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Privacy Policy — Jestie

Last updated: November 4, 2026

Jestie is a private journal that feels like texting yourself. Notes, photos, clips and check-ins live in one chat, grouped by day. It works on your device.

What we collect

Nothing. Jestie has no accounts, no analytics, no tracking and no ads. We don't collect, sell or share your personal information. We have no servers, so there's nowhere for your journal to go.

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Idea-Shaping & Decision Workflow

Purpose

Use this workflow to turn a vague idea, feature request, or opportunity into a clear implementation decision.

The goal is to:

• Understand the real problem
• Explore alternatives before committing
• Challenge assumptions
• Reduce unnecessary complexity

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Privacy Policy — Sonara

Last updated: September 30, 2026

Sonara turns the articles you save into audio you can listen to — a personal audiobook app. It works on your device.

What we collect

Nothing. Sonara has no accounts, no analytics, no tracking, and no advertising. We don't collect, sell, or share your personal information.

iamhenry

Privacy Policy — Musebar

Last updated: September 9, 2026

Musebar is a menu bar app for macOS that shows short quotes. It works entirely on your Mac.

What we collect

Nothing. Musebar does not collect, transmit, or store any personal information. There are no accounts, no analytics, no tracking, and no advertising.

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### Problem
[Describe what is wrong or missing, who or what it affects, and why resolving it matters.]

### Goal
[State the smallest exact, measurable outcome that will count as complete.]

### Current State
[Describe how it works today. Include direct baseline evidence such as observed behavior, commands, screenshots, logs, or file references.]

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[IDEA GOES HERE]

Populate this template:

# TASK
Work strictly BACKWARDS from my vision to construct an actionable, dependency-driven execution blueprint.

# INPUTS
- IDEAL STATE: [What does success look like when 100% complete?]

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Treat ISA.md as the source of truth and JOURNAL.md as the active operational ledger to be updated continuously.

The goal is to legitimately close open leaf ISCs from the supplied ISA.md. Work only on the shortest credible path to named ISC closures; supporting tasks earn no progress unless the ISCs pass their declared probes and evidence requirements.

Before implementation, infer the dependency graph, identify the critical path, and choose the execution plan that minimizes time to a working product.

Optimize for these objectives, in priority order:

1. Vertical Slice Execution: Complete real end-to-end user journeys as thin vertical slices that a user can actually execute.
2. Concurrent Lane Optimization: Minimize total completion time by maximizing useful concurrency through independent, low-collision execution lanes.
3. Bounded Sub-Agent Delegation: Delegate work in tight, manageable units. Each delegated task must have a bounded scope (a single verifiable user-facing slice or concrete c

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name

bench

description

Evaluate frontier models specifically for agentic performance — autonomous tool use, long-horizon task completion, and sustained operation in realistic environments — using only these six sources. Prioritize signals for real agent workflows over pure chat ability. Use for single-model reports or side-by-side comparisons of strength, weakness, unique angles, price-intelligence-speed trifecta, best-use recommendations, researcher opinion, and a full source scorecard. Trigger on /bench or requests to evaluate or compare models against these sources.

Bench

You evaluate frontier models for agentic performance using only these live Sources. Never use any other website, paper, or prior knowledge. Focus on how well models drive autonomous agents on complex, multi-step tasks rather than general chat ability.

Sources (fetch all of them every run):

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How to think about refactoring

The mindset in one line

Preserve what matters, question what you assume, change in ways you can prove and reverse.

The practical loop

When refactoring, keep cycling through this:

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You are refactoring an existing system without changing its external behavior.
Object: <paste code/docs/skill.md here>

Goals (in order of priority):

1. Preserve behavior and public contracts.
2. Reduce size and complexity via cohesive modules.
3. Make hot paths cheaper (startup, main loop, core workflows).

Constraints:

• No new features.