Across our ~600 engineers adoption of AI coding tools is about 85 percent, and there is an undeniable uptrend in activity as a result. Parking for a moment if there is any value to that increase in activity, the question is about what benefits are there beyond that. We have found a couple of things that have caused us to change the way we do things: - by using AI supported and facilitated team alignment practices as per some of the spec-driven development ideas but separate from the coding parts, we can get teams to traction on a new initiative in about half the time, from about 4 weeks to less than two. That might not sound impressive, but initiatives typically sit around 20 weeks long, so 2 weeks is a ten percent speed up. - by taking pieces of work that are relatively shallow but need to be done in a large number of systems, we have stopped farming those asks out to the teams and started centralising those with the highly leveraged application of AI to make the changes and raise PRs on behalf of teams that they review and merge. This applies to things like resource tagging and new language translation support for new markets. these have worked well and we'll be doing more of it. In a world where 3 to 4 PRs is the norm per developer, those doing this kind of work are doing hundreds.

LLM assisted operations is very interesting. I can but only brainstorm...

Very recently I have been thinking about bash and systems automation because Luna is quite cheap and decent at command line interaction.

Incident automation -> have Luna poll emails and distribute work requests/assignments.

Maybe log those requests and let a larger model analyze them for insights.

We are a small R&D company where everyone is a core contributor so we're stuck in that 1st mode you mentioned...

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