In my experience an LLM does 2 things:
1. Bring you up to some average-LLM level when you don’t have the skills/knowledge to actually do what you want.
2. Work at 80-90% of your capacity but WAY faster than you physically could depending on how much context you provide it. If you don’t provide it sufficient context to do what/how you want it to do, of course it might default to something you don’t want.
This is very important to write on. A lot of people believe news is worth consuming for the truth and often cite it as a primary source of information. News producers may not necessarily lie but they cherry pick to maximize reach and that
content plays on peoples belief that what they see on the news is all the information you need.
The news in a vacuum can actually be quite misleading and I too believe people should realize that it is not the ‘whole’ truth.
I’ve been using hacks to do this for a long time. I wish it was simpler in C++. I love C++ typing but hate the syntax and defaults. It’s so complicated to get started with.
Following the paper, if you end up with a gender balanced workforce, it implies there is surely a bias in one of the variables - the candidate pool (like you say) or the evaluation of a candidate or other related things. However the bias must also reverse to equalize once the balance tips the other way or actually disappear once the desired ratio is achieved.
Edit: it should go without saying that once you hire enough people to dwarf the starting population of the startup + consider employee churn, the bias should disappear within the error margin in the real world. This just follows the original posted results and the paper.
Going by this: https://www.aeaweb.org/conference/2025/program/paper/3Y3SD8T... which states “… founding teams comprised of all men are most common (75% in 2022)…”
it might actually make sense that the LLM is reflecting real world data because by the point a company begins to use an LLM over personal network-based hiring, they are beginning to produce a more gender-balanced workforce.
Not sure why OP is getting downvoted. Going the premise of the cited doctrine, it follows precisely that even doing illegal things are acceptable as long as there is net gain until the expected future.
OP doesn’t even claim this is the one true doctrine or anything of the sort.
The way I put it was meant to be slightly amusing, but let me put it a different way for the sake of discussion.
For this purpose, I like to mentally model a country (or territory which decided to hold an election of some kind) as a collection of points in a multidimensional space of values or policies which are up for election. Each point represents where a voting person would stand according to their personal principles.
Now, I’d like to postulate that the act of voting corresponds to finding the center of the cluster of points. In this model, it’s easy to imagine the scenario where a very large portion of people (and possibly even the majority) are deeply dissatisfied with the result because they are too far on many dimensions from the center that was elected. This is actually further exacerbated by many factors in real life like the asymmetry of information between people.
The obvious solution would be to create more cluster centers instead of one fat cluster that leaves everyone not very happy. This corresponds to states or provinces within a country in the real world.
So coming from that line of thinking, it leads me to believe that sometimes to maintain a democracy, you need to cut the outliers away to move/make the center such that people are happy with the result. Essentially you enforce the will of the majority by cutting away the minority until it is no longer a tyranny to do so.
Of course this is gross simplification of real life. It is ignoring factors such as external threats, instability, unpredictability, information availability and so on. However, I think it’s useful to think this way for many purposes.
Addressing your extreme example of a criminal, that’s a point so far off in the space that you definitely want to cut it out of the system.
Ironically, tyranny of the majority is a very very good definition of democracy if you drill the majority down into subcategorical majorities recursively until you reach the individual who might be severely disconnected from the averages.
The article is nice and the intention seems to be good, but it’s not backed up by the fundamentals. The salary range posted in the job listing is low enough that anyone who would answer the questions to my satisfaction (personal opinion) and is an autodidact would instantly ignore the entire listing or leave a year after joining once they realized how valuable the skill they have actually is.
Having conducted 50+ interviews with Indian and international candidates, including those from FAANG, you can’t just discount these skills by 50-75% of their value and then post an article asking where they are in good faith.
There’s no real connection to junior developers in this article. It’s a piece about how the author feels people are using AI and how the author feels people should use AI.
The quick fix vs deep understanding positions are mired by many factors; two of the big ones being time and how much one cares none of which are actually discussed.
The reason here is Microsoft is trying to make copilot a platform.
This is the essential step to moving all the power from OpenAI to Microsoft. It would grant Microsoft leverage over all providers since the customers would depend on Microsoft and not OpenAI or Google or Anthropic. Classic platform business evolution at play here.
Officially banning fake reviews to introduce liability is a good start, but the real challenge with reviews is the incentive structure.
For positive reviews, a business will figure out customers who they already know had a positive experience (quick delivery, continuous usage, etc) and only send them invites to review. This is perfectly legal and the fundamental business model of many review websites - selling the ability to push invites and “manage” reviews.
For negative reviews - no business wants these, and customers with bad experiences are likely to post them by themselves.
What gets left out is the average experience because reviews are essentially cherry picked from the head and tail ends of the normal curve of experiences. This doesn’t render reviews useless, of course. Having a large number of positive reviews is still a positive signal but it is nowhere close to free from manipulation.