Ferenc Huszár Portfolio
Professor of Machine Learning at the University of Cambridge (currently on leave leading Reasonable, an LLMs-for-programming startup), previously a senior ML researcher at Twitter; his "inFERENCe" blog has run since around 2014 with technical posts on generative models, causal inference, and diffusion models, including a widely cited "10 years on" retrospective on why deep learning works.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Theoretical rigor applied to trend-driven topics
Posts on diffusion models or LLM behavior are grounded in probability theory and statistics rather than restating whatever is trending, which is what makes the analysis hold up years after publication.
Long-view retrospectives, not just reactions to new papers
The "10 Years On" post revisits an old claim about deep learning against a decade of subsequent evidence — a rarer form of intellectual honesty than only ever writing about the newest result.
A single-author blog sustained across more than a decade
The archive runs to at least twelve pages of posts, evidence of a genuinely sustained personal research practice rather than a blog active for one hype cycle.
What AI Researchers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Ground trend-topic posts in the underlying theory (probability, statistics, optimization) rather than restating the trend — it's what keeps writing relevant after the trend passes.
Revisit your own older claims publicly against new evidence — it's a stronger credibility signal than only ever writing about what's new.
Sustain a single-author blog over years rather than a burst tied to one hype cycle — the archive itself becomes the credential.
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