rosspackard

Half the tasks they were not allowed to use AI.

Great for the authors. But everyone else seems to be extrapolating. Authors have a responsibility and should recognize how their work will be used.

Metr may overall have an ok mission, but their motivation is questionable. They published something like this to get attention. Mission accomplished on that but they had to have known how this would be twisted.

It was just published. Too new for someone to conduct a direct study to critique and journals don't just publish critiques anyway. It would have to be a study that disputes the results.

They used 16 developers. The confidence intervals are wide and a few atypical issues per dev could swing the headline figure.

Veteran maintainers on projects they know inside-out. This is a bias.

Devs supplied the issue list (then randomized) which still leads to subtle self-selection bias. Maintainers may pick tasks they enjoy or that showcase deep repo knowledge—exactly where AI probably has least marginal value.

Time was not independently logged and was self-reported.

No possible direct quality metric is possible. Could the AI code be better?

The Hawthorne effect. Knowing they are observed paid may make devs over-document, over-prompt, or simply take their time.

Many of the devs were new to Cursor

Bias in forecasting.

One mediocre paper/study (it should not even be called that with all the bias and sample size issues) and now we have to put up with stories re-hashing and dissecting it. I really hope these don't get upvoted more in the future.

16 devs. And they weren't allowed to pick which tasks they used the AI on. Ridiculous. Also using it on "old and >1 million line" codebases and then extrapolating that to software engineering in general.

Writers like this then theorize why AI isn't helpful, then those "theories" get repeated until it feels less like a theory and more like a fact and it all proliferates into an echo chamber of AI isn't a useful tool. There have been too many anecdotes and my own personal experience to ignore that it isn't useful.

It is a tool and you have to learn it to be successful with it.

I have a suspicion it does use budget forcing. The word "alternatively" also frequently show up and it happens when it seems logically that a </think> tag could have been place.

What books or material on learning marketing would you suggest?

Looks like they do but aren't really documented yet:

https://github.com/infiniflow/ragflow/pull/119

Looks like they do but aren't really documented yet:

https://github.com/infiniflow/ragflow/pull/119

Could this be a case like investment alpha? If you have a real life use case and share it then you could lose the opportunity.

So some "experts" could be staying quiet because they don't have one. But some may stay quiet because they are working on or benefiting from it?

What do you mean when you use the word taught for open-interpreter?

Looking at the OI docs wasn't too helpful.

"I did spend a non-trivial amount of time fiddling with the prompts" was it writing prompts?

I am really interested and this seems like a cool use case that I want to explore. Could you share the prompts on a github gist?

Which James Clear articles did you like the best? There are a lot to choose from.

Fully anonymized medical history/demographics data across countries and open to the public.

This would allow crowd sourcing and statistics to be applied by the best minds in the world.

I know regulations can make this difficult but many difficult boundaries are being pressed now.

Would you share anything about your lifestyle business?

What industry? How long you worked on it, etc?

IANAL but you should be careful about sharing private information about a large publicly traded company

I don't work at gitlab but one great avenue is working on large open source rails apps. And gitlab is actually open source. Try to get it up and running and tackle an issue they have.

https://gitlab.com/gitlab-org/gitlab-ce/issues

also some other rails apps to look at:

discourse https://github.com/discourse/discourse

dev.to https://github.com/thepracticaldev/dev.to

just recently mentioned in another thread consul https://github.com/consul/consul

Or search for large open source rails apps.

International law is complex. Having a full-time employee located in another country can lead to a lot of legal unknowns. At the very least this requires a company to spend the money to understand if they are exposing themselves to any international laws that are unknown to them. I am not a lawyer but have run businesses in the past and legally that would scare the hell out of me. Also, benefits were brought up by the other commenter but it wasn't addressed in the legal context. How do ADA and FMLA and other benefits work when the employee is living in another country? These are all questions that are a burden on a company. Why should they legally have to shoulder the burden?

Having fully remote employees within the US is a large burden in and of itself. It is very very hard unless a company has resources that can address that. Each employee in a different state or location is basically treated like a separate office. You are basically asking a company to open an office in Mexico.

Can you point me to some of the Tony Robbins stuff that is good for this in particular? I find myself worrying more and more about losing what I am gaining.

In publicly traded companies the shares are taxed when they vest. So when they give you the 100k in stock, you are taxed on the 100k (usually they withhold a portion of the stock in vesting programs).

Look into Axis aligned Bounding Box Tree algorithms

Could you elaborate. Is LR, linear regression?

I am really curious how the first method/process claim was granted.

'1. A method of treating a symptom of diabetes, the method comprising: identifying a subject exhibiting symptoms caused by pancreatic beta cell destruction, the subject having a normal caloric intake; and administering multiple cycles of a diet protocol to the subject wherein a fasting mimicking diet is provided for a first time period and a re-feeding diet is provided for a second time period, the fasting mimicking diet providing less than about 50% of the normal caloric intake of the subject with both protein restriction and sugar restriction and the re-feeding diet providing 60-100 percent of the normal caloric intake of the subject, depending on a need to lose weight wherein the first time period is from 2 days to 6 days and the second time period is from 7 days to 85 days.'

This seems like un-patentable subject matter based on USPTO guidelines (https://www.uspto.gov/sites/default/files/web/offices/pac/da...). It seems to me that it fails the machine or transformation test for a process patent (https://en.wikipedia.org/wiki/Machine-or-transformation_test). The only way I could reason that it was patentable is if the 'diet protocol' has to be followed strictly based on the definition in the patent. The diet protocol has a long section with very specific micro-nutrient content. So that is the 'product', a calorie restricted diet with a narrowly defined micronutrient supplementation.

