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Yes, I 100% agree with saying that.

Author here. I said this in the post, and I'll say it again here. If I hear from any reliable sources at Meta that I'm wrong, I'll gladly retract and issue a public apology.

Author here. The main reason not to share is that if you share at the beginning of the process, they will inevitably ask you how things are going with Company X, Y, and Z. Let's say you get rejected from a few of them. It's going to be increasingly hard to hide that, and now you lost leverage. Better to share when you control the flow of information.

I wrote a bunch about why sharing doesn't make sense and how to avoid it, while still managing your timeline: https://interviewing.io/blog/sabotage-salary-negotiation-bef...

Author here. We sell mock interviews at interviewing.io, in the styles of specific companies. Sales data lets us see what's popular, which in turn, let's us gauge how much hiring is happening there.

Our sales data has lined up with previous events like hiring freezes and hiring booms (e.g., when Amazon briefly when on a hiring spree after Google and Meta froze in 2022). You can see more historical data here: https://interviewing.io/blog/when-is-hiring-coming-back-pred...

In Longevity by Peter Attia, there are some possible interventions, mostly around diet and exercise. None are a sure thing, but they may move the probability distribution a bit.

I'm not the author (perhaps he'll chime in as well), but I'm the CEO of interviewing.io (we're the ones who ran the experiment).

I think it depends on whether the interviewer has agreed to make the interview "open book". Looking up stuff on Stack Overflow during the interview can be OK or can be cheating, depending on the constraints.

In this experiment, the interviews were not "open book". That said, I am personally in favor of open book interviews.

We're actually testing those popups right now. Cycling through a few preexisting ones to see if engagement moves at all, and then we'll make them realtime.

Author here. These ranges are typically wide because your compensation depends on your interview performance and perceived seniority/value add to the company.

There's no way to tease this out before you interview, except that if you're looking for compensation that's significantly above the top of the band, it might be hard to get. (A little above the top is possible, and sometimes a lot is possible too... that's where the negotiation comes in.)

Please take a listen to the interview (bottom of the post) and judge for yourself.

Founder of interviewing.io here. The author of the post is not an engineer, period. This means he has never written code, and he has certainly never designed systems.

OP and founder of interviewing.io here. We spent hundreds of hours interviewing current & former FAANG interviewers to write this piece. We really wanted to peel back the curtain on what happens during these interviews, what actually matters to them, how these companies train their interviewers (if at all), make hiring decisions, and so on.

I'll be around to answer questions, and I'll tag in Kevin, the author, as well (I just edited it).

Post updated. Thanks for the feedback!

Author here. I replied to a similar comment, but I'll reply to this one too because it's a really good point, and I think I should have done a better job of calling out in the post that humans suck at hiring as well.

Most recruiters are terrible, and they're kinda set up to fail because the data isn't there in exactly the same way. The difference is that good human recruiters can make some meaningful warm intros, let 2 engineers get in a room together to see if there's chemistry, and get the hell out of the way.

Author here. 100%. Most recruiters are terrible, and they're kinda set up to fail because the data isn't there in exactly the same way.

The difference is that good human recruiters can make some meaningful warm intros, let 2 engineers get in a room together to see if there's chemistry, and get the hell out of the way.

I should make this clearer in the post. "Recruiters can't do hiring... and neither can AI. Both can't because the data's not there."

Venture backed CEO here. I'm a "her". Also, please see the first footnote in the post.

"If I were you, I’d be justifiably skeptical at this point. Wow, a recruiter is writing about how AI can’t do recruiting. Classic Luddite trope, right? Let’s burn down the shoe factory because it can never compete with the artisanal shoes we make in our homes. In this case, though, you’d be wrong. I walked away from a very lucrative recruiting agency that I built to start interviewing.io, precisely because I wanted to be the guy who owned the shoe factory, not the guy setting it on fire out of spite. Recruiting needs to change, it needs disintermediation, and it needs more data. It’s the only way hiring will ever become efficient and fair. I just don’t think AI is going to be that change. If I’m wrong, I’ll be the first in line to pivot interviewing.io to an AI-first solution."

Do you know how many shares are outstanding, i.e., what % of the company you'd be getting?

Moreover, you should absolutely ask for the preferred price at the last fundraise. They should be able to give you that figure, just like they gave you the strike price.

Also ask about liquidation preferences: do investors get paid out at a multiple?

Finally, you should adjust that figure depending on how long ago the raise was. A 2021 valuation is very very different from a 2022 and 2023 valuation.

