Michael Ruescher
I am building Bitter : a software factory for making AI-built software real.
The thesis is the bitter lesson in operational form: general methods that scale with compute matter most, and the next frontier is aligning those methods with goals, tools, evidence, and systems people can actually use.
The short version: frontier agents can now produce surprisingly useful apps and workflows. Turning those into real software still takes prepared repos, wired services, deploys, credentials, verification, product signals, and evidence.
That is the terrain I am working on: the gap between "AI helped me build this" and "real people can use this."
If you have an AI-built prototype, internal workflow, or agent-assisted app stuck between magic demo and real product, I would like to compare notes.
Current Focus
My current focus is the operating layer around capable models: persistent workspaces, tool access, deployed services, verification, logs, memory, and strategic feedback loops that let useful work compound.
I am also building the Bitter runtime around the same pattern: prepared workspaces, agent runs, services, signals, receipts, and next actions in one operating context.