Reliable browser automation
Almost every flaky automation bug is the same bug: acting on a page that has moved on since you looked at it.
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Almost every flaky automation bug is the same bug: acting on a page that has moved on since you looked at it.
Read moreYou cannot assert an exact sequence against a non-deterministic system, and you cannot run your suite against real employers. Both problems have the same answer.
Read more"Universal" is the wrong goal. A handful of systems carry most volume; the design that works optimises those and degrades gracefully for the rest.
Read moreThe variety is unbounded but not random. Most novel questions are old questions in new words, and exploiting that is what makes the problem finite.
Read moreThe model is the easy part. Getting readable text out of a two-column PDF is where resume parsers actually fail.
Read moreThe skills section is the least reliable part of a CV. The real evidence is in the experience bullets, and extracting it is a different job.
Read moreA job description is a wish list written defensively. Treating every item on it as a requirement is why matching systems reject candidates who would have got the job.
Read moreA single percentage is the least useful thing you can produce. The same 72% can mean a near-perfect fit in the wrong country or a mediocre fit next door.
Read moreA fabricated line on a CV is not a model quality issue for the vendor. It is a credibility problem for the candidate, in an interview, months later.
Read moreGenerating one tailored CV is a prompt. Generating hundreds and still knowing what each one claimed is a system, and the difference is version control.
Read moreTailoring was always good advice that almost nobody followed, because it cost an hour a job. That constraint has gone — which changes less than you would think.
Read moreMost generated cover letters fail for one reason: they were produced from the job description alone, which every other applicant also had.
Read moreMost embedding matching disappoints for one reason: embedding whole documents. What you embed matters more than which model you use.
Read moreThree techniques, each wrong in a different direction. Used in the right order they cover each other; used alone, each fails predictably.
Read moreMost job boards do not need a dedicated vector database. Knowing when you cross that line saves an unnecessary piece of infrastructure.
Read moreSemantic search is not a replacement for keyword search. They fail on opposite inputs, which is precisely why the good systems run both.
Read moreKeeping vectors next to the rows they describe removes a whole category of problems — chiefly that your two data stores can disagree.
Read moreEmbeddings solve the cold-start problem that sinks most job recommenders — and then quietly create a filter bubble if that is all you use.
Read moreYou can get a long way with one database and no new infrastructure — and knowing exactly how far is what keeps the architecture honest.
Read moreThe whole recommender fits in one database: a profile vector, a filtered similarity query, and a scheduled job that keeps both honest.
Read moreReading about ATS screening helps. Running your actual resume through one helps more.
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