This comment is extremely prejudicial[1]. Not only is language a part of people's heritage and culture, which in itself provides value, but also what you've said is incredibly biased and also simply incorrect. For example, both Mandarin and Spanish have more first-language speakers than English[2]. Almost 40 languages have over 50 million speakers, each.
I would love more time and money put into real-world problems by these companies. Climate, food insecurity, pollution, technology for convenience and/or to help people have a higher quality of life.
I'm sure they must do some of this type of work, right?
Sure but the people who tend to get into leadership positions are people that are primarily concerned with personal wealth and gain in the short term. It's the prisoner dilemma except with more players that all assume everybody else is going to screw them over too. Because there are basically zero personal downsides to being the one to screw everyone else over.
I suppose it depends on which types of problems you're targeting. There is a lot of physical science, and theoretical science, that has gaps because there aren't enough people working on tooling to assist in things like calculation, generation, simulation, etc.
I agree, some of the problems are more difficult. I don't think that's the case for all of them. And, besides, these companies could be demonstrating how to approach problems and where their users could spend tokens to help with these problems.
Should not these companies try to work on these problems _because_ they are difficult?
This is something I have been thinking about for quite a while. I readily embrace the advances AI may bring to mathematics and the hard sciences, but those advances were largely expected even predictable.
What I had hoped for was something more ambitious: using AI as an arbiter in economic, political, and social debates, one capable of weighing evidence, exposing trade-offs, and helping us make decisions that produce better outcomes over the medium and long term, even when those decisions conflict with powerful private interests.
I suspect, however, that this is not a particularly urgent goal for the people funding and directing these systems, many of whom live far removed from scarcity and its consequences.
I'm not sure if this is meant to be rhetorical. If it isn't: money and manpower. The LLM companies have the money, they have the manpower, and so they could likely spare to target some of the problems they are also helping cause.
There is boundless technology we have not yet discovered. I think that we understand we have problems. I don't believe we actually know how to solve them all.
And yeah, the lack of collective political will sucks. It would be naïve, however, to think that there is no value in ensuring longevity in our current and future infrastructure. And improving it to sustain the population giving these companies their value is an obvious win.
We only know how to do it by means of considerable sacrifice. That's why nobody wants to do it. Solving the issue would be doing it without sacrifice or somehow getting us to do it regardless.
Not really. They're an AI company: they develop AI and sell it. There's some room for pulling off flashy marketing stunts, but not all that much.
This is division of labor, and it's a good thing. I'm sure OpenAI employees, who are very well paid, donate some money from their salaries to others working on the areas you're citing: probably more than you think, I say that from having attended some EA parties back in the day.
But that isn't my point: my point is that a company which makes brushless motors should put most of its time and money into solving the "make and sell brushless motors" problem, and if they or their investors feel like they need to do more for the world, give money to the people who have the time and ability to do things about that. There are a lot of quality-of-life improvements which need brushless motors.
Next question is how useful their product (OpenAI, I mean) is to more focused do-good-in-the-world professionals. I'm sure that varies quite a bit. For getting the homeless off the street? I conjecture, not very useful. For 'complete the transition off fossil fuels'? Extremely useful, no one in that field knows how to do their job without AI in summer 2026. I'm certain of this.
We know how to solve food insecurity (in 1st world countries). We have plenty of food. Capitalism requires though throwing out food that can't be sold because billionaires find giving away things anathemic to their worldview. Get rid of billionaire sociopaths.
The worst part is that several of the replies are probably AI generated too. So AI feeds AI, essentially. Is this going end forums like HN? I hope not.
You might be approaching PR comments differently than I've seen. When a comment is something to be addressed, it's either put into a new development task (i.e. on something like Jira), or it is completed before the PR merges. I'm not sure that having comments in the code surfaces that information in a useful manner. The code is for the code, not for what the code could be. The comments on what it could be should be handled outside the code at a different abstraction layer.
Agreed. Putting comments in code is good for adding context around the code, but the actual action item needs to be tracked in the same place that all other action items and issues are tracked.
An exception would be for information that doesn't yet qualify as an action item, but could become an action item if someone changes something in the code. Like if removing a conditional check would trigger the need for some other work or a refactor. Then it's good to put it close to the code so anyone touching that code will know they need to make the action items if they go down that route.
Perhaps in a small codebase without issue tracking this might be something leverage. It's just not reasonable with 100k+ lines of enterprise grade code.
Some subscriptions offer "unlimited tokens" for certain models. i.e. GitHub co-pilot can be unlimited for GPT-4o and GPT-4.1 (and, actually, GPT-5 mini!). So: I spent some time with those models to see what level of scaffolding and breaking things down (hand holding) was required to get them to complete a task.
Why would I do that? Well, I wanted to understand more deeply how differences in my prompting might impact the outcomes of the model. I also wanted to get generally better at writing prompts. And of course, improving at controlling context and seeing how models can go off the rails. Just by being better at understanding these patterns, I feel more confident in general at when and how to use LLMs in my daily work.
I think, in general, understanding not only that earlier models are weaker, but also _how_ they are weaker, is useful in its own right. It gives you an extra tool to use.
I will say, the biggest findings for "weaknesses" I've found are in training data. If you're keeping your libraries up-to-date, and you're using newer methods or functionality from those libraries, AI will constantly fail to identify with those new things. For example, Zod v4 came out recently and the older models absolutely fail to understand that it uses some different syntax and methods under the hood. Jest now supports `using` syntax for its spyOn method, and models just can't figure it out. Even with system prompts and telling them directly, the existing training data is just too overpowering.
Find another developer and pair/work together on a project. It doesn't need to be serious, but you should organize it like it is. So, a breakdown of tasks needed to accomplish the goal first. And then many pull requests into the source that can be peer reviewed.
This comment is extremely prejudicial[1]. Not only is language a part of people's heritage and culture, which in itself provides value, but also what you've said is incredibly biased and also simply incorrect. For example, both Mandarin and Spanish have more first-language speakers than English[2]. Almost 40 languages have over 50 million speakers, each.
1. https://en.wikipedia.org/wiki/Linguistic_discrimination 2. https://en.wikipedia.org/wiki/List_of_languages_by_total_num...