Hamel Husain Portfolio
ML engineer with prior roles at companies including Airbnb and GitHub; now focuses his blog and courses specifically on LLM evaluation practice, co-teaching an "AI Evals for Engineers and PMs" course that has enrolled 5,000+ students.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A single, narrow specialty within a broad field
Rather than covering "AI" broadly, the site is almost entirely about one specific, high-value problem — evaluating LLM outputs — which is a harder and more differentiated position than generalist AI content.
Opinionated, contrarian post titles
"'It's Hard to Eval' Is a Product Smell" states a specific, debatable claim rather than a neutral how-to title, which is what gets a technical post actually discussed rather than skimmed.
A course validated by enrollment numbers
Citing 5,000+ students on a specific course is a concrete, checkable measure of demand for the expertise being sold, not a vague claim of authority.
What AI Engineers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Pick one narrow, hard problem within your field (not the whole field) to become known for — narrow and deep beats broad and shallow.
Write post titles that state a specific, debatable claim rather than a neutral how-to — it's what gets technical work actually discussed.
If you teach a course or workshop, publish real enrollment or outcome numbers rather than describing it only in qualitative terms.
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