Python development
Python development for automation, APIs, and tools.
I use Python for backend features, scripts, internal tools, data tasks, and automation systems that save time and reduce repetitive work.
Best fit projects
APIs, scripts, workflow automation, internal dashboards, scraping, and utility tools for founders and small teams.
Start with the repetitive problem
The useful starting point is usually a frustrating manual task: copying information between tools, preparing a report, checking an inbox, or making the same API request many times. I break that task into inputs, rules, output, and failure cases before deciding whether the right answer is a script, an API, a scheduled job, or a small internal tool.
Python beyond quick scripts
Python is a good fit for automation, data processing, web scraping where it is appropriate and permitted, API integrations, and backend features. A short script can be valuable, but production work also needs configuration, logging, secure credentials, and a clear way to recover when an external service fails. That is where disciplined backend work makes the automation dependable.
How it connects to your stack
I often use Python alongside Django, PostgreSQL, Redis, and n8n. n8n can orchestrate a business process, while Python handles custom rules or data transformation that need more control. The result is a system that is practical for the team using it, rather than a workflow that only makes sense to its original builder.
What I focus on before delivery
I test the normal path and the awkward cases: missing values, duplicate records, rate limits, and temporary API failures. I also document the required accounts, environment variables, and expected output. Small details like these decide whether an internal tool saves time every week or becomes another job to maintain.