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PUBLIC VALIDATION BRIEF

Support token or context-window percentage watchdog thresholds

Evidence observed in mvschwarz/openrig, a Coding project.

8 comments1 positive reactions2 days openProject Radar 92
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DEMAND CONFIDENCE

REPEATED ACROSS 3 PROJECTS

Related friction appears in 3 independent repositories. This is stronger than one backlog item, but still requires direct user validation.

Project strength and demand confidence are measured separately.
SOURCE EVIDENCE

Start with what users actually said

Reporter context: What are you trying to do, and what stops you today? I want a context-usage watchdog to notify an operator at a chosen point in a managed seat's actual context window. Transcript-file size is not a stable substitute for tokens or window percentage: the v0.6.9 watchdog help warns that transcript density varies by…Excerpted from the public Issue. Read the complete thread before interpreting it.

Read original GitHub Issue ↗
CODING VALIDATION LENS

Recruit: Recruit contributors who can replay the problem in a representative repository and development environment.

Guardrail: Use a fixed test case and compare completion, regressions and recovery against the unchanged baseline.

01 · Context & memory boundaries

Write the problem hypothesis

For [user], the agent loses or retrieves [information] incorrectly across [scope], causing [wrong action or repeated work].

You can name one user, one situation and one measurable consequence without proposing a feature.
02 · EVIDENCE INTERVIEW

Interview five developers or engineering leads

  • Which information must persist?
  • For how long and within what scope?
  • Show the last wrong or missing recall.
  • What data must never cross boundaries?
  • How do you correct memory today?
At least three people independently describe the same painful workflow with recent examples.
03 · MINIMUM TEST

Run the smallest experiment

Test one constrained memory scope with a small real dataset and a written expected-recall benchmark.

The system retrieves the right evidence across repeated sessions without leaking or inventing context.Use a fixed test case and compare completion, regressions and recovery against the unchanged baseline.
04 · DECISION GATE

Make a build decision

  • Build: repeated pain and active commitment
  • Narrow: pain is real but the audience or job differs
  • Stop: weak frequency or no behavioral proof
Do not let GitHub engagement replace direct validation.

Why this brief exists

Information has value only when it changes action. This page turns one public signal into a bounded validation exercise. It is a research aid, not proof of demand, investment advice or a product recommendation.