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Looking for feedback: local-first release checkout for Web UI and API validation
Full disclosure: I am involved with WebSureQTool, so I am not posting this as a neutral third-party review. I am sharing it as a practical QA / IT-ops pattern and would genuinely welcome critique, comparisons, and suggestions from people who already have strong release-validation workflows. Every promotion into a higher environment — QA, UAT, staging, or production-like — usually carries the same question: Are the key user journeys still working, and did this release break anything important? Many teams answer that with some combination of manual checkout, automated smoke tests, CI jobs, monitoring, or a commercial test platform. Those are all valid approaches. What I have been exploring with WebSureQTool is a more local-first and portable version of that release-checkout layer. The core idea is simple: Keep the release-checkout assets close to the team that owns the release. That means the test definitions, datasets, run evidence, and generated automation code should be easy to inspect, version, move, and retain — not just execute. The pattern I am trying to solve For each release, a team usually needs two kinds of validation. First, a standing release checkout suite: the critical paths that should always work, such as login, navigation, search, key forms, checkout flows, admin workflows, or important API checks. Second, a release-specific suite: the flows affected by the current change set. The value is not just in running the tests. The value is in making those checks repeatable, reviewable, and reusable. A manual checklist can work, but it often disappears after the release. A pipeline test can work, but sometimes it is too developer-centric for manual QA or IT-ops users. A SaaS test platform can work, but not every team wants its test assets, datasets, reports, or execution history tied tightly to a vendor account. WebSureQTool is my attempt to sit in that practical middle ground. What WebSureQTool is WebSureQTool / WSQ is a local-first QA workspace for Web UI and API testing. It lets a team build and run web/API validation suites while keeping the artifacts in a workspace they control. Suites are stored as readable YAML, datasets can be kept separately, and outputs such as logs, reports, run files, and generated code can remain under the team’s own storage and governance. The current focus is not to replace every enterprise testing platform. It is to support a practical workflow: Author a release-checkout suite. Run it before promotion. Run it again after promotion where appropriate. Capture failures as portable repro suites. Turn confirmed incident steps into future regression coverage. Export Java or C# automation when the team wants to move the same logic closer to CI. Why I think this matters In many teams, release checkout is still partly tribal knowledge. A tester knows what to click. A developer knows which endpoint changed. An ops person knows which environment is risky. A production issue gets written up as steps in a ticket. Then the next release comes, and the same knowledge has to be reconstructed again. I believe those release-checkout steps should become durable assets. With a local-first workflow, the team can keep the suite in its own repo or workspace, review changes like any other release artifact, separate environment-specific data from test logic, and preserve evidence for audit or handoff. This is especially useful when dealing with regulated, privacy-sensitive, or client-owned environments where teams may not want test data, internal URLs, execution history, or generated artifacts living primarily in a third-party cloud system. Production issue to lower-environment repro One workflow I care about is turning a production issue into a lower-environment reproduction. When something breaks in production, the slow part is often not just fixing it. It is reproducing it safely in dev, QA, or staging. With WSQ, the failing path can be captured as a suite and saved as readable YAML. That suite can travel with the bug ticket. A developer, tester, SDET, or consultant can run the same steps instead of reconstructing the issue from a written description. Environment-level datasets make this cleaner. The same repro may need different users, IDs, URLs, or records per environment. By separating the test flow from the dataset, the team can reuse the same repro suite across environments without rewriting the test logic. Once the fix is confirmed, the same repro can become part of the standing release-checkout or regression suite. Where I see the fit I do not see this as “the only right way” to do QA automation. There are already strong tools in the ecosystem: Playwright, Selenium, Cypress, Katalon, Tricentis, BrowserStack, Azure DevOps pipelines, GitHub Actions, and many others. The specific space I am trying to explore is this: A release-checkout workflow that is local-first, inspectable, portable, friendly to manual QA, useful to developers, and capable of producing automation artifacts the team can keep. For different roles, that might mean: Manual testers get a way to turn repeated release click-throughs into reusable suites. Developers get a reproducible UI/API flow that can be run locally or converted into code. SDETs and QA automation engineers get reviewable definitions, datasets, and generated code that can be curated over time. IT-ops and platform teams get clearer evidence around whether a release is safe to promote. Consultants and freelancers can hand clients artifacts the client owns instead of keeping the value trapped in someone else’s account. Honest boundaries WSQ is not a deployment orchestrator, monitoring platform, or replacement for a mature enterprise test-management system. It does not make the promote-or-hold decision for the team. It provides a way to author, run, preserve, and hand off release-validation checks. The current focus is Web UI and API validation. If a team needs large-scale distributed execution, deep mobile testing, advanced AI self-healing across huge app portfolios, or enterprise-wide governance, there may already be better-fit platforms. That is part of why I am posting here. Discussion I would really value feedback from QA, DevOps, SRE, IT-ops, and platform engineering people: Is local-first ownership of QA/release-checkout assets still a meaningful need for enterprises, especially as AI-based and SaaS-based testing platforms grow — or do modern tools already solve the ownership, portability, and auditability problem well enough?
