Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
EU AI Act is in force: what providers and deployers need to do now
A practical breakdown of the first obligations under the European AI regulation for those building or deploying AI systems.
RAG in production: why a large context window does not solve the problem
Why RAG architecture often disappoints in production, and where the real bottleneck sits.
GPT-4o and the normalisation of real-time multimodal UX
What the GPT-4o announcement means for companies designing AI-powered interfaces: voice, vision, and text in a single stream is becoming a standard expectation.
RAG vs fine-tuning: the decision a manager actually needs to make
A practical framework for choosing between RAG and fine-tuning when applying AI to business processes - without unnecessary technical detail.
NVIDIA Blackwell and the economics of the next inference wave
What the Blackwell architecture announcement means for companies planning or already running AI systems in production: on cost, availability, and strategic decisions.
LLM context windows: what the limit means for business applications
Why the context window constraint in language models is not a technical footnote but an architectural decision that determines what can actually be built.
AI in 2023: what actually changed and what is still open
A mid-November account of what the year delivered in practical terms - not a hype recap but an honest read of where things moved and where the gaps remain.
DevDay, long context, and the tooling shift toward LLM production systems
What OpenAI's DevDay announcements mean for companies thinking about moving from LLM pilots to working production systems.
LLM operational economics: how to model costs before you scale
Why token costs for language models need to be modelled in advance, and how to avoid an unexpected invoice when load grows.
Fine-tuning GPT-3.5: when it makes sense and when it does not
OpenAI opened fine-tuning for GPT-3.5 Turbo in August 2023. Here is a practical read on the use cases where it delivers and the ones where prompt engineering is still the right call.
Llama 2 and open weights: what it means for enterprise
A look at why larger organisations should pay attention to open-weight language models, and where the real boundary between opportunity and illusion lies.
From pilot to product: the gap that breaks AI projects
A language model works beautifully in a demo - and falls apart in real use. I look at where the gap is and how to bridge it.