On May 15 2026, xAI published xai-org/x-algorithm — a Rust-first rewrite of X's recommendation system with a Grok-1 transformer at the core. We ran the release past GPT-5, Gemini 2.5 Pro, and Grok 4, then layered our own analysis. Below: what they all agreed on, where each one saw something different, and what it means for anyone selling X engagement data.
The repo ships five working modules plus a Rust trait framework. Models, training, and serving infra are not included — and that absence is the tell.
We sent the same prompt to all three. These five points came back from every one of them, in different words but identical substance. That convergence is the signal.
Same prompt, same repo, three different angles. Worth noticing what each chose to elevate — and where they all stayed quiet.
None of the three frontier models touched the political / moderation-policy context — verified-account amplification, content-moderation rollback under Musk, hate-speech policy changes since 2024. All three stayed in pure ML/engineering critique. Either alignment training is making them shy on contested topics, or those features simply are not in this release. Worth knowing which.
Beyond what the frontier LLMs converged on, four insights that change how to interpret the release.
They took Grok-1's transformer and used it as the recommendation ranker's backbone. The marginal cost of scaling Grok now improves both the LLM and the X feed. This is the same compute-flywheel play TPUs gave Google and PyTorch gave Meta. If it works, "Grok improves → feed improves" becomes a structural advantage.
The 256-dim, 2-layer model is a runnable demo to silence "where's the code." The actual production system runs on weights that are not in the repo. You can inspect the architecture but you cannot validate any behavioral claim against the real model. Transparency theatre, not transparency.
Independent candidate scores mean you cannot game ranking through batch composition — no faking co-occurrence patterns to surface low-signal content. Quietly the most underrated piece of the design. Twitter has historically struggled with this exact attack class.
Multi-action prediction with explicit negative weights for block/mute/report tells you exactly what the algorithm optimizes for and against. For anyone selling X engagement data, this rewrites how to score "quality" engagement vs raw volume. Suppression detection becomes possible.
Three direct implications for LunarCrush — and any institutional buyer of X social engagement signals.