models are accurate, safe, and fair. The framework’s scalability and flexibility make it suitable for both small and large-scale projects, effectively integrating into existing AI workflows.

With comprehensive metrics, including responsible AI measures, AWS ensures LLMs meet high ethical and performance standards. Real-world applications, like Amazon Q Business and Bedrock Knowledge Bases, show its practical benefits. Overall, AWS’s framework enables businesses to optimize and scale their AI systems confidently, setting a new standard for generative AI evaluations.

[nareshitofficial]

Full Stack Software Testing is a comprehensive approach that involves testing both the front-end (UI) and back-end (server, database, APIs) of a software application. It includes manual testing, automation testing (using tools like Selenium), API testing, performance testing, and database validation. Full stack testers have a complete understanding of the software architecture, which enables them to ensure quality at every layer of the application.

Naresh i Technologies offers industry-oriented Full Stack Software Testing training with real-time projects, expert faculty, and placement assistance to help students become skilled and job-ready professionals.

[blockchaindevelopmenttechnology]

How to Build a Solana Trading Bot: A Complete Guide

Introduction

In today’s rapidly evolving crypto landscape, algorithmic trading is no longer just for hedge funds—it’s becoming the norm for savvy traders and developers. Trading bots are revolutionizing how people interact with decentralized exchanges (DEXs), allowing for 24/7 trading, instant decision-making, and optimized strategies.

If you're planning to build a crypto trading bot, Solana blockchain is a compelling platform. With blazing-fast transaction speeds, negligible fees, and a thriving DeFi ecosystem, Solana provides the ideal environment for high-frequency, scalable trading bots.

In this blog, we'll walk you through the complete guide to building a Solana trading bot, including tools, strategies, architecture, and integration with Solana DEXs like Serum and Raydium.

Why Choose Solana for Building a Trading Bot?

Solana has quickly emerged as one of the top platforms for DeFi and trading applications. Here’s why:

🚀 Speed: Handles over 65,000 transactions per second (TPS)

💸 Low Fees: Average transaction cost is less than $0.001

⚡ Fast Finality: Block confirmation in just 400 milliseconds

🌐 DeFi Ecosystem: Includes DEXs like Serum, Orca, and Raydium

🔧 Developer Support: Toolkits like Anchor, Web3.js, and robust SDKs

These characteristics make Solana ideal for real-time, high-frequency trading bots that require low latency and cost-efficiency.

Prerequisites Before You Start

To build a Solana trading bot, you’ll need:

🔧 Technical Knowledge

Blockchain basics

JavaScript or Rust programming

Understanding of smart contracts and crypto wallets

🛠️ Tools & Tech Stack

Solana CLI – For local blockchain setup

Anchor Framework – If using Rust

Solana Web3.js – For JS-based interactions

Phantom/Sollet Wallet – To sign transactions

DeFi Protocols – Serum, Raydium, Orca

APIs – RPC providers, Pyth Network for price feeds

Set up a wallet on Solana Devnet or Testnet before moving to mainnet.

Step-by-Step: How to Build a Solana Trading Bot

Step 1: Define Your Strategy

Choose a trading strategy:

Market Making: Providing liquidity by placing buy/sell orders

Arbitrage: Exploiting price differences across DEXs

Scalping: Taking advantage of small price changes

Momentum/Trend Trading: Based on technical indicators

You can backtest your strategy using historical price data to refine its effectiveness.

Step 2: Set Up Development Environment

Install the essentials:

Solana CLI & Rust (or Node.js)

Anchor framework (for smart contract development)

Connect your wallet to Solana devnet

Install Serum/Orca SDKs for DEX interaction

Step 3: Integrate with Solana DeFi Protocols

Serum DEX: For order-book-based trading

Raydium & Orca: For AMM (Automated Market Maker) trading

Connect your bot to fetch token pair information, price feeds, and liquidity data.

Step 4: Build the Trading Logic

Fetch real-time price data using Pyth Network

Apply your chosen trading algorithm (e.g., RSI, MACD, moving averages)

Trigger buy/sell actions based on signals

Handle different order types (limit, market)

Step 5: Wallet and Token Management

Use SPL token standards

Manage balances, sign and send transactions

Secure private keys using wallet software or hardware wallets

Step 6: Testing Phase

Test everything on Solana Devnet

Simulate market conditions

Debug issues like slippage, front-running, or network latency

Step 7: Deploy to Mainnet

Move to mainnet after successful tests

Monitor performance using tools like Solana Explorer or Solscan

Add dashboards or alerts for better visibility

Key Features to Add

For a production-ready Solana trading bot, include:

✅ Stop-loss and take-profit functionality

📈 Real-time logging and analytics dashboard

🔄 Auto-reconnect and restart scripts

🔐 Secure environment variables for keys and APIs

🛠️ Configurable trading parameters

Security & Risk Management

Security is critical, especially when handling real assets:

Limit API calls to prevent bans

Secure private keys with hardware or encrypted vaults

Add kill-switches for extreme volatility

Use rate limits and retries to handle API downtime

Consider smart contract audits for critical logic

Tools & Frameworks to Consider

Anchor – Solana smart contract framework (Rust)

Solana Web3.js – JS-based blockchain interaction

Serum JS SDK – Interface with Serum’s order books

Pyth Network – Live, accurate on-chain price feeds

Solscan/Solana Explorer – Track transactions and wallet activity

Real-World Use Cases

Here are examples of Solana trading bots in action:

Arbitrage Bots: Profiting from price differences between Raydium and Orca

Liquidity Bots: Maintaining order books on Serum

Oracle-Driven Bots: Reacting to real-time data via Pyth or Chainlink

These bots are typically used by trading firms, DAOs, or DeFi protocols.

