TL;DR
Thorsten Meyer AI has published Outcome-First Decisions, an open-source framework for portfolio reviews built around keep, change, or kill verdicts. The project is framed as decision support for operators deciding whether ongoing work still earns its cost.
Thorsten Meyer AI has published Outcome-First Decisions, an open-source decision framework that asks operators to judge projects by current outcomes and ongoing costs, then assign one of three verdicts: keep, change, or kill.
The framework centers on what the source material calls the Worth Filter: whether an initiative’s outcome is worth the cost of continuing it. The stated aim is to move portfolio reviews away from sunk cost, prior effort, or identity-based attachment.
According to Thorsten Meyer AI, the framework is available on GitHub under the AGPL-3.0 license. The source describes it as local-first, provider-agnostic, and intended as decision support rather than an automated decision-maker.
The dispatch places Outcome-First Decisions inside a broader operator portfolio and says it completes a decision layer made up of validation, planning, and review. The source material does not provide adoption figures, external evaluations, or usage data.
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 Work Gets Formalized
The release matters because many project portfolios carry work that continues without clear evidence that it still pays for itself. The framework gives teams and solo operators a named process for ending work, changing direction, or continuing with intent.
Its most direct audience is operators managing multiple products, experiments, channels, or internal commitments. For those readers, the practical value is capacity: ending low-return work may free time, budget, and attention for higher-return activity.
The framework’s framing is also a governance statement. It treats stopping a project as a normal verdict rather than a failure, while still requiring review against outcomes and costs. That makes it potentially useful for teams that struggle to make end-of-life decisions.
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Built In Public Portfolio Review
Outcome-First Decisions appears in Thorsten Meyer AI’s Built in Public series as Day 8 of 19. The dispatch describes the product as part of “the operator portfolio” and connects it to a larger set of tools and projects.
The source material describes a decision layer sequence of validate, plan, and review. Outcome-First Decisions is presented as the review step that closes that loop by forcing a verdict on whether existing work should continue.
The project is described as open source and inspectable, with the AGPL-3.0 license attached. The source also states that the framework is provided “as is” and that its verdicts may be wrong.
“The hardest decision isn’t what to start — it’s what to stop.”
— Thorsten Meyer AI dispatch
PROJECT MONITORING AND EVALUATION; TOOLS AND TECHNIQUES
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Adoption Data Still Missing
It is not yet clear how widely the framework is being used, whether outside users have tested it, or how it performs in real portfolio reviews. The source material does not include case studies, independent assessments, or examples from third-party teams.
It is also unclear from the supplied material what inputs the GitHub project requires, how verdicts are generated in practice, or whether the tool includes safeguards against weak or incomplete inputs.
Evidence-Guided Practice
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GitHub Use Will Show Traction
The next test is whether operators use the framework beyond the Built in Public dispatch. GitHub activity, issue reports, forks, and implementation examples would give early signals about whether the keep, change, or kill model is useful outside the author’s own portfolio.
Future updates may also clarify how the framework handles disputed outcomes, uncertain costs, and cases where teams disagree on whether to continue or end an initiative.
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Key Questions
What is Outcome-First Decisions?
Outcome-First Decisions is an open-source framework from Thorsten Meyer AI for reviewing initiatives and assigning one of three verdicts: keep, change, or kill.
What is the Worth Filter?
The Worth Filter asks whether the outcome an initiative is producing is worth the ongoing cost of continuing it. The source says past effort and sunk cost are excluded from that judgment.
Is the framework making decisions automatically?
No. The source describes it as decision support and says users should verify independently before acting.
Is Outcome-First Decisions open source?
Yes. According to the source material, the project is on GitHub and licensed under AGPL-3.0.
What remains unknown about the release?
The supplied material does not show adoption figures, independent testing, detailed usage examples, or evidence of results from outside users.
Source: Thorsten Meyer AI
