thinkerflake

“Clear thought precedes powerful action.” - ChatGPT

About

Thinkerflake is a thinking space for power users organizing themselves or a team. It guides you through a structured, reasoning-driven process to turn ideas into actionable outcomes. It begins by capturing all relevant knowledge — notes, data, and references — organized in a branching mind map. From there, you formally model the problem, defining goals, constraints, and dependencies using logical structures. This reasoning foundation allows you to derive a precise, step-by-step solution workflow, automatically translating logic into actionable tasks. Finally, after executing the solution, you reflect on the outcome by producing informal feedback notes — preserving insights, lessons, and context for future projects. This cycle ensures that every decision is reasoned, every action is intentional, and every experience adds to a growing body of usable knowledge.

The Process

Knowledge -> Problem -> Solution -> Feedback:

1. Capture Knowledge: Collect notes, source materials, and technical data for a project or problem. Keep informal notes within a unique branching mind map.
2. Model the Problem: Use formal reasoning structures to map out goals, constraints, and dependencies. Formally model decisions and plans with structured logic, not intuition alone.
3. Derive a Solution: Deduce a clear, actionable solution workflow directly from the reasoning model. Automatically generate tasks and sequences based on the logical problem setup.
4. Produce Feedback: Create informal notes describing the solution to the problem. Keep the solution notes for the future reference.

Who It’s For

Thinkerflake is meant to be used by power users to help with note keeping and decision making processes, especially for:

• Systems thinkers solving technical problems
• Team leads managing employee workflow coordination
• Knowledge ops leads managing decision-heavy projects

Example Use Cases

We choose to enumerate three representative use examples.

1. Architect a Modular Software System

• Capture Knowledge: gather system requirements, API specs, and tech constraints.
• Model the Problem: logically connect module dependencies, risk areas, and interface protocols.
• Derive a Solution: auto-generate a development plan and testing milestones derived from the reasoning tree.
• Produce Feedback: record notes on implementation outcomes, challenges faced, and lessons for future builds.

2. Coordinate a Complex, Multi-Phase Team Project

• Capture Knowledge: gather tasks, deadlines, team capacity, constraints, and notes on risks or clients.
• Model the Problem: map task dependencies, bottlenecks, and align work with skills and schedules.
• Derive a Solution: auto-generate a phased plan with priorities, conflicts, and decision points.
• Produce Feedback: document project outcomes, workflow issues, and team insights for future improvements.

3. Decide Between Competing Product Ideas

• Capture Knowledge: list market data, feature ideas, and constraints.
• Model the Problem: reason through feasibility, market need, and risk tradeoffs.
• Derive a Solution: generate a roadmap for the selected concept and a revisit plan for the others.
• Produce Feedback: note market reactions, performance data, and decision hindsight for future product cycles.