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.
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.
As Tejas Kumar explains, AI isn’t about the tools themselves - it’s about reclaiming time, boosting creativity, and using what’s already at your fingertips in clever, unexpected ways.