AI News, Research and Tool Reviews
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Common fine-tuning mistakes that ruin Llama 3.3 enterprise deployments
Avoid deployment failures by managing data quality, quantization precision, and catastrophic forgetting. While Llama 3.3 70B offers cost advantages, its 0.11 completion score in banking highlights significant domain-specific…
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Memory Bandwidth, Not FLOPs, Is What Limits LLM Inference
Accelerators are sold on peak FLOPs, but token generation is bound by how fast weights move out of memory. What to actually…
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How 15 Top LLMs Perform on Classification: Accuracy vs Cost
Classification is the task most often hiding inside a production system, and the one where per-call cost compounds fastest. How to compare…
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How o1 Changes the LLM Training Picture, Part 2: Search, Reward and Cost
What reasoning models borrow from game-playing systems, why the reward signal is the bottleneck outside maths and code, and when the extra…
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How o1 Changes the LLM Training Picture, Part 1: Why Imitation Hits a Ceiling
Pre-train, fine-tune, align. The standard recipe produced remarkable models and could not produce reasoning. Why that limit is structural rather than a…
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How Text Clustering Works: From Embeddings to Labelled Topics
Fifty thousand support tickets nobody has read. The pipeline that turns them into named topics, and why k-means is usually the wrong…
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The Comprehensive Guide to LLM Evaluation
The four kinds of evaluation, which metric fits which task, and how to assemble a suite you will still be running in…
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Embedding-Based Evaluation Metrics for LLM Outputs
Two sentences can share no words and mean the same thing. Embedding metrics close part of that gap – and quietly mislead…