Show HN: ai-tokenizer – 5-7x faster than tiktoken with AI SDK support (coder.github.io)

ai-tokenizer is a new tokenizer tool pitched as a high-performance, drop‑in alternative to tiktoken, claiming 5–7× speed improvements while offering built-in support for popular AI SDKs. The web UI/demo lets you pick a model, paste text, and immediately see token and character counts plus an encoding selection — useful for quick iteration and debugging token budgets. Token counts are labeled as approximations (typically 95–100% accurate) and the project provides an accuracy table to show where slight differences may occur across models or contexts. For the AI/ML community this matters because tokenization is a regular bottleneck in data preprocessing, batching, and real‑time systems; faster tokenizers reduce latency, lower CPU cost, and speed up pipeline throughput. The SDK support suggests easy integration into existing inference and training workflows, and the model-aware encoding handling helps avoid mismatches that can cause off‑by‑one token errors. The main caveat is that counts are approximate and model encodings can differ, so teams should validate the tokenizer against their target model for strict accounting or billing-sensitive use cases. Overall, ai-tokenizer promises a pragmatic performance uplift for practitioners who need faster token counting and encoding in production systems.