Writing
From the lab
Writing on building, running, and owning AI agents in production.
Jev: a model that answers questions instead of writing text
Most of what an agent runtime does is not generation. It is small judgements, dozens per run, where the answer is a bit or a label. TypeSafe's Jev only does judgement, in parallel, with a probability attached. How it works, how Cruq uses it for semantic guardrails, and what we are building on it next.
Physical AI: the agent in the supervisory loop
Most of the physical world a business runs on is not a robot. It is a fridge, a truck, a pump, a door sensor, already wired with sensors and actuators that nobody watches well. What an agent in that layer has to be, the escalation ladder that makes it safe, and what we measured when we built one for a cold chain.
The latency budget of a voice AI agent
A voice agent has under a second to reply before a call feels broken. That budget is spent across speech-to-text, a language model, and text-to-speech, and the model is usually the bottleneck. A field guide to where the milliseconds go and how to claw them back.
GraphRAG: when a knowledge graph beats vector search
Vector RAG retrieves chunks that look like your question. It falls down on questions that need connecting facts across documents, or summarizing a whole corpus. GraphRAG builds a knowledge graph first. How it works, when it wins, and what it costs.
Top AI agent development companies in Dubai (2026)
Dubai has become one of the fastest markets in the world for agentic AI. A practical guide to the companies building production AI agents in the UAE, what separates a real agent platform from a demo, and how to choose a partner.
Agentic model optimization for the edge
Getting a trained model to run fast on a phone, an NPU, or a microcontroller is a brutal, coupled search across the whole stack. The shift in 2025 and 2026 is handing that search to agents that work from a goal. A field guide to the four capabilities that make it work.
Quality Intelligence: engineering trust into AI features
Shipping AI features that hold up in production takes more than model testing. It takes a Quality Intelligence framework built on reliability, clarity, and measurable excellence.
Why RL environments beat prompt engineering for edge cases
Prompts are instructions. Environments are practice. Here's why the distinction matters when your agent keeps failing on the same class of inputs.
The hidden cost of frontier models in enterprise workflows
Most teams don't realize how much of their API bill comes from a small set of repetitive tasks. We traced the pattern across 12 deployments.
Trace capture without slowing down your agent
Observability shouldn't be an afterthought. Here's our async capture architecture that adds less than 5ms overhead to any LLM call.
What we learned building private SLMs for three different verticals
Finance, legal, and ops all have different failure modes. Here's what we found when we trained domain-specific models for each.
