Hey!
I rumbled in front of camera for 9 and a half minutes about bias in hiring caused by third party vendors of automated systems used by many companies that can reject a single candidate from multiple advertised job positions by multiple hiring teams.
https://t.co/jSn8TdhUjk
@PeterDiamandis The bottleneck will be how much intelligence we have put out there. Let's not forget intelligence in this sence is just weights in a tensor tuned by historically recorded intelligence
INSTEAD OF WATCHING NETFLIX TONIGHT.
Spend 1 hour with this.
Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything.
The people who watch this tonight will wake up tomorrow with a new skill.
Watch it and Bookmark it now.
Hi @Airtel_Ug
You installed this Fibre thing more than two months ago at my home. I never received internet. This feels like an expensive and unnecessary art on my wall.
Location
Najjera, Bulabira, Opengates church.
Can I ask for a refund?
In this tutorial, I walk through a complete end-to-end AWS data engineering workflow where we generate synthetic data using Python (Boto3 and Faker), store it in Amazon S3, and then use AWS Glue to automatically discover the schema through a crawler.
https://t.co/cXBCWuIfue
Just read about frustration-driven development. If something keeps annoying you (slow builds, manual work, clunky workflows), it’s probably a system problem. The best engineers don’t live with friction. They remove it. Your frustrations are pointing to your next big improvement.
Without that understanding, it becomes easy to misuse algorithms, misinterpret results, or deploy models that perform well in testing but fail in the real world.
Machine learning isn’t magic, it’s built on statistical principles, optimization techniques, and mathematical foundations that determine when a model works and when it fails.