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#Trail

8 pages

5 min read

How do you know an AI isn't making things up?

The usual answer is a promise. Trail gives a different one: it measures it every night, against an answer key — with a control that proves the measurement can tell the difference.

4 min read

When the chatbot has no answer, the question should not disappear

Four routes a question can take with Aidan and our other chatbots — and the one line we do not move: a quote is never sent without a person.

5 min read

From article to podcast without a microphone

Five links, one human — and five bugs we only found by running the whole chain. How our podcast pipeline is put together.

4 min read

Knowledge you don't have to remember to save

Trail has gained two new ways of capturing knowledge: Ambient, which watches along while you work, and Web Clipper, which saves the page you are on — as content, not as a bookmark.

5 min read

Chat With Your Site — Update Content, in Multiple Languages, in One Sentence

"Chat with your site" isn't a future vision — it's the screen that greets you at login. 60+ tools, translation on command, and the same engine available as a headless CMS via REST/MCP.

9 min read

Three architectures of agent memory — and why Trail picked Compile

Karpathy, Tan, and Liu all start from the same diagnosis — your agent is a retriever, not a thinker. They reach three different architectures: retrieve (RAG), compile (LLM Wiki), and act (Fat Skills / GBrain). Trail picked Compile, deliberately.

7 min read

Supervised Fine-Tuning: When Should You Train the Model — and When Should You Not?

Supervised Fine-Tuning has become the new buzzword — but it's one tool out of four. We walk the full staircase from prompt engineering through RAG to SFT, and add our own fourth path: Trail, a compile-at-ingest knowledge engine.

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trail

Trail is your company second brain — a living knowledge engine you can chat with and look things up in.

Hi — I'm Aidan