Guides for small businesses, implementation examples, and technical articles for people who want to look inside AI, automation, and integration work.
The blog collects longer guides and technical articles. If you are looking for shorter updates, comments, and notes from the process, go to the MorenaTech newsroom.
Articles about automation, Google Workspace, Apps Script, data, and AI written from the perspective of business decisions, everyday work, and first implementations.
How to organize the process from order to dispatch: checking transfers, invoice status, courier label, client email, and manual exception review.
Audience: Small businesses handling orders, payments, and shipping
A practical step-by-step guide: Google Sheet, Apps Script, DRY_RUN mode, test without sending, and a controlled batch of Gmail messages.
Audience: Small businesses testing small outbound batches from Google Sheets
Examples of automatic reports from Google Sheets: sales, deadlines, forms, PDFs, and exception reports for small businesses.
Audience: Small businesses reporting from Google Sheets
A practical checklist before automating Google Sheets: columns, statuses, data formats, identifiers, logs, permissions, and test data.
Audience: Teams working in Google Sheets
A practical comparison of Apps Script and Make for a small business: when to choose Google Workspace scripts, when to choose an integration platform, and how to make the decision.
Audience: Small businesses choosing an automation tool
Five practical uses of Apps Script that reduce manual work, organize recurring processes, and help a small business get time back.
Audience: Small businesses and operations teams
How to organize data, documents, and processes before automation and AI so the tools help instead of adding more disorder.
Audience: Teams preparing data and processes before an AI rollout
Not every small business should start with AI. See when it is better to organize processes and implement straightforward automation first.
Audience: Small businesses planning the first AI implementation
A practical path for introducing AI in a small business: choose one problem, check the data, run a small pilot, keep a human in the loop, and measure the effect.
Audience: Small businesses planning the first AI implementation
A simple method for choosing the first process to automate: repeatability, input data, number of exceptions, effect, and business risk.
Audience: Small businesses choosing the first process
How to plan automatic reminders about payments, tasks, missing data, and deadlines without turning the company into a notification factory.
Audience: Small businesses managing deadlines and tasks
How to organize reminders, payment statuses, receivables reports, and information flow around invoices without implementing a large system.
Audience: Small businesses managing payment deadlines
Not sure what to automate first? See 7 processes a small business should usually start with before thinking about more complex AI projects.
Audience: Small business owners
Texts for people who want to look under the hood of AI solutions: MCP, assistant memory, agents, RAG, architecture, and technical experiments.
A practical guide: extract audio from video with FFmpeg, run it through a local Whisper model, and save the result to a text file with timestamps.
Audience: Technical users and small businesses working with recordings
AI speeds up code changes, but Git lets you inspect the diff, work on a branch, save decisions in commits, and avoid turning the project into a chain of random experiments.
Audience: A small team working with AI on code, scripts, or automation
What a prototype looks like when it searches for companies, organizes contact data, and prepares outreach without scraping Google Maps and without an uncontrolled sending bot.
Audience: Technical users and small business owners
How to connect your own MCP server to ChatGPT and turn it into a technical semi-agent for one workspace. Folder, Python, PowerShell, FastMCP, ngrok installation, smoke test, and common pitfalls.
Audience: Technical users and power users
A context search engine alone is not enough when AI has to work for longer than a few minutes. See why assistants need memory, how it differs from RAG, and why many implementations end in operational amnesia without it.
Audience: Technical users and teams building useful AI assistants
A technical explanation of what MAPI-local-medium is: a local MCP server that lets a language model use project memory, tools, context bootstrap, and a controlled working environment.
Audience: Technical users, power users, and automation builders
