Enhance pull requests by using Playwright to automatically screen-record a demonstration of the new feature. This video is then attached to the PR, giving code reviewers immediate visual context of the changes, far beyond what static code can show.
Go beyond static prototypes by using text-to-video tools like Flow or Sora to create promotional clips. This final step allows stakeholders to visualize the product in a real-world context and emotionally connect with the user experience, making your pitch significantly more persuasive.
Writing detailed documentation is a task most employees avoid. By recording a quick video walkthrough of a process (e.g., how to pull a report), that video can be shared, referenced, and then automatically transcribed by AI into a structured SOP, eliminating the friction of manual writing.
Go beyond static AI code analysis. After an AI like Codex automatically flags a high-confidence issue in a GitHub pull request, developers can reply directly in a comment, "Hey, Codex, can you fix it?" The agent will then attempt to fix the issue it found.
Solo developers can integrate AI tools like BugBot with GitHub to automatically review pull requests. These specialized AIs are trained to find security vulnerabilities and bugs that a solo builder might miss, providing a crucial safety net and peace of mind.
Use Playwright to give Claude Code control over a browser for testing. The AI can run tests, visually identify bugs, and then immediately access the codebase to fix the issue and re-validate. This creates a powerful, automated QA and debugging loop.
When product marketers create a video walkthrough of the complete customer journey for a campaign—from social post to in-product upgrade—they are forced to test every step. This acts as a forcing function for quality assurance, allowing the team to identify friction points or broken links before launch.
To find tasks ripe for AI automation, simply screen record yourself performing a repetitive, hour-long task. Then, upload the video to a multimodal LLM like Gemini 3 and ask it what parts can be automated and how much time you could save. This provides concrete, actionable suggestions.
You can instruct an AI browser to navigate through your product's user flows page-by-page. The agent will document each step and can even include screenshots, automating what is typically a very manual and time-consuming process for product teams.
Overcome the hurdle of documenting processes by recording a screen-share video of yourself performing a task while talking through the steps. AI tools can then automatically convert the recording into a written playbook, eliminating the need to set aside dedicated writing time.
Product reviews are conducted using live demos that the entire meeting can interact with. Team members can fork the prototype in real-time to build on ideas collaboratively, making reviews a dynamic, creative session rather than a passive presentation.