The author highlights that effective project estimates in IT should clearly define the scope of work to allow clients to compare proposals accurately. A standard estimate typically includes tasks, costs, hours, specialists, and hourly rates.
For larger projects, simple tables become insufficient. The author suggests incorporating project stages, executor roles, and implementation plans. However, he warns against over-detailing, noting that estimates exceeding a thousand lines often obscure project clarity.
Project timelines in commercial proposals should be treated as plans rather than absolute deadlines, as they must account for team workload, vacations, and approval processes. Gantt charts are recommended as a formal tool for visualizing these timelines.
In a separate observation, the author compares AI models based on the 'Cost per Task' metric. He notes that the GPT-5.6 Luna (max) model costs $0.18 per task, while the GPT-6 Astra (max) model costs $3.26, with an Artificial Analysis Intelligence Index of 38 and 53 respectively.
