Outcome-First Decisions: Keep, Change, or Kill

TL;DR

Thorsten Meyer AI has published Outcome-First Decisions, an open-source framework for reviewing portfolios by current outcomes and ongoing cost. The framework returns three verdicts: keep, change, or kill.

Thorsten Meyer AI has published Outcome-First Decisions, an AGPL-3.0 open-source framework that helps operators decide whether to keep, change, or kill initiatives based on current outcomes rather than past effort.

The framework centers on what the source calls the Worth Filter: whether an initiative’s current and expected outcome is worth its ongoing cost. The source says the filter ignores sunk cost, effort already spent, and identity-based attachment.

Outcome-First Decisions produces three verdicts. “Keep” applies when the outcome justifies continued support. “Change” applies when the underlying opportunity may still be useful but the current version is not working. “Kill” applies when the outcome no longer justifies the cost.

The project is described as local-first, provider-agnostic, and open source under AGPL-3.0. The source also says the tool is meant as decision support, not as an automated final authority.

Built in Public · Day 8 / 19 ThorstenMeyerAI.com · the operator portfolio
The Decision Layer · Day 08 Dispatch

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.

01 The Worth Filter
The Worth Filter
is the outcome worth the ongoing cost?
judged forward (outcome) — not backward. Ignored: sunk cost · effort spent · identity
✓ Keep
Affiliate cluster A
compounding revenue
Channel E
reach still growing
↻ Change
Product C
right problem, wrong shape
alter deliberately — don’t drift
✕ Kill
Experiment B
flat · high upkeep
Side project D
zero traction · sunk cost
3verdicts: keep · change · kill outcomesthe only input that counts AGPLopen source · local-first
02 Why stopping is the leverage
kill
the verdict everything in human nature avoids — made normal, not a failure.
forward
judge what it will produce next, not what you’ve already spent. Sunk cost is gone either way.
capacity
killing dead work reclaims the focus and capital trapped in it — the cheapest growth there is.
03 The thesis the whole series inherits
01
Local-first
Reviews run on owned compute — cheap enough to run as often as honesty requires.
02
Provider-agnostic
The reasoning isn’t welded to one model. Swap freely; no lock-in.
03
Non-developer build
A small, opinionated framework — AGPL-3.0, open so the method stays inspectable.
04
Edit by subtraction
The whole product is subtraction — killing what no longer earns its place.
04 The operator constellation
18 products · one foundation
Today: Outcome-First lit — the keep/change/kill review that closes the loop. The Decision layer is complete: validate → plan → review.
Content
DojoClaw
RoundupForge
Stenvrik
ChannelHelm
IdeaNavigator
Decision
IdeaClyst
Threlmark
Outcome-First
Platform
Grimfaste
Delvasta
Open / Reg
Glasspane
QAtrial
Markets
Polybot
TradingAgents
Defense / Intel
Argus
VigilSAR
VigilSAR-Bench
Diagnostic
World Model Readiness
Local-first · Provider-agnostic foundation

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.

ThorstenMeyerAI.com · Built in Public · Day 8 of 19 · © 2026 Thorsten Meyer

A Stop Rule for Portfolios

The release matters for founders, operators, and teams managing many projects because it targets a common portfolio problem: initiatives that continue consuming time, money, and attention after they stop producing enough value.

By making “kill” one of the normal verdicts, the framework attempts to reduce the social and emotional friction around ending work. That framing could be useful for teams that add projects more readily than they retire them.

Amazon

portfolio decision support software

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

Part of the Decision Layer

Outcome-First Decisions was published as Day 8 of Thorsten Meyer AI’s 19-day Built in Public series. The source places it inside “the Decision layer,” following earlier tools or concepts described as validate, plan, and review.

The material lists Outcome-First among a wider operator portfolio of 18 products. It describes the framework as the review step that closes a loop across that portfolio.

Amazon

initiative evaluation framework

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

Open Questions on Adoption

The source does not provide user numbers, GitHub activity, independent testing, or examples of outside organizations using the framework. It is also not clear from the provided material how the Worth Filter is implemented in practice or what inputs it requires.

The framework’s business impact is a claim that would depend on how teams apply it, the quality of their inputs, and whether leaders act on the verdicts.

Amazon

outcome-based decision making tools

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

Repository Review Comes Next

The next milestone is likely scrutiny of the public GitHub repository, including its license, documentation, examples, and any issues or pull requests from early users. Readers evaluating the framework should review the repository and test it against real portfolio decisions before relying on its output.

Amazon

project portfolio management software

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

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 do?

The Worth Filter asks whether an initiative’s outcome is worth its ongoing cost. According to the source material, it does not count sunk cost, past effort, or identity-based attachment as reasons to continue.

Is the framework making final decisions automatically?

No. The source describes it as decision support. It says users should verify independently before acting.

What license applies to the project?

The source says Outcome-First Decisions is open source under AGPL-3.0 and provided without warranty.

Source: Thorsten Meyer AI

Wellness content on this site is informational and not a substitute for professional medical guidance.
You May Also Like

The Good List: 6 Things to Bring Delight to Your Day

A new list highlights six everyday items that can bring joy and comfort, offering practical ways to enhance daily well-being.

The unbundling of the budget app. Why a conversational finance surface absorbs what the personal-finance apps charge for, and what survives the absorption.

A new trend in personal finance sees conversational interfaces absorbing traditional app functions, reshaping how users manage money and what features remain exclusive.

The Memory Squeeze: Why Your RAM Bill Doubled

DDR5 prices have jumped to three to six times recent lows as AI demand shifts wafer capacity toward HBM, raising PC build costs.