TL;DR
Thorsten Meyer AI published Outcome-First Decisions, an open-source framework for deciding whether initiatives should be kept, changed, or ended. The framework centers on a Worth Filter that weighs current and expected outcomes against ongoing cost, while excluding sunk cost and effort already spent.
Thorsten Meyer AI has published Outcome-First Decisions, an AGPL-3.0 open-source framework that gives operators a structured way to decide whether to keep, change, or kill projects based on the outcomes they are producing now and the cost of continuing them.
The framework is built around what the source calls the Worth Filter: a forward-looking review that asks whether an initiative’s current or expected outcome is worth its ongoing cost. According to Thorsten Meyer AI, the filter excludes sunk cost, effort already spent, and identity attachment from the decision.
Outcome-First Decisions returns three possible verdicts. A project can be kept if its outcome justifies the cost, changed if the underlying opportunity still appears useful but the current form is not working, or killed if the outcome does not justify further upkeep.
The source describes the framework as local-first, provider-agnostic, and open source under AGPL-3.0. It is presented as a decision-support tool, not an automated authority; the published disclaimer says the framework’s verdicts may be wrong and should be independently verified before action is taken.
Outcome-First Decisions — keep, change, or kill
The hardest decision isn’t what to start — it’s what to stop. Judge every initiative by the outcome it produces now, not the effort already spent.
Independent commentary, produced with AI assistance under human editorial oversight. The views are the author’s own and may change. Outcome-First Decisions is open source under AGPL-3.0, provided “as is” without warranty; see the repository LICENSE. The framework’s verdicts are reasoning aids based on the inputs given and may be wrong — decision support, not decisions; verify independently before acting. Product and company names are trademarks of their respective owners; mention does not imply endorsement.
A New Stop Rule
The release matters because portfolio work often rewards launches while leaving weak projects alive. Thorsten Meyer AI argues that underperforming initiatives consume attention, maintenance time, and capital even when their cost is not visible as a direct line item.
For operators managing several products or experiments, the framework’s main value is a repeatable stop rule. Its core claim is that ending low-return work can free capacity for stronger bets without requiring a new launch or new funding.
That claim remains the publisher’s thesis, not an independently measured result. The source material does not provide usage data, case studies, or performance outcomes showing how teams have applied the framework in practice.
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Built In Public Day Eight
Outcome-First Decisions was published as Day 8 of Thorsten Meyer AI’s 19-part Built in Public series. The source places it inside an operator portfolio that includes 18 products and a shared local-first, provider-agnostic foundation.
The dispatch says the “decision layer” is now complete, with a sequence described as validate, plan, and review. Outcome-First Decisions is positioned as the review layer that closes that loop by forcing a keep, change, or kill verdict.
The publication also frames the tool as part of a broader build approach centered on subtraction. The stated method is to remove work that no longer earns its place rather than keep adding new projects to an already crowded portfolio.
“The hardest decision isn’t what to start — it’s what to stop.”
— Thorsten Meyer AI
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Adoption Data Is Missing
It is not yet clear how many users or teams have adopted Outcome-First Decisions, how the GitHub project is being maintained, or whether the framework has been tested outside the publisher’s own portfolio.
The source material also does not state an exact publication date, release version, repository activity level, or roadmap. Those details would affect how readers assess the maturity of the tool.
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Repository Activity To Watch
The next markers are likely to be GitHub activity, public examples, and any follow-up posts in the remaining Built in Public series. Readers evaluating the tool should look for implementation details, maintenance signals, and evidence of use beyond the initial dispatch.
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Key Questions
What is Outcome-First Decisions?
It is an open-source framework from Thorsten Meyer AI for reviewing initiatives and assigning one of three verdicts: keep, change, or kill.
What does the Worth Filter measure?
According to the source, it asks whether the outcome an initiative is producing now, or is likely to produce next, is worth the cost of continuing it.
Does the framework make final decisions automatically?
No. The publisher describes its verdicts as reasoning aids that may be wrong and should be checked independently before action is taken.
What license applies to the project?
The source says Outcome-First Decisions is open source under the AGPL-3.0 license and provided “as is” without warranty.
Source: Thorsten Meyer AI
