So following the HN community too closely can stir feelings of insecurity and inferiority and the imposter syndrome. And yeah, I feel it sometimes, too. :-)
You can't know everything and you can't keep up with this firehose of new knowledge that is published every day.
Most of that is not relevant to your field anyway - whatever it might be, so you don't actually have to know any of it.
I mean, you're free to take it all in, but don't expect anyone to quiz you on it in a professional environment.
Hacker News is :
1- a jungle of expertise.
" tripping over each other's areas of expertise which were literally years in the making. "
" I shouldn't compare my personal knowledge with the combined knowledge of a community "
" A lot of the trends people get fixated on tend to fade away in a few months "
" The four topics you list are all high BS "
" HN bubble will always make readers feel behind when in reality 90% of the industry hasn’t even begun to catch-up "
" No need to keep up with the "bleeding edge" of technology "
1- Try to just enjoy it
" Stop taking fomo quite seriously. I suggest that you just stop giving a shit. "
" JOMO (joy of missing out) "
" here for the smart(er) people with experience in different areas. The thing that makes you anxious is the reason I enjoy being here "
" stressed that they HAVE to learn more. "
" feelings of insecurity and inferiority and the imposter syndrome "
" If you want to feel relieved you could try reading the comments section when some topic you know stuff about is posted "
" prioritize whatever you want or need in life "
" allocation your effort toward things you need to know for progress, and indexing those things you dont need to know right now "
" you've been working in software for almost 20 years and all of the stuff that doesn't concern you actually doesn't concern you. "
If I find something I genuinely want to learn, it’s just a matter of spending focused time learning it. I also believe it’s important to know when to cut off an interest that is no longer serving you. While discipline is important to push through discomfort and overcome adversity, it’s really silly to be wholly invested in a topic or hobby that brings you no value in life. That value can be monetary or happiness or growth. But it’s important to know that you don’t have to know everything and it’s really silly to do so. There will always be something you don’t know.
For you, maybe ask yourself why do you want to know all these things? Maybe you can identify if they serve you in a way. Of those that you believe will make your life better, spend 30 minutes every day or every other day learning from the ground up about the topic. Maybe put it into practice, or just take a course on it. Regularly reflect on if this is what you want to be doing, if your making meaningful progress towards whatever goal you have, and celebrate your successes!
The best way of coping is a JOMO (joy of missing out) and moderation. You don't need to know everything and you surely don't need to spend all your time trying to. It's okay to not know. That's the beauty of life.
1. What problem is this trying to solve?
2. What are the strengths / weaknesses?
Usually you don't have to go very deep to get a feel for these questions. I figure that if I know these things, then in future if I encounter a problem where tech X is useful I can go learn the details. If I never do, I can at least prioritize within whatever free time/energy I have for additional side learning.
As an example, for quantum computing the problem it's solving is surprisingly hard to identify. Breaking (some) cryptographic algorithms is the classical example, but that's not a problem I plan to try and solve anytime soon. Other use cases tend to be vague - ML is often mentioned but actual players in the AI space don't seem to be investing into QC much. Quick eval result: pass for now until use cases become clearer.
What about ML? Many useful problems that can be solved with that - anything where you can't easily express an algorithm to solve a problem but one may be learnable by example. Strengths: capable of doing things no other technique can do. Weaknesses: requires lots of clean(ish) data, advanced ML may need specialists, training/inference can be expensive, finding ways to actually apply it to business problems is remarkably tricky due to its capacity for random failure and difficulty in coming up with fixes for that, which doesn't fit the needs of most automation projects ... but can be OK if you can predict fast enough to speed up a human who's already in the loop, or if mistakes are cheaply correctable. The exact details of how it works? I enjoy reading about it. There's a good article on the GPT-3 DNN architecture on the HN front page right now actually, but it's just some casual hobby learning. I don't feel stressed if I don't grok all the details right now because it's not necessary to do so.
In other words, focus on knowing the outlines of these topics and learning when/how to apply them, then forget the rest. That doesn't take as much time as you'd expect.
I cope by reminding myself that I shouldn't compare my personal knowledge with the combined knowledge of a community - multiple people here are knowledgeable about and post about different areas
I would dabble in many other domains like AI, blockchain, other languages, etc. to test out their effectiveness, but the best advice I have is to focus on your problem first and then find a technical solution that solves that problem. If you start with the technology first and then go looking for a problem, it’s not as effective IMO.
> Also that the HN bubble will always make readers feel behind when in reality 90% of the industry hasn’t even begun to catch-up. I felt behind with K8S in 2016, for example, not realizing we were just seeing the wave form when I felt like it was cresting. [0]
Crypto seems to have outlived its utility as a way to get rich quick. ML is still just a technique to average out giant datasets to find/generate things based on trends (these things work on training data after all and not from a true blank slate). Quantum computing is a long way off and the most practical way it’ll affect most people here is making our encryption keys too simple and some new quantum resistant version will be needed.
Just watch chatGPT - in 6 months, it’ll feel like a distant memory here! And people will be raving about some new JS/wasm thing instead
But there really are maybe 2000 weird sub-worlds in tech that are a big deal to people in them and almost unknown outside. So you see a lot of posts that are like “Oh we’ve just dropped version 39 of Gryptorflux, so it the beginning of a new era” and you look and there are 50,000 users of Gryptorflux and Electronic Arts is sponsoring it and you’ve never even heard of it before today.