If there is anyone well versed, what is your opinion on the patentability of the first claim?

I think you are mixing up 'Patent application' vs 'Patent'. Anyone can submit a patent application. Whether it makes it through and is granted is a different story. Also the title and abstract of the invention don't really mean much. The claims are what defines the patentable invention.

I only have anecdotal evidence but I helped a colleague use this successfully. Sourcing companies to email on angelist then using google/linkedin to find contact info of the appropriate person instead of going through the career page. Cold emailed 5 companies and was able to initiate conversations with 3 without sending a resume. The 3 showed interest but after the conversations he only wanted to follow up with 1 and was able to tailor the resume very specifically. Lead to an offer.

Please don't say this:

'Can you work as a sw dev? I'm probably not as skilled as a professional dev. I would never be time-efficient as a professional.'

I have seen many fellow developers say things like this (impostor syndrome). Productivity as a code monkey is rarely relevant to jobs and probably not relevant to the jobs you want. You have worked in a variety of roles and have lots of life experience. This may make you many times more efficient in other essential parts of software engineering. Clear communication, problem solving, planning, understanding actual customer and business needs. The most efficient software engineers I have seen don't type [insert some huge number] lines of code a day, they ask good questions and fully understand the domain before the tackling a problem.

As for your problem finding a job, I would be working on creating a portfolio of code samples/projects in the field I wanted to work. Also I would make sure to tailor my resume depending on who I sent it to and what the job description lists.

One successful approach I have seen is contacting companies you want to work for (works best on smaller companies) and trying to talk to someone before giving them a resume. This can be accomplished at meetups or cold calling/emailing :shudder:. When you talk to them you identify what real problems they are facing. Then tailor the resume to those specific problems (or even throwing in a case-study of how you would approach and solve them).

Those are all goals that with a little work should be surmountable.

When CS majors or programmers estimate how fast/efficient code is they generally are intuitively applying big 'O' notation or some similar concept. This can be gained from an intro level algorithms class. Or you could look up and read about the topic.

The classical coding interview questions mostly stem from an intro algorithms and data structures class. Or you could get a book on interview prep and work through a couple a day.

Finally, the coding style issue usually is ancillary knowledge and not found (or at least not done well) in typical CS curricular. I would suggest the book "Clean Code" by Robert Martin. It goes through code smells, commenting, general style, and is well written.

So overall, you could look at Algorithms and Data Structures MOOC's. Also read a book on CS interview prep and writing maintainable code. Hope this helps a little. Good Luck!

So you are arguing that weak foundations are one of the greatest sources of problematic learning outcomes. And yet you are advocating for a system that produces those poor foundations and learning outcomes. I just don't understand why you are so hung up on university/school/establishment dictated 'core/prereq' classes as being defined as the foundation.

I would argue that the current pre-req system is responsible. It lumps a full class as a pre-req. And someone can pass a full class while not understanding pieces of it. Those pieces could be the essential ones needed for the specialty knowledge a person pursues. If instead of saying generally, this requires a 200 level linear algebra course and instead enumerated the topics from that course that were necessary than people would know the foundation that is needed. You saying that 'fluency' is required in some broad course is intellectual elitism.

"I see the pursuit of quick and shallow ways of learning advanced subjects"

Also in what way is clearly defined prerequisite knowledge 'shallow'. And where is this coming from:

"People who only ever search for the quick secret trick to learning X"

You are injecting biases into this debate that weren't there in the first place to make it seem like I am advocating some kind of get rich scheme for learning things. I am actually advocating clearer and more descriptive knowledge ontologies. This would allow for better learning efficiency and overall may benefit the field. People would be able to spend more time on depth in the field they want to pursue.

Also using 'never' in an argument is a habit you need to break. It is hyperbolic and usually trivial to find counter examples.

What kinds of things do you tend to be insecure about?

Thanks. And thats why my comment advocates for a 'better list' which would have a list of knowledge required. Then a student can choose whether they know enough that they just need to cherry pick or if they would need to take a whole different class to prepare.

"The course prerequisite list is, I think, usually a good balance of the right amount of information."

I strongly disagree with this statement. I see many college curricula as hugely time inefficient. Especially when you want to learn deeply on a specific topic. I personally disregarded the prerequisites for many elective engineering classes because of scheduling constraints. And anecdotally I found you only needed small pieces of the prereq's.

You can argue for a 'foundation' all you want. I was simply stating that I would like a detailed list of knowledge required to take a course instead of a generic 'you need a 100 level linear algebra course.' A detailed list would then allow people to decide if they know/remember enough info or need to learn/brush up on a topic. Speaking of linear algebra, seems like a struck a nerve with my made up example.

I also think it is a disservice to say that you need 'understanding [of] those subjects deeply if you want a high ROI.' These kind of statements discourage learning. I have seen many people struggle with so called necessary prereq's only to flourish in more advanced classes.

I shouldn't have used the word curriculum. I went to a top university and I know how frequently those prerequisite courses can be totally off base (my degree is chemical/biomolecular engineering). I used the term 'pre-requisite knowledge' because I would prefer a list of what ideas/concepts are needed to be fully understood before taking the course (ex/ instead of saying linear algebra is a prerequisite, say you need an understanding of solving linear equations using matrices but you don't need to understand linear spaces. Then you can focus your effort better and not take a 40 hour pre-req that you only need 4 hours from)