What we learn from the problems we work on
We write about artificial intelligence, mathematics and software, and about what we learn while turning research into systems people use.
When should you use synthetic data?
Synthetic data can help when real data is scarce, sensitive, or unavailable. When is it worth using, and how can you tell whether it improves results?
Read article
How do you prepare data for LLM fine-tuning?
Turn existing data into useful fine-tuning examples: format, chat templates, cleaning, deduplication, redaction, synthetic data, and pre-training checks.
Read article
How to build an AI chatbot with your own data
Connecting documents to a model is the easy part. Learn what a company chatbot needs: source rules, system connections, access control, handover, and testing.
Read article
Self-hosted LLM or API? Rent the generalist, own the specialist
Self-host an LLM or use an API? Use an API for open-ended work. For narrow, repeated tasks, a small model you run can win on cost, privacy, and control.
Read article
How much does an AI agent cost? It depends on what you let it do
AI agent projects can cost $8,000 to $250,000 or more. Estimate how permissions, integrations, exceptions, data readiness, and oversight affect the budget.
Read article
AI token economics: what you pay, what it costs, and who covers the difference
AI tokens keep getting cheaper, yet production workflows can cost more. See how credits, retries, agent loops, and human review change the cost of an AI task.
Read article