Meet Anca Mihalache, a person who believes real impact lives where purpose meets meaningfulness. With a sharp eye for details and a healthy skepticism,
Stories
Get to know AscentCore through the eyes of our talented team members.
Discover the story of Horia Balc, a Software Engineer who brings the same determination to code as he does to the starting line of a
Discover the story of Laura Marinoiu, an Engineering Manager who sees leadership as an ongoing journey shaped by curiosity, trust, and strong teams. Guided by
At AscentCore, our mentoring program creates space for employees to learn from one another, share perspectives, and improve the way they work. These relationships build
Amalia Drăguș: A Journey of Leadership and Growth from Engineer to Associate Director in Engineering
Discover the inspiring journey of Amalia Drăguș, one of our leaders who balances professional life with personal growth. From leading distributed teams to embracing creativity
In this edition of our Employee Stories, we reconnect with Corina Velea, one of AscentCore’s talented Technical Leads. Corina’s journey reflects an inspiring evolution from
Would you like to see all of your AI initiatives, their workflows, and their tools under one fully auditable and traceable view? Every AI initiative
“The future of enterprise AI will not belong to the smartest agents running the most expensive loops, but to the systems disciplined enough to know
“A word that names everything from a chatbot reply to a swarm of self-directing systems has stopped naming anything at all, and the cost
“The most expensive mistake in enterprise AI is not choosing the wrong model, it is misunderstanding which game you are playing.” What is our AI
An organisation that cannot remember what it decided, or why, is condemned to decide the same things over and over again, each time believing it is the first.
Large language models respond to emotionally charged inputs with contextually appropriate outputs, but the mechanism by which they represent, propagate, and modulate emotional tone through their internal layers remains poorly understood. Do emotions "live" in specific layers? Is the signal carried by the attention mechanism, the MLP, or the residual stream itself? And when a model is instructed to be a "helpful assistant," does its internal representation remain emotionally neutral, or does it mirror the user's emotional state?
Would you like to see all of your AI initiatives, their workflows, and their tools under one fully auditable and traceable view? Every AI initiative
“The future of enterprise AI will not belong to the smartest agents running the most expensive loops, but to the systems disciplined enough to know
“A word that names everything from a chatbot reply to a swarm of self-directing systems has stopped naming anything at all, and the cost
“The most expensive mistake in enterprise AI is not choosing the wrong model, it is misunderstanding which game you are playing.” What is our AI
An organisation that cannot remember what it decided, or why, is condemned to decide the same things over and over again, each time believing it is the first.
Large language models respond to emotionally charged inputs with contextually appropriate outputs, but the mechanism by which they represent, propagate, and modulate emotional tone through their internal layers remains poorly understood. Do emotions "live" in specific layers? Is the signal carried by the attention mechanism, the MLP, or the residual stream itself? And when a model is instructed to be a "helpful assistant," does its internal representation remain emotionally neutral, or does it mirror the user's emotional state?