CrimpCity

Lately I’ve been using caffeinate to run long running scripts without interruption from sleep on Mac. Nothing crazy but could be useful to newer devs.

That’s interesting since this has been my exact opposite experience.

What type of coding are you doing? Did you locally roll your own coding assistant with a local model of DeepSeek or are you prompting via the web?

I studied civil engineering at an ok engineering school however I was lucky to be there in the “golden era” of the department. I caught the very best professors before they retired. They were old school and really pushed us without worrying about the drop out rate. They took cheating and academic rigor very seriously.

After graduation I got a job at a top 10 nationally ranked engineering firm which has a very strong reputation. My coworkers were the top of their class coming from much better engineering schools. Compared to them my education was better as judged by the number of calculation mistakes in my work vs theirs. I also had a better understanding of some core concepts. So comparatively I do not regret my degree given I landed where they did, with fewer student loans and a comparable education but I definitely got lucky.

Overall I would say my studies prepared me very well for certain things like working hard, preserving through difficult problems, learning how to be more analytical etc but it really didn’t prepare me for basic usage of the tools, learning the building code or working professionally with a manager or on a real engineering team as opposed to a team of students working on a BS design project in some engineering class where the professors don’t really grade the final output.

I think your experience is similar to mine. University teaches a “foundation” level of basic understanding and leaves out a lot of the basic professional skills or when they do try to “simulate” real work it is extremely phoned in. At first I did feel some amount of regret that my degree didn’t “fully” prepare me for professional working life but I think that’s just a result of dealing with “gaps” in development since we can’t possibly learn everything in our degree. If I had gotten internships during my undergrad degree then I think I would have probably had a more complete education with less buyers remorse.

As universities become more market as opposed to academic dominated I think this dynamic might invert.

The real regret came when I saw my paycheck and when I heard what software engineers make. I later switched into software and have never looked back.

Nope. OP stipulated that at some point mortal combat happens and also suggests that it happens within arms length for example dropping a large rock on an even larger Rock.

Short answer no. This is because what gets exhausted is aerobic capacity. When you get within striking range The Rock will still have access to fast twitch muscles & some reduced amount of glycogen for energy release. This would probably be enough to grab you and choke you out. Basically the Mountain vs the Viper fight from game of thrones.

If you were of similar size then yes. Basically being smaller really sucks in a fight. So if you can reasonably deflect the Rocks wrestling & grappling offensive you could rope a dope him causing him to gas out and then win. Higher weight MMA fighters & boxers use this tactic all the time. They let their opponent “punch themselves out” and then get off their own offense.

Dang I was hoping the article would have been about non-traditional higher level fractional factorial designs. Like for example you have an experiment with 3 factors and 5, 4, 3 levels each but can only run 20 experiments.

Life was considerably more difficult. Logistically a lot has been simplified. Yes modern life is more complex today but we manage it through specialization. This stress takes a toll on people and they look older.

What practical lessons did you take from these books?

Thank god

Watching them work at that age made me super sad.

Where might someone find this Fast Development workshop?

There may be a lot of cost savings in labor but there’s a huge risk in technology transfer & following lab protocols. In biotech lab culture and best practices are actually a tighter package than “low” skill factory work. So you get really variable outcomes when you unbundle the lab.

I think it’s more competitive than that.

Company B isn’t just looking at company A’s “success”. Company B’s levels of attrition are increasing because its employees are going to company A. So company B has no other option than to open the hiring floodgates or else they can’t meet bare bones maintance tasks let alone growth.

Not your comment but most of the comments on this thread are whacky. It’s like people completely forgot how hot the software market was during the pandemic. Anecdotal but I had 2 friends trying to break into the software game at the same time. That didn't happen during the pre 2019 boom so who knows what that means lol

Clickbait title but I generally agree being direct in code reviews is great especially if it reduces the feedback loop & doesn’t drag out the code review process.

Triton is pretty hot right now you can check it out.

https://openai.com/blog/triton/

https://triton-lang.org/master/index.html

The paper you shared is WAY more interesting than the original post.

Maybe this is a naive criticism but if the goal of the model is to "emulate the correct reasoning function" by which is implied that it learns prepositional logic rules then shouldn't the model have some architecture that tries to model generic logic rules and then using that score the input data and predict the result?

Currently it seems like the model is simply scoring each predicate since each of it's reasoning layers performs one step of forward chaining, adding some predicates to the Proved Facts.

My naive (and way undercooked) way of conducting this experiment would have been to try to use a GAN where the generator model comes up with new predicates and then the discriminator model that tries to classify them as real/fake. I would then try to train an MLP on top of the generator to classify the result and swap out the MLP depending on the sampling method basically the generator model becomes the pre-trained thing we care about to see if it can generalize.

