Readit Newshttps://en.wikipedia.org/wiki/Founders_at_Work
Note: YC didn't have HN to begin with, it was launched 1-year later (Oct 2006 as seen by pg profile). And the book was launched the subsequent year (2007).
Does anyone else keep a similar collect or have advice for finding more?
(For example Gurwinder sometimes posts lists like this: https://www.gurwinder.blog/p/30-useful-concepts-spring-2024 - that's what I'm talking about.)
> In Theory There Is No Difference Between Theory and Practice, While In Practice There Is
I for one used to believe in a cross functional team. I used to believe that everyone in a team should be able to do every task in the team. I still believe it somewhat but my ego is shattered.
I worked on one team where the lead believed in this more than I ever did. Consequently, I was doing tasks I sucked at and therefore didn't enjoy s lot more because as she said, it will help me improve.
Long story short, I didn't improve. I just got frustrated and I quit. I guess it was all fine from the leader's perspective as her team stayed the way she wanted anyway.
I went down this tangent to remind people that when things are going well, we can say a lot of things that are nice like kumbaya my lord but when things are tough is when our ideals and morals are actually put to the test.
When poop hits the fan, will leadership throw someone under the bus? Will team members feel like leadership will throw people under the bus? Kind of a difficult question that we can't answer until we are there and at that point it is too late.
Where I've worked, a cross functional team is one made up of functional experts from different groups. A team where everyone could do the work of everyone else was a team that was cross trained.
As someone who's gone through the mathematical ringer, the analogy doesn't ring true to me, but it does sound pedagogically useful still (my students will be CS majors, so the math will be for training rather than an end). Even at the highest levels the definitions are of prime importance, though I suppose once you get to "stage 3" in Terry Tao's classification (see elsewhere in the thread) definitions can start to feel inevitable, since you know what the theory is about, and the definitions need to be what they are to support the theory.
Personal aside: In my own math research, something that's really slowed me down was feeling like I needed everything to feel inevitable. It always bugged me reading papers that gave definitions where I'm wondering "why this definition, why not something else", but the paper never really answers it. Now I'm wondering if my standards have just been too high, and incremental progress means being OK with unsatisfactory definitions... After all, it's what the authors managed to discover.
To be clear, this does not mean memorizing all the theorems. Getting to know the theorems (and solving problems) is what helps you internalize the subject. Math is the art of what's certain, and knowing exactly what the objects of the subject are is necessary for that. Theorems are derived from the definitions, but definitions can't be derived.
In my experience with a math (undergrad and PhD), I realized I had to know definitions to feel competent at all. In my teaching, it's hard to convince students to actually memorize any definitions — so many times students carry around misconceptions (like that "linearly independent" just means that no vector is a scale multiple of any other vector), but if they just had it memorized, they might realize that the misconception doesn't hold up. Math is weird in that the definitions are actually the exact truth (by definition! tautologically so), so it does take some time to get used to the fact that they're essential.
I don’t have any musical training, but I related it back to the practice and warm up sessions we had before we’d play an actual game in the sports I played as a kid.
Perhaps some explanation like that will get it to click with someone.
I also learned of the existence of soft question tags on Math Overflow and Math Stack Exchange that contained an incredible amount of guidance that I think was never possible in lectures. Sharing links to those websites in the syllabus may be helpful for the odd student that actually looks at the syllabus.
The calculus books we used were not set up like this and the books that focused on learning the CAS or numerical methods weren't structured any better. I think this only worked because it was a small program aimed at technical education with a faculty that cared about developing a unified curriculum.
When I transferred to a different university to finish a degree as a stats major, all of our courses and most of the textbooks were structured in a way to use R. We did some problems on simple linear regression by hand, but very quickly it becomes impractical do to it any other way. This seemed very natural to me, but apparently it was not the typical experience of studying statistics.
Perhaps there are some calculus books out there that do a good job of both teaching calculus concepts and using CAS / numerical methods, but my narrow minded view is that calculus is a tool for physics, engineering, or other applications, and you'll be bogged down in teaching the relevant domain knowledge to get interesting examples. If you're looking for your own examples, perhaps this could be done purely through the differential calculus topics of related rates and optimization or the integral calculus topics of simple ordinary differential equations.
There are way too many software engineers with lofty ideas about how physical engineers can magically know all the answers to all the problems they could ever have.
I'm assuming you mean a single course? If so, this material would not be a standalone course. It would be baked into the entire bachelor's degree program. Some of the topics would maybe be more advanced or something that need to be demonstrated by being an EIT or writing the appropriate exams. For example, chapter 15 on engineering economics is a single class, but chapter 17 on mathematical foundations would cover at least 4 classes (discrete math, differential calculus, integral calculus, probability).
The US of A did have a software engineering principles and practices of engineering (PE) exam, but it's been discontinued, and I haven't managed to find an archived snapshot of the exam spec. I'm not American, but I think there is a common fundamentals of engineering (FE) exam [1] that has to be written to register as an EIT and then the PE [2] has to be written to be licensed and given the PE.
I'm not familiar with which American schools were ABET accredited in software engineering, but in Canada, several schools do have accredited software engineering majors. You can review the curriculum and see a fair amount of alignment to the SWEBOK topics. Again, some of these chapters could be split across multiple courses, but some chapters look more like a couple of weeks in one class.
For comparison, there is a 61 page industrial and systems engineering body of knowledge [3] available from the IISE (Institute of Industrial and Systems Engineers), which is really just a couple short paragraphs on each topic, a list of key areas within each, and a list of reference books. At a quick glance, all of the areas correspond to sections in the industrial FE [4] and the industrial PE [5].
> There are way too many software engineers with lofty ideas about how physical engineers can magically know all the answers to all the problems they could ever have.
I'm not an engineer. I did an associates in engineering technology in Canada, so I'm a "pretengineer" at best. As far as I know, engineers in Canada have a discipline and then areas of practice. For industrial, there are 9 different areas of practice, but people are generally licensed to practice in 1 to 3.
In my region, software is not even broken out into its own areas of practice. Software is an area within computer engineering. I think software is way too vast right now and the expectations are much too big. So, the traditional engineers have much more limited scope problems. But I could be limited by my perspective and lack of license.
[1] https://ncees.org/exams/fe-exam/
[2] https://ncees.org/exams/pe-exam/
[3] https://www.iise.org/Details.aspx?id=43631
Links to PDFs
[4] https://ncees.org/wp-content/uploads/2022/09/FE-Industrial-a...
[5] https://ncees.org/wp-content/uploads/2024/10/PE-Ind-Oct-2020...
The original posting is about new tools and algorithms, with some more analysis. Well beyond my background from undergrad courses in LP and OR, but probably more relevant and insightful to you.
[1] https://www.asc.ox.ac.uk/people