TildAlice – Tech Blog for Developers
Python, AI, Deep Learning, Data Analysis, Computer Vision, Reinforcement Learning โ tips and tutorials
Latest Posts
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LLM Context Windows: Why 128K Tokens Break at 50K
Discover why LLM context windows fail before their limits and learn proven techniques to maximize token usage in production applications.
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Why Memorizing LeetCode Patterns Won’t Land You the Job
Memorizing LeetCode patterns isn't enoughโlearn why problem-solving skills and adaptability matter more for landing your dream coding job.
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MoE Token Routing: DeepSeek-V3 vs Mixtral Explained
Compare MoE token routing in DeepSeek-V3 and Mixtral architectures. Discover why auxiliary-loss-free load balancing changes everything.
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PPO Training Diverges After 1M Steps: Clipping & LR Fixes
PPO training collapse after 1M steps? Learn how gradient clipping and learning rate schedules prevent policy divergence in deep RL implementations.
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INT8 vs INT4 Quantization: 2x Latency Drop on ARM Cortex-M
INT4 quantization cuts Cortex-M inference latency in half โ but costs 18KB flash, breaks on residual nets, and drops accuracy 4-6% on edge cases.
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Python slots=True: 8x Memory Cut in 10M Dataclass Instances
Cut Python memory usage by 87% with slots=True in dataclasses. Real benchmark: 10 million instances, 8x efficiency gain, zero code complexity added.
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Speculative Decoding: Why 2x Faster Inference Fails
Speculative decoding promises 2x faster LLM inference, but real-world gains often disappoint. Debug the hidden bottlenecks killing your speedup.
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LSTM Encoder-Decoder vs Seq2Seq Transformer: CMAPSS RUL Benchmark
LSTM Encoder-Decoder vs Seq2Seq Transformer on NASA CMAPSS: 18% RMSE improvement on FD004 but LSTM wins on simple data. Full PyTorch benchmark inside.