Another nitpick is that the authors claim that BERT has enough "capacity" to solve SimpleLogic BUT this isn't actually what they want to achieve since solving != learning reasoning. So it feels like a bait and switch since IMO if a model has the capacity for something then it has some architectural aspect to it that can be used to show that it can achieve a smaller version of what you want and they didn't prove BERT can learn ANY prepositional logic rule.

pycharm is also MUCH smarter figuring out import paths.

Didn’t see that but just saw the top voted comment here and yeah def feel like I called it haha :)

That’s great it sounds like Twitter and social media in general isn’t a big value add for you.

I think for Paul Graham it’s a different story since he talks to other influencers and occasionally goes viral. That sort of feedback loop well that’s quite addictive. There’s a reason why there’s no obvious Twitter competitor.

He’ll be back.

Both things can be true at the same time. I dropped of CH because the synchronicity was too much for me. But apparently other people still use Twitter spaces.

> What has Twitter innovated on in the past decade?

They basically stole clubhouses’ thunder with spaces and tbh clubhouse has lost a lot of it’s shine.

Now did Twitter actually innovate? I would say not really but they did reimplement the wheel and so far seems to be working for them. I’d say the execution still counts as being innovative.

This is true but the gains are surprisingly limited. From Algorithmic game theory there’s the concept of “the price of anarchy” it measures the gap between cooperation (a centrally designed or coordinated solution) and competition (where each participant is independently trying to maximize the outcome for themselves). Selfish routing basically what we have now has price of 4/3. This means the greedy status quo is only 33% worse than perfect coordination.

I’ll quote from one of my favorite books Algorithms to Live By:

“It’s true that self-driving cars should reduce the number of road accidents and may be able to drive more closely together, both of which would speed up traffic. But from a congestion standpoint, the fact that the price of anarchy is only 4/3 as congested as perfect coordination means that perfectly coordinated commutes will only be 3/4 as congested as they are now.”

The article is gated so I can’t see if they address this but when companies make such announcements isn’t differential laying off / firing due to race illegal?

Actually I like having a smaller phone in my pocket instead of a brick so I for one will be looking at foldable phones once I am back on the cell phone market.

Studies looking at WW1 and WW2 show that 40 hours a week is optimal. https://www.researchgate.net/publication/262809555_The_Produ...

You can use scihub to get the paper or here's a secondary pop-sci source: https://www.bbc.com/worklife/article/20190912-what-wartime-m...

I think I understand your criticism. There is no inherent ground truth in the image. The mass example is great since a 2D plane can't capture the quantity mass it's literally impossible the dimensions don't work. At best a 2D plane could show you correlations of mass (mass vs something plotted out). Hence this is just modern AI aka pattern-matching on steroids.

I think a counter argument would be that if there is SOME signal in the photos AND there's enough training data that does have the correct ground truth signal that the scientists are matching up then you can have SOME level of accuracy. If the training set can reasonably cover the space of possibility that we're interested in then we can get reasonable interpretations.

However in this case the insane number of physical phenomena will always be larger than any training set so this approach should NEVER generalize there will always be way too much noise which is what the scientists have figured out here. So I agree with you that it's extremely limited but I don't think I'd call it pseudoscience there might be very limited domains where for example the only data we have available are images and so such a tool may be appropriate.

I definitely share your frustration though since any half way decent scientist should have just done a thought experiment instead and figured that this wouldn't work well. This smells like BS academic marketing where they always inflate their own impact and significance.

This is cool and I personally believe this type of work may lead to breakthroughs in messy data rich fields like biology where we can arrive at a higher levels of abstraction maybe not exactly to "laws" like physics but highly correlative rules around phenomena. I think this is more on the side of knowledge creation and is human friendly as opposed to being more of a black box prediction like deep neural networks. Though I think both things are complimentary since human curiosity isn't satisfied by prediction alone.

If anyone else is interested in this line of work I recommend checking out Kathleen Champion, Steve Brunton, and J. Nathan Kutz's work on Discovering governing equations from data by sparse identification of nonlinear dynamical systems(https://www.pnas.org/doi/full/10.1073/pnas.1517384113).

Also this intro video is great! https://youtu.be/Z-l7G8zq8I0

Also many of the links I spot checked were dead or broken. I may just have unpopular interests tho

Yup! The change order strategy or nickel and dime strategy tho autocorrect suggested “buckle and dime” and that would also work.

Unfortunately as much as engineers and owners reps complain about this way of doing business it’s quite accepted since people generally want to keep their reputations. No one wants to be known as “difficult to work with”. So only very excessive transgressions get punished.