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Adjust claude cron to real-world inference challenges
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.github/workflows/create-challenge.yml

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Pick the next available challenge number that is NOT already taken by a merged challenge or an open PR. Also avoid creating a challenge on the same topic as any pending PR, even if the number differs.
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THEME — REAL-WORLD INFERENCE KERNELS:
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Focus on challenges inspired by real-world ML inference workloads. Think about the building blocks of modern neural networks (transformers, diffusion models, LLMs, vision models) and the GPU kernels that make them fast. Good examples:
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- Transformer components: multi-head attention, KV-cache updates, rotary positional embeddings (RoPE), RMS normalization, grouped-query attention
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- Inference optimizations: flash attention, paged attention, speculative decoding verification, quantized matmul (INT8/INT4), fused MLP blocks
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- Diffusion model ops: denoising steps, classifier-free guidance fusion, cross-attention
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- Sequence/token operations: top-k/top-p sampling, beam search step, KV-cache rotation, causal masking
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- Model architecture blocks: full transformer decoder block (like the existing GPT-2 challenge), mixture-of-experts routing, SwiGLU/GeGLU activations, LoRA forward pass
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- Serving primitives: batched inference with variable sequence lengths, continuous batching, prefix caching
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Look at `challenges/medium/74_gpt2_block/` as the gold standard for this style of challenge. The solver should implement a meaningful, self-contained inference building block — not a toy operation.
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HARD RULES:
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- Do NOT create trivial element-wise challenges. We have way too many (sigmoid, relu, silu, clipping, etc). If your idea is just "apply f(x) to every element", pick something else.
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- Do NOT duplicate existing challenges — check both the merged challenges in the repo AND the open PRs listed above.

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