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Analyzed a taxi trip dataset using Python to uncover how time-based factors — month, day of week, and hour — along with payment method, influence tip earnings. Cleaned and prepared the raw data, then explored and analyzed patterns through visualizations to identify key trends. The analysis found that drivers earn the most tips and orders during 6 AM–6 PM shifts, while early morning (4–6 AM) and late evening (8–10 PM) shifts yield higher tips per trip, giving drivers a data-backed way to plan their schedules
The network for creativity
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AI-powered financial analytics platform built with FastAPI, Pandas, DuckDB, and Gemini API for data analysis, REST APIs, natural-language-to-SQL queries, and automated business insights.
Thinking about moving from Excel or Google Sheets to a CRM? Don’t migrate the problems with the data.
Before choosing or configuring a CRM, I usually check five things:
• Does every active lead have a clear owner?
• Is there always a defined next action?
• Are follow-up dates consistently tracked?
• Are lead statuses clear and used consistently?
• Could another team member understand the spreadsheet without asking for help?
If several answers are “no”, the problem may not be the CRM yet — it may be the workflow behind it.
A CRM works much better when the sales process is clear before migration starts. I created a free CRM Readiness Self-Check for small teams that want to identify these gaps before investing more time or money in a CRM.
If you’re currently working from spreadsheets and considering a CRM, comment “CHECK” or send me a message and I’ll send it to you.
#CRM #CRMReadiness #SalesOperations #RevOps #WorkflowOptimization #GoogleSheets #Excel #BusinessProcess
Most agent bugs are invisible: the agent loops, drops context or calls a tool it shouldn't, and all you see is a wrong answer. I wrote an agent harness from scratch in Python — loop control, context compaction, permissioned tools, sub-agents — and Harness Studio, a web app that replays every run: one lane per agent, each event explained, and the exact context each agent held when it stopped.
