Blog | Women in AI Prague
Insights from Women in AI Prague meetups, AI workflows and practical community notes.
What came up at the first Women in AI Prague meetup
Six practical ideas from the discussion about AI workflows, tools and how to start without unnecessary pressure.
How AI Project Memory helps me keep track of my own projects
Codex, Claude Code, Fireflies, Git and validation scripts now give me one shared view: what moved forward, what still holds true and where I need to step in.
How to keep project context across Codex, Claude Code and other AI tools
A simple way to store the current state directly with the project so both a person and another AI tool can understand where things stand.
When one PROJECT_MEMORY.md is not enough: how to create a shared view across AI projects
How I turned individual project memories into a shared decision view, and why I separate source checks from AI interpretation.
AI Project Memory under the hood: a technical case study
How I check sources, where I involve the model, what I changed when token usage was too high and which limits remain open.
AI Adoption Does Not Start With a Tool. It Starts With a Work Mode
A practical look at AI adoption in companies: why introducing a tool is not enough and what has to change in briefing, testing and decision-making.
An AI Content System: How Not to Lose Your Voice
How to set up AI for content creation so it helps with outlines, preparation and repurposing without damaging brand voice or reputational context.
Safe AI Adoption Starts With Data Discipline
Why AI adoption is not only about a tool and a prompt. A practical frame for data, access rights, privacy and output review.
What AI Should Do, and What Must Stay With Humans
A practical framework for leaders: what AI can prepare, what humans need to review and who carries responsibility in AI workflows.
Dashboards as a Dialogue With Data
How internal AI dashboards help marketing and business connect data, find patterns and make better decisions.
Trafikante: A Personal AI Filter Against Information Noise
A practical example of an AI agent that filters sources, evaluates relevance and sends briefings based on work context.
Vibe Coding as a Bridge Between Idea and Prototype
A practical look at vibe coding: how it helps product and business teams prepare options, clarify briefs and work better with development.