Data science as engineering, not a science project
Data science still gets treated as an academic exercise. Engineering constraints, caching, timeouts and safe fallbacks are what turn a model into something you can actually run on a client site.
Category: AI
Filter Pandas DataFrames: cleaner ways than boolean masks
Nested boolean masks are hard to read and harder to debug. Cleaner ways to filter Pandas DataFrames with .query(), .isin(), .between() and the .str accessor.
Vector search optimization: flatten your JSON before embedding
Raw JSON in an embedding model spends your token budget on braces and quotes. Flattening product data into plain sentences gives the model the kind of text it was trained to read.
ML in production: what shipping a model actually takes
A notebook demo is not production. This is what sits between the two: an API contract, a container, infrastructure that suits the workload, and monitoring that catches drift before the predictions quietly go bad.
How convolutional neural networks learn musical similarity
Metadata filters only take music discovery so far. How CNNs read audio as Mel-spectrograms, how contrastive learning with InfoNCE loss shapes the embeddings, and why the inference belongs outside WordPress.
Modeling urban walking risk with H3 hexagons and XGBoost
Fastest-route logic says nothing about what a walk is actually like. How the StreetSense project models urban walking risk with Uber’s H3 indexing, sine and cosine time encoding, and XGBoost under a Tweedie objective.
Distributed training data transfer: fixing the multi-GPU stall
Multi-GPU training usually stalls on communication, not compute. What nvidia-smi topo and Nsight show you about NCCL overhead, and the DDP settings I change: static graph, bucket views, PowerSGD compression and smaller buckets.
Neural machine translation when the data barely exists
Dongxiang has almost no parallel data, so we fine-tuned Meta’s NLLB-200 instead of prompting a general model. Cleaning the bilingual corpus, checking subword fertility, registering a new language tag, and why Adafactor beat AdamW on one GPU.
WordPress AI core: what has to land before 7.0 Beta 1
Notes from the AI contributor meeting: AI Experiments 0.2.0 is out, the Core Post Management abilities PR is under review, PHP AI Client v0.4.0 shipped, and the MCP Adapter stays a canonical plugin. February 19 is the Beta 1 cutoff.
WordPress AI Client: what changed on the road to 7.0
The WordPress AI Client is being lined up for a core inclusion proposal. January 2026 contributor notes cover streaming leaving the PHP client, a kill switch filter for locked-down sites, and MCP for tool discovery.