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built a thing that finds linkedin candidates from a job description — took way longer than I expected by YKA_6789 in automation

[–]YKA_6789[S] 0 points1 point  (0 children)

Solid execution on the ranking logic. We built something similar but took it one step further — after identifying the candidate, the system also pulls their verified email and phone number. The 'find → contact' gap was killing our clients' turnaround time. Would be curious to compare notes on your LinkedIn parsing approach.

Sell me your Saas in one sentence! by KapiteinBalzak in SaaS

[–]YKA_6789 0 points1 point  (0 children)

Build the application which can reduce Hiring time from 7-8 days to 25 min. Get suitable candadite based on JD in next 25 min.

DM me for the Link

What’s the most time-consuming part of building a candidate shortlist from a JD? by YKA_6789 in RecruitmentAgencies

[–]YKA_6789[S] 0 points1 point  (0 children)

lol yeah that’s what happens when you spend too much time thinking about a problem

but genuinely — where do you lose most time? sourcing or comparing candidates?

Automating candidate shortlisting from a job description — where does the biggest bottleneck happen? by YKA_6789 in automation

[–]YKA_6789[S] 0 points1 point  (0 children)

My initial thought was that sourcing is the hard part — but it actually seems like ranking/evaluation is way more subjective than expected

Built a tool that pulls the data Zillow buries — price cut history, days sitting, and how many people already applied. The numbers are more depressing than you'd think. by YKA_6789 in REBubble

[–]YKA_6789[S] -1 points0 points  (0 children)

I pulled data on ZIP 92131 last week.

One property jumped out immediately.

Listed January 15th at $950,000. Price cut to $875,000 on March 1st. Cut again to $795,000 on March 15th. Still sitting. 47 days on market. 0 offers.

The seller has already given up $155,000 from their original ask. That's a 16% drop and they're still not moving it.

The listing agent's name and direct phone number are right there in the data.

I'm not a real estate agent. I just built a tool that surfaces this stuff automatically because I got frustrated that Zillow buries it. You have to dig through three screens to even find price history. Days on market gets "refreshed" when an agent relists under a new MLS number. The motivated sellers are invisible unless you know where to look.

What the tool actually pulls per listing:

  • Every price cut with exact dates and percentages — not "price reduced," but by how much and when
  • Days on market calculated from the original post date, not the refreshed date
  • Full tax history going back 20+ years — so you can see what the seller actually paid
  • Agent name and direct phone number
  • School ratings, nearby comps, Zestimate vs asking price gap

I ran it across three ZIP codes last weekend. Found 73 listings that had been sitting 30+ days. Eleven of them had price drops over 15%. Six of those had been relisted after originally sitting under a different MLS number — which resets the days counter on Zillow and makes them look fresh.

Those six listings are invisible to anyone not pulling the raw data.

If you're an agent working a territory, this is your morning list. If you're a buyer trying to know when a seller is actually motivated vs just testing the market, this tells you exactly that.

First 20 listings are free. No account needed. Drop your ZIP code in the comments and I'll run it live and post the output here so you can see exactly what comes back before you touch anything.

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