drgiggles

Weird timing, I just stumbled upon this Brian Beckman video recently https://youtu.be/ZhuHCtR3xq8?si=ifeR6wddsPRdOw1N

It’s possible (even true in my opinion) that garbage collected functional languages and low level languages like Zig are both great, and serve different purposes.

I actually ship stuff in Haskell believe it or not. I also think Zig is very cool and have played around with it quite a bit. Yes, garbage collection hurts performance, but the reality is that the overwhelming majority of all software does not suffer from the performance loss between well written code in a reasonably performant functional gc language and a highly performant language with manual memory management. It’s just not important. But not having to deal with the cognitive overhead of managing memory and being able to deal in domain specific abstractions only is a massive win for developer productivity and code base simplicity and correctness.

I think OxCamls approach of opting in to more direct control of performance is interesting. I also think it’s great that many functional patterns are making their way into imperative first languages. Language selection is always about trades offs for your specific use case. My team writes Haskell instead of Rust because Haskell is plenty fast for our use case and we don’t have to write lifetime annotations everywhere and think about borrowing. If we needed more performance we would have no choice but to explore other languages and sacrifice some developer experience and productivity, that’s very reasonable. I’m also not saying performance doesn’t matter (if you’re writing for loops in Python, stop). But this read to me like “because better performance exits with manual memory management, all garbage collectors are bad, so I’ll force zig to be something it’s not in order to gain performance I probably don’t need”. Which to me is an odd take. A more measured way of thinking about this might be, it can be useful to leverage functional patterns where appropriate in low level languages, if you find yourself needing to write code in one.

This. It's valuable b/c if you have many thousands of python devs using astral tooling all day, and it tightly integrates with subscription based openai products...likelihood of openai product usage increases. Same idea with the anthropic bun deal. Remains to be seen what those integrations are and if it translates to more subs, but that's the current thesis. Buy user base -> cram our ai tool into the workflow of that user base.

I am pretty far to one end of the spectrum on need for comments. Very rarely is a comment useful to help you/another developer decipher the intent and function of a piece of code.

Easily 99% of comments generated by LLMs are useless.

There are mountains of data that show it actually has long term benefits beyond weight loss (beyond even the obvious health markers that improve due to losing weight). I wouldn’t be surprised at all if the majority of the population ends up taking next gen drugs in this space, most of them purely for longevity.

Anyone that’s worth anything at all in this field is “self taught”, some of them just went to school first.

I am a strategist at a smallish boutique quant investment firm. This is how we think about hiring a junior person. It's not all that different for a more senior person, but actual development experience would likely be more important, we would expect more contribution sooner from a more experienced person. More senior jobs also might have more specific responsibilities and therefore require more specific knowledge of technologies, etc. Many junior analyst roles support the team as a whole and there is less concern around experience with specific technologies, typically.

When we hire a junior person we are interested in math background, ability to communicate real world value of various models to our investment process and familiarity with computer science and software engineering concepts more than we care about experience with specific languages or technologies. That being said, C++ does still dominate this space so having exposure to it certainly would not hurt.

I work in quantitative finance and have wanted to to start using OCaml at work for years. I just find that unless you are at a shop like Jane Street with a well developed proprietary code base, internally developed tooling, etc, there just isn't the ecosystem available for me to be nearly as productive as I can be in other well accepted languages in the quant dev space...which is a bummer. It's been a little while since the last time I investigated this though.

If you were to buy a property today in any major market, the average unlevered cap rate on leasing residential property is nowhere near 20%. In many cases it’s not even profitable without a significant housing price return assumption.

This is only part of what drives inflation. Yes, increase in money supply creates upward pressure on inflation. Higher interest rates reduce borrowing, which decreases demand for goods and services, this has downward pressure on inflation. This explains Fed activity over the last couple of years…the goal is to reduce inflation, not create higher interest payments on national debt…

This is exactly my point. Let subject matter experts in their respective disciplines handle what they know and communicate through the lingua franca of R. Most data scientists/statisticians probably shouldn't be writing production code, I think that's ok. It's a failing of management to think that coding is coding and not understand the value of true engineering ability.

Unfortunately it seemed pretty clear from the start that this is what data science would turn into. Data science effectively rebranded statistics but removed the requirement of deep statistical knowledge to allow people to get by with a cursory understanding of how to get some python library to spit out a result. For research and analysis data scientists must have a strong understanding of underlying statistical theory and at least a decent ability write passable code. With regard to engineering ability, certainly people exists with both skill sets, but its an awfully high bar. It is similar in my field (quant finance), the number of people that understand financial theory, valuation, etc and have the ability to design and implement robust production systems are few and you need to pay them. I don't see data science openings paying anywhere near what you would need to pay a "unicorn", you can't really expect the folks that fill those roles to perform at that level.

No, I comment on posts about pc fan grills at 6am...I'm single :)

hmm, I always assumed there was some other benefit. I'm taking all mine off. Seems pretty easy to not ram my finger in it.

The point of a grill is to block dust and debris, which presumably I want. Otherwise I would just not use one and my fans would be the quietest and cool most effectively. What is the trade-off between the two metrics sited and effectiveness of each type of grill at its intended purpose?

I really love Scala for all of these reasons, it also has the benefit of being used frequently in industry (big data, etc).