Senior Tech Journalist interested in how technology impacts our lives, as well as how programming and a new era of development will shape the future of society.
Because AI agents are probabilistic and can't be trusted on their own, developers need to build layered safeguards around them, and the cost of running them in production deserves as much attention as safety.
NVIDIA’s Igor Dmochowski explained that LLMs are no longer limited just by model size or benchmark scores; the cost of longer reasoning means runtime, context length, memory use, and throughput matter just as much.
As infrastructure gets more complex, "inside the network" is no longer enough. Users, devices, and AI agents need their own identity and only the access they need.
Everyone is busy asking how big the model is, but the real question is what you fed it first - because if your PDFs are mangled and your OCR is sloppy, even the smartest LLM starts from the wrong answer.
We asked engineers how far AI can really go in software delivery, and their answer was simple: it can speed things up, but people still have to make the decisions.
I spoke with four veteran software engineers to explore how they’re approaching long-term career resilience and adapting their skills to stay effective in the field.
As AI tools become part of developers’ everyday workflows, a lot of engineering leaders assume that getting started is just a matter of buying the right software.
Building with LLMs is nothing like traditional software. If we want something that actually works in production, we have to test it, monitor it, and keep iterating on real customer data.
> subscribe shift-mag --latest
Sarcastic headline, but funny enough for engineers to sign up
Get curated content twice a month
Written by people, not robots - at least not yet. May or may not contain traces of sarcasm, but never spam. We value your privacy and if you subscribe, we will use your e-mail address just to send you our marketing newsletter. Check all the details in ShiftMag’s Privacy Notice