jvans

Lots of fun counter examples to this. Complex numbers were introduced in the 1600s with no practical application for almost 300 years until they were used in electromagnetism and quantum mechanics.

i notice a huge difference between working on large systems with lots of microservices and building small apps or tools for myself. The large system work is what you describe, but small apps or tools I resonate with the automate coding crowd.

I've built a few things end to end where I can verify the tool or app does what I want and I haven't seen a single line of the code the LLM wrote. It was a creepy feeling the first time it happened but it's not a workflow I can really use in a lot of my day to day work.

it's used extensively in java and it would be my first choice when starting a java project. I don't think I'd use it in Go though.

i like reactive programming in other languages but it is at odds with Go's philosophy of simplicity and avoiding big abstractions

Ya except people with innate talent are frequently lazy and take their talent for granted. You can often outwork these people and get better than them

I kind of agree with you. On the one hang OP is logically correct, on the other it's very sad and a form of a tragedy of the commons. If everyone gave candid feedback we'd all be better off.

Even when it is the case you can logically come to a resolution but if you don't emotional feel it, the problem/conflict is not solved and will come up again. In my experience this manifests in non obvious ways that are far removed from the original problem

Good luck with that :)

I think this blind spot exists because the pure engineering/logic mindset is such a massive superpower in so many elements of life, people fail to consider that it might not always be the right way to think about the world.

One obvious example where it falls laughably short is in interpersonal relationships. Trying to logic your way out of an emotional conflict just does not work

A lot of people should be mad at Marc Tessier-Lavigne, not just HN folks. He lied for personal gain at the expense of scientific progress and millions of patients who suffer

The controversy over the amyloid hypothesis comes from a Stanford professor faking data[1] and setting the field back decades. The amount of harm this individual caused is hard to overstate. He is also still employed by Stanford.

[1] https://stanforddaily.com/2023/07/19/stanford-president-resi...

in poker AI solvers tell you what the optimal play is and it's your job to reverse engineer the principles behind it. It cuts a lot of the guess work out but there's still plenty of hard work left in understanding the why and ultimately that's where the skill comes in. I wonder if we'll see the same in math

For comparison if you run your own hardware and do a memcached KV lookup with a different server on the same rack, p99 times are slightly under 1ms. Given the guarantees of cosmosdb ~10ms isn't that bad for a p100

I had a lightbulb moment recently where I had a recruiter ask me if I had any experience building recommendation systems. While I don't use that word on my resume, my resume is full of technologies and projects that point toward recommendation system experience.

The recruiter was tasked to find candidates with a recommendation system background but the only way they know to do that is look for that exact word.

only take action when there is actual evidence and proof of harm being done.

Sounds like a good way to kill a lot of people

Imagine a scenario where option B does 10x better than option A during the morning hours but -2x worse the rest of the day. If you start the multi armed bandit in the morning it could converge to option B quickly and dominate the rest of the day even though it performs worse then.

Or in the above scenario option B performs a lot better than option A but only with the sale going, otherwise option B performs worse.

git bisect is an absolute power feature everybody should be aware of. I use it maybe once or twice a year at most but it's the difference between fixing a bug in an hour vs spending days or weeks spinning your wheels

Figure out the core technologies they use and become an expert at them. Read the documentation of those technologies like a book. Is kafka at the heart of the stack? Learn everything related to kafka and setup a small dummy instance to play around with. This can take some time but after a couple of months you'll be more knowledgeable about the technology than many people on the team who have been there longer.

this is a great write up, thank you

but there isn't actually a second set of eyes because the second set of eyes you're thinking about is complaining about formatting or slamming the approve button without actually looking

It would obviously be crazy to roll your own numpy or lightgbm. But there's plenty of bloat in the library space where the cost of integration and upkeep is far more expensive than a simpler implementation of your own

It's crazy because the JDs will be so extremely specific to a niche skillset and I still won't get a reply.

The internet is unusable without ublock origin and privacy bafger. When I use my friend's computers without ad block I have to fix that first

If I'm not having fun learning something I can't stick with it. Efficiency without tenacity/grit is useless. It's kind of a tautology that "more efficient is better", but I agree it's pointless without fun. Being less "efficient" but having fun will yield better results imo

I really do expect we will all be on this

When antibiotics were first invented some people thought we'd be taking them daily as a vitamin. Turns out that's not such a good idea despite them being life saving in some scenarios

metrics sell, even when they're not that accurate, and even when people know that.

fitbits and similar are fine for measuring awake vs asleep but really terrible at breaking up the stages

EASE and/or MMA

https://sleepapneasurgery.com/about-dr-kasey-li/

unless you're in the weeds everyday of a specific problem, it can be hard to ask the right questions. often times the most important details are the ones that are omitted

How do you personalize to the specific signals of the product, do they ingest into your infrastructure? What happens if a customer discovers a bug in a feature they're ingesting, how do they have control of retrains/pinning model versions? Who handles monitoring, the customer or your service?