Challenges to Be Aware Of

❗ Network congestion during high demand

🧩 Rapid updates in SDKs and APIs

📉 Slippage and liquidity issues

🔄 DeFi protocol changes requiring frequent bot updates

Conclusion

Building a trading bot on Solana blockchain is a rewarding venture—especially for developers and crypto traders looking for speed, cost-efficiency, and innovation. While there are challenges, Solana's robust ecosystem, coupled with developer support and toolkits, makes it one of the best choices for automated DeFi solutions.

If you're looking to take it a step further, consider working with a Solana blockchain development company to ensure your bot is scalable, secure, and production-ready.

[rsayoub]

Lionbridge Language AI Unleashed: Transforming Localization with Vincent Henderson

In the latest episode of the Localization Fireside Chat, I had the privilege of speaking with Vincent Henderson, Vice President of Language AI Strategy at Lionbridge, one of the leading global companies in localization and AI-driven language solutions. Our conversation focused on how Lionbridge is leveraging AI to revolutionize localization processes, transforming efficiency, quality, and…

[qprofessionals]

Automation vs. Manual Testing – Which One Do You Need?

When it comes to software testing, choosing between automation and manual testing can be challenging. Both have their advantages, and at Quality Professionals, we help businesses find the perfect balance between the two.

Manual testing is ideal for exploratory, usability, and ad-hoc testing, where human intuition and creativity are essential. On the other hand, automation testing is perfect for repetitive tasks, regression testing, and performance evaluations, saving time and increasing efficiency.

A successful QA strategy incorporates both approaches to ensure software reliability, speed, and cost-effectiveness. Not sure which testing method suits your project? Let’s discuss your needs and create a customized QA plan for your business. Reach out to Quality Professionals today!

[leonbasinwriter]

Building Business Universes: Ethics and AI Combined

Where AI, Ethics, and Stardust Collide to Build Universes of Impact 🚀 Launching Cosmic Business Architecture 3.0 ∞ The universe of business is expanding—fast. Old leadership constellations are collapsing, and new galaxies of innovation are being born. Today, I’m thrilled to introduce The Cosmic Business Architect—a fusion of AI’s precision, cybersecurity’s integrity, and the raw creativity of…

[yethiconsulting]

Shaping the Future of QA Test Automation in 2025

As software delivery accelerates, QA test automation is evolving into a strategic driver of product quality. In 2025, the focus is shifting from merely automating tests to enabling smarter, faster, and more resilient testing at scale. AI and machine learning are now playing a central role—powering self-healing tests, predictive analytics, and intelligent test generation to reduce flakiness and maintenance efforts.

Low-code/no-code automation tools are empowering non-technical testers to contribute, breaking silos and expanding automation coverage. Meanwhile, DevOps and CI/CD pipelines are demanding tighter integration, where automated tests trigger instantly with every build, across diverse environments.

Cloud-based and containerized testing platforms like Selenium Grid on Kubernetes or device farms for mobile testing are becoming standard for scalability and flexibility. Also, there’s a growing emphasis on shift-left and shift-right testing—embedding QA from the earliest stages and continuing into production monitoring.

The future is also about test data management, security testing, and performance under real-world loads. QA professionals must now blend skills in development, analytics, and business understanding.

In 2025, automation isn’t just about speed—it’s about intelligent quality engineering that aligns with agile business goals.

[2ribu]

Evolusi Framework AI: Alat Terbaru untuk Pengembangan Model AI di 2025

Kecerdasan buatan (AI) telah menjadi salah satu bidang yang paling berkembang pesat dalam beberapa tahun terakhir. Pada tahun 2025, teknologi AI diperkirakan akan semakin maju, terutama dengan adanya berbagai alat dan framework baru yang memungkinkan pengembang untuk menciptakan model AI yang lebih canggih dan efisien. Framework AI adalah sekumpulan pustaka perangkat lunak dan alat yang digunakan…

[voittoinsights]

Project Mandala: Can it revolutionize the FinTech Ecosystem ?

In today’s rapidly evolving financial landscape, the pressure on financial institutions to stay compliant with complex regulatory requirements is at an all-time high. Frauds are increasing exponentially and regulators are finding ways to plug the gap sooner. The emergence of digital finance, driven by fintech innovations, is racing ahead, leaving traditional compliance methods struggling to…

[agiledock]

JavaScript Testing Best Practices: Frameworks for Success

Unlock the secrets to successful JavaScript testing with our detailed infographic! From unit testing in JavaScript to the most effective frontend testing frameworks, this visual guide showcases the top JavaScript testing frameworks, libraries, and best practices to ensure robust and efficient test automation.

[technologyequality]

Executing and Expanding: How the P.R.I.S.M.© Method is Elevating My Momentum

Executing and Expanding How the P.R.I.S.M.© Method is Elevating My Momentum If you’ve been following along, you know we’ve been diving into the art of scaling smart with AI—growing with intention, not just speed. But let’s get personal for a second. Right now, I’m in the thick of the Execute and Expand phase myself, and let me tell you… this is where things start to get real. Scaling isn’t…