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-# Announcing React Native Evals
-
-## Scaffold
-
-- Working title: Announcing React Native Evals
-- Audience: React Native engineers, engineering managers, and AI tooling teams evaluating coding models on practical mobile tasks.
-- Tone: practical, technical, direct; short sections; concrete claims; end with a clear call to action.
-
-Section plan:
-
-1. What is React Native Evals?
-2. Why we made it
-3. What models and categories are currently benchmarked
-4. What categories are planned
-5. Our methodology
-6. Check out the repository
-
----
-
-## Draft Article
-
-React Native teams are moving fast with AI-assisted coding, but it is still hard to answer a basic question with confidence: **which model actually performs better on real React Native work?**
-
-Today we are open-sourcing **React Native Evals**, a benchmark suite designed to evaluate coding models on practical React Native tasks.
-
-### What is React Native Evals?
-
-React Native Evals is a task-based benchmark for model-generated code.
-
-Each eval is a self-contained task in `evals/<category>/<eval-id>/` with:
-
-- a task prompt (`prompt.md`)
-- judgeable requirements (`requirements.yaml`)
-- a baseline app scaffold (`app/`)
-- a reference implementation (`reference/`)
-
-The repository currently includes **136 evals** across seven category groups:
-
-- `animation` (13)
-- `async-state` (13)
-- `device-permissions` (24)
-- `expo-sdk` (1)
-- `lists` (18)
-- `navigation` (49)
-- `storage` (18)
-
-### Why we made it
-
-We built this benchmark to make model quality discussions less anecdotal and more reproducible.
-
-Most model comparisons in app development are still based on demos, one-off prompts, or generic coding tasks. That does not reflect the edge cases mobile teams hit every day: navigation state correctness, virtualization performance pitfalls, permission handling, offline persistence, and threading constraints in animations.
-
-React Native Evals focuses on those implementation details so teams can compare models on tasks that look closer to production work.
-
-### What models and categories are currently benchmarked?
-
-Based on current repository run artifacts (as of **February 27, 2026**), benchmark runs include:
-
-- `gpt-4.1-mini`
-- `gpt-5.3-codex`
-- `noop` reference baseline mode (used to validate the judging pipeline without solver generation)
-
-Category coverage is currently:
-
-- animation
-- async state
-- device permissions
-- Expo SDK
-- lists and virtualization
-- navigation
-- storage/offline
-
-### What categories are planned?
-
-There is no fixed public roadmap list of future category names in-repo yet.
-
-Current direction is to:
-
-- continue expanding depth and coverage in the existing seven categories
-- add new categories through the documented category workflow (`docs/adding-new-category.md`)
-- prioritize categories with clear, judgeable implementation constraints and strong primary-source API guidance
-
-If you publish this post with a committed roadmap, replace this section with your concrete next category list.
-
-### Our methodology
-
-React Native Evals uses a split pipeline:
-
-1. **Generation stage** (`bun runner/run.ts`)
-
-- discovers evals from `requirements.yaml`
-- runs a solver model against each eval prompt + baseline files
-- writes generated outputs plus a manifest
-
-2. **Judge stage** (`bun runner/judge.ts`)
-
-- reads generated outputs
-- evaluates each declared requirement with an LLM judge
-- writes per-eval results and run summaries
-
-Scoring is requirement-based with optional requirement weights.
-
-At the eval level, passed requirements are normalized into a `scoreRatio`.
-At the run level, `weightedAverageScore` is the mean of successful eval `scoreRatio` values.
-
-This keeps the benchmark transparent: you can inspect prompts, requirements, generated files, and judge outputs directly.
-
-### Check out the repository
-
-If you want to run it, extend it, or benchmark your own model setup, start here:
-
-- Repository: [github.com/callstackincubator/evals](https://github.com/callstackincubator/evals)
-- Methodology whitepaper: `paper/benchmark-methodology-whitepaper.tex`
-- Quick start:
-  - `bun runner/run.ts --model openai/gpt-4.1-mini --output generated/my-generated`
-  - `bun runner/judge.ts --model openai/gpt-5.3-codex --input generated/my-generated`
-
-If you run experiments with it, share your findings and setup details so results are reproducible across teams.