Curious whether the biggest value has been performance debugging itself or helping developers understand system behavior they otherwise wouldn't have visibility into.
Sometimes the observability layer ends up being more valuable than the optimization layer.
Curious how many people are finding that context management has become a bigger bottleneck than model quality.
We've experimented with a few different workflows and the biggest failures usually aren't because the model can't code—they happen when the model loses track of project conventions, previous decisions, or why something was built in the first place.
Has anyone found a workflow that solves that well at scale?
Curious what you're seeing from users after the initial setup.
Do most people want a private environment primarily for security reasons, or because they eventually want long-running agents and workflows that they can fully control?
We've found that grounding works reasonably well until the underlying knowledge starts changing frequently, at which point retrieval quality becomes much more of a state-management problem than a search problem.
Are most users treating Tkcore as a knowledge layer for relatively static information, or are you seeing teams use it for fast-changing operational data as well?
Curious whether the biggest value has been performance debugging itself or helping developers understand system behavior they otherwise wouldn't have visibility into.
Sometimes the observability layer ends up being more valuable than the optimization layer.