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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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Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
🔗 Verify Certificate:
Validation Number: 74c5808986a64b84addcaa9db725b8bd
Verification Link: https://aws.amazon.com/verification
#AWS #MachineLearning #AI #CloudComputing #DataAnalysis #MLOps
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.
What if there were a festival where the opulence of the Roaring Twenties met the vibrant spirit of Woodstock?
GREATSTOCK is a fictional festival happening today, named for the two legendary parties it borrows from: the Jazz Age glamour of The Great Gatsby and the camaraderie of Woodstock in 1969. I found its look in Gatsby’s era, in George Barbier’s fashion plates from Falbalas et Fanfreluches, the Gazette du Bon Ton, and Le Bonheur du Jour (1913 to 1924), now in the Rijksmuseum’s public domain collection. I built a Recraft Style from ten of those plates and used it to tell the festival in ten scenes, from the first poster to the morning after. Barbier supplies the 1920s, the guests bring 1969 in crochet, tie-dye, and flares, and today arrives as QR code charm bracelets in place of wristbands, drone shows, and glowing silent-disco headphones drawn as ornament.
Camaraderie, Celebration, Champagne.
Link to Recraft Project: https://www.recraft.ai/project/f9c7da6e-4fe9-41a3-b5f6-b709d3fd9949
Link to Recraft Style: https://www.recraft.ai/styles/17e9d70d-6b1a-4510-b0a4-8640696ede02
That fusion of 1920s Art Deco elegance with Woodstock's free-spirited vibe is brilliant, Kelsey! The poster design and editorial layouts capture that timeless glamor perfectly.
Building software with AI requires more than a good prompt.
I've been developing my own Autonomous Engineering Harness to structure how I build software with AI.
The idea is simple: instead of relying on AI to generate code one task at a time, I wanted a more structured process that connects specifications, task planning, implementation, code review, and QA.
The harness organizes the workflow into clear stages:
- Specifications: define what needs to be built.
- Task planning: break requirements into manageable units.
- Implementation: coordinate AI-assisted development.
- Review: check the result against the intended requirements.
-QA: validate the software before considering the work complete.
The goal isn't to make AI autonomous for the sake of it. It's to make AI-assisted engineering more structured, repeatable, and easier to validate.
I'm applying this approach to my own projects and continuing to refine the framework as I learn what works in practice.
I wrote more about the approach, the engineering decisions behind it, and how the framework works on my website.
Read the full story: https://yitsymosqueda.com/autonomous-engineering
I'd carry acceptance criteria from the spec into QA as explicit checks, with a result attached to each one. When the spec changes mid-build, does the workflow flag which completed tasks and QA results need to be revisited?