Documentation Index
Fetch the complete documentation index at: https://www.aidonow.com/llms.txt
Use this file to discover all available pages before exploring further.
Articles in this section present structured retrospectives and cost analyses with specific metrics. The goal is to produce assessments useful for organizational planning — not to validate AI development universally, but to characterize where it generates measurable return and where it does not.
Metrics & Retrospective
Weeks 1–4 Retrospective
Quantitative assessment of the first four weeks of AI-assisted development: velocity, defect rate, context-window failure modes, and the workflow adjustments that addressed them.
Weeks 5–8 Retrospective
The second retrospective cohort: how the workflow changes from weeks 1–4 performed under increased system complexity and multi-agent coordination requirements.
ROI: Cost vs. Value
A structured cost-benefit analysis of AI tooling investment — API costs, productivity multipliers, defect rates, and the organizational conditions under which the return is positive.
Ollama at Home: Real Cost
The actual infrastructure cost of running local LLMs for development tasks — power consumption, hardware amortization, latency profile, and the use cases where local inference is and is not competitive with API access.