I don’t have anxiety because I know that my world of Context-Driven Software Testing looks weird and obscure to almost everyone, too. The reason I am here is to pick up clues, not conquer the universe.
I've been in this space since 1995, and I've learned that most of the things that pass by simply don't interest me. I don't really keep up with JS/CSS/HTML stuff any more, since it's a saturated space in a work sense. I focus on the back end exclusively, where things move in much slower, provable steps.
There is joke going around on LinkedIn where somebody asked for x years Kubernetes experience, and it was not even released that long by Google.
LinkedIn is just a harvesting farm for recruiters to meet their quotes - my last contracts I found via word of mouth and connections in the industry.
Definitely. You spend a couple of years learning new tech and methodologies and then the industry moves on and you’re behind on something different.
Ai is going to rock our world. I personally don’t know what to grab hold of to keep from getting tossed overboard during the coming storm.
Great time to start an ai company though, if you can get financing. If you’d like to fund me, I have video game related ideas I’d pursue if I could ^^
I could imagine fears about job security, maybe some sense of falling behind? A simple fear missing of missing out on all the fun in specific sub-fields?
We have such a large field in general, I don’t think anyone can know it all, but that can be something to celebrate too. Never a dull moment :)
HN also has better comments that don't tend to go towards insulting or argumentative.
i feel like a lot of other computing-related concepts are approaching that same level of sophistication.
i've usually been pretty good at not worrying about true understanding of various technologies, so the fact that i can't explain ai well, or that most ai is not even capable of offering an explanation of how they make their own decisions, is not so anxiety-producing to me (tho, parts of ai, and explainability, are real problems).
but i do get anxious from constantly reading headlines from various substacks and other clickbaity and attention-seeking entities about how some new tech has basically already ended life on earth so we might as well just give up.
i'm anxious enough already with real threats to the world -- nukes, global warming, social media, inequality/fascism/authoritarianism, etc.
It's okay to not be Donald Knuth.
I wish I could filter these out and just stick to the technology.
Just try to remember that nobody could possibly keep up with everything, and that you don't need to anyway. Follow what stokes your interest and/or pays the bills, and don't worry about the rest
I'm here for the smart(er) people with experience in different areas. The thing that makes you anxious is the reason I enjoy being here.
I suspect that's true of most people whether or not they believe it.
But it's only a suspicion.
Good luck.
If you don’t, you have nobody to blame except yourself.
some of these things, are things you wont ever need to know, but observation is enrichment.
part of a critical knowledge base is allocation of effort toward things you need to know for progress, and indexing those things you dont need to know right now.
Not really surprising considering the vast majority of people here seem to have probably not received higher education (university/college) in regards to STEM. I didn’t think this was the case before I joined though.
Some people think anxious thoughts, which they identify with and take seriously as if they are objectively real and significant occurrences, which then produces physiological changes that are interpreted as feelings of anxiety, about things they read on HN.
𝐂𝐫𝐲𝐩𝐭𝐨 —- the main use case is separating fools from their money. Missing out on that one is one of the best things that can happen to you.
𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 —- people starting writing papers on this around 1998 when I got my physics PhD. You could not get a postdoc in it at that time. It got fashionable maybe 10 years later. I am sure there are some people getting tenure in it right now but in 10 years it might be remembered as just another field that ran its course and had better days and it may get a reputation, just as chaos and fractals got in the 1990s as something you just could not make a career in. Sure there will be ‘quantum supremacy’ for particular problems (maybe even that box that is the McGuffin in Sneakers) but you are not going to be using a quantum web server to host your content.
𝐃𝐚𝐭𝐚 —- I hear ‘data is the new oil’ and I think of an oil tanker spill like the Exxon Valdez or maybe like Spider-Man, ‘with big data becomes big responsibility’. I think how TikTok’s recommendation algorithm is so good because they collect better data: they give you a very limited set of choices so the data collect is meaningful. When YouTube shows you 30 links on the other hand you can come to no conclusion that the viewer didn’t like those things.
Data is a big part of everything we do in computing. In plain ordinary boring applications development the most important thing is getting your data structures straight. There is a whole art of online transaction processing and the arts of doing analytics after the fact or in real time.
𝐌𝐋 —- data is the currency of ML. If you have a real problem to solve and real test and training data you can do ok with basic algorithms out of scikit-learn. If you don’t have appropriate data you can dream about the latest algorithms and accomplish nothing. You need to know the basics of linear algebra and statistics: the courses you take as a math major are great.
Like most other things there is a part of the field which is almost eternal (I think about things Yan LeCun wrote about 20 years ago almost every day and think ‘Neural Networks: Tricks of the Trade’ can turn a failing project into one that succeeds even though it was written before deep networks.) and other parts that are ephemeral. For work I did a lot with LSTM networks for text, then it was CNN networks, those have been forgotten almost because transformers can do things that would be difficult or impossible without attention. Personally I am really interested in those radiance field algorithms for scene synthesis, I haven’t done a project yet but it was clear early on that the algorithms would get much more efficient and that has been happening the whole time I’ve been watching from the sidelines.
There are ‘data scientists’ who do quantitative marketing with a lot of smak and pow and get paid really well because they really can increase revenue 850% in two years. They don’t necessarily know a lot about algorithms. If you were working in the field as a scientist you would be one of the people who writes one of the 20 papers on radiance fields a week that would never get a second vote on HN, but as a practitioner you can master the basics and solve real problems for fun and profit.