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🚀 Building Smarter Software with AI-Powered QA & Release Management
Modern software teams need more than bug tracking—they need intelligent insights.
I'm currently working on an AI QA & Release Dashboard concept that helps development and QA teams deliver faster, with greater confidence.
✨ Key Features
✅ AI-powered defect analysis
✅ Playwright & Selenium automation reports
✅ REST API testing dashboard
✅ CI/CD pipeline monitoring
✅ Release readiness score
✅ Performance & stability analytics
✅ Real-time bug tracking
✅ Team productivity dashboard
✅ Deployment history
✅ AI-generated testing insights
💻 Tech Stack
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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The workflow maps the different paths a lead can take, including successful contact, missed calls, continued follow-up, and human handoff. It also keeps the relevant tasks, contact updates, and next steps organized inside the CRM.
The goal was to create a consistent lead-management process without relying on scattered notes or manual reminders.
Good automation does not replace the sales conversation. It makes sure the right conversation happens at the right time.
I help service businesses with GoHighLevel workflow design, CRM organization, lead routing, follow-up automation, and system audits.
For a construction company specifically, that missed call path is probably doing more revenue recovery than any other branch. guys on a job site are not picking up mid pour so leads default to voicemail & just go cold without something automatically pulling them back in
Hype, hype and hype... It's the first page of the notebook that I write in tells me.
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Hype around just one piece of the puzzle, LLMs. They just predict the next word while generating... No magic.The better they are trained, the better the output. Super helpful in a lot of contexts...
Tool calling. Helpful, but if LangChain and RAG + LLMs, for example, are not included where appropriate, ta ta, bye bye.
Automation. Someone bringing me a glass of water, for example, isn't something to be automated or waste resources or tokens on...
This brings me back to structural understanding, especially python + machine learning basics where you train a model based on a small piece of dataset you download from hugging face, for example... a lot becomes clear here
I love working with Krea Ai when I am working on marketing and design projects.
