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Recordings of phone screenings for employment

Hacker News

Recordings of phone screenings for employment

I have installed a program on my phone called ACR, which records all incoming and outgoing phone calls. I have recorded 26 phone screens from the past 3 or 4 months of trying to find a job as a full stack web developer. Some of the interviews are non-technical screens, some of them get technical. I have not gone through and beeped out any names or personal details. I maybe should have, but it was bad enough I had to live through these moments once, I didn't want to have to relive them by listening through them again to add in the beeps. Some people may get mad at me for giving away their company's interview details, but it is what it is. The main reason why I'm posting them is because there's this myth that is commonly spoken about on the internet that the unemployment rate is at an all time low, and that companies are dying to hire developers, and if you're a developer with basic skills and a few years of experience, they you'll be flooded with job offers. Hopefully these recordings will put an end to those myths. No one is throwing jobs at me, as these recordings make very clear. It is also very clear that the burden is on me to prove to the company that I'm the person they should hire, not on the company to convince me to work for their company. By the way, not s single one of these phone screens have lead to a job offer. Most of these screens are with startup companies, but some of them are established companies as well. About half way through these recordings, I began the habit of asking how many people the company has interviewed before me. In very few cases do they say that they've interviewed less than 10 people. If programming jobs are easy to get, then companies shouldn't get to interview 10 or 15 people before finding a person to hire. Anyways, here's the link: https://soundcloud.com/freework/sets/phone-interviews

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: recordings, single · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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