Blog
Blog
Notes on search visibility, content operations, and ad performance. One piece at a time, we unpack how we find, ship, and improve.
The AI Marketing OS TRAIL Labs Builds: A System, Not Tools
TRAIL Labs builds an AI marketing OS that links finding, creating, and proving. How it differs from juggling tools, and why automation must be a system.
145 posts
Cutting LLM agent costs: delegate decisions to a 4B model
Cutting LLM agent costs: delegating choices to a small decision model saved 81 to 89 percent, but only with thresholds fitted on the agent's own runs.
How ChatGPT search works: the language changed in 4 days
ChatGPT moved its search calls from JSON to a pipe format: five call types, freshness windows, and why AI visibility tracking must keep running.
How to build a small LLM: Sheared-LLaMA's 3% compute path
How to build a small LLM, what LLM pruning is, whether small models are enough for AI agents, and the cost of building one, read through Sheared-LLaMA.
The world model that teaches a robot from video: V-JEPA 2
Whether you can teach a robot from video alone, what an AI world model is, and how it differs from a video generator, read through Meta's V-JEPA 2 paper.
Does AI search use your exact question? 70,000 queries show
How AI search turns a question into queries, why uncited pages are not irrelevant, and what retrievers miss, from Perplexity's Q2D-Web benchmark paper.
Is agent memory better than RAG over the raw conversation?
A 2026 UIUC study of why agent memory systems trail RAG over raw history, what LLM summaries lose, and how a path back to the source cuts the loss.
Why RAG misses the evidence an answer needs
A 2026 MBZUAI study of why RAG retrieves only part of the evidence multi-fact questions need and how chunking erases document identity.
Do AI search sources change with the user's location?
A 2026 University of Toronto audit of how user location changes the sources ChatGPT and Gemini cite, and which channel each engine reads location from.
How AI product recommendations vary across tools and APIs
A 2026 study of how decisively AI chatbots recommend products, which sources they display, and whether API audits represent the consumer screen.
How often do AI answers name people, and what rosters miss
A 2026 study of 2,400 grounded AI calls measures how often AI names individual professionals, and how much a roster-based count misses.