the candidate experience angle is definitely worth its own post because i don't think it's black and white. plenty of companies get thousands of applications and are still dissatisfied with the talent quality (although i'm skeptical of this, i don't dismiss it entirely). others get thousands and simply can't process the volume, with 75% left unreviewed (ask me how i know).
if you work in big tech and you've ever referred an extremely qualified friend who's also in big tech and watched it get ignored despite 10 open support engineer or 20 account executive roles they'd be perfect for, that's exactly the incentives and infrastructure problem i'm describing.
AI might help with these problems for both recruiters and candidates though. pre-AI the pitch for many recruiting tools was about skills and keywords (garbage in, garbage out). but now we can use LLMs to reason positive signals related to qualification (compared against the job description, does resume/application show experience match based on years, sales quotas, industries, programming languages, etc.). the goal is surfacing more qualified candidates for human review, which matters a lot when a recruiter is literally waiting 5-10 seconds for one application to load in workday while every incentive they have pushes them to just source someone off linkedin instead.
author here! i originally intended this to be a technical write-up on everything i learned building an ATS (as someone who's in HR). the original title was "how to build an ATS and why you probably shouldn't". i debated mentioning what i built, but it felt hollow to _ramble_ about schemas, architecture, components with nothing to show for it.
the more i wrote and reflected, the more i thought about why the market never corrects itself despite the tools being expensive and badly designed. i've worked with hundreds of recruiters and most use spreadsheets. that's not a workflow quirk but i think an indictment of something bigger which traces back to everything in the post -- the buyer who never uses the product, the integrations racket, the "AI-native" tools bolted on top of a broken foundation, etc. etc.
so i ended up writing the first half. it's drawn from my experiences frustratingly buying an ATS for a small business, and watching the dysfunction of procurement/integration/lack of adoption play out at the enterprise level.
admittedly, HR/recruiting tech is a very niche audience, so the technical section probably lands better with engineers who've been handed a recruiting project than with anyone actually working in HR. so i wanted to offer a resource from that perspective.
Thank you! Oftentimes when playing Wordle, I use the address bar to write out the letters that I got right like so: T__ST. This allows me to visualize how I might fill the gaps. Playing in the spreadsheet definitely feels easier for me.
Since the Settings sheet hosts the list of past and future Wordles, this also serves as an archive and a way to play the next games.
Also, the user can add a timed trigger to reset the game (and move on to the next word) on a 24 hour cycle, just like the real Wordle. But I left it as a manual item to click in the menu.
if you work in big tech and you've ever referred an extremely qualified friend who's also in big tech and watched it get ignored despite 10 open support engineer or 20 account executive roles they'd be perfect for, that's exactly the incentives and infrastructure problem i'm describing.
AI might help with these problems for both recruiters and candidates though. pre-AI the pitch for many recruiting tools was about skills and keywords (garbage in, garbage out). but now we can use LLMs to reason positive signals related to qualification (compared against the job description, does resume/application show experience match based on years, sales quotas, industries, programming languages, etc.). the goal is surfacing more qualified candidates for human review, which matters a lot when a recruiter is literally waiting 5-10 seconds for one application to load in workday while every incentive they have pushes them to just source someone off linkedin instead.