Once you have all of these figures, you can build yourself a probability-weighted model that factors in dilution and a few different outcomes (company ends up being worth nothing, company gets sold to PE at some small multiple, company has an IPO etc) come up with an expected value for the worth of your equity.

If you'd like, email me, and I'll share a redacted version of the model I give my employees at interviewing.io to do some reasoning about the value of their equity: aline@interviewing.io

P.S. Odds are that this equity is going to be monopoly money (because that's true for every company)... and odds are your stake is very small even if it's not because you're coming in late.

Author/OP here. Just heard an interesting and relevant tidbit from someone who read the post. Apparently a while ago, Google fed resumes of employees into an ML model and correlated several metrics to the success of a specific engineer at Google. There was a lot of interesting data gathered from that but one that stood out was exactly that the number of certifications listed was inversely proportional to the engineer's success (measured as career progress).

Hey, founder of interviewing.io here. We actually did zero thinking about SEO. We had someone look at this guide at the end through an SEO lens, but then we decided that that would make the content not as good and would make the guide less readable.

(As an aside, I think in recent years, the spirit of SEO has become more about just making good stuff and less about hacking...)

Anyway, it wasn't for SEO. It's hard to edit stuff well because we're so passionate about what we wrote here, and we probably should do another pass.

It's on its own axis because the blue line represents the number of open tech jobs across all disciplines, whereas the red line is engineers only.

I also just fixed the first graph. It still has 2 axes, but the order of magnitude is more realistic.

Author here. Publishing this post brings us no pleasure. I originally had that in a footnote and then moved it up after seeing these comments.

One might say that because we’re a mock interview platform, publishing this post is self-serving. The reality is that we take no joy in this content. Ever since hiring freezes and layoffs have started dominating the news cycle, we’ve lost about half of our interview volume, many of the prominent employers who were hiring through us as recently as Q2 of this year have paused. So, trust us, we’d much rather be writing about something else while living in a hiring boom. And look, whether we want to see it or not or whether we have vested interest in publishing it or not, the data is the data, and the data doesn’t care. Realistically, neither will the employers who are still interviewing and hiring.

TrueUp doesn't have historical data from before 2022. We couldn't find another index of open tech jobs, sadly. If you know of one, please lmk, and we'll rerun the numbers.

Here's the raw TrueUp data: https://www.trueup.io/job-trend

We commented on this in the post. While we are targeting a specific slice of the market, FAANG and FAANG adjacent, so are our users. It's true that these companies run a process that's not indicative of the whole industry. However, because they’re relative rather than absolute, we feel reasonably confident that these numbers can be generalized to the overall hiring market.

We also felt that even if this data WEREN'T entirely generalizable, it would be interesting to a big enough subset of the HN population to merit sharing.

We used 2 different axes because the values for each of the lines were different. For instance, in the graph comparing open tech jobs to the bar, the bar is the average technical in interviews from 1 to 4. It wouldn't have made sense to put that on the same axis as the number of open tech jobs (hundreds of thousands).

just saw this. thank you. that means a lot.

Founder here. This piece took my team and I months to research and tag and write. We spent hours counting individual layoffs in spreadsheets and on LinkedIn and running and rerunning the numbers. And the conclusion wasn't even what I wanted. When I started writing, I really thought that the layoffs were overblown and weren't affecting engineers. That's not what's happening at all.

Now, you're right, why do we put effort into pieces like this? It's to get some eyeballs on interviewing.io.

But this piece is not flimsy, and I think it's not correct or fair to assume that just because something serves our business that it's of poor quality.

Normally I wouldn't say anything, but the "flimsy" thing really got me.

Author here. For what it's worth, I added the conclusion pretty late in the game. When I first ran the data, it looked like engineers were barely getting laid off (because of engineers not opting in to layoff lists as much as others). And the graph about the bar going up came in as a zero hour suggestion from someone who proofread the piece.

Believe me, I'd be much happier if eng hiring were back in full swing. With all the freezes and layoffs, we've taken a bigger hit than we can hope to gain back by shilling interview practice.

Author here. Good call, just fixed to a more generic link about layoffs on Blind. No shortage of recent conversations.

no, we didn't adjust for demographic biases because, look, we're an anonymous platform. we periodically survey our users to get demographic data, but it's not something we ask in the app because we've never been able to resolve "tell me your race & gender" with "hey we're anonymous, and you'll be judged on your performance and nothing else".

last thing i want is to perpetuate stereotype threat inadvertently. it's possible to do this right, i think, but we haven't gotten there yet.

HAH.