Context: In recent pricing debates in the automation space, a familiar friction around open-core models has resurfaced. The code is public and the community contributes, but billing ends up placing a meter where there used to be autonomy. Teams accept paid features and enterprise support, but they react when the meter comes between them and their own infrastructure.
My stance: In contrast, I built an alternative. WFGY is distributed as a single, very small PDF or TXT file. Attach it to GPT or Claude and you're done. There's no server, no account, no feature gates, no per-execution meter. It's MIT. Even if someone wanted to pay me, they couldn't. That restriction is deliberate and completely eliminates the meter.
Comparison for engineers: Instead of a server application with locked enterprise features, WFGY is a single document. Instead of a hybrid license with commercial tiers, WFGY is MIT end to end. Where others apply per-seat or usage billing on self-hosted infrastructure, here there's no billing surface: a text can't be metered in practice. Typical lock-in vectors like dashboards, hosted features, and support channels disappear because there's no account, no telemetry, and no API dependency. Time to first result stops being deploy, secure, and scale, and becomes about 60 seconds in a new chat. Failure management is described in a Problem Map with 16 reproducible modes and solutions. The cost of heavy use falls solely on the LLM the user is already using. Reproducibility improves because everyone uses the same file, which makes verification easier.
What WFGY is: It's a reasoning overlay based on mathematical formulas for language models, packaged as a tiny document. The document encodes operators for stability, constraint maintenance, multi-track progression, collapse and recovery, and attention buffering. It doesn't replace the model; it shapes it.
Reproduce in about 60 seconds: 1 Open a new chat in GPT or Claude. 2 Upload the MIT PDF or TXT with the WFGY layer. 3 Paste the appropriate instruction asking the model to execute the formulas and procedures in the document rather than just summarizing it. The usual result is a tighter reasoning chain, with fewer detours, better constraint compliance, and an explicit transition to bridging and recovery when the chain gets stuck.
Evidence and tests: GitHub repository with steps and notes at https://github.com/onestardao/WFGY. Problem map with 16 failure modes and fixes at https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md. Formulas used in the overlay at https://github.com/onestardao/WFGY/blob/main/SemanticBlueprint/wfgy_formulas.md. Social proof: over 550 stars on GitHub in about 60 days from a cold start, verifiable in the repository statistics. I'm especially interested in counterexamples; if WFGY doesn't help in your case, share a short trace and I'll map it to the right fix or say it's out of scope.
Why I chose a meter-free artifact: Trust. If the instrument is a file, there's nothing to revoke or throttle. Reproducibility. The same artifact across different teams makes comparisons honest. Alignment. I only win if the math helps you; if not, you close the tab and there are no sunk-cost traps.
Notes on the broader debate: Open source isn't just a license; it's also the placement of restrictions. When the restriction lives on the billing surface in self-hosting, engineers feel it immediately. When the restriction is removed and the artifact is plain text, users retain power regardless of my future decisions. That's the purpose.
License and privacy: MIT. No registration, no tracking, no ads, and no analytics.
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