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qdrant-landing/content/articles/turboquant-quantization.md
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@@ -218,7 +218,7 @@ TurboQuant gives Qdrant a new path on the compression ladder: 8× compression at
**Engineering deep-dives by the team that built this:**
* **[TurboQuant in Qdrant: tricks and ideas behind the implementation](https://turbo-quant-personal-site.pleshkov-ivan.workers.dev/blog/turboquant/)** — by Ivan Pleshkov. Every algorithmic extension explained: renormalization, the reversible LCG + Fisher-Yates Hadamard rotation, anisotropy compensation, P-Square calibration, support for L2 and unnormalized dot. The post walks through each idea against the companion [Python showcase](https://github.com/IvanPleshkov/turboquant-qdrant-showcase) — a readable toy-implementation to see exactly how each piece works in practice.
* **[TurboQuant + RaBitQ: a hybrid approach in Qdrant](https://turbo-quant-personal-site.pleshkov-ivan.workers.dev/blog/turboquant/)** — by [Ivan Pleshkov](https://www.linkedin.com/in/ivan-pleshkov/). Every algorithmic extension explained: renormalization, the reversible LCG + Fisher-Yates Hadamard rotation, anisotropy compensation, P-Square calibration, support for L2 and unnormalized dot. The post walks through each idea against the companion [Python showcase](https://github.com/IvanPleshkov/turboquant-qdrant-showcase) — a readable toy-implementation to see exactly how each piece works in practice.
**Background:**