ALL STORIES.

Retrieval changes what evidence reaches a model; fine-tuning changes aspects of its learned behaviour.

The central question is where your software should allow a model to choose its next step.

Extra computation is valuable when it improves the decision the application actually needs.

A larger model is not always the most useful place to spend a system’s time and budget.

Model access is only one part of the responsibility you accept when choosing an AI system.

The deployment decision changes who carries the work of running, updating and governing a model.

Choosing Glean or a custom knowledge assistant is a decision about ownership as well as retrieval.

DeepL and a general AI assistant should be compared on meaning, terminology and the surrounding translation workflow.

Ollama and LM Studio offer routes into local-model work, but the model and configuration remain central to the result.

Granola and Fathom can be compared by how their meeting workflows support attention, review and follow-up.

Otter and Fireflies are best compared through the accuracy and usefulness of the meeting record.

n8n and Gumloop should be evaluated on control, visibility and the operating work around an AI process.