TL;DR
Thorsten Meyer AI has published Outcome-First Decisions, an open-source framework for reviewing initiatives by present outcomes and ongoing cost. The project centers on a Worth Filter that returns three verdicts: keep, change, or kill.
Thorsten Meyer AI has released Outcome-First Decisions, an open-source framework meant to help operators decide whether to keep, change, or kill ongoing initiatives based on current outcomes and continuing cost.
The framework, described by Thorsten Meyer AI as part of its Built in Public series, uses a mechanism called the Worth Filter. The filter asks whether an initiative’s outcome is worth its ongoing cost, while excluding sunk cost, effort already spent, and identity from the decision.
According to the source material, Outcome-First Decisions returns one of three verdicts. “Keep” applies when an initiative’s result justifies the cost of continuing. “Change” applies when the problem or opportunity still appears valid but the current form is not working. “Kill” applies when the outcome no longer justifies the cost.
The project is described as open source under the AGPL-3.0 license and local-first. The source also states that the framework is provider-agnostic, meaning its reasoning layer is not tied to a single AI model provider.
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.
Stopping Gets a Formal Process
The release matters because portfolio work often rewards starting new projects more than ending weak ones. Thorsten Meyer AI frames the framework as a way to make stopping a normal verdict rather than a failure.
For operators managing many products, tools, channels, or experiments, the practical effect is capacity. A project that remains alive without strong results can consume maintenance time, attention, and money. The framework’s stated aim is to make that cost visible and force a forward-looking decision.
The tool does not claim to make decisions on behalf of users. The source describes the verdicts as reasoning aids based on the inputs given, and says users should verify independently before acting.
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Part of a Decision Layer
Outcome-First Decisions appears as Day 8 of Thorsten Meyer AI’s 19-day Built in Public sequence. The source places it inside a broader operator portfolio and describes it as the review component that closes a decision loop.
The source says the broader decision layer follows a sequence of validate, plan, and review. Outcome-First Decisions occupies the review step by judging whether existing initiatives still earn their place.
The dispatch names examples such as affiliate clusters, channels, products, experiments, and side projects. These examples are presented as portfolio review categories, not as audited performance claims.
“The hardest decision isn’t what to start — it’s what to stop.”
— Thorsten Meyer AI
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Adoption and Accuracy Still Open
It is not yet clear how widely the framework will be used outside Thorsten Meyer AI’s own portfolio. The source material does not provide user numbers, repository activity, third-party audits, or examples of independent teams applying the method.
The quality of each verdict also depends on the inputs supplied. The source itself says the framework’s outputs may be wrong and should be treated as decision support, not final decisions.
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Repository Use Comes Next
The next step is practical use through the open-source repository and future entries in the Built in Public sequence. Readers evaluating the framework can inspect the AGPL-3.0 project, test it against live initiatives, and compare its verdicts with their own review process.
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Key Questions
What is Outcome-First Decisions?
Outcome-First Decisions is a framework from Thorsten Meyer AI for reviewing initiatives and assigning one of three verdicts: keep, change, or kill.
What does the Worth Filter do?
The Worth Filter asks whether the outcome an initiative is producing now is worth the cost of continuing it. It excludes sunk cost and past effort from the decision.
Is this a finished commercial product?
The source describes it as an open-source AGPL-3.0 framework. It does not provide commercial adoption figures or customer data.
Does the framework make final decisions?
No. The source describes its verdicts as decision support based on the inputs given, and says users should verify independently before acting.
Source: Thorsten Meyer AI
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