标签：#productdesign

An activity log tells us what an agent did. A decision log should also tell us what it considered and rejected. Without rejected options, a later reviewer sees a clean path that never existed: model B was selected, the task restarted, the result succeeded. Missing are the reasons model A was unsuitable, why staying put was worse, and what new evidence would change the choice. That information matters for trust and recovery. It lets people challenge a decision without reconstructing the entire session. Execution history is necessary, but different The MonkeyCode model-switch record at commit c58bcd4 stores the task and user, from/to model IDs, request ID, whether to load the session, success, message, session ID, and timestamps. The switch use case creates that switch record, restarts the task with the target configuration, and records the result. That is valuable execution history. It answers “what switch was requested and what happened?” The expanded rejected-options structure below is my design proposal , not a claim about MonkeyCode's current schema or interface. Add the decision before the outcome A reusable record can separate choice from execution: { "decision_id" : "task-42-model-switch-7" , "context" : "The task needs the required tool-call contract." , "chosen" : { "option" : "model-b" , "reason" : "Passed the declared capability contract" , "evidence" : [ "evaluation/capability-model-b.json" ] }, "rejected" : [ { "option" : "model-a" , "reason" : "Required tool-call case failed" , "evidence" : [ "evaluation/capability-model-a.json" ], "revisit_when" : "Adapter version changes" } ], "execution" : { "request_id" : "req-switch-7" , "result" : "success" , "session_id" : "session-9" } } The key field is revisit_when . “Rejected” should not mean universally bad. It should mean unsuitable under a specific context and evidence set. Design the interface for progressive disclosure Do not paste this JSON into the main task timeline. Use three layers: Timeline: Switch

In Part 1 , we introduced the idea that meaningful product design isn't about adding more — it's about knowing what to remove. Now let's examine this principle through a specific lens: meditation and mindfulness products. The Paradox of "More Mindfulness" Walk through any app store's health & wellness category and you'll find a strange contradiction: apps that promise to reduce your mental clutter by adding more things to your daily routine. Daily meditation streaks Guided breathing exercises (14 varieties) Sleep stories narrated by celebrities Mood tracking with 47 emotion labels Community challenges, leaderboards, badges AI-generated personalized recommendations The message is clear: "To feel less overwhelmed, here are 12 more things to do every day." This isn't just ironic — it's counterproductive. The cognitive load of managing a wellness routine can itself become a source of stress. The Feature Ceiling I've been studying meditation products for the past few months, and a pattern emerges across the market: Product Core Feature Total Features After 2 Years Calm Guided meditation ~40+ (stories, music, masterclasses) Headspace Guided meditation ~35+ (focus music, move, sleep casts) Balance Personalized meditation ~15 (singles, plans, skills) The most interesting case is Balance, which has fewer features but higher per-session engagement. Users spend more time meditating, not more time navigating. This isn't accidental. There's a cognitive principle at work: decision fatigue applies to self-care too. Every additional feature is another decision the user has to make before they can simply be still . What OneZen Gets Right OneZen takes the subtraction principle to its logical endpoint. Instead of asking "What can we add?" the product asks "What can we remove while still delivering value?" The result is a meditation tool that doesn't feel like a tool at all. It feels like breathing room. Three design choices worth studying: 1. No onboarding questionnaire. Most apps ask