mk_chan

Just shows the offset openai feels like it has to add to ‘equalize’ the average discourse of its training material

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.

Microsoft ban any % speedrun soon.

Is this what the employees do nowadays while their AI is generating code?

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.

https://github.com/Mk-Chan/libchess/blob/master/internal/Met... https://github.com/Mk-Chan/libchess/blob/master/Square.h

That should happen if the training dataset (which is presumably based on the real world) reflects that happening.

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.

Diagnosis: Misaligned incentives correlate with acute miscomprehension

I don't actually use the program in my stead, I just put it up against other programs in computer chess tournaments.

It’s the same reason I stopped playing chess, but it only hit me later that I lost interest because of the program I wrote.

Now I just play casually if a friend wants to.

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.

I’ll believe their proficiency claims when they replace all their software developers, knowledge workers and PhDs with this stuff.

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.

Yes this seems likely because important things like government websites and banks seem to always come up.

Lack of thorough testing by the people who make the call for release maybe. I’m sure the people doing the grunt work brought it up.

Yes, you have to expect the future (or even better if your manager/boss already has expectations you can adopt to begin with) and then choose the right way to tackle the changes required. That's why I laid out 3 possible cases the last of which points out that I prefer to refactor primarily when there's a lot of work incoming on the codebase. Personally, I don't see much value in refactoring code significantly if you alone are going to be editing it because refactoring for ease of editing + the cost of editing in the refactored codebase is often less than just eating the higher cost of editing in the pre-refactored codebase and you don't reap the scaling benefits of refactoring as much. However, like I mentioned in the above paragraph, it depends. In the end it's all about managing the debt to get the most out of it in a _relatively_ fixed time period.

I’m not sure why there’s so much focus on refactoring or improving it. When a feature needs to be added that can just be tacked onto the code, do it without touching anything else.

If it’s a big enough change, export whatever you need out of the legacy code (by calling an external function/introducing a network layer/pulling the exact same code out into a library/other assorted ways of separating code) and do the rest in a fresh environment.

I wouldn’t try to do any major refactor unless several people are going to work on the code in the future and the code needs to have certain assumptions and standards so it is easy for the group to work together on it.

This is the perspective I’ve come to learn as well. Fact of the matter is what you want to do (and perhaps sell) is what actually matters, not the tools you use to achieve it. Programming language, frameworks, monitoring tech, monolith, microservices etc don’t matter at all as long as you are trading fine, able to deal with failures and move on with checks for next time, able to retain/convert customers and so on. As long as the business is served by the tech within the business constraints, the tech can be abstracted away from the business. Not to say it isn’t important because you are in fact enabling or accelerating the business, but the business is what matters.

The tools only start mattering when the maintenance and usage of the tools becomes a heavy constraint to doing what you actually want to do (big tech). That doesn’t actually happen very easily.

I used chatgpt4 to generate python code which generates c++ code for a hobby project of mine using a library I've never used before. The iteration speed is ridiculously good and no one in any of the IRC or discord channels I visited pointed me even in the general direction of such a simple solution.

https://chat.openai.com/share/d041af60-b980-4972-ba62-3d41e0... https://github.com/Mk-Chan/gw2combat/blob/master/generate_co...

It is indeed an unfair advantage to have bosses who care deeply about the products in addition to revenue!

Pick something you know how to build already, a familiar concept reduces a variable in the learning process and build something for yourself using the new tool or subject. Whether it be writing a blog/review about it or coding a medium sized project in the new language.

Find a reference or a sample codebase (which may be a tutorial or someone else’s project/blog or a by-example documentation) and copy, copy, copy, copy and copy. You always learn better by copying first and innovating later.

Predict how the the new language/subject should work according your current knowledge and iteratively improve your predictions based on subsequently being proven wrong again and again till you know all the good ways to do something and why this particular project/language/book chose to do it this way. Then pick up another book/reference on the same topic and repeat. Never trust a single source, always aggregate information and judge for yourself, only giving little weight to authority.

If you want to get really good at something you have to spam it. To that end find an IRC or discord channel meant for that topic and interact on the topic there as well.

It’s not so simple. Many manufacturers set a minimum advertised price which would require legislation to change.

Additionally advertising a certain price and actually selling at a different price is a surefire way to get delisted from google for example. This is why the whole promotion codes/voucher concept is popular.

Just a bit of fun :D “As you once did for the vacuous Rom, grant us eyes, grant us eyes. Plant eyes on our brains, to cleanse our beastly idiocy.” - Micolash, Bloodborne

It's a tight rope arguing who deserves what when a lot of business are built on this chain of platforms (like a supply chain). News agencies have many channels, probably some of the largest being FB and G (hence this post) and now they're looking to cut out the middle man because they believe it doesn't provide them value and apparently detracts from it. However, note that there's an inversion of power at some point when a platform becomes much bigger than its customers. News agencies will pretty much die out without G and FB (if they are indeed by far the largest channels) because someone will just post the news from their platform onto G and FB obfuscated enough to bypass copyright because people want news on G and FB.

It stopped being about "I want news, oh cool G and FB have it!" And started being about "I read news on G and FB. What is this WSJ you are talking about" a long time ago.

Platform inversion of power taken to its extreme, some might even say perhaps Google and Facebook should demand revenue share from ISPs and computer/mobile manufacturers for incentivizing people to buy plans and devices to get on the internet?