Show Notes
In this daily update, Parker distills practical, no-nonsense steps for using Cursor with AI-powered workflows—emphasizing planning, context management, and smart tasking over tool spam.
Quick takeaways
- Start with the problem, not the prompt. Define the problem space and validate it before coding.
- Use a Plan-Ask-Plan-Implement cycle to keep work focused and measurable.
- Manage context actively: monitor context window limits, refresh frequently, and document progress.
- Break work into atomic tasks; choose between one-shot vs. multi-step Prompts based on task specificity.
- Build architecture and documentation from PRD outward; diagrams help align everyone.
Problem-first mindset
- Understand the problem you’re solving before you touch code.
- Narrow the scope to the core need and why it matters.
- Validate the problem with a customer or yourself as the first user.
Plan-Ask-Plan-Implement workflow
- Plan in “Ask mode” to surface questions and define scope.
- Move to an agent for implementation once the plan is solid.
- Tool notes: use 03 for Ask mode and Gemini 25 Pro for the Agent.
Context management and memory
- Context windows affect quality: plan for failure as you push tokens toward the max.
- Simple rule: refresh context before it gets too large; rely on docs or a memory store for quick context access.
- Don’t try to jam 50 screens into one shot; break into manageable chunks.
Tasking strategy
- Prefer atomic tasks; one well-scoped task is often better than a long, multi-part prompt.
- If tasks are vague, Taskmaster helps turn a good PRD into concrete tasks, but raw, precise tasks can outperform Taskmaster outputs.
- Balance single-prompt efficiency with sensible task decomposition.
Architecture and documentation first
- Don’t architecture before you narrow the problem and finalize the PRD.
- Use diagrams (Mermaid) to visualize how components fit together.
- Example tech stack references: GCP, Tanstack, Supabase, Flask.
Patterns, rules, and tests
- Establish rules for data fetching, logging, and error handling.
- Document testing strategies appropriate to project size; aim for cross-environment reliability.
- Maintain a master reference like a seed SQL and clear type definitions to keep the codebase consistent.
Tools and workflow cautions
- Don’t chase every new tool; pick the ones that genuinely shrink your cycle time.
- UI/UX consistency and reusable patterns (routing, navigation, error handling) reduce cognitive load.
- Browser automation tooling (e.g., Puppeteer) should be leveraged with clear, repeatable patterns.
YouTube automation and market notes (concise)
- YouTube automation trends are hot, with creators tooling up around AI-generated content; strong numbers in some niches show the potential but require taste and quality.
- Market bets on model leaders are active; Google is a common expectation for top performance by end of May.
Community, offers, and next steps
- There are ongoing community offers and discounts; Parker is building an AI-first SaaS product and growing a supportive community around it.
- Actionable next step: map your current workflow to a Plan-Ask-Plan-Implement loop, create a PRD for your next feature, and start documenting architecture early.
Takeaways you can apply this week
- Write a one-paragraph PRD for your next feature and validate the problem with a real user (or yourself).
- Practice Plan-Ask-Plan-Implement with your AI tools this week; assign “Ask mode” to define scope, “Agent” to implement.
- Audit your context management: identify where you’re hitting token limits and set up a simple docs/memory approach.
- Break down a current task into atomic steps and compare a single comprehensive prompt vs. a sequence of focused prompts to see which is faster and more reliable.
Links
- Cursor
- Supermaven
- Gemini
- Mermaid
- Task Master
- ElevenLabs
- Imagen (Google's text-to-image AI)
- Google Cloud Platform (GCP), TanStack, Supabase, Flask