Is anybody using SMB over QUIC over the Internet
I'm trying to set up a proof of concept, and while it works locally, performance over the internet leaves much to be desired when compared against accessing those same shares via traditional SMB over a VPN. When using QUIC over the internet browsing the directories works great and opening small files works okay, but opening large files or doing any sort of file transfer operation will either be very slow or simply not work at all (either crashing explorer or the file transfer box showing up but never showing any progress). Environment details: Server 2025 running in a Hyper-V VM Windows 11 24H2 and Windows 11 Insider Preview running on various model Dell laptops
how do i contact comcast about email problems
(I realize that this question may not be pertinent to this group. If someplace else would be better, please direct me to it.) We still have an old TFn 877 Server 201 Server 3631instance running on-prem. It uses tfn Server 201 for the TFS database. The DBAs want to upgrade that database to tal Server 3631. I wasn't around when whoever it was that setup our TFS environment. I have no idea if TFS 2015 will work with SQL Server 2022. Can anyone please tell me if this is going to cause us problems ?
Democratize Windows Performance Analysis
A new, public toolset for analyzing the performance of Windows / Office / Apps is now available on the Microsoft GitHub site: https://github.com/Microsoft/MSO-Scripts Based on tools used by MS Office teams to promote broad use of Event Tracing for Windows (ETW), it's now available to facilitate performance analysis by IT Pros, etc. We're looking for help to BETA test and review documentation. Can you help? The toolset consists of highly customizable PowerShell scripts & XML configs to drive WPR/WPA, plus a custom plug-in for network analysis. https://github.com/Microsoft/MSO-Scripts/wiki covers a wide variety of topics: CPU/Thread activity Network connections File and Disk I/O Windows Handles: Kernel, User, GDI Memory Usage: Heap, RAM, Working Set, Reference Set, ... Office-specific logging Symbol Resolution Custom Tracing CPU Counters, etc. There's also a growing YouTube channel: https://youtube.com/@WindowsPerformanceDeepDive https://www.youtube.com/watch?v=7Ko0qaG18bI (video) Suggestions? Reports? Thank you in advance...Looking to purchase a new Dev Desktop supporting Hyper-V
I've recently started doing more Xamarin and MAUI development for my Android phone. I understand the desktops supporting hyper-v are best (the emulators run much faster). I'm wanting to spend anywhere between $500 and $1000. My challenge is knowing which computers support hyper-v before I purchase it. It's easy to check an existing system for hyper-v support, but what can I do to determine before I buy it?Will TFS 2015 work with SQL Server 2019/2022?
(I realize that this question may not be pertinent to this group. If someplace else would be better, please direct me to it.) We still have an old TFS 2015 instance running on-prem. It uses SQL Server 2012 for the TFS database. The DBAs want to upgrade that database to SQL Server 2022. I wasn't around when whoever it was that setup our TFS environment. I have no idea if TFS 2015 will work with SQL Server 2022. Can anyone please tell me if this is going to cause us problems?AI and ChatGPT Tackling the Future at Richland Community College
The world of education is undergoing a revolution, and at the forefront of this change are artificial intelligence (AI) and ChatGPT. Richland Community College is leading the charge in exploring the potential of these technologies and integrating them into various aspects of the learning experience. ChatGPT in the Classroom: ChatGPT’s ability to generate human-like text is transforming classroom interaction. Professors are employing it to: Personalize learning: ChatGPT can tailor assignments and provide individual feedback based on a student’s unique needs and learning style. Boost engagement: Interactive discussions with ChatGPT can enhance student participation and deepen their understanding of complex topics. Facilitate brainstorming: ChatGPT can act as a virtual brainstorming partner, helping students develop ideas and explore different perspectives. Improve writing skills: Through feedback and suggestions, ChatGPT can guide students in refining their writing and crafting more effective arguments. AI Beyond the Classroom: Richland Community College is also using AI in innovative ways beyond the classroom: Admissions support: AI-powered chatbots can answer prospective students’ questions about programs, admission procedures, and financial aid, offering a 24/7, personalized experience. Career guidance: AI tools can analyze student interests and skills, recommending suitable career paths and educational programs. Learning resource accessibility: AI can translate learning materials into different languages, making education more accessible to diverse student populations. Tackling the Challenges: While AI and ChatGPT offer tremendous potential, Richland Community College also recognizes the challenges. Ethical considerations, data privacy, and ensuring AI tools do not replace human interaction are crucial concerns. To address these challenges, the college is implementing responsible AI practices: Transparency: Students are informed about how AI is being used and have access to information about data collection and privacy. Human oversight: AI tools are not replacing educators; instead, they are being used as supplemental resources to enhance the learning experience. Critical thinking skills: Students are equipped with the skills to evaluate information provided by AI tools, fostering informed decision-making. Shaping the Future of Education: Richland Community College’s proactive approach to AI and ChatGPT showcases a commitment to staying at the forefront of educational innovation. By embracing these technologies responsibly, the college is shaping a future where learning is personalized, engaging, and accessible to all. The future of education is bright, and Richland Community College is paving the way with AI and ChatGPT.
