Vibes-checking AI output is the new bottleneck. What actually moved my Claude Code performance: terminal permissions for the agent, a test requested with every feature, and a no-stop rule until they pass.
Prefill is compute-bound, decode is memory-bound, and running both on one GPU wastes most of it. Splitting them into separate pools, the KV-cache transfer tax that comes with it, and when it pays off.
A synthetic dataset can pass every standard check and still lose the correlations, rare events and privacy margin that your model depends on. Here are the three checks I run before shipping one, with the Python for two of them.
My Clippings.txt is an append-only mess, and the expanded highlights inside it will wreck any summary you generate from the raw file. Here is the parser, the deduplication logic and the local Ollama call I use instead.
An ad hoc prompt works once, then quietly stops matching the result you got the first time. Claude Code Skills wrap the workflow in a SKILL.md file, with structured JSON for the data and Python for the parts that have to be deterministic.
On EMNIST Letters, a GMVAE reached 80% accuracy from roughly three labeled samples per cluster, where XGBoost needed 35 times the supervision. Most of that comes down to soft decoding instead of hard cluster assignment.
Google’s TurboQuant compresses the LLM KV cache 5x with near-zero accuracy loss. How PolarQuant’s randomized rotation flattens outliers, and how QJL residual correction keeps the attention dot product unbiased.
Bigger context payloads buy accuracy and charge you latency and tokens. How KNN-based prefiltering with scikit-learn trims an ICL payload without losing predictive accuracy, and whether to run it client side or service side.
AI slop is code that reads fine until it meets real traffic. This is the problem, hypothesis, test, conclusion loop I run on AI written WordPress and WooCommerce work, including a benchmark that settles metadata API versus a custom table.
Retrieval gets worse as the memory pool grows, while the confidence score keeps climbing. Here is what causes RAG memory decay and the four fixes I put into production systems: topic routing, semantic deduplication, relevance based eviction, and lexical reranking.