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Tag: AI

AI, Development

What the data science career path actually looks like

The data science career path in 2026 asks for more than Python. Notebook work is the easy half; the rest is MLOps, domain knowledge, and keeping your own judgment while agents handle the tedious parts.

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AI, Development

Career advice for the AI era: what gets juniors hired now

AI has absorbed the task-level work, and juniors who sell themselves as code writers are getting ghosted. After 14 years in WordPress development, here is what I look for instead: work you actually owned, and a repo that shows a human debugged it.

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AI, Development

Intent-based chaos engineering for WordPress resilience

Most WordPress resilience testing is refreshing the checkout page and hoping. Intent-based chaos engineering starts from a stated hypothesis about behavior instead, with fallback paths around external calls and abort signals tied to checkout completion rather than p99 latency.

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AI, Development

Ghost database for AI: why agents need throwaway Postgres

Ghost is an agent-first Postgres platform. Notes from using its MCP server and database forking to let AI agents test schema changes and index strategies without touching a shared staging database.

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Accessibility, Design, Front-end

Streaming UI stability: fixing scroll jumps and layout shifts

Streaming interfaces jump around and drag the scrollbar back to the bottom. What fixes it: tracking whether the user scrolled away, batching renders into the animation frame, and marking the container up as an ARIA log region.

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AI, Development

Credit scoring model stability: monotonicity and PSI in Python

A credit model can score well and still fall apart in production if its variables drift. Two checks before estimation: whether each variable holds a consistent risk direction, and what PSI says about distribution shifts between datasets.

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AI, AI in WordPress

Stacking ensembles, and why one model is not enough

One best model is one point of failure. I go through the three layers of a stacking ensemble, how predictions become features for the next layer, and what the extra training time actually costs you.

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AI, Development

Multimodal RAG without the multimodal embeddings

Multimodal RAG usually fails at retrieval, not at vision. I walk through the proxy-pointer approach: parse a document into a section tree, keep image pointers on each node, and retrieve whole sections instead of sliding-window chunks.

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AI, Development

Calling Rust from Python when a loop will not vectorize

Some loops will not vectorize, and Python object overhead eats whatever is left. Here is how PyO3 and Maturin let you move a hot loop into Rust, how Rayon uses the rest of your cores, and what the throughput looks like afterwards.

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AI, AI in WordPress, Development

Where agentic AI token savings actually come from

Prompt caching only pays off when the prefix stays byte-identical. I go through the mistakes that break it, a semantic cache layer for WordPress, loading tool definitions on demand, and what to keep or drop between turns.

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