Stack decisions,
compared honestly.
Framework and tooling comparisons written from production experience rather than documentation summaries — including the cases where the popular choice is the wrong one.
RAG vs Fine-Tuning
RAG supplies knowledge; fine-tuning changes behaviour. A practical decision guide, including why the answer is often both and rarely fine-tuning first.
PyTorch vs TensorFlow
PyTorch won research and most of production. Where TensorFlow still makes sense, what actually differs now, and why the choice matters less than it did.
pgvector vs Dedicated Vector DB
When Postgres with pgvector is enough and when a purpose-built vector database earns its complexity. Scale thresholds, filtering, and operational reality.
XGBoost vs Neural Networks
On tabular business data gradient-boosted trees usually beat deep learning. Why that is, when neural networks win, and how to choose without wasting months.
Batch Inference vs Real-Time Inference
Real-time serving costs far more per prediction than batch scoring. How to tell which your feature actually needs, and the hybrid pattern that usually wins.
Open-Weights LLMs vs Proprietary LLM APIs
Hosted APIs win on capability and speed to market; open-weights models win on data control and unit cost at volume. A practical decision framework.
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intelligent.
Tell us what you're trying to build — we'll tell you honestly whether AI is the right tool, and what it would take.
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