Bring a language, a constraint, or a hard case.

Alefba needs more than code contributions. It needs difficult source material, independent evaluation, linguistic judgment, threat models, and reviewers willing to preserve uncertainty.

THE PROPOSITION

The most valuable contribution is a reproducible case where meaning changes, disappears, conflicts, or becomes unsafe during machine translation.

06.1

Six contribution surfaces

Choose the layer where you can produce inspectable evidence.

Specification contributors refine types, invariants, examples, and migration rules. Codec contributors implement target profiles without contaminating canonical fields.

Language and culture contributors test whether the representation preserves distinctions that English-first datasets routinely erase.

APIR schemas and conformance fixtures
Provider and harness profiles
AlefBench tasks and paired baselines
Language, culture, and legal-context reviews
Security and privacy threat models
Documentation, governance, and release review

06.2

Start with a work packet

A useful proposal is bounded enough to reproduce and difficult enough to teach the architecture something.

Name the source context, the critical commitments, the target surface, the expected failure mode, the evidence required, and the data restrictions.

Declare what would falsify the proposal. A contribution is stronger when its rejection criteria are visible before implementation begins.

01CASE
02BOUNDARY
03FIXTURE
04BASELINE
05EVIDENCE
06REVIEW

06.3

The review gate

Public compatibility and performance claims require more than a successful demo.

Schema changes need examples, tests, version notes, and migration impact. Benchmark changes need paired results, total compiler cost, and reproducible artifacts.

Critical-loss cases, provenance gaps, credential exposure, or cross-tenant paths stop the release until the boundary is restored.

Implementation and documentation agree.
Evidence identifies its exact scope.
Unsupported cases remain explicit.
Rollback and migration paths are documented.

06.4

Connected experiments

Alefba remains the semantic layer while adjacent projects explore identity, education, cultural memory, and governed execution.

These links are neighboring initiatives, not mandatory dependencies or proof of Alefba conformance.

Use the founder channel to propose a bounded work packet and identify the evidence you can contribute.

samiei.aiFounder, research direction, and collaboration context.orcest.aiOptional governance and orchestration research.gs1ai.onlineTrusted identification research for AI-era systems.alefgpt.irExecutive education from foundations to fluency.irani.fyiLanguage, memory, and cultural-interface experiments.