> "what was the hardest day of your life" is a pretty standard
I would suggest that this is a misremembering. As someone who's hosted thousands of interviews at companies big and small, all of the questions were scoped to professional work. Why? because when you ask things like "what was the hardest day in your life" you have a non-trivial chance of getting your interviewee tell you about the time they saw someone die, cleaned up a suicide attempt, or developed a new fear. That or you see someone make something up on the spot.
Its just not a useful question. If they answer honestly, then they are going to just going to remember that sad feeling of re-living trauma. If they don't answer honestly, they are more than likely going to be pissed off at the weird prying question.
These questions are emotionally expansive, you could have been getting on really well, shared a joke, had a great conversation. All of that will be blotted out by remembered pain.
The reason why people ask "can you tell me a time you overcame a big obstacle to achieve a business outcome" is threefold:
1) can you describe a blocker with the right amount if context
2) can you talk about improving things without insulting the people blocking you
3) can you think of ways to non-destructively overcome problems
Asking about when your pet died doesn't give you useful information
New CEO walks into the office for the first day. Looks at the budget to see what they can do to "Make a mark"
[Budget proposal officer]: "Here is the new cleaning budget"
[CEO]: "looks around the office, the office is clean, why do we need cleaners? much less expensive cleaners"
[Budget proposal officer]: "The cleaners keep the office clean though"
[CEO]: "Nonsense! I read a substack about this, lets get rid of the cleaners"
//Six months later in an ALL hands//
[CEO]: "Look I know that we have had issues, The washing facilities were sub standard, and in some cases people were poisoned by poor hygene. We have insistuted a new mandatory training called `keeping the office clean, you're the first link in the chain` to help keep everyone happy and healthy"
At the FAANG I was at, we were pushed to interview interview interview, and the company tripled in size in two years.
We constantly questioned the motive for more engineers, when we had plenty already, constantly seeking alignment. The rationale was more engineers meant we could make more products more quickly.
It never worked, and caused huge headaches that never really went away. It didn't help that team size was made a specific goal of a number of VPs. So it because a goal to grow team size, rather than a business need
And because the VPs were doing it, a whole bunch of people down the hill started copying then, using team size as a forcing function for power.
But, I would gently point out that people are actively looking to move away from github because its unreliably
People hate shit when its not working, we are in a bubble where people allow AI products to have shit outcomes, because they either sound human, or do amazing things most of the time (generate that image, sort out that spreadsheet, generate that code or answer that question)
Because, ironically, there is lots of money about to subsidise anything with an AI sheen (how very ZIRP) people are making all sorts of shit with it. Some times it produces value. A lot of the time it doesn't.
If we take a step away from SWE opinion and talked to the product users, I think you'll find they just want that one feature they use your thing to work, they want those bugs to be fixed, and they want your product to work when they need it. They don't give a shit about AI, they fucking hate your redesign, and they really really get pissed off when you move stuff about and re-name it.
> There is no way these people have the resources to train a fully fledged LLM, so claiming that is their goal makes me think they don't intend for the LLM to be useful.
Depends on what they are doing and why. but at most big labs, only the final model training happens on the big clusters. a lot of experimentation happens on <500 gpus per dev.
This is the use case for the small NVIDIA boxes that a researcher can have on their desk for $5k and do useful experiments before spending all the grant money on a huge training run for the final product.
but that only gets you so far, you need bigger multi-GPU setup to do the higher dimension stuff. You can use a DGX, but again thats limiting up to a certain point.
I've noticed that it also imposes american moral judgements on certain things, even though it reasons (sometimes) in the native language.
I was trying to work out how and when to use swear words, and the relative power index of them. it translated english swear words into the target language then lectured me on not using them.
It took a bunch of prodding for it to actually think as the target language to then get the (mostly) correct response.
Would be curious about the model and the prompt for this.
Not kidding at all. I had a similar issue with a project where I needed to classify images into specific demographics, and Gemini, while capable, was entirely not going to do the task… until in my JSON response I left room for it to tell me why this was not a good idea and why it was culturally insensitive. Then boom… full JSON array: hair color, eye color, skin color, fitness level, likely ethnicity, likely country of origin, and about 10 other values.
You’re probably wondering what on earth I was working on. I was matching Ai gen headshots to Ai voices so that in an app the voice picker had human (Ai) faces.
we are going through our second AI transformation, the first one didn't work that well because the tools were shit.
Whats happening now and whos driving it is interesting. The CEO has a license for this new tool (think one of the top 4, Qwen Claude, Gemini, openAI) and really likes it. So much so that they (non coder) are making lots of little single page web apps.
The COO is bollocks deep in AI, and is saying that we cannot buy any SaaS products anymore. We must make it ourselves.
The engineering manager has seen this as an opportunity to build out a brand for engineering (its a small department in a medium sized company) by delivering quickly what the large year long efforts cant.
This has formed a slopnexus where PoCs are spun up left right and centre, but there isn't much time or thought going in to making them sustainable.
What started out as a (simple ish) asset management tool, neatly scoped into a deliverable PoC has morphed into a 5 product as one monster.
Its a mess that will either lead to burn out or disaster.
Just...wow. That sounds awful, and I'm sorry for you, but I believe many other companies will or are already following that same path.
And myself being an infrastructure guy that needs to maintain all these PoCs that are now suddenly critical for production, it's the perfect nightmare.
And mind you, that dynamic always existed to a certain degree (laptop on a desk that runs some ugly Python script that does half the work of the BizOps team? Check. GCP account attached to the GSuite running a random instance for finance when the company is 101% on AWS? Check. Spreadsheet with macros that sends emails via Outlook as a mailing list manager? Check.) but at least when you discovered that you could scold them and tell them that we need to migrate this to a proper system because security.
But nowadays with vibe-coded internal apps...it's a challenge.
There is probably some opportunity here for a centralized, internal only LLM proxy which injects AGENT.md and skills and permits switching backend providers.
I would suggest that this is a misremembering. As someone who's hosted thousands of interviews at companies big and small, all of the questions were scoped to professional work. Why? because when you ask things like "what was the hardest day in your life" you have a non-trivial chance of getting your interviewee tell you about the time they saw someone die, cleaned up a suicide attempt, or developed a new fear. That or you see someone make something up on the spot.
Its just not a useful question. If they answer honestly, then they are going to just going to remember that sad feeling of re-living trauma. If they don't answer honestly, they are more than likely going to be pissed off at the weird prying question.
These questions are emotionally expansive, you could have been getting on really well, shared a joke, had a great conversation. All of that will be blotted out by remembered pain.
The reason why people ask "can you tell me a time you overcame a big obstacle to achieve a business outcome" is threefold:
1) can you describe a blocker with the right amount if context
2) can you talk about improving things without insulting the people blocking you
3) can you think of ways to non-destructively overcome problems
Asking about when your pet died doesn't give you useful information
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