Free learning path
The Path From Frontend to AI Engineer
A step-by-step path from frontend developer to AI engineer: Python, SQL, prompting, RAG, agents, evals, and system design — with real, checked courses.
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Notes from shipping, not from theory.
What I’ve learned building AI product features, frontend platforms, and plugin systems in production. Written for people who have to make these decisions, not to fill a content calendar.
Job-market study
What AI Engineer Jobs Actually Require
I read 68 AI Engineer job postings at 48 companies — agents, RAG, evals, tools, languages, pay — and what the ideal candidate looks like in 2026.
Read the study →July 18, 2026 · 7 min read
A practical performance diagnostic for complex React products
Most React performance work starts with a guess and ends with a rewrite proposal. This is the diagnostic order I use instead — user-visible symptoms, render profiling, memory, bundle cost, data loading — and how I decide what to fix first once the causes have names.
Read the article →July 18, 2026 · 6 min read
Architecture lessons from shipping plugins into other people’s platforms
Shipping plugins inside Figma, Adobe, Canva, VS Code, WordPress, Chrome, and ChatGPT ecosystems forces architectural discipline nothing else does. What the shared core should actually share, why auth design starts with the most hostile sandbox, and when the right call is not to support a host at all.
Read the article →July 18, 2026 · 6 min read
From AI prototype to production: the workflow is the product
Calling a model is the easy part. What separates an impressive AI demo from a feature enterprises will actually roll out is the workflow around it: grounding, identity, approval states, and observability. Here is how I approach that layer, and the sequence I recommend to teams building it.
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