Notes on data, AI, IT
and security
No marketing fog. The way I think about real problems with founders and managers.
Multimodal models: what is actually useful for business right now
A practical look at AI models that work with text and images together - without the marketing fog.
OpenAI DevDay 2024: what the announcements mean for product teams
A short reading of the October DevDay announcements - real-time API, prompt caching, fine-tuned evals - focused on what changes for teams building on top of OpenAI.
Agentic AI: the first questions a manager should ask
What AI agents are, how they differ from standard LLM tools, and which questions to ask before deploying them.
LLM hallucinations: why they happen and what it costs the business
A practical explanation of why language models confidently state things that are not true - and how to decide whether that risk is acceptable in your case.
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.