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🚀 AI + Existing Test Framework = Massive Productivity Boost
Today I tried something interesting.
I already had an API automation framework built using Playwright + TypeScript, with a few test cases like:
✅ CreatePet
✅ GetPetById
✅ GetAvailablePets
I needed to add a new test case: UpdatePet.
Instead of writing everything manually, I simply:
1. Provided the cURL request to AI Copilot in IntelliJ.
2. Let it analyze the existing project structure and patterns.
3. Within a few minutes, it generated the new UpdatePet.spec.ts test case that fit perfectly into the framework.
The real value of AI isn't just generating code—it's understanding an existing codebase and extending it consistently.
As SDETs, AI allows us to spend less time on repetitive coding and more time on:
🔹 Test design
🔹 Edge cases
🔹 Better assertions
🔹 Quality engineering
The future isn't AI replacing testers.
The future is testers who know how to leverage AI becoming significantly more productive.
#AI #GitHubCopilot #IntelliJ #Playwright #TypeScript #APITesting #TestAutomation #SDET #QualityEngineering #SoftwareTesting #AutomationTesting #DeveloperProductivity #GenerativeAI #QA #SoftwareEngineering
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Niva - AI Automation Agency Template (Contact page)
📌 What's Included
13+ fully designed pages, covering the entire buyer journey:
Home — hero, problem/solution framing, services, industries served, integrations, process, stats, case study previews, testimonials, pricing, FAQ, and final CTA
About — origin story, mission, values, team, and company stats
Services — a full breakdown of each service line with outcomes and use cases
Case Studies — a CMS-powered index plus a detailed single-case-study template (problem → approach → solution → results)
Blog — a CMS-powered index and article template, ready for SEO content
Contact — a conversion-focused form paired with direct calendar booking
404, and legal pages — Privacy Policy, Terms of Service, and Cookie Policy included
📌 Ideal For
AI automation agencies and consultancies
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Custom AI agent developers
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Professional work
An AI engineer should make the path from input to output inspectable. In Tenderseal, I shaped an AI-assisted workflow where users READ source material, ASK focused questions, and DRAFT a response while keeping the underlying material close to the work. I build with approvals around consequential actions, read-back after integrations, and a clear boundary between model output and human judgment. If your AI feature needs a dependable workflow, message me.
Great Design :)
What if your PC could actually listen, understand, and do the work for you?
I’m building that idea into something real.
Meet AURA — my personal AI voice assistant for Windows.
Instead of building another chatbot that only answers questions, I’m experimenting with an AI agent that can actually interact with my computer and help with everyday work.
So far, AURA can:
• Have live voice conversations
• Convert speech to text and respond with voice
• Open applications like Excel, Power BI, Chrome, and Windows Settings
• Search the web and YouTube through voice
• Perform basic PC and file operations
• Understand requests through Gemini
• Execute actions through a safety-controlled automation layer
• Continue listening after completing a task
But this is only the beginning.
My long-term goal is to make AURA understand my actual workspace — including what’s happening on my screen and inside applications — so I can simply say:
“Open my Excel file, check the sales data, clean the date column, and add a formula for total revenue.”
And let AURA handle the workflow.
I also want it to become useful for my own Data Analytics work — from Excel and Power BI workflows to repetitive desktop tasks, research, and automation.
I’m building AURA with Python, AI, voice technologies, desktop automation, Excel, Power BI, and LLMs, while keeping safety and human control at the core.
It’s still far from the final vision.
But that’s exactly what makes the project interesting.
I’m not just learning how AI agents work.
I’m trying to build one that I can actually use every day.
Building in public. Learning by building. 🚀
#AI #AIAgents #Python #AIAssistant #VoiceAI #DesktopAutomation #DataAnalytics #PowerBI #Excel #Automation #BuildInPublic
The screen-reading step is where I would put the guardrail. Once AURA can read what is in the window, everything on that screen is input - and a page it opened from a voice search can hold a sentence written to be read as an instruction. I spent a while trying to rob my own...