Outcome-First Decisions: Keep, Change, or Kill
AIThis post was created with the assistance of artificial intelligence (AI).

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TL;DR

Outcome-First Decisions is a framework that guides organizations to evaluate whether to keep, change, or kill initiatives based on current outcomes. It aims to improve portfolio health by encouraging pruning of underperforming projects.

A new decision framework called Outcome-First Decisions is now available, designed to help organizations evaluate whether ongoing initiatives should be kept, changed, or terminated based solely on their current outcomes.

Outcome-First Decisions is an open-source framework that emphasizes assessing initiatives by their present results rather than past investments or emotional attachment. It introduces the Worth Filter, a mechanism that prompts decision-makers to judge each project by its current outcome and whether continuing is justified by that outcome. The framework produces three verdicts: keep, change, or kill, with a bias toward making kill decisions straightforward. It is provider-agnostic, runs locally, and is licensed under AGPL-3.0, ensuring openness and extensibility. The framework aims to address the common problem of long tail projects that persist without meaningful results, consuming resources and attention. By focusing on outcomes, it encourages pruning and reclaiming capacity, which is often more valuable than generating new initiatives. The process closes the decision loop in portfolio management, providing a disciplined approach to stopping projects that no longer justify their costs. However, experts caution that outcome measurement can be gamed and that emotional resistance remains a challenge, as the framework cannot replace judgment or courage in making tough decisions.
Outcome-First Decisions — Keep, Change, or Kill · Built in Public Day 8/19
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

Why Outcome-First Decisions Reshape Portfolio Management

This framework offers a structured approach to addressing the persistent problem of resource drain from ongoing, underperforming projects. By prioritizing current outcomes over sunk costs and emotional attachment, organizations can improve efficiency, reduce waste, and free capacity for more impactful work. Its open-source nature and local-first design make it accessible and adaptable, potentially influencing how organizations approach portfolio pruning and decision-making at a fundamental level.

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Background on Portfolio Pruning and Decision Frameworks

Traditional portfolio management often struggles with the tendency to keep projects alive due to sunk costs, organizational identity, or effort justification. This leads to a long tail of initiatives that neither succeed nor are actively terminated, draining focus and resources. Existing methods typically focus on starting new projects or optimizing existing ones but lack a disciplined mechanism for stopping them. Outcome-First Decisions addresses this gap by providing a clear, outcome-based evaluation process, inspired by principles of effective pruning and resource reallocation. Its development aligns with ongoing efforts to improve operational discipline and portfolio health in complex organizations.

“Outcome-First Decisions is about stopping the noise and focusing on what truly produces value now. It’s the garbage collection for your portfolio.”

— Thorsten Meyer, creator of the framework

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Limitations and Risks of Outcome-Based Judgments

While the framework promotes objective evaluation, it relies heavily on accurate outcome measurement. There is a risk of mismeasuring or gaming metrics, which could lead to prematurely killing valuable initiatives or keeping underperformers. Additionally, emotional resistance and organizational culture may hinder the adoption of such disciplined pruning, as decision-makers might be reluctant to terminate projects with past investments or personal significance. The framework cannot replace judgment, courage, or the nuanced understanding required for complex decisions, and these human factors remain a significant challenge.

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Next Steps for Adoption and Refinement

Organizations interested in Outcome-First Decisions can access the open-source framework on GitHub, where ongoing updates and community discussions are taking place. Future developments may include tools for more robust outcome measurement, integration with existing portfolio management systems, and case studies demonstrating practical application. Adoption will likely vary depending on organizational culture and willingness to confront difficult decisions, but the framework provides a structured starting point for fostering disciplined pruning practices.

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  • Versatile Pruning Combo: Includes pruning shears and snip for all tasks
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Key Questions

How does Outcome-First Decisions differ from traditional portfolio management?

It emphasizes evaluating ongoing initiatives strictly based on current outcomes and whether they justify continued investment, rather than past effort or sunk costs, promoting more disciplined stopping decisions.

Can this framework be applied to all types of projects?

While designed to be provider-agnostic and flexible, its effectiveness depends on the ability to measure relevant outcomes accurately. It is most useful in environments where outcomes can be clearly defined and tracked.

What are the main challenges in implementing Outcome-First Decisions?

Key challenges include establishing reliable outcome metrics, overcoming emotional and cultural resistance, and maintaining discipline to act on the verdicts rather than delaying tough decisions.

Is the framework suitable for small organizations or only large portfolios?

It can be adapted for organizations of any size, but its benefits are most apparent in larger portfolios where resource allocation and pruning have significant impact.

How does the licensing affect the use of the framework?

The open-source AGPL-3.0 license ensures that the framework remains freely available and that any modifications or extensions also remain open, encouraging community-driven improvement.

Source: ThorstenMeyerAI.com

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