“How do I vectorise this?” It is a reasonable question after Part 1. We know how useful vectors can be. We can compare entities, maintain centroids, discover groups and track change. But where do the coordinates come from when the thing we care about is…
In Part 1, we looked at how to characterise an entity. We chose observations, encoded the relationships we cared about, and used vectors and centroids to summarise what we had seen. That entity could be a shopper, a service or a device. Now we can ask the next…
You buy a kettle, and the shop spends the next fortnight recommending kettles. You browse a birthday present, and suddenly your entire personality is scented candles. Meanwhile, the thing you actually came back to find is three pages down. Recommendation…
This is the next instalment in both the StyloAgent and GEPA article series. It continues The StyloAgent Workflow, Part 2 and Evolving Decision Policies with GEPA and Decision Models, bringing persistent agent coordination and evaluated policy evolution…
In the previous part, we checked whether generated prose contains an answer and whether evidence supports its claims. We kept those checks separate from whether the answer reads well. Now suppose those checks keep finding the same mistakes: assistance becomes…
How do we check that generated prose contains the right information, rather than merely sounding like a good answer? Ask an agent for a refund status when the authoritative tool says pending. “Your refund is complete” answers the question but contradicts the…
lucidRESUME needs to turn one complete career transcript into distinct, credible résumés. Select an applied-AI job: foreground agent architecture, evaluation and tool controls. Select a database job: foreground queries and performance. An early version found…
The first form extracts perfectly. Then the address moves, the label changes, or the next document calls it something else. We add another rule. This series explores how to retain what those exceptions teach us: the structure we want, the evidence that…
In Part 1, I suggested a fairly simple approach to interviewing developers: prepare properly, make the process clear, and let candidates discuss code they understand. Then explore the decisions behind it. I'm now researching how parts of that process could be…
In the first StyloMail article, I used Jev to ask specific questions about email and get typed, probabilistic answers back. Nimble, from Bespoke Labs, brings that technique to a model I can host myself through Ollama. From C#, I can send the same kind of state…