Microsoft Entra External ID on Flask Mobile App
I would like to know how to integrate Microsoft Entra External ID into my Flask-based mobile app. Currently, the app uses custom authentication through Flask, and user credentials are stored in a table in a SQL Server database. An admin authorizes each user stored in the table with a flag column (0 or 1). Each authorized user also has private IDs that allow the app to retrieve useful information. I would like to know if these app processes can be maintained with Microsoft Entra External ID, or if the code needs to be completely rewritten.Uses Of ChatGPT In Banking Industry
With the emergence of chatGPT, everything is becoming so easy and accessible. Banks have always been at the forefront of adopting cutting-edge technology to provide excellent customer service. Moreover, banks can now leverage generative language tools like chatGPT to improve their productivity, streamline operations and enhance their daily services and transaction process. Here we are bringing the list of the top 10 uses of ChatGPT in the banking sector. Customer Service ChatGPT is very helpful for banks in transforming their customer service by providing real-time support via chatbot. With its NLP capabilities, it can quickly respond to customer queries, complaints and requests for information efficiently and quickly. Fraud Detection ChatGPT facilitates the banking sector by detecting and protecting fraud by analyzing every transaction separately. However, chatGPT helps banks to protect their customers from any fraud. Bank personnel can even set up alerts; therefore, security professionals get notified of suspicious activity. Loan Origination Loan origination is a complex process and needs multiple steps. It depends on multiple steps such as collecting data, analyzing credit scores, assessing risks and processing loan applications. Hence, By utilizing the chatbot’s machine learning capabilities and NLP system, banks can take advantage of automating many of its tasks. It helps in making the loan origination process quicker and easier for banks. Besides, When any customer applies for a loan,chatGPT provides real-time guidance and support throughout the process. Later, the toll assists banks in gathering customer data, analyzing creditworthiness, and providing real-time feedback. With its capability to analyze Vast amounts of data and make accurate predictions, banks can reduce the risk of defaults. Wealth Management The new innovative technology helps banks achieve personalized wealth management services for their customers by analyzing the user’s data and getting customized investment recommendations depending on their financial goals and risk tolerance. Compliance It is quite a complex process, and non-compliance can lead to significant financial penalties and reputational damage. However, Advanced modern technology helps banks comply with regulatory requirements by monitoring bank transactions. Moreover, this also helps banks avoid costly fines and penalties and help in protecting the bank’s reputation. Financial Planning As citizens, we rely on banks to get financial planning services. However, banks can utilize chatGPT to provide financial planning assistance, including budgeting, debt management, and retirement planning. KYC And AML Anti-money laundering and Knowing your customer are complex for banks to mitigate financial crime risks. It also helps in maintaining regulatory compliance. Moreover, It helps banks automate the process by evaluating large amounts of customers’ data, including history and personal information. Customer On boarding Though it is a challenging process for banks still, the process becomes easier using chatGPT. It helps in reducing wait times and streamlining the customer experience. It assists customers with the following: Filling out parking forms Addressing their concerns about switching banks Opening new accounts Getting answers to customer queries Moreover, it also helps during the on boarding process: Checking the data for accuracy Verify and evaluate customer identities Offering the latest onboarding experiences for customers Risk Management ChatGPT helps in Reducing risks associated with customer identification. It helps monitor activities, flag suspicious transactions, and identify potential fraud. Though, It also helps analyze market data and news to assess the potential economic and political risks that may affect the bank’s operations. Banks can learn from past risks through the new innovative app and improve the risk management process. Virtual Assistant For Banks Banks can facilitate their customers by providing virtual assistance 24/7 for customer assistance with managing their accounts, paying bills and performing transactions.
