- Choosing an Embedding Loss Function
MNRL, triplet, CoSENT and batch-triplet losses: what each one optimises, which data it needs, and a playground that fine-tunes a model in your browser.
12 min - The Case for Calibrated Hybrid Retrieval
RRF is a useful default for hybrid search, but it throws away score magnitude. Calibrated retrieval gives us a cleaner path.
9 min - PyLate Late Interaction Retrieval
PyLate makes ColBERT style late interaction practical in Python. Better than bi encoders for precision, costlier to store. Here is when it is worth it.
7 min - Matryoshka Representation Learning
MRL trains embeddings so any prefix of the vector is independently useful. How the multi-scale loss works, which models support it, and how to use it.
10 min - OpenAI Codex Agentic Coding
OpenAI's new Codex runs coding tasks in cloud sandboxes, async and in parallel. Here's what that means for ML engineers.
5 min - Model2Vec Fast Static Text Embeddings
Model2Vec distills a sentence transformer into a fast static lookup table. How it works, where it fits, and where it falls short.
6 min - Claude Code CLI Agent
A month in with Anthropic's research preview CLI agent — what actually works, what costs you, and why the terminal model changes things for ML engineers.
6 min - Fine-Tuning Embeddings with Contrastive Learning and MRL
Contrastive Learning and Matryoshka Representation Learning.